Article

A holistic aging model for Li(NiMnCo)O2 based 18650 lithium-ion batteries

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Abstract

Knowledge on lithium-ion battery aging and lifetime estimation is a fundamental aspect for successful market introduction in high-priced goods like electric mobility. This paper illustrates the parameterization of a holistic aging model from accelerated aging tests. More than 60 cells of the same type are tested to analyze different impact factors. In calendar aging tests three temperatures and various SOC are applied to the batteries. For cycle aging tests especially different cycle depths and mean SOC are taken into account. Capacity loss and resistance increase are monitored as functions of time and charge throughput during the tests. From these data physical based functions are obtained, giving a mathematical description of aging. To calculate the stress factors like temperature or voltage, an impedance based electric-thermal model is coupled to the aging model. The model accepts power and current profiles as input, furthermore an ambient air temperature profile can be applied. Various drive cycles and battery management strategies can be tested and optimized using the lifetime prognosis of this tool. With the validation based on different realistic driving profiles and temperatures, a robust foundation is provided.

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... Calendar and cycle aging were considered separately. This approach and the parameterization process is described in detail by Schmalstieg in [19]. The aging behavior of the Li-Tec HEA40 cell was tested in accelerated aging tests in the laboratory at the institute for power electronics and electrical drives (ISEA) at RWTH Aachen. ...
... This in turn can lead to crack-and-repair of the solid-electrolyte-interface (SEI) that consumes lithium (capacity loss) and increases its inner resistance. Furthermore active material particles can loose contact to the electrode (capacity loss) [19]. ...
... Other exponents for the time dependency can be found in literature, such as 0.75, which was found to describe the time dependence for calendar aging in [19]. In [36], Ecker et al. used a square root time dependency for the fit which was motivated by the assumption of solid electrolyte interface on the negative electrode as the dominating aging factor. ...
Article
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A comprehensive electric vehicle model is developed to characterize the behavior of the Smart e.d. (2013) while driving, charging and providing vehicle-to-grid services. To facilitate vehicle-to-grid strategy development, the EV model is completed with the measurement of the on-board charger efficiency and the charging control behavior upon external set-point request via IEC 61851-1. The battery model is an electro-thermal model with a dual polarization equivalent circuit electrical model coupled with a lumped thermal model with active liquid cooling. The aging trend of the EV’s 50 Ah large format pouch cell with NMC chemistry is evaluated via accelerated aging tests in the laboratory. Performance of the model is validated using laboratory pack tests, charging and driving field data. The RMSE of the cell voltage was between 18.49 mV and 67.17 mV per cell for the validation profiles. Cells stored at 100% SOC and 40 °C reached end-of-life (80% of initial capacity) after 431–589 days. The end-of-life for a cell cycled with 80% DOD around an SOC of 50% is reached after 3634 equivalent full cycles which equates to a driving distance of over 420,000 km. The full parameter set of the model is provided to serve as a resource for vehicle-to-grid strategy development.
... The impedance spectra are subject to change over time and due to operation loads, allowing the aging behavior to be concluded. Another approach is to extend the electric model by a separate aging model, as shown in several studies [67][68][69][70][71][72]. In this case, aging experiments at, e.g., different temperatures or SoCs are carried out beforehand, providing insights into the capacity fade and resistance increase as a function of time and cycles. ...
... Thus, a more generic modeling approach 3 with a focus on capacity fade as proposed by Motapon et al. [164] was further developed and implemented within this work. The underlying assumption that capacity loss due to calendar and cycle aging is linearly independent is a common simplification of more complex interdependencies and is used in many publications [69,70,72,165]. The applied aging model incorporates all relevant stress factors concerning calendar and cycle aging. The individual aging effects due to calendar and cycle aging, Q cal loss and Q cyc loss , are then superimposed to create a combined aging model calculating Q loss , as shown in Equation (4.2). ...
... The calendar and cycle aging models' basic concept is that of weighted time and charge throughput, respectively. This approach can often be found in the literature [69][70][71][72]. Weighting factors are multiplied with a function of time and FECs at each time step. ...
Thesis
The past decade has seen a rapid transformation of electric power systems in many countries worldwide through integrating renewable energy sources. However, the associated increase in irregular power generation and consumption forces system operators to compensate for resulting power fluctuations. Battery energy storage systems are predestined for integrating renewable energy sources smoothly but demand proper energy management. Research in this area has clearly established that coordinated control is an essential contributor to the reliable operation of energy storage systems and thus the power grid. The development of battery energy storage systems requires operating strategies to efficiently manage the power flow under rapidly and continuously changing power requirements. Therefore, this work aims to identify, quantify, and evaluate the potentials and sensitivities of power flow control strategies for heterogeneous battery energy storage systems in several applications and system designs. Moreover, it aims at developing a versatile power flow control strategy for battery energy storage systems. The experimental research design was used to analyze