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Influence of the vehicle-to-grid strategy on the aging behavior of lithium battery electric vehicles
Abstract
The main goal of this paper is to study the effect of a vehicle-to-grid (V2G) strategy on the lifetime of two different lithium-ion batteries. The work investigates how the aging effect on the electric vehicles' (EV) battery packs due to the additional V2G use can be reduced: it is assumed that the grid is able to identify the cars within the fleet for which the ulterior aging effects caused by V2G usage are restrained in respect of the others. The chosen EVs have to contain enough energy to satisfy the grid requests in terms of power regulation. In order to analyze the possible effects on the EVs due to the mentioned strategy, a V2G simulation environment has been implemented. The system consists of 100 EVs and a grid management strategy subsystem. Each EV is represented by a battery electrical model based on electrical impedance spectroscopy (EIS) data and an aging prediction model parameterized through accelerated aging tests. In order to reproduce real scenario conditions, both the electrical battery model and the aging prediction model have been parameterized for two different cells, a LiFePO4-cathode based and an NMC-cathode based lithium-ion cell. In particular, the accelerated aging tests have been carried out for more than one year, both for calendar and cycling operation, involving around 45 cells for each of the two technologies. The grid subsystem is represented by an algorithm which is able to consider information in terms of aging and type of battery installed in the EV. This subsystem helps to make decisions related to the optimal additional use of each car for a V2G operation. In order to show the applicability and feasibility in terms of battery pack lifetime of the considered V2G management strategy, different scenarios for a period of one year have been simulated. These scenarios consider two different locations with two significantly distinct ambient temperatures, in which the starting conditions of each car in terms of aging state have been selected randomly. The implemented system can be used as a perfect tool to test different grid strategies, taking the aging of the EVs as well as the request in terms of grid power regulation at the same time into account. Furthermore, the entire strategy has been tested including in the system two assembled battery packs, with two li-ion battery chemistries as mentioned earlier. The individual battery management system (BMS) for each technology has been developed in terms of hardware and software requirements. Moreover, the information exchange in terms of aging data between grid and BMS for the V2G strategy has been implemented and tested on a real-time simulation unit.
... To avoid such situations, an effective battery management system (BMS) is required [15][16][17]. Though BMS has been used to provide function both at component-level and system-level [15,16,18], fewer studies have been made about the impact made by grid ancillary services on vehicle battery [19][20][21][22]. These studies have focused on practical wear cost model for EVs charge scheduling applications in V2G programs [19], the effect of a vehicleto-grid (V2G) strategy on the lifetime of different lithium-ion batteries [20], impact of bidirectional charging of V2G technology on commercial li-ion cells used in EVs [21], in-cell variation analysis of vehicle batteries and their impact of propagation with each involved component [22]. ...
... Though BMS has been used to provide function both at component-level and system-level [15,16,18], fewer studies have been made about the impact made by grid ancillary services on vehicle battery [19][20][21][22]. These studies have focused on practical wear cost model for EVs charge scheduling applications in V2G programs [19], the effect of a vehicleto-grid (V2G) strategy on the lifetime of different lithium-ion batteries [20], impact of bidirectional charging of V2G technology on commercial li-ion cells used in EVs [21], in-cell variation analysis of vehicle batteries and their impact of propagation with each involved component [22]. The rapid charging and discharging operation of EVs during ancillary services could lead to severe variations and deviations in connections and components of BMS [23]. ...
... Using the calculation from (17) to (20), the weighted matrices for cells c1 and c2 can be expressed in closed-form as: ...
The rapid introduction of electric vehicles (EVs) in the transportation market has initiated the concept of vehicle-to-grid (V2G) technology in smart grids. However, where V2G technology is intended to facilitate the power grid ancillary services, it could also have an adverse effect on the aging of battery packs in EVs. This is due to the instant depletion of power during the charge and discharge cycles, which could eventually impact the structural complexity and electrochemical operations in the battery pack. To address this situation, a median expectation-based regression approach is proposed for parameter estimation of vehicle batteries in V2G systems. The proposed method is built on the property of uncertainty prediction of Gaussian processes for parameter estimation while considering the cell variations as an exogenous function. Firstly, a median expectation-based Gaussian process model is derived to predict the fused and individual cell variations of a battery pack. Secondly, a magnitude-squared coherence model is developed by the error matrix to detect and isolate each variation. This is obtained by extracting the cross-spectral densities for the measurements. The proposed regression-based approach is evaluated using experimental measurements collected from lithium-ion (Li-ion) battery pack in EVs. The parametric analysis of the battery pack has been verified using D SAT Chroma 8000ATS hardware platform. Performance evaluation shows an accurate estimation of these dynamics even in the presence of injected faults.
... A proper definition of the test matrix and test procedure allows the test engineer to investigate and evaluate the dependencies of aging. Based on these test results, aging models can be developed and parameterized that map the influence of application-related loads-well-known as stress factors-to the effects of lithium-ion cell aging [1][2][3][4][5][6][7]. For modeling, it is essential to investigate and simulate all aging-relevant stress factors. ...
... These findings lead to the relevance of ∆DOD and to its strong interaction with ∅SOC: The aging relevance of one of the two stress factors must be assessed in relation to the actual level of the other [1,3]. The relevance of ∆DOD is generally detected, but diversely evaluated [2,11,13,[19][20][21]. The relevance of the discharge current is also controversially discussed, ranging from having no influence [2] to a strong influence [13,18,20,22]. ...
... The relevance of ∆DOD is generally detected, but diversely evaluated [2,11,13,[19][20][21]. The relevance of the discharge current is also controversially discussed, ranging from having no influence [2] to a strong influence [13,18,20,22]. This controversy allows for the following conclusions. ...
As storage technology in electric vehicles, lithium-ion cells are subject to a continuous aging process during their service life that, in the worst case, can lead to a premature system failure. Battery manufacturers thus have an interest in the aging prediction during the early design phase, for which semi-empirical aging models are often used. The progress of aging is dependent on the application-specific load profile, more precisely on the aging-relevant stress factors. Still, a literature review reveals a controversy on the aging-relevant stress factors to use as input parameters for the simulation models. It shows that, at present, a systematic and efficient procedure for stress factor selection is missing, as the aging characteristic is cell-specific. In this study, an accelerated sensitivity analysis as a prior step to aging modeling is proposed, which is transferable and allows to determine the actual aging-relevant stress factors for a specific lithium-ion cell. For the assessment of this accelerated approach, two test series with different acceleration levels and cell types are performed and evaluated. The results show that a certain amount of charge throughput, 100 equivalent full cycles in this case, is necessary to conduct a statistically significant sensitivity analysis.
... Battery cell wear decreases battery capacity, increases inner resistance, increases power loss, and causes a variation in [12], [14], [16], [24], [30], [32]- [34], [56], [65], [72] Li(NiMnCo)O 2 or Li(Ni 1/3 Mn 1/3 Co 1/3 )O 2 [11], [12], [16], [19]- [21], [27], [29], [30], [32], [33], [35], [41], [44], [46], [51], [56], [65], [89] LiCoO 2 [14], [31], [ [6]- [8], [10], [14]- [16], [18], [23]- [26], [28], [36], [37], [42], [44], [45], [48], [50], [52], [53], [56], [57], [59], [61]- [66], [72], [102], [103] LiNi 0.5 Mn 1.5 O 4 ...
... Battery cell wear decreases battery capacity, increases inner resistance, increases power loss, and causes a variation in [12], [14], [16], [24], [30], [32]- [34], [56], [65], [72] Li(NiMnCo)O 2 or Li(Ni 1/3 Mn 1/3 Co 1/3 )O 2 [11], [12], [16], [19]- [21], [27], [29], [30], [32], [33], [35], [41], [44], [46], [51], [56], [65], [89] LiCoO 2 [14], [31], [ [6]- [8], [10], [14]- [16], [18], [23]- [26], [28], [36], [37], [42], [44], [45], [48], [50], [52], [53], [56], [57], [59], [61]- [66], [72], [102], [103] LiNi 0.5 Mn 1.5 O 4 ...
... In Li(Ni 1/3 Mn 1/3 Co 1/3 )O 2 aging tests, the capacity degradation rate influenced the EV range as well as decreased the discharge capacity during V2G operations [44]. This understanding enables us to establish a quantitative physical model. ...
Electric vehicle owners need vehicle to grid operations information before being able to participate in power grid support. Specifically, the lack of understanding of the effect of battery charge and discharge on the lifetime of battery cells inhibits full participation. Batteries are expensive, but increasing their lifetime is one solution to reduce costs. Several battery aging models have been developed to calculate the lifetime and cost of vehicle to grid operations. This review contributes to what is already known by connecting measurement data, driving data, and vehicle to grid operations to the battery cycle aging model. The review identified time, temperature, and the state of charge influence calendar aging. In addition, a number of cycles, depth of discharge as well as charging rate influence cycle aging. Results indicated the batterys naturally decreasing lifetime can be reduced and scheduled charging can extend lifetime. The implications of vehicle to grid operations on battery durability plays an important role in the convenience for electric vehicle owners to support the power grid with ancillary service contribution.
... Frequency domain approaches have been extensively employed in battery SOH studies [17,18]. The EIS-based approaches have been studied generally in laboratory settings to estimate ECM parameters [19,20] and these approaches cannot yet be used in real-life systems. Implementation of EIS in real-life battery applications requires additional hardware in order to generate and sense high-frequency excitation signals and their responses. ...
... The subscripts 4 in (18) and (19) indicate that the model corresponds to Model 4, as in Figure 1d. Equivalent circuit models 1-3 are discussed later in Section 3. The noise in the voltage drop in (17) is written as ...
Battery management systems (BMS) are important for ensuring the safety, efficiency and reliability of a battery pack. Estimating the internal equivalent circuit model (ECM) parameters of a battery, such as the internal open circuit voltage, battery resistance and relaxation parameters, is a crucial requirement in BMSs. Numerous approaches to estimating ECM parameters have been reported in the literature. However, existing approaches consider ECM identification as a joint estimation problem that estimates the state of charge together with the ECM parameters. In this paper, an approach is presented to decouple the problem into ECM identification alone. Using the proposed approach, the internal open circuit voltage and the ECM parameters can be estimated without requiring the knowledge of the state of charge of the battery. The proposed approach is applied to estimate the open circuit voltage and internal resistance of a battery.
... Cell chemistry Target value Baghdadi et al. 2016 [4] NCA, NMC x x Ecker et al. 2012 [5] NMC x x Grolleau et al. 2014 [6] LFP x -Hahn et al. 2018 [7] NMC x -Hoog et al. 2017 [8] NMC x -Marongiu et al. 2015 [9] NMC, LFP x x Naumann et al. 2018 [10] LFP x x Rumberg et al. 2020 [11] NMC x x Schmalstieg et al. 2014 [1] NMC x x Smith et al. 2012 [12] NCA x x Xu et al. 2018 [3] LMO, LFP, NMC x characterization usually includes a capacity measurement and a pulse test, to determine the cells capacity and resistance . Optionally, other supplementary measurements such as an Electrochemical Impedance Spectroscopy (EIS) or charge/discharge cycles at small current rates are added. ...
... means, that the information was not available in the cited source. 3 ----Marongiu et al. [9] CC/CV 1C CC 1C 1 n.a. n.a. ...
To optimize costs and ensure safety, investigation and modeling of battery aging is very important. Calendar aging analysis consist of a periodic sequence of calendar aging and cell characterization. The influence of the characterization on the results of the calendar aging investigation has been assumed to be negligible so far. Nevertheless, the characterization measurements of different studies, especially the important capacity measurement, differ fundamentally. This could have an impact on the results of capacity and resistance change in calendar aging. In this paper, the effect of characterization is quantified for the first time, using periodic characterization measurements. A significant influence in the form of capacity increase and resistance decrease is found. It can be explained by an increase in the active electrode surface area caused by the characterization measurement. Therefore, the cell characterization is a new possible source for capacity increase in calendar aging. As an important result, in future studies capacity measurements should be performed with small currents below 1C to reduce the influence of characterization on the results. Additionally, a correction of the characterization effect can be done according to the presented method.
... To date, most of the models developed for estimating the calendar loss of NMC-based LIBs during the storage period are semi-empirical [30][31][32][33]. For example, a square root time dependency function based on the SEI formation theory was commonly used [32]. ...
... To date, most of the models developed for estimating the calendar loss of NMC-based LIBs during the storage period are semi-empirical [30][31][32][33]. For example, a square root time dependency function based on the SEI formation theory was commonly used [32]. A nearly linear trend of the capacity degradation curve was observed if the square root time dependency function was used [8,30]. ...
Li-ion batteries with nickel-manganese-cobalt (NMC) cathode and graphite anode are popularly used in portable electronic devices and electric vehicles. Calendar loss of the lithium ion battery is a dominating factor in battery degradation. However, few modeling work was reported on studying the calendar capacity loss of NMC-graphite Li-ion batteries. In this work, an electrochemical model for NMC-graphite Li-ion battery was developed to investigate its calendar loss behavior. Various factors affecting the calendar loss of the NMC-graphite batteries were systematically investigated, with the results validated with experimental data of a Sanyo 18,650 cylindrical cell. It was found that at 25 o C working temperature and 100% SOC, the capacity drops 6.4% of its original capacity after 10 months. Also, when the anode particle size decreases from 26.2 {\mu}m to 6.55 {\mu}m, the capacity drop ratio is over 22% after 10 months under the same operation condition. Our simulation results demonstrate that a smaller SOC, a lower cell working temperature and a larger particle size could prolong the battery life during the storage period. This modeling work can help better understand the calendar loss behavior of NMC-graphite Li-ion batteries, and serve as a robust reference for the battery performance optimization in future.
... High discharge rates lead to accelerated battery ageing [53,54]. Therefore, discharge during V2G is limited to a SOC of 20%, which is consistent with literature data and real V2G applications [55,56]. ...
A key factor in limiting global warming is the conversion of conventional electricity generation to renewable energy sources. However, a major obstacle is that renewable energy generation and energy demand often do not coincide in time, and energy must therefore be stored temporarily. Vehicle to grid (V2G) can be used to store excess renewable energy in battery electric vehicles (BEVs) and feed it back into the electric grid when needed. For effective V2G operation, the grid may have to be expanded, as the energy needs to be transported to BEVs. However, the grid should only be strengthened where renewable energy demand exceeds current grid capacity due to high grid expansion costs. This requires a method that determines the spatial distribution of V2G potential at a high resolution. Since such a method has not yet been reported in the existing literature, and so is developed in this paper. The method is demonstrated for the city of Berlin and its 448 sub-districts. For each sub-district, the method allows determining the percentage of residential and BEV energy demand that can be met by renewables if V2G is deployed, and answers the question of whether a full renewable supply is possible. The results show that BEVs can be effectively used as intermediate storage for renewable energy. If 30% of the BEVs participate in V2G, more than 99% of the energy demand of households and BEVs in Berlin can be covered by renewables on certain days. On the other hand, V2G deployment increases the average peak load in the districts by up to 100% and results in a nearly double load on vehicle batteries. High shares of renewable energy can be observed in districts with a high degree of motorization, which are predominantly found in the outskirts of the city.
... In this section, the model is validated by comparing the estimated C loss output to other known measured C loss . For Cycling Aging, data needed to estimate the C loss and compare it to a measured one is obtained from (Marongiu et al., 2015). Table 4, first column shows a summary of the data that used to estimate the Cycling Aging C loss . ...
Understanding battery capacity degradation is instrumental for designing modern electric vehicles. In this paper, a Semi-Empirical Model for predicting the CapacityLoss of Lithium-ion batteries during Cycling and Calendar Aging is developed. In order to predict the Capacity Loss with a high accuracy, battery operation datafrom different test conditions and different Lithium-ion batteries chemistries were obtained from literature for parameter optimization (fitting). The obtained modelswere then compared to experimental data for validation. Our results show that the average error between the estimated Capacity Loss and measured Capacity Loss isless than 1.5% during Cycling Aging, and less than 2% during Calendar Aging. An electric mining dumper, with simulated duty cycle data, is considered as an application example.
... Dubarry et al. [37] studied the influence of V2G service on battery degradation and concluded that a V2G step (1 h discharging at peak demand for electricity) twice a day increased the capacity loss by 75% and the resistance by 10%. Besides, due to the uncertainty in BEV behavior, V2G services will cause uneven degradation extents among different BEVs in the V2G system [38]. Actively controlling the distribution can make V2G services fairer and meet the wishes of users. ...
A new Vehicle-to-Micro-Grid (V2μG) network is studied in this paper. • The degradation of BEV batteries can be reduced by introducing FCEVs to the system. • System configuration, capacity and working modes are optimized using NSGA-II. • The proposed V2μG network can contribute to 516 t CO 2 emission reduction annually. A R T I C L E I N F O Keywords: Off-grid building energy system Vehicle-to-grid network Electric vehicles Energy storage A B S T R A C T To fully exploit the potential of decarburization in the transport sector (e.g., electric vehicles (EV)) and energy sector (e.g., building energy system), this paper proposes a new concept of 'Vehicle-to-Micro-Grid (V2μG) network' that incorporates the off-grid building energy system with flexible power storage/supply provided by battery EVs (BEVs) and fuel cell EVs (FCEVs). The work is conducted with three main contributions: 1) a rule-based energy management strategy is proposed to study the impact of the V2μG interactions on the EV battery degradation; 2) a scenario analysis based on four working modes is conducted to evaluate the energy efficiency , costing, environmental impacts, and component ageing of the proposed V2μG network; 3) the optimum settings for system configuration, capacity, and operation strategy of the V2μG network are obtained by the NSGA-II algorithm. The study suggested that the degradation of lithium-ion batteries in BEV can be reduced by 13% compared to the network without FCEVs. In a community with 160 households and 200 EVs, the optimal V2μG network can reduce carbon dioxide emissions by 515.56 tons annually compared to the conventional off-grid building energy system powered by internal combustion engines.
... The authors of [17] assessed the battery aging considering different technologies in V2G applications using RCP in Real-Time Simulator architectures. Particularly, they tested two real batteries modules equipped with two different lithiumion battery chemistries in 4 different scenarios. ...
... To estimate the SoH utilising input data as current, voltage and temperature. generally feed forward neural networks and convolutional neural networks are utilised [16][17][18][19][20][21][22]. ...
Electric Vehicles (EVs) are becoming more and more financially viable as the operating costs of EVs fall dramatically in comparison to Internal Combustion Engine Vehicles (ICEVs). To boost consumer trust in EVs even further, accurate State of Health - SOH measurement is essential. SOH in a battery is determined by a number of parameters, including current, voltage, age, and temperature. Estimating the SOH of a Lithium -ion battery chemistry is of a difficult task. Because lithium-ion batteries are extremely nonlinear, time-variant, and complicated electrochemical systems, this is the case. Two machine learning techniques are used in this article to estimate SOH from Lithium-ion battery cell experimental test data. Experiments are carried out using data from NASA's Prognostic Center of Excellence.
... For example, a high SOC over 80% accelerates solid electrolyte interphase (SEI) growth (Barré et al. 2013). Other stressors accelerating battery ageing are high charge and discharge C-rates 1 as well as a high ∆ (Gewald et al. 2020;Marongiu, Roscher, and Sauer 2015). Known battery stressors are qualitatively displayed in Table 1. ...
Due to the global transition to electromobility and the associated increased use of high-performance batteries, research is increasingly focused on estimating and forecasting the state of health (SOH) of lithium-ion batteries. Several data-intensive and well-performing methods for SOH forecasting have been introduced. However, these approaches are only reliable for new battery types, e.g., with a new cell chemistry, if a sufficient amount of training data is given, which is rarely the case. A promising approach is to transfer an established model of another battery type to the new battery type, using only a small amount of data of the new battery type. Such methods in machine learning are known as transfer learning. The usefulness and applicability of transfer learning and its underlying methods have been very successfully demonstrated in various fields, such as computer vision and natural language processing. Heterogeneity in battery systems, such as differences in rated capacity, cell cathode materials, as well as applied stress from use, necessitate transfer learning concepts for data-based battery SOH forecasting models. Hereby, the general electrochemical behavior of lithium-ion batteries, as a major common characteristic, supposedly provides an excellent starting point for a transfer learning approach for SOH forecasting models. In this paper, we present a transfer learning approach for SOH forecasting models using a multilayer perceptron (MLP). We apply and evaluate it on the method presented by von Bülow, Mentz, and Meisen (2021) using five battery datasets. In this regard, we investigate the optimal conditions and settings for the development of transfer learning with respect to suitable data from the target domain, as well as hyperparameters such as learning rate and frozen layers. We show that for the transfer of a SOH forecasting model to a new battery type it is more beneficial to have data of few old batteries, compared to data of many new batteries, especially in the case of superlinear degradation with knee points. Contrarily to computer vision freezing no layers is preferable in 95% of the experimental scenarios.
... In this chapter, an equivalent degradation cost of charging/discharging cycles for a certain depth of discharge (DOD) is proposed to the objective function as follows: The battery has generally two types of degradations: self (calendar fade) and cycle [418,419]. Self-degradation represents the corrosion of internal materials that has no relationship with charging/discharging cycles, and therefore is considered as a constant value, for example, if the self-life of a battery is 10 years, then the degradation per day can be calculated as 100% 365 * 10 = 0.02739%. ...
This dissertation focuses on the energy management of a community microgrid to
minimise its operational cost under uncertain power generation, power demand and
electricity prices. The study proposes some effective solution approaches to minimise the operational cost of the microgrid. Firstly, a real-time energy management
scheme that is free from the effect of uncertainty is developed and compared with
existing management schemes to efficiently control the battery energy of a microgrid.
In the objective function for the scheme, a dynamic penalty function is added to
incentivise battery charging during low electricity prices. The proposed method can
reduce operational cost by 12 per cent as compared to a well-established existing one
over a time horizon of 96 hours. Secondly, a two-hour ahead energy management
approach considering the degradation cost of the battery and a penalty function to
reflect the true operational cost is proposed. The optimisation problem formulated
is solved using a particle swarm optimisation algorithm which is designed. The pro-
posed energy management approach reduces electricity cost by up to 44.50 per cent
compared to a baseline method and 37.16 per cent compared to another existing
approach. Finally, a day-ahead scheduling of the battery energy is proposed while
considering its degradation costs due to charging-discharging cycles. The degradation costs with respect to the depth of charge are modelled and added to the objective
function to determine the actual operational costs of the microgrid. A framework
to solve the optimisation problem formulated is developed in which particle swarm
optimisation, the Rainflow algorithm and scenario techniques are integrated. Uncertainties of variables, such as power generation and electricity prices, are also
discussed in the study. Simulation results demonstrate that the proposed method
for a day-ahead scheduling program can reduce the operational costs by around 40
per cent compared to the baseline method. Results also reveal that uncertainty in
power generation and power demand has no influence on the energy schedule of the
battery, but variation in electricity prices has an impact on the outcome. Several
pragmatic tests verify the effectiveness of the proposed methods.
... The operating fluctuations originated from traffic flow dynamics, varying mechanical forces and road conditions on the battery performance have been studied in [13]. Other factors that affect the EV battery in vehicle-to-grid (V2G) applications and its aging have been explored in [14][15][16]. However, more research in relation to EV applications is still needed as development of comprehensive analytical battery lifetime models and the extension of battery lifetime models in a probabilistic manner to perform reliability analysis are yet to be carried-out. ...
... Note that studies that only discuss cyclic ageing have inherently incorporated calendar ageing as well. In this case, the superposition of calendar ageing is assumed to be nonexistent [98], [106] or negligible compared to the cycling induced ageing. However, in these cases, the fast sequential cycling tests mask the effect of calendar ageing and, therefore, lead to estimation errors in applications with significantly more time between cycles. ...
Battery ageing is one of the critical problems to be tackled in battery research, as it limits the power and energy capacity during the battery’s life. Therefore, optimizing the design of battery systems requires a good understanding of ageing behaviour. Due to their simplicity, empirical and semi-empirical models are frequently used in smart charging studies, feasibility studies, cost analyses studies, among other uses. Unfortunately, these models are prone to significant estimation errors without appropriate knowledge of their inherent limitations and the interdependency between stress factors. This paper presents a review of empirical and semi-empirical modelling techniques and ageing studies, focusing on the trends observed between different studies and highlighting the limitations and challenges of the various models. First, we summarize the main ageing mechanisms in lithium-ion batteries. Next, empirical modelling techniques are reviewed, followed by the current challenges and future trends, and conclusion. Our results indicate that the effect of stress factors are easily oversimplified, and their correlations often not taken into account. The provided knowledge in this paper can be used to evaluate the limitations of ageing models and improve their accuracy for various applications.
... Ces modèles décrivent l'évolution de la capacité et/ou l'impédance en fonction des [118,131], polynômiales [141] ou les deux à la fois [35,102]. A noter que pour le vieillissement calendaire, la baisse de la capacité est souvent exprimée en fonction de la racine carrée du temps écoulé [78]. ...
Les batteries au lithium ion sont considérées comme un des vecteurs principaux de la transition énergétique. Elles sont dotées d’une importante densité d’énergie, combinée avec un faible effet de mémoire. Grâce à ces avantages, la technologie Li-ion est largement privilégiée pour des applications de stockage embarqué, notamment à bord des véhicules électriques. Cependant, l’intégration réussite des batteries Li-ion est confrontée à un défi majeur qui est le vieillissement de ces éléments. En effet, les performances de ces batteries se dégradent au fil du temps et d’usage. Ceci se traduit par la diminution de la quantité d’énergie et la perte de la puissance délivrée par la batterie. Dans ce cadre, deux objectifs principaux sont visés à travers ce travail. D’une part, l’étude expérimentale du comportement ainsi que du vieillissement des batteries Li-ion sous différentes conditions d’opération. Et d’autre part, l’élaboration d’une nouvelle approche pour l’estimation de l’état de santé des batteries. Ainsi, une intense activité de caractérisation est conduite après la prise en main d’un nouveau dispositif expérimental d’une échelle industrielle. Ensuite, la question de l’estimation du SoH est abordée. D’abord, une synthèse exhaustive des méthodes existantes est réalisée. Puis, deux solutions sont proposées afin de répondre au mieux aux exigences d’une application en ligne.La première campagne expérimentale a été menée pour étudier le comportement d’un paramètre clé des batteries qui est la tension en circuit ouvert. Ce dernier rentre en jeu lors de l’élaboration des modèles de simulation. Ainsi, deux technologies de batteries ont été testées à plusieurs niveaux de températures. Cette étude a permis d’apporter une nouvelle évaluation de l’impact des variations de l’OCV sous l’effet de la température sur le comportement des éléments du circuit équivalent de la batterie. La deuxième campagne expérimentale était consacrée à la mise en place du vieillissement accéléré des batteries. Le protocole des tests est élaboré en alternant des phases de sollicitions dynamiques émulant deux modes d’opération en véhicule tout électrique et hybride. Ensuite, un test de référence fut réalisé afin de mesurer les caractéristiques des batteries et ainsi quantifier leurs niveaux de dégradation.Une nouvelle méthode d’estimation de la dégradation est développée dans ce travail. Elle consiste en la combinaison d’un modèle de comportement sous forme de circuit équivalent et un modèle d’évolution de la dégradation qui est le processus de Wiener. Dans cette configuration, le premier modèle fourni au deuxième l’information sur l’état actuel des paramètres internes de la batterie. Ce dernier délivre une projection future de l’évolution de l’état de la batterie. Cette nouvelle combinaison permet de réaliser deux objectifs à la fois. D’abord, la RUL peut être prédite grâce à l’estimation de l’évolution de la dégradation qu’offre le processus de Wiener. Ce dernier a été toujours employé d’une façon hors ligne, où ses paramètres sont mis à jour en se basant sur une information qui n’est pas obtenue en temps réel. Grâce à la solution proposée, l’information sur la dégradation est acquise à travers le circuit équivalent pour mettre à jour le processus de Wiener en temps réel. Ceci est le deuxième objectif réalisé. En somme, les deux modèles se complètent pour tirer le maximum de profits de leurs propriétés respectives.Enfin, une amélioration des techniques d’apprentissage automatique pour l’estimation de la dégradation des batteries Li-ion est proposée. En particulier, l’attention a été protée aux caractéristiques qui représentent les signatures de la dégradation. D’abord, un grand nombre est extrait englobant pour la première fois toutes les caractéristiques citées en littérature dans un seul modèle d’estimation. Ensuite, nous avons intégré la méthode du « meilleur sous ensemble » pour tirer l’information la plus pertinente des caractéristiques.
... Yue Yu et al., [16] presented a framework of optimal EV charging/discharge strategies for ancillary services of a smart grid by proposing a multi-objective optimisation task to minimise the system losses and battery cycling degradation. Marongiu et al., [17] studied the effect of different V2G strategies on the lifetime of two different liion batteries types to show how the ageing effect of the batteries could be reduced. However, most of the studies focus on evaluating either calendar ageing or cycling ageing under fixed operational conditions. ...
Transport electrification is a key enabler to reduce fossil fuel depletion and related carbon dioxide emissions. However, critical barriers exist in terms of battery costs and their expected life. Vehicle-to-grid technology can bring benefits to both the electrical power grid and electric vehicle owners, while its practical implementation faces challenges due to concerns over accelerated battery degradation. This paper presents a comprehensive study on reduced Lithium-ion battery degradation through state-of-charge pre-conditioning strategies that allow an electric vehicle to participate in vehicle-to-grid operations during periods in which the vehicle is parked. Energy capacity reduction of the electric vehicle battery are predicted using semi-empirical ageing models, which have been built and validated to capture the degradation behaviours of the battery with respect to both calendar and cycling ageing. Five charging strategies for battery state-of-charge pre-conditioning have been developed to evaluate the ability to mitigate battery ageing before commencing vehicle-to-grid operation. Simulation studies on battery degradation utilizing such charging mechanisms under two different operational profiles have been undertaken. The analytical results show that the proposed charging strategies do not accelerate battery degradation and are capable of mitigating the total ageing process from 7.3 – 26.7% for the first 100 days of operational life and gradually vary to 8.6 – 12.3% for one-year continual operation compared to the reference standard charging approach.
... In this section, the model is validated by comparing the estimated C loss output to other known measured C loss . For Cycling Aging, data needed to estimate the C loss and compare it to a measured one is obtained from (Marongiu et al., 2015). Table 4, first column shows a summary of the data that used to estimate the Cycling Aging C loss . ...
Understanding battery capacity degradation is instrumental for designing modern electric vehicles. In this paper, a Semi-Empirical Model for predicting the Capacity Loss of Lithium-ion batteries during Cycling and Calendar Aging is developed. In order to redict the Capacity Loss with a high accuracy, battery operation data from different test conditions and different Lithium-ion batteries chemistries were obtained from literature for parameter optimization (fitting). The obtained models were then compared to experimental data for validation. Our results show that the average error between the estimated Capacity Loss and measured Capacity Loss is less than 1.5% during Cycling Aging, and less than 2% during Calendar Aging. An electric mining dumper, with simulated duty cycle data, is considered as an application example.
... This study takes a practical approach towards battery aging to limit the complexity and avoid non-linear constraints. Empirical studies show that for C-rates 2 of 1 c or less, which can be expected as the outcome of the presented model, the capacity fade is close to linear [48,49]. ...
The possibility of electric vehicles to technically replace internal combustion engine vehicles and to deliver economic benefits still mainly depends on the battery size and the charging infrastructure costs as well as on annual mileage (utilizing the lower variable costs of electric vehicles). Current studies on electric vehicles’ total cost of ownership often neglect two important factors that influence the investment decision and operational costs: firstly, the trade-off between battery and charging capacity; secondly the uncertainty in energy consumption. This paper proposes a two-stage stochastic program that minimizes the total cost of ownership of a commercial electric vehicle under uncertain energy consumption and available charging times induced by mobility patterns and outside temperature. The optimization program is solved by sample average approximation based on mobility and temperature scenarios. A hidden Markov model is introduced to predict mobility demand scenarios. Three scenario reduction heuristics are applied to reduce computational effort while keeping a high-quality approximation. The proposed framework is tested in a case study of the home nursing service. The results show the large influence of the uncertain mobility patterns on the optimal solution. In the case study, the total cost of ownership can be reduced by up to 3.9% by including the trade-off between battery and charging capacity. The introduction of variable energy prices can lower energy costs by 31.6% but does not influence the investment decision in this case study. Overall, this study provides valuable insights for real applications to determine the techno-economic optimal electric vehicle and charging infrastructure configuration.
... They also found the impact of bi-directional charging for maximizing the profit of EV users by using commercial Li-ion cells. Another study made by[112] used an empirical model to find the V2G viability taking into consideration the energy cost and battery longevity for battery degradation.Habib et al., 2015[113] made a comparative review on the charging strategy of an EV in addition to V2G technology to investigate their impact on the power distribution network. They also stated that the charging strategy and vehicle aggression could make V2G technology economically viable. ...
Electric vehicles are an important option for reducing emissions of greenhouse gases. Electric vehicles not only reduce the dependency on fossil fuel but also diminish the impact of ozone depleting substances and promote large scale renewable deployment. Despite comprehensive research on the attributes and characteristics of electric vehicles and the nature of their charging infrastructure, electric vehicle production and network modelling continues to evolve and be constrained. The paper provides an overview of the studies of Electric Vehicle, Hybrid Electric Vehicle, Plug-in-Hybrid Electric Vehicle and Battery Electric Vehicle penetration rate into the market and discusses their different modelling approach and optimisation techniques. The research on the essential barriers and insufficient charging facilities are addressed for a developing country like India that makes the study unique. The development of new concept of Vehicle-to-Grid has created an extra power source when renewable energy sources are not available. We conclude that taking into account, the special characteristics of electric vehicles are so important in their mobility.
... Frequency domain approaches are suited only for active estimation, where a predetermined current applied as system excitation signal. In the literature, EIS based approaches have been widely used in laboratory setting for ECM model identification [2] and for state of health modeling [3]. • Time-domain approaches. ...
Real-time identification of electrical equivalent circuit models is a critical requirement in many practical systems, such as batteries and electric motors. Significant work has been done in the past developing different types of algorithms for system identification using reduced equivalent circuit models. However, little work was done in analyzing the theoretical performance bounds of these approaches. Proper understanding of theoretical bounds will help in designing a system that is economical in cost and robust in performance. In this paper, we analyze the performance of a linear recursive least squares approach to equivalent circuit model identification and show that the least squares approach is both unbiased and efficient when the signal-to-noise ratio is high enough. However, we show that, when the signal-to-noise ratio is low - resembling the case in many practical applications - the least squares estimator becomes significantly biased. Consequently, we develop a parameter estimation approach based on total least squares method and show it to be asymptotically unbiased and efficient at practically low signal-to-noise ratio regions. Further, we develop a recursive implementation of the total least square algorithm and find it to be slow to converge; for this, we employ a Kalman filter to improve the convergence speed of the total least squares method. The resulting total Kalman filter is shown to be both unbiased and efficient in equivalent circuit model parameter identification. The performance of this filter is analyzed using real-world current profile under fluctuating signal-to-noise ratios. Finally, the applicability of the algorithms and analysis in this paper in identifying higher order electrical equivalent circuit models is explained.
... The benefits and drawbacks to both the vehicle owner and the energy provider of these strategies have been well-documented [6][7][8][9][10][11][12][13][14][15][16][17][18], and the main obstacle was identified to be the additional usage on the cells [19][20][21], among other challenges [22]. Few experimental studies [23][24][25][26][27][28] attempted to account for the change in battery degradation resulting from the implementation of these strategies. To accurately account for the change in usage, the path dependence of degradation needs to be considered in the estimation [29]. ...
The usage of electric vehicle batteries to assist the main electric grid for the storage of energy provided by intermittent sources should become an essential tool to increase the penetration of green energies. However, this service induces additional usage on the cells and, therefore, could degrade them further. Since degradation is path-dependent, it is of paramount importance to test the impact of all the different grid applications on the batteries. In this work, we tested the additional usage induced by using electric vehicle batteries for frequency regulation at moderate rates during rest or charge and found no detrimental effect after around 2000 cycles on the cells.
... Such capacity loss was shown to be dependent to the state of lithiation swing (which could be approximated by the DOD) of the electrode, during lithiation [23]. Similar experimental results were reported in [24][25][26]. ...
Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing.
In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model.
The first paper of the series focussed on the systematic modelling and experimental verification of cell degradation through calendar ageing. Conversantly, this second paper addresses the same research challenge when the cell is electrically cycled. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 124 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 26 tested cells achieves an overall mean-absolute-error of 1.04% in the capacity curve prediction, after being validated under a broad window of both dynamic and static cycling temperatures, Depth-of-Discharge, middle-SOC, charging and discharging C-rates.
... It is not only a crucial parameter for drivers but also an index in the battery management system (BMS) [3]. Without an accurate SOC knowledge, EVs would not meet the requirements for a safe and reliable operation, as the BMS would not manage to predict the driving range accurately nor to balance the battery cells [4]. On the other hand, the state of power (SOP) represents the available charging and discharging power for batteries. ...
In this paper, an improved method for estimating the state of charge (SOC) of lithium-ion batteries is proposed, which is developed from the particle filter (PF). An improved genetic particle filter (GPF), owing to the advantages of the PF and genetic algorithm, is proposed to overcome the disadvantage of the traditional particle filter: lacking the diversity of particles. Firstly, the relationship between SOC and open-circuit voltage (OCV) is identified on the low-current OCV test. Secondly, a first-order resistor and capacitance (RC) model is established, then, the least-squares algorithm is used to identify the model parameters via the incremental current test. Thirdly, GPF and the improved GPF (IGPF) are proposed to solve the problems of the PF. The method based on the IGPF is proposed to estimate the state of power (SOP). Finally, IGPF, GPF, and PF are employed to estimate the SOC on the federal urban driving schedule (FUDS). The results show that compared with traditional PF, the errors of the IGPF are 20% lower, and compared with GPF, the maximum error of the IGPF has declined 1.6% SOC. The SOC that is estimated by the IGPF is applied to estimate the SOP for battery, considering the restrictions from the peak SOC, the voltage, and the instruction manual. The result shows that the method based on the IGPF can successfully estimate SOP.
... However, this gives rise to the following intriguing questions: 1) Will the installed sensors be able to accurately monitor the incipient in-cell variations happening at pack-level? 2) Do the conventional battery model-based methods [21,22] take into account the dynamic variations between the composition of two parallel cells, that are connected in a string? Despite of the claimed BMS accuracy in the literature, fewer studies have tested the impact of grid ancillary services on vehicle battery [23,24]. V2G systems and the ancillary services could introduce performance imperfections in cells, such as: 1) create errors of capacity and power efficiency fade due to cyclic aging, SoC saturation limits, temperature variations, etc. [25], 2) cause random dynamic deviations and offsets of in-cell measurements, and 3) propagation of faults due to these errors and variations. ...
Vehicle-to-Grid (V2G) technology enables bi-directional charging of electric vehicle (EV) and facilitates power grid ancillary services. However, battery pack in EV may develop in cell dynamic variations over time. This is due to the structural complexity and electrochemical operations in the battery pack. These variations may arise in V2G systems due to 1) additional charging and discharging cycles to power grid, 2) external shocks, and 3) long exposures to high temperatures. A particular source of these variations are due to faulty sensors. Therefore, it can be argued that the battery packs in EV are highly reliant on the monitoring of these in-cell variations and their impact of propagation with each involved component. In this paper, a prediction-based scheme to monitor health of variation induced sensors is proposed. Firstly, a propagation model is developed to predict the in-cell variations of a battery pack by calculating the covariance using a median-based expectation. Secondly, a hypothesis model is developed to detect and isolate each variation. This is obtained by deriving a conditional probability-based density function for the measurements. The proposed monitoring framework is evaluated using experimental measurements collected from Li-ion battery pack in electric vehicles. The in-cell variation profiles have been verified using D-SAT Chroma 8000ATS hardware platform. The performance results of the proposed scheme shows accurate analysis of these emerged variations.
... Recently, the vehicle-to-grid strategy has also been studied as a stress factor [49]. ...
Commercial lithium-ion battery modules are commonly used to form battery packs for hybrid and electric non-road mobile machinery. The characterization of batteries is a time-consuming task, because very slow dynamics are present and the characteristics are strongly affected by the state of charge, discharge rate, and temperature. Batteries also exhibit reversible heat generation, which is associated with the entropy change in the electrodes resulting from structural changes caused by the intercalation of lithium ions during charging and discharging. The entropy change characteristics, which can be either exothermic or endothermic, are strongly dependent on the materials and composition of the electrodes, and thus, the entropy change characteristics need to be obtained experimentally.
This dissertation proposes efficient and effective methods for the electrical and thermal characterization of lithium-ion batteries. The aim is to reduce the time and effort involved in the experimental testing and parameterization as well as to improve the accuracy of the
resulting model. A semi-empirical approach, in which the battery model consists of coupled lumped-parameter electrical and thermal models and the characterization is performed on the basis of current, voltage, and temperature data, is adopted in this dissertation.
A systematic methodology is presented for the characterization of the capacity, open-circuit voltage, internal impedance, entropy change, and thermal properties by using only two types of experiments, which can be applied for commercial battery modules: (i) galvanostatic intermittent discharging and charging and (ii) continuous thermal loading. Module-level characterization inherently includes the manufacturing tolerances between the cells in a module as well as the effects of the cooling system into the model. Furthermore, it results in a low-order model that can be scaled for any battery pack configuration.
The use of a conventional potentiometric method for entropy change characterization takes several weeks to complete. In this dissertation, a novel entropy change characterization method is presented, which uses the empirical temperature data from the galvanostatic intermittent discharging and charging experiments and the corresponding estimated temperature data to extract the entropy change characteristics. The advantage of the method is that no dedicated characterization experiment is needed. Furthermore, because the entropy change is not dependent on the rate or temperature, the characterization can be performed with the data from several experiments, which improves the accuracy of the results.
Battery life prediction is steadily becoming more relevant due to the increased use of batteries in stationary and mobile applications. Current semi-empirical aging models are often supplemented by empirical model equations and parametrized on the entire available measurement data set. This practice limits their extrapolation capability and transferability. The model in this work describes the two important anode aging mechanisms, solid electrolyte interface (SEI) cracking and reforming and cracking of the active material, by completely physically based equations. A simple incremental capacity analysis (ICA) method is introduced to allow targeted parameterization of the model equations with measurement data, in which the aging modes associated with the respective aging mechanism are present. The overall model can accurately describe the battery capacity loss under dynamic frequency containment reserve loading. It is transferable to all graphite-based battery cell chemistries and provides a basis for future semi-empirical aging models, describing the capacity loss in a wide variety of applications by considering further aging mechanisms.
Free access to the paper until March 16, 2023: https://authors.elsevier.com/a/1gUFs1M7w0ay24
Electrification is one of the main decarbonization strategies for transportation. While uncontrolled electric vehicle (EV) charging can challenge the electricity system, controlled EV charging can offer flexibility. Using an agent-based model, we simulate combinations of two elements of EV charging, plug-in behaviors and controlled-charging processes, and measure flexibility goals with four metrics: total load shift, increase in midday load, peak reduction, and flatness of the load curve. We reveal trade-offs between these flexibility goals, which indicates that the most beneficial combinations are specific to spatial areas and their flexibility goals. Furthermore, we find that controlled-charging processes show higher impact on the flexibility metrics than plug-in behaviors, particularly with high EV and charging-station diffusion, but less so in rural areas. Incentivizing beneficial combinations can increase the flexibility potential of EV charging and potentially avoid grid reinforcements.
Lithium-ion (Li-ion) batteries with nickel-manganese-cobalt (NMC) cathode and graphite anode are popularly used in portable electronic devices and electric vehicles. Calendar loss of the lithium-ion battery is a dominating factor in battery degradation during long-term usage. However, only a few physics-based modeling works were reported on studying the calendar capacity loss of NMC-graphite Li-ion batteries, while none of them can depict the complete voltage behavior during the storage period. In this work, a Pseudo-2D model for an NMC-graphite Li-ion battery was developed and applied to investigate its calendar loss behavior. Various factors affecting the calendar loss of the NMC-graphite batteries were systematically studied, with the results validated using experimental data of a Sanyo 18,650 cylindrical cell. It was found that at 25 °C working temperature and 100% state of charge (SOC), the capacity drops 6.3% of its original capacity after 10 months. Our simulation results demonstrate that a lower SOC and a proper cell working temperature could prolong the battery life during the storage period. This modeling work can help improve understanding of the calendar loss behavior of NMC-graphite Li-ion batteries and provide valuable guidance for battery performance optimization in the future.
Currently, several methods for battery state of health (SOH) prediction exist which are applicable to battery electric vehicles (BEV). However, only few research has been conducted on SOH forecasting based on features that encode causes for battery ageing applicable in real world applications. This paper proposes a machine learning method for SOH forecasting applicable for BEV fleet managers and battery designers in real world applications. As model inputs, we use the battery's operation time within certain operation ranges defined by combinations of the battery signals current, state of charge (SOC) and temperature. Different variants of this temporal aggregation of the battery operation time and of the operation ranges of the battery signals are examined. Our findings state that combining different cycle window widths ww to one training data set improves the generalization of the model. Also, we find that the fineness of the operational ranges of the signals does not limit the model's performance if ww is larger than 100 cycles or different ww are combined.
In the work, a battery model is developed based on experiments performed on lithium ion cells to estimate the degradation that a battery will undergo, given its usage profile. This battery model is used to perform a case study to evaluate the profits that an ancillary service provider may expect if they use a fleet of electric vehicles (EVs) to provide different ancillary services on the electricity market. The battery model computed the battery degradation in the battery of the EVs in the presence and absence of service provision and this additional degradation is considered as a cost to the service provider. This also helps to design a remuneration strategy for the owners of the individual EVs. The services considered are frequency containment reserve, peak shaving and a combination of the two. The results show that, considering an investment of € 1000 per V2G charger (borne by the service provider), providing a combination of frequency containment reserve and peak shaving is the most profitable of all the ventures, with a net present value of € 19500 after 10 years, while providing peak shaving and frequency containment reserve individually have a net present value of around € 11000 and € −21100 after 10 years, respectively.
Vehicle-to-grid (V2G) technology could increase electric vehicle (EV) revenues and grid flexibility. Prior analyses have quantified potential V2G revenues, but each has ignored at least one of four factors that could drive future V2G revenues: future grid changes, large EV numbers, V2G interactions with electricity prices, and V2G operational responses to shifts in electricity prices. Our innovation is the capture of all four factors to quantify net revenues of a fleet of V2G-enabled EVs in California through 2030 by co-simulating optimization models for V2G and power system operations. We find V2G-enabled EVs generate an average of $32-$48 more annual net revenues than smart-charging EVs. Declining electricity prices through 2030 due to decarbonization and V2G participation decreases V2G revenues, and ignoring fleet-wide V2G participation significantly overestimates V2G annual net revenues. Overall, these results indicate V2G revenues could be modest and decrease in the future, underscoring the value of our co-optimization framework.
Vehicle-to-grid (V2G) technology could turn electric vehicles (EVs) into a potentially valuable solution to the problem of increased load demand caused by large-scale EV integration. Successful market penetration of V2G relies not only on developing the technology itself, but also on EV drivers' willingness to participate in this technology. This paper aims to explore Dutch EV drivers' preferences for participating in V2G contracts. In particular, we conduct a context-dependent stated choice experiment to examine the impact of EV recharging technology on the V2G contract preferences. Two contexts have been designed: the current EV recharging time and fast recharging. Our results show that in the context of current recharging time, Dutch EV drivers in general prefer not to participate in V2G contracts, while the opposite is true in the context of fast recharging. With regard to specific V2G contract attributes, Dutch EV drivers are most concerned about ‘discharging cycles’. Also important to them is ‘the guaranteed minimum battery level’, but its importance drops significantly in the fast charging context. In addition, ‘monthly remuneration’ and ‘plug-in time’ also influence people's preferences for adopting V2G. From these findings, we draw the implications for the aggregator and policy makers.
Due to their impressive energy density, power density, lifetime, and cost, lithium-ion batteries have become the most important electrochemical storage system, with applications including consumer electronics, electric vehicles, and stationary energy storage. However, each application has unique, often conflicting product specifications, requiring a balanced overall assessment. The Ragone plot is commonly used to compare the energy and power of lithium-ion battery chemistries. Important parameters including cost, lifetime, and temperature sensitivity are not considered. A standardized and balanced reporting and visualization of specifications would greatly help an informed cell selection process.
Energy and power density of batteries are commonly compared using standard short-term test protocols. Non-standard parameters, e.g., battery cost, are usually not considered. Even though cost is an essential criterion in many applications, it is also the most controversial, since individual cell prices are not openly shared and depend on non-technical factors such as production volume and operating margin. Second, lifetime comparisons of lithium-ion batteries are widely discussed in the literature, but these comparisons are especially challenging due to the high sensitivity of lithium-ion battery lifetime to usage conditions (e.g., fast charge, temperature control, cell interconnection, etc.). Additionally, the metrics for lifetime are not standardized, and conclusions about lifetime performance generally depend on the choice of metrics used (e.g., relative vs absolute capacity or energy; see Supporting Information, Figure S1). Battery degradation is often non-linear but is nevertheless commonly reflected by a single parameter from a linear fit, e.g., the slope. Despite known challenges and shortcomings, standardized reporting and visualization of parameters is required for both a fundamental understanding of cell performance and practical cell choice. Bubble plots have been used to compare four material parameters of lithium metal electrodes. Here, we extended this approach by leveraging the increasing number of open-source battery datasets. ENPOLITE plots compare several hundred battery cells in a single bubble plot derived from a raw dataset exceeding 1000 GB.
The selection of the lithium-ion battery chemistry is a crucial step when designing a certain application that includes an energy storage device, as it could limit the lifetime of the system. This paper presents two empirical cycling degradation models designed for NMC and LFP lithium-ion battery chemistries. The novel contribution of the models consists on representing the effect of the degradation stress factors as function of battery chemistries, rather than single cell references as typically approached in the literature. This know-how is claimed to be potentially valuable in order to compare characteristics of different lithium-ion battery chemistries and therefore help in the selection of the definite cell reference to be integrated in a certain application. A methodology is proposed for the development of such empirical degradation models, which are built upon large sets of degradation data collected from the literature and include information of diverse cell references and stress factors. A review of the state-of-art reveals that the data of NMC and LFP cathode chemistries is enough rich to develop degradation models that consider the main cycling stress factors accelerating battery ageing – number of full equivalent cycles, operation temperature, depth-of-discharge, charge and discharge current rate, and state-of-charge. Eventually, the cycling degradation models are constructed based on data from 232 degradation tests for NMC and 85 degradation tests for LFP. The obtained models are validated and mean absolute errors of 2.49% (NMC model) and 2.18% (LFP model) are achieved. An analysis of the models is conducted focusing on the effect that each stress factor has on the overall degradation of both lithium-ion battery chemistries. Finally, some case studies based on typical applications are proposed in order to calculate the lifetimes of each battery chemistry and evaluate the most appropriate option for each application.
The uptake of electric vehicles supports decarbonization and increasingly interconnects the electricity and transport system. While the integration of electric vehicles could challenge electricity grids, bidirectional power flows between vehicles and grids could support grid operations. Despite the globally increasing number of Vehicle-to-X trials, including Vehicle-to-Grid and Vehicle-to-Customer, an in-depth understanding of trial implementations and expert experiences has largely been overlooked although they are both crucial for technological development and deployment. Based on our analysis of a global Vehicle-to-X trial database and 47 interviews with experts from industry and academia, we (i) provide an overview of the implementation status of Vehicle-to-X and analyze predominate trial configurations, i.e. combinations of characteristics, (ii) identify important technical, social and regulatory challenges for the implementation of Vehicle-to-X and assess and discuss expert evaluations of these challenges and (iii) derive implications for different actors.
The most predominate trial configurations are Vehicle-to-Customer and transmission-level services provided by commercial fleets that charge at work due to current practical advantages of centralized approaches. From a technical standpoint, we find that although Vehicle-to-X can defer or even mitigate grid reinforcement at the distribution level, this potential is highly dependent on local conditions. Regarding social aspects, incentives and Vehicle-to-X operations need to be tailored to different vehicle users. Concerning regulation, it is imperative to avoid double taxation of electricity, simplify market participation for small providers, and further develop Vehicle-to-X standards. Implications for actors include the evaluation and enablement of portfolios with different flexibility assets, and stacking of services to increase revenue streams and reduce risk resulting from variations in driving patterns and charging behavior.
The battery models used in system studies are generally based on electrical models with a single tank ("one tank model"), to which are coupled semi-empirical aging models predicting the evolution of the capacity of this one tank model.In these models, the state-of-health of a cell is therefore represented by a single value, which is too limiting. Moreover, these approaches make it difficult to understand the phenomena of capacity slope failures observed experimentally.In this thesis, empirical or physics-based aging models are coupled to a dual tank model. The first empirical approach aims to directly predict the evolution of the capacity of each electrode and the offset between electrode potential signals. The second, inspired by the physical phenoma that can occur within the battery, introduces the notion of parasitic current at the origin of the loss of cycling lithium.These different approaches are implemented using experimental calendar results from the MOBICUS project.
Real-time identification of electrical equivalent circuit models is a critical requirement in many practical systems, such as batteries and electric motors. Significant work has been done in the past developing different types of algorithms for system identification using reduced-order equivalent circuit models. However, little work was done in analyzing the theoretical performance bounds of these system identification approaches. Given that both voltage and current are measured with error, proper understanding of theoretical bounds will help in designing a system that is economical in cost and robust in performance. In this paper, we analyze the performance of a linear recursive least squares approach to equivalent circuit model identification and show that the least squares approach is both unbiased and efficient when the signal-to-noise ratio is high enough. However, we show that, when the signal-to-noise ratio is low – resembling the case in many practical applications – the least squares estimator becomes significantly biased. Consequently, we develop a parameter estimation approach based on total least squares method and show it to be asymptotically unbiased and efficient at practically low signal-to-noise ratio regions. Further, we develop a recursive implementation of the total least square algorithm and find it to be slow to converge; for this, we employ a Kalman filter to improve the convergence speed of the total least squares method. The resulting total Kalman filter is shown to be both unbiased and efficient in equivalent circuit model identification. The performance of this filter is analyzed using real-world current profiles under fluctuating signal-to-noise ratios. Finally, the applicability of the algorithms and analysis in this paper in identifying higher order electrical equivalent circuit models is explained.
Lithium-Ion battery lifetimes from cyclic and calendar aging tests of more than 1000 cells were compared employing novel plots termed ENPOLITE (energy-power-lifetime-temperature). Battery cell data from in-house measurements and published data were combined into a uniform database; the total dataset size exceeds 1000 GB. At a glance, ENPOLITE plots inform about the nominal capacity, cell format, cell chemistry, average aging test duration, measurement temperature, specific power employed for testing, energy density, and the achieved lifetime for every cell. A battery lifetime coefficient was derived, allowing the comparison of lithium-ion batteries with different weights or volumes, capacities, and cell chemistries. The combination of multiple parameters in ENPOLITE facilitated a thorough comparison of various batteries' respective lifetimes. In addition to the cell-specific parameters during cycling, the specific stored energy and the storage temperature were depicted in a calendar ENPOLITE-Plot.
Electric vehicles have great potential to improve local air quality and reduce greenhouse gas emissions especially in combination with green electricity production. Vehicle-to-grid technology presents an opportunity to increase penetration rates of renewable energy sources by balancing their intermittent nature. Local energy systems, or microgrids, provide a clear use case for early adoption of vehicle-to-grid by executing energy balancing locally. This work focuses on how well the current standards and interoperable EV charging market fit the requirements of the specific case of vehicle-to-grid in local energy systems. The paper identifies and presents the possible configurations to integrate vehicle-to-grid into a local energy system. The paper presents further specific requirements of these configurations with regards to communication and compares them to the current standards for communicatiom in the European context.
The increase in the number of electric vehicles (EVs) has led to increased global expectations that the application of this technology may result in the reduction of CO2 emissions through the replacement of conventional petrol vehicles and ensure the flexibility of power systems such as batteries. In this paper, we propose a residential demand response (DR) evaluation model that considers the degradation mechanism of the EV battery and examines the effective battery operation. We adopted the already-proposed NiMnCo battery degradation model to develop an EV DR evaluation model. In this model, the battery operation is optimized to minimize the electricity and degradation costs affected by ambient temperature, battery state of charge (SOC), and depth of discharge. In this study, we evaluated the impact of the relevant parameters on the economics of the DR of EV batteries for 10 all-electric detached houses with photovoltaic system assuming multiple EV driving patterns and battery (dis)charging constraints. The results indicated that the degradation costs are greatly affected by the SOC condition. If a low SOC can be managed with a DR strategy, the total cost can be reduced. This is because the sum of the reduction of purchased cost from the utility and calendar degradation costs are higher than the increase of the cycle degradation cost. In addition, an analysis was conducted considering different driving patterns. The results showed that the cost reduction was highest when a driving pattern was employed in which the mileage was low and the staying at home time was large. When degradation costs are included, the value of optimized charging and discharging operations is more apparent than when degradation costs are not considered.
Plug-in electric vehicles (PEVs) have the highest promise for dramatically reducing transportation emissions. No other option has comparable emission reduction potential or as a promising pathway. Still, PEVs can offer more than green transportation. In particular, their onboard storage can further serve the society by providing an energy buffer to increase the reliability, affordability, and sustainability of electric services. These benefits are only achievable by fully exploiting the multifaceted flexibility provided by PEVs' mobility, charging adaptability, and bidirectional flow of power, as well as adopting effective decision-making and control algorithms, while minding the likely unfavorable side effects, such as shortened battery life span. This work takes a closer look at different elements of this puzzle. The main subject of this survey is behind the meter energy management with vehicle to building (V2B). We focus on different V2B application ideas and review energy management methods in smart buildings with V2B integration. Recent findings on battery capacity fade resulting from the bidirectional flow of power and extra discharging cycles with V2B are reviewed, and the methods for integrating the battery degradation in energy management formulation are discussed. Finally, the main findings of this review and research gaps are summarized and clarified.
Electric vehicles, whether fueled by chemical batteries or by liquid or fuel cells providing electricity onboard, provide benefits to grid operators as battery storages and power resources. In vehicle to grid operations, batteries provide ancillary services such as regulation up and down services to electric utilities. Balancing voltage frequency keeps power grids stable and sustainable. Vehicle to grid operations provide economic benefits to power grid operators, aggregators and to electric vehicle owners. An electric vehicle operator shares battery power with the power grid, an electric vehicle operator provides information about the coming journey, departure time and traveling distance as next trip requires. An automatic charging control can mitigate communication, and estimate the next trip with automatic charging control. Electric vehicle operators are not required to share information about planned travel as battery management charges batteries automatically. The proposed topic is interesting and worthy of investigation in order that the impact of vehicle to grid operations on battery durability plays a key role for the convenience of electric vehicle owners in supporting the electricity network with this kind of ancillary services. Main findings are lifetime reduction is decreased in vehicle to grid operations and a lifetime can be extended. For electric utilities, the increased battery storage provides benefits such as power system reliability and lower costs and facilitates the integration of intermittent renewable energy resources such as solar energy and wind power.
The battery is one of the most important parts of electric vehicles (EVs), and the establishment of an accurate battery state estimation model is of great significance to improve the management strategy of EVs. However, the battery degrades with the operation of EVs, which brings great difficulties for the battery modeling issue. This paper proposes a novel aging phenomenon considered vehicle battery modeling method by utilizing the cloud battery data. First of all, based on the Rain-flow cycle counting (RCC) algorithm, a battery aging trajectory extraction method is developed to quantify the battery degradation phenomenon and generate the aging index for the cloud battery data. Then, the deep learning algorithm is employed to mine the aging features of the battery, and based on the mined aging features, an aging phenomenon considered battery model is established. The actual operation data of electric buses in Zhengzhou is used to validate the practical performance of the proposed methodologies. The results show that the proposed modeling method can simulate the characteristic of the battery accurately. The terminal voltage and SoC estimation error can be limited within 2.17% and 1.08%, respectively.
Bus fleet electrification achieves momentum and inspiration within public transport aiming at further improving the mobility sustainability. In many countries, such as Sweden, China, and the USA, there are several ongoing demonstration projects of electric buses and many research projects. The charging technology development and implication is key for the expansion of electric buses and to foster it. An investigation of characteristics and benefits of various existing and future charging technologies has been created in this paper. The main types of charging infrastructure are depot charging, station charging, and inductive charging. The choice of different types is highly related to infrastructure construction, investment, and daily operation. The detailed illustration and analysis of them can provide a solid foundation to the near-future large-scale electric buses’ operation.
Aging characteristics of lithium-ion cells are investigated on the basis of commercially available LiMn2O4 cells. The effects of time, cell state of charge (SOC), and temperature are examined for the case of calendar aging. Likewise, charge throughput, depth of discharge (DOD), average SOC, charge rate, and realistic discharge profiles are analyzed for cycle aging based on a thorough aging test campaign. Aging properties are described by means of capacity, resistance, and differential voltage analysis (DVA). In addition to the square root-like capacity decrease over time and charge throughput, a significantly lower capacity loss is observed for low SOC scenarios. The influence of continuous cell chemistry improvement and a change of the cell chemistry from LMO to LMO-NMC is investigated over a period of 3 years. Initial characterization tests show a decrease in resistance and increase in capacity over the years. DVA and incremental capacity analysis (ICA) help to reconstruct the cell development progress by observing the rise of the NMC-induced ICA peak at about 3.5 V. A comparison of the three cell batches which were purchased over a period of three years indicates that, although the ICA changes, the capacity and resistance characteristics vary only slightly.
This paper describes and verifies a Li-ion cell electro-thermal model and the associated data analysis process. It is designed to be adaptable and give accurate results across all variations of operating conditions and cell design based only on time domain voltage, current and temperature measurements. The creation of this model required an analysis process ensuring consistency in expressing the underlying cell behavior. This informed a flexible modelling structure adaptable both to cell performance variations and the limitations of the available test data. The model has been created with a combined thermal and electrical approach enabling 1D nodal distribution adaptable to both cylindrical and prismatic cells. These features combine with an intelligent parameter identification process identifying model structure and parameterization across the usage range, adaptable to any Nickel-Manganese-Cobalt Li-Ion cell. It is designed to retain physical meaning and representation to each circuit element across the temperature operating range. The model is verified in several different operating conditions through representative automotive cycling on an 18650 cell and a BEV2 format prismatic cell, representing the extremes of automotive cell design. The consistency of the model parameters with real phenomena is also analyzed and validated against Electrochemical Impedance Spectroscopy data.
This paper presents a comparative study of twelve equivalent circuit models for Li-ion batteries. These twelve models were selected from state-of-the-art lumped models reported in the literature. The test data used is obtained from a battery test system with a climate chamber. The test schedule is designed to measure key cell attributes under highly dynamical excitations. The datasets were collected from two types of Li-ion cells under three different temperatures. The multi-swarm particle swarm optimization algorithm is used to identify the optimal model parameters for the two types of Li-ion cells. The usefulness of these models is then studied through a comprehensive evaluation by examining model complexity, model accuracy, and robustness of the model by applying the model to datasets obtained from other cells of the same chemistry type.
In recent years, lithium-ion batteries have often been proposed as part of various hybrid energy systems. Examples of such applications are hybrid vehicles, photovoltaic-battery or wind-battery systems and power distribution grids. One of the key functions of a battery is to be source of an additional power when the main power source in the system cannot cover the power demands. The power capability of the battery is described by its impedance characteristic. It depends significantly on the battery state-of-charge (SoC), the temperature, the current and the previous history. In this paper, these dependencies and their variations over the battery lifetime are investigated on a 40 Ah lithium-ion cell produced by Kokam (Type SLPB100216216H) with nickel manganese cobalt oxide (NMC) cathode material as an example. For this purpose, a full characterisation of the battery impedance is performed in the new and aged states using electrochemical impedance spectroscopy (EIS) and the current-pulse technique. The results obtained for the new and aged states are compared. The results show, for example, that the SoC range, in which the battery operates with high efficiency, decreases due to significant aging. For the first time, the nonlinearity (current dependency) of the battery resistance is investigated extensively. It varies slightly with the SoC and considerably with the temperature, and it also changes during the battery lifetime. Furthermore, the dependency of the lithium-ion battery impedance on the short-time previous history is shown for the first time for a new and aged cell. The influence of the measured dependencies of the battery impedance on potential applications is discussed.
The aging behavior of a commercial 2.3 Ah graphite/LFP cell during a year of cycling or storage at either 25 or 45°C is investigated.
The performance decline of the cells during the aging period is monitored by non-destructive electrochemical techniques and
is discussed in detail. An in-depth analysis of the aging results reveals that aging manifests itself more in terms of capacity
loss rather than in terms of impedance increase, regardless of the cycling or storage conditions and of the temperature. The
capacity fade is larger at 45°C than at 25°C, regardless of the cycling or storage conditions, and at a same temperature,
cycling conditions are always more detrimental to capacity fade than storage conditions. The loss of cyclable lithium is identified
as the main source of capacity fade in all cases, and for the cells aged at 45°C, a partial loss of graphite active material
is suspected as well towards the end of the aging period.
Incorporating plug in electric vehicles (PEVs) to power systems may address both additional demand as well as mobile storage to support electric grid spatially. Better utilization of such potential depends on the optimal scheduling of charging and discharging PEVs. Charging management malfunction of PEVs may increase the peak load which leads to additional generation. Therefore, charging and discharging of PEVs must be scheduled intelligently to prevent overloading of the network at peak hours, take advantages of off peak charging benefits and delaying any load shedding. A charging and discharging schedule of PEVs with respect to load curve variations is proposed in this paper. The proposed methodology incorporates integrated PEVs; the so-called parking lots; into the unit commitment problem. An IEEE 10-unit test system is employed to investigate the impacts of PEVs on generation scheduling. The results obtained from simulation analysis show a significant techno-economic saving. (C) 2013 Published by Elsevier B.V.
Plug-in Electric Vehicles (PEVs) provide new opportunities to reduce fuel consumption and exhaust emission. PEVs need to draw and store energy from an electrical grid to supply propulsive energy for the vehicle. As a result, it is important to know when PEVs batteries are available for charging and discharging. Furthermore, battery energy management and control is imperative for PEVs as the vehicle operation and even the safety of passengers depend on the battery system. Thus, scheduling the grid power electricity with parking lots would be needed for efficient charging and discharging of PEV batteries. This paper aims to propose a new intelligent battery energy management and control scheduling service charging that utilize Cloud computing networks. The proposed intelligent vehicle-to-grid scheduling service offers the computational scalability required to make decisions necessary to allow PEVs battery energy management systems to operate efficiently when the number of PEVs and charging devices are large. Experimental analyses of the proposed scheduling service as compared to a traditional scheduling service are conducted through simulations. The results show that the proposed intelligent battery energy management scheduling service substantially reduces the required number of interactions of PEV with parking lots and grid as well as predicting the load demand calculated in advance with regards to their limitations. Also it shows that the intelligent scheduling service charging using Cloud computing network is more efficient than the traditional scheduling service network for battery energy management and control.
In this paper, we survey two kinds of mathematics-based battery models intended for use in hybrid and electric vehicle simulation. The first is circuit-based, which is founded upon the electrical behaviour of the battery, and abstracts away the electrochemistry into equivalent electrical components. The second is chemistry-based, which is founded upon the electrochemical equations of the battery chemistry.
An extensive set of accelerated aging tests has been carried out employing a Li-ion high energy 18650 system (2.05 Ah), negative electrode: carbon, positive electrode: Li(NiMnCo)O2). It is manufactured by Sanyo, labeled UR18650E, and is a commercial off-the-shelf product. The tests comprise both calendar life tests at different ambient temperatures and constant cell voltages and cycle life tests operating the cells within several voltage ranges and levels using standard test profiles. In total, 73 cells have been tested. The calendar life test matrix especially investigates the influence of SOC on aging in detail, whereas the cycle life matrix focuses on a detailed analysis of the influence of cycle depth. The study shows significant impact of the staging behavior of the carbon electrode on cycle life. Furthermore a strong influence of the carbon potential on calendar aging has been detected. Observed relations between aging and the different influence factors as well as possible degradation mechanisms are discussed. Analysis of C/4 discharge voltage curves suggests that cycle aging results in different aging processes and changes in material properties compared to calendar aging. Cycling, especially with cycles crossing transitions between voltage plateaus of the carbon electrode seems to destroy the carbon structure.
The provision of vehicle-to-grid (V2G) services to an electric grid by electric vehicles (EVs) can potentially reduce the cost of vehicle ownership through revenue generation. Recent studies indicate that yearly vehicle profit from V2G may not be sufficient to induce widespread participation. This paper investigates the feasibility of a premium tariff rate for V2G power, similar to current feed-in-tariff (FIT) programs for renewable energy. Using Ontario, Canada as a case study, an hourly time-series model for a fleet of commuter EVs is created. Tariff rates for V2G peak power are calculated based on the same return on investment as the current FIT for renewable energy in Ontario. The tariff rates are competitive with the renewable energy tariffs, especially when EVs are allowed to provide ancillary services to the grid in addition to peak power. Despite the guaranteed rate of return, yearly vehicle profit is low. Two variations are considered to increase vehicle profit, thereby enhancing the attractiveness of V2G. A higher return on investment is favored over direct benefits offered to EV owners. A higher return on investment may be justifiable based on the higher level of risk inherent in V2G when compared to renewable energy.
Main objective of this work is the model-based investigation of the impact of different load scenarios on the estimated useful life of a traction battery. An energy-based battery model is used for simulation of the available energy of the battery. Reduction of the energy storage capability of the battery is considered by an aging model. The aging model consists of a calendar and a cycle aging model. All models are implemented for simulation of present and future lithium-ion technologies. Hence, the range of battery characteristics and the aging behavior can be varied. In 2020 electric vehicles presumably will have reached a significant number and feed-back of electrical power from the vehicle to the power grid can be expected as an implemented ancillary service. Hence the battery's load scenarios comprise different combinations of driving cycles, charging strategies, and peak-shaving. Therefore the impact of these scenarios on battery aging can be identified by means of the model-based investigation of the battery lifetime. The presented battery model considers aging effects and is a useful tool for the design of an electric vehicle, for the dimensioning of a battery system depending on climate and user behavior, as well as for cost calculations.
Battery lifetime prognosis is a key requirement for successful market introduction of electric and hybrid vehicles. This work aims at the development of a lifetime prediction approach based on an aging model for lithium-ion batteries. A multivariable analysis of a detailed series of accelerated lifetime experiments representing typical operating conditions in hybrid electric vehicle is presented. The impact of temperature and state of charge on impedance rise and capacity loss is quantified. The investigations are based on a high-power NMC/graphite lithium-ion battery with good cycle lifetime. The resulting mathematical functions are physically motivated by the occurring aging effects and are used for the parameterization of a semi-empirical aging model. An impedance-based electric-thermal model is coupled to the aging model to simulate the dynamic interaction between aging of the battery and the thermal as well as electric behavior. Based on these models different drive cycles and management strategies can be analyzed with regard to their impact on lifetime. It is an important tool for vehicle designers and for the implementation of business models. A key contribution of the paper is the parameterization of the aging model by experimental data, while aging simulation in the literature usually lacks a robust empirical foundation.
The profitability of plug-in hybrid electric vehicles (PHEVs) is significantly influenced by battery aging and electricity costs. Therefore a simulation model for PHEVs in the distribution grid is presented which allows to compare the influence of different charging strategies on these costs. The simulation is based on real-world driving behavior and European Energy Exchange (EEX) intraday prices for obtaining representative results. The analysis of comprehensive lithium-ion battery aging tests performed within this study shows that especially high battery states of charge (SOCs) decrease battery lifetime, whereas the cycling of batteries at medium SOCs only has a minor contribution to aging. Charging strategies that take into account the previously mentioned effects are introduced, and the SOC distributions and cycle loads of the vehicle battery are investigated. It can be shown that appropriate charging strategies significantly increase battery lifetime and reduce charging costs at the same time. Possible savings due to lifetime extension of the vehicle battery are approximately two times higher than revenues due to energy trading. The findings of this work indicate that car manufacturers and energy/mobility providers have to make efforts for developing intelligent charging strategies to reduce mobility costs and thus foster the introduction of electric mobility.
One hundred seven commercially available, off-the-shelf, 1.2-Ah cells were tested for calendar life and CS cycle- and CD cycle-life using the new USABC PHEV Battery Test Manual. Here, the effects of temperature on calendar life, on CS cycle life, and on CD cycle life; the effects of SOC on calendar life and on CS cycle life; and the effects of rest time on CD cycle life were investigated. The results indicated that the test procedures caused performance decline in the cells in an expected manner, calendar
The testing methods to estimate the life cycles of lithium ion batteries for a short period, have been developed using a commercialized cell with LiCoO2/hard carbon cell system. The degradation reactions with increasing cycles were suggested to occur predominantly above 4V from the results of operating voltage range divided tests. In the case of the extrapolation method using limited cycle data, the straight line approximation was useful as the cycle performance has the linearity, but the error is at most 40% in using the initial short cycle data. In the case of the accelerated aging tests using the following stress factors, the charge and/or discharge rate, large accelerated coefficients were obtained in the high charge rate and the high temperature thermal stress.
A systematic semiempirical way to analyze the variation in dc polarization with the operating time of electrochemical energy conversion devices is suggested. This includes the differentiation of the constituents of the total cell impedance, followed by the estimation of the time-dependent elementary contributions to the total polarization with the help of the theoretical analysis of an equivalent circuit. This unique method enables us to design high power energy conversion devices and, at the same time, effectively diagnose the power degradation of a cell. A comparative analysis of a fresh cell with an aged cell and the effect of temperature on polarization are exemplified. In particular, time-dependent contributions of elementary polarizations have been quantitatively suggested in each case with a strategy for the design of the materials. From this approach, power degradation after repeated battery cycling was ascribed to a rise in polarization due to the interfacial charge-transfer resistance of the cathode. Furthermore, the charge-transfer resistance of the cathode proved to be the most important factor in high rate battery discharging at ambient and low operating temperatures.
We measured the internal resistance of high-power lithium-ion secondary batteries for next-generation electric vehicles. We
succeeded in separating the two elements of the electrode (positive and negative)/electrolyte interfacial resistances
by measuring the ac impedance spectrum at a low state of charge and low temperature. In addition,
took a value close to the capacity ratio between small-
and large-capacity
cells when the internal resistances of the two cells calculated from the ac impedance spectrum were compared. These results
indicate that interfacial degradation, which is one of the main factors causing battery degradation, can be estimated by analyzing
using a smaller cell.
Electrochemical characteristics of advanced lithium-ion batteries (ALBs) have been investigated using a three-electrode system.
The reference electrode is a piece of lithium inserted between the anode and cathode. Electrochemical impedance spectroscopy
shows that both the solid electrolyte interface (SEI) and charge-transfer resistances depend on the battery’s state of charge
(SOC). In a fully charged ALB, the main contribution to the resistance comes from the SEI of the anode; whereas, in a fully
discharged state, the contribution comes from the charge-transfer resistance of the cathode. The discharge capacity under
high currents is limited by the diffusion of lithium ions within the cathode; the effects of the anode are small. The charge-transfer
resistance of the cathode increased most, by approximately 200%, after 200 cycles at
and thus this value has the largest effect on the cycle life of the ALB. During low-temperature discharge, the anode shows
the largest cell resistance of 2.8 Ω, but near the completion of discharge, the cathode contribution starts to dominate (0.4-2.7
Ω), and the anode resistance decreases to 1.3 Ω. © 2004 The Electrochemical Society. All rights reserved.
Electric vehicles are commonly seen as one of the alternatives to reduce the oil dependency and the greenhouse gas emissions in the transport sector.
The aim of this paper is to evaluate the impact of different electric vehicle charging strategies on the national grid including the storage utilization of electric vehicles (V2G-vehicle to grid). Furthermore, an economic analysis of electric vehicle utilization is performed and the results are compared with the conventional diesel vehicle.
To accomplish this aim the availability of passenger cars in Germany to be plugged into the grid showed to be high at any time over the day (>89%), which is advantageous for the V2G concept.
The impact of the different electric vehicle charging strategies is investigated by employing three scenarios. The first scenario (unmanaged charging) shows that 1 mil. electric vehicles only impacts slightly on the daily peak electricity demand. In the second scenario (Grid stabilizing storage use) a maximum reductions of grid fluctuations of 16% can be achieved with the use of 1 mil. electric vehicles as storage. The last scenario (profit maximization by power trading) the maximum daily revenues from V2G activities are calculated to be 0.68 EUR2009.
An 18650 lithium ion cell was evaluated using dynamic stress test (DST) protocol for cycle life study. Reference performance tests were applied every 60 DST cycles to quantify capacity fade and peak power capability (PPC) degradation with cycles to the end of life. A quantitative incremental capacity analysis (ICA) was applied to identify extrinsic and intrinsic contributions to capacity fading, whereas the open circuit voltage (OCV) measurements were utilized to determine the correct state of charge (SOC) in order to accurately correlate the capacity fade with SOC. The analysis also helps us identify that cell polarization resistance change in the cycles is the primary culprit that bifurcates to both extrinsic and intrinsic origins in capacity fade and PPC degradation. This analysis allows us to develop better understanding in predicting battery performance and life in the rechargeable lithium batteries.
The capacity fading of an 18650 LiFePO4-based lithium ion cell was studied using the dynamic stress test (DST) schedule in a cycle life evaluation. Intermittent reference performance tests were conducted to quantify capacity loss and peak power capability degradation with cycle number to the end-of-life. An incremental capacity analysis was applied to identify various contributions to capacity loss, whereas the open circuit voltage measurements were utilized to trace the correct state of charge as the cell degrades in order to accurately correlate the capacity degradation with SOC. Our non-invasive, in situ analyses are in general consistent with current understanding of the degradation mechanism in this chemistry derived from post-mortem analysis. Loss of lithium inventory is the main cause of capacity degradation, in addition to the loss of active materials. The degree of under-discharge and under-charge is quite minimal under the test protocol.
There is a growing need to explore path dependence of aging processes in batteries developed for long-term usage, such as lithium-ion cells used in hybrid electric vehicle (HEV) or plug-in hybrid vehicle (PHEV) applications that may then be “retired” to be utilized in grid applications. To better understand the foremost influences on path dependence in the PHEV context, this work aims to bridge the gap between ideal laboratory test conditions and PHEV field conditions by isolating the predominant aging factors in PHEV service, which would include, for example, the nature and frequency of duty cycles, as well as the frequency and severity of thermal cycles. These factors are studied in controlled and repeatable laboratory conditions to facilitate mechanistic evaluation of aging processes. This work is a collaboration between Idaho National Laboratory (INL) and the Hawaii Natural Energy Institute (HNEI). Commercial lithium-ion cells of the Sanyo Y type (18650 configuration) are used in this work covering two initial independent studies of path dependence issues. The first study considers how the magnitude of power pulses and charging rates affect the aging rate, while the second seeks to answer whether thermal cycling has an accelerating effect on cell aging. While this work is in early stages of testing, initial data trends show that cell aging is indeed accelerated under conditions of high discharge pulse power, higher charge rates, and thermal cycling. Such information is useful in developing accurate predictive models for estimating end-of-life conditions.
The influence of the operation conditions temperature and state of charge (SOC) on the performance of a commercial high-power lithium-ion cell is investigated by electrochemical impedance spectroscopy. Based on the results of several preliminary tests, measurements were run covering the complete range of automotive applications.The cell impedance is presented and analyzed. A strong nonlinear temperature correlation is shown for all frequency ranges. Although the ohmic resistance is nearly unaffected by variation in SOC, the mass transport impedance reduces from 100% to 60% SOC and increases significantly again for lower SOCs.
This article examines the problem of estimating the aggregate load imposed on the power grid by the battery health-conscious charging of plug-in hybrid electric vehicles (PHEVs). The article begins by generating a set of representative daily trips using (i) the National Household Travel Survey (NHTS) and (ii) a Markov chain model of both federal and naturalistic drive cycles. A multi-objective optimizer then uses each of these trips, together with PHEV powertrain and battery degradation models, to optimize both PHEV daily energy cost and battery degradation. The optimizer achieves this by varying (i) the amounts of charge obtained from the grid by each PHEV, and (ii) the timing of this charging. The article finally computes aggregate PHEV power demand by accumulating the charge patterns optimized for individual PHEV trips. The results of this aggregation process show a peak PHEV load in the early morning (between 5.00 and 6.00 a.m.), with approximately half of all PHEVs charging simultaneously. The ability to charge at work introduces smaller additional peaks in the aggregate load pattern. The article concludes by exploring the sensitivity of these results to the relative weighting of the two optimization objectives (energy cost and battery health), battery size, and electricity price.Highlights► We optimize the charging patterns of plug-in hybrid electric vehicles (PHEVs). ► The objectives are to reduce energy cost and battery degradation, simultaneously. ► We obtain the aggregate PHEV grid load by accumulating individual charge patterns. ► We study the effects of charging location, battery size, and electricity price. ► To enhance battery longevity PHEVs adopt last-minute off-peak charging.
In the late 1950s there was a group of teachers and research assistants at the London School of Economics interested in linear
programming and its extensions, in particular Helen Makower, George Morton, Ailsa Land and Alison Doig. We had considered
the ‘Laundry Van Problem’ until we discovered that it was known as the Traveling Salesman Problem, and had looked at aircraft
timetabling, until quickly realizing that even the planning for the Scottish sector was beyond our capability! Alison Doig
(now Harcourt) had studied the paper trim problem for her Masters project in Melbourne before coming to England.
The rechargeable lithium-ion battery has been extensively used in mobile communication and portable instruments due to its many advantages, such as high volumetric and gravimetric energy density and low self-discharge rate. In addition, it is the most promising candidate as the power source for (hybrid) electric vehicles and stationary energy storage. For these applications in durable equipment, the long-term cycling and storage behaviour becomes of increasing interest. In this paper, the mechanisms of lithium-ion battery ageing are reviewed and evaluated.
Test results and life modeling of special calendar- and cycle-life tests conducted on 18650-size generation 1 lithium-ion battery cells were presented. The test consisted of a once per day discharge and charge pulse, which was designed to produce minimal impact on the cell but yet establish its performance over a period of time. Results indicated that the discharge and regen resistances increased non-linearly as a function of the test time. The magnitude of the resistances depended on the temperature and states-of-change at which the test was conducted.
As the light vehicle fleet moves to electric drive (hybrid, battery, and fuel cell vehicles), an opportunity opens for “vehicle-to-grid” (V2G) power. This article defines the three vehicle types that can produce V2G power, and the power markets they can sell into. V2G only makes sense if the vehicle and power market are matched. For example, V2G appears to be unsuitable for baseload power—the constant round-the-clock electricity supply—because baseload power can be provided more cheaply by large generators, as it is today. Rather, V2G's greatest near-term promise is for quick-response, high-value electric services. These quick-response electric services are purchased to balance constant fluctuations in load and to adapt to unexpected equipment failures; they account for 5–10% of electric cost—$ 12 billion per year in the US. This article develops equations to calculate the capacity for grid power from three types of electric drive vehicles. These equations are applied to evaluate revenue and costs for these vehicles to supply electricity to three electric markets (peak power, spinning reserves, and regulation). The results suggest that the engineering rationale and economic motivation for V2G power are compelling. The societal advantages of developing V2G include an additional revenue stream for cleaner vehicles, increased stability and reliability of the electric grid, lower electric system costs, and eventually, inexpensive storage and backup for renewable electricity.
The effects of combined driving and vehicle-to-grid (V2G) usage on the lifetime performance of relevant commercial Li-ion cells were studied. We derived a nominal realistic driving schedule based on aggregating driving survey data and the Urban Dynamometer Driving Schedule, and used a vehicle physics model to create a daily battery duty cycle. Different degrees of continuous discharge were imposed on the cells to mimic afternoon V2G use to displace grid electricity. The loss of battery capacity was quantified as a function of driving days as well as a function of integrated capacity and energy processed by the cells. The cells tested showed promising capacity fade performance: more than 95% of the original cell capacity remains after thousands of driving days worth of use. Statistical analyses indicate that rapid vehicle motive cycling degraded the cells more than slower, V2G galvanostatic cycling. These data are intended to inform an economic model.
Vehicle-to-grid power (V2G) uses electric-drive vehicles (battery, fuel cell, or hybrid) to provide power for specific electric markets. This article examines the systems and processes needed to tap energy in vehicles and implement V2G. It quantitatively compares today's light vehicle fleet with the electric power system. The vehicle fleet has 20 times the power capacity, less than one-tenth the utilization, and one-tenth the capital cost per prime mover kW. Conversely, utility generators have 10–50 times longer operating life and lower operating costs per kWh. To tap V2G is to synergistically use these complementary strengths and to reconcile the complementary needs of the driver and grid manager. This article suggests strategies and business models for doing so, and the steps necessary for the implementation of V2G. After the initial high-value, V2G markets saturate and production costs drop, V2G can provide storage for renewable energy generation. Our calculations suggest that V2G could stabilize large-scale (one-half of US electricity) wind power with 3% of the fleet dedicated to regulation for wind, plus 8–38% of the fleet providing operating reserves or storage for wind. Jurisdictions more likely to take the lead in adopting V2G are identified.
In the classical linear programming problem the behaviour of continuous, nonnegative variables subject to a system of linear inequalities is investigated. One possible generalization of this problem is to relax the continuity condition the variables. This paper presents a simple numerical algorithm for the solution of programming problems in which some or all of the variables can take only discrete values. The algorithm requires no special techniques beyond these used in ordinary linear programming, and lends itself to automatic computing. Its use is illustrated on two numerical examples.
Records of world climate data (historical weather averages) Available from
- Worldclimate
- Com
Worldclimate.com [Internet]. Records of world climate data (historical weather averages). Available from: <http://www.worldclimate.com> [updated 05.01.05].
AC impedance study of high-power lithium-ion secondary batteries-effect of battery size
- S Seki
- N Kihira
- Y Mita
- T Kobayaashi
- K Takei
- T Ikeya
Seki S, Kihira N, Mita Y, Kobayaashi T, Takei K, Ikeya T, et al. AC impedance
study of high-power lithium-ion secondary batteries-effect of battery size. J
Electrochem Soc 2011;158:A163-6.
Records of world climate data (historical weather averages
- Worldclimate
Worldclimate.com [Internet]. Records of world climate data (historical
weather averages). Available from: <http://www.worldclimate.com>
[updated 05.01.05].