the causal relationships between the inputs and the outputs of heterogeneous battery energy storage systems. In this context, a methodological framework was developed that includes a validated simulation model of a battery energy storage system and methods to systematically evaluate and visualize these causal relationships for different power flow control strategies and applications. The results showed that the trade-offs between the target indicators “performance,” “efficiency,” and “service life” can be quantified accurately. Furthermore, the individual influences of the power flow control strategies and applications on the target indicators were analyzed. It was shown that, for example, a heterogeneous battery energy storage system could influence the service life of the batteries in different ways depending on the applied power flow control strategy. The findings of this work show that the applied power flow control strategy, system design, and application are essential factors to consider when operating heterogeneous battery energy storage systems. These factors influence the resulting power distribution within the system, which is, in turn, a decisive point for reliable and sustainable operation. In many cases, a trade-off between the target indicators “efficiency” and “service life” was observed, requiring a decision on a more equal or a more individual power-sharing. A more individual power-sharing, for example, might be beneficial in the case of the peak shaving scenario, especially in terms of efficiency. However, this often decreases the service life and demands a premature replacement of a single battery. Generally, this work contributes to the body of knowledge on power flow control strategies for battery energy storage systems by incorporating a methodological framework for researchers and industries to analyze and develop battery energy storage systems. The benefits gained from the methodological framework address system operator needs across a wide range of different applications. This work focuses, among other aspects, on the successful implementation of the methodological framework for heterogeneous battery energy storage systems. However, most battery energy storage systems are included in higher-level systems, resulting in new challenges to be addressed. A further study could assess the potentials and sensitivities of operating strategies for microgrids or other higher-level systems using the methodological framework of this work.
... The aging of lithium-ion battery is a complicated aspect with enormous electrochemical processes subjected to different operational circumstances which gradually resulted in both cycling and calendar aging capacity loss effects. When batteries are passed through calendar aging, capacity degradation is resulted which is commonly caused by storage SoC and temperature stress factors [18][19][20][21][22]. So far, for calendar aging prediction of lithium-ion batteries, different types of modeling techniques have been implemented [23,24]. ...
... The previous aging studies did not consider the impact of the base-loss effect on the estimation of capacity fade evolution by the developed model. In addition, most of the above lifetime investigations were focused on low nominal capacity cells (mostly less or equal to 20 Ah), and validation of the models were also performed with a limited cycle number of dynamic profiles [22,26], and short range of impact factors. Therefore, to alleviate the beforementioned gaps, and hence provide a compromise between complexity, accuracy, and computational difficulties, in this paper, a semi-empirical fitted ECM based combined lifetime model is proposed. ...
... For the 80% DoD cycling, similarly, the higher charge cycling degraded with a significantly higher speed while a 2C discharge cycling performed the same as the 1C discharge cycling condition. Therefore, it is proved that depth of charge and discharge cycling rate is a significant factor considered in the study of cycling aging [16,22]. From Fig. 4(a), it can be seen that, at 3C charge and discharge rate the cell degrades faster as compared to 1C charge and discharge rate which is found to be similar to each other. ...
Article
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This paper presents the development of a combined lifetime model used to estimate the capacity fade evolution and internal resistance increase of 43 Ah big capacity nickel-manganese-cobalt oxide (NMC) cell playing the leading role in the automotive industry. However, these NMC based lithium-ion cells passed through capacity fade uncertainty problems during their cycling and calendaring periods. Therefore, to investigate the nonlinear aging behavior of the NMC cell, an extensive lifetime characterization of the cell was performed with an efficient testing methodology accompanied by large and quality datasets collected for more than two years. According to the analysis result of the experimental dataset, the cycling capacity fade was strongly affected by the depth of discharge (DoD), temperature, C-rate, and slightly affected by middle state of charge (Mid-SoC). On the other hand, the calendar capacity fade was affected by high storage state of charge (SoC), and high temperature. In the previous studies, limitations related to complexity, accuracy, and computational difficulties of aging models are observed. Therefore, to compromise these challenges, in this paper, a semi-empirical fitted equivalent circuit model-based lifetime model is proposed. The validation of the developed model is performed by using a highly dynamic Worldwide Harmonized Light Vehicles Test (WLTC) profile and a root mean square error (RMSE) of 2% was found. In this study, new knowledge is gained with the developed lifetime model which benefits car manufacturers using big capacity NMC-based cells and hence can utilize the model to estimate the available capacity and lifetime of the battery cells.
... At high SOC, i.e., at higher terminal voltages, the SEI layer thickens with the electrolyte decomposition and Li-ion deposition, leading to a higher aging rate [95]. The combination of high SOC and low temperature charging also accelerates the aging phenomenon as a non-linear function of time [96] with the occurrence of Li-ion deposition side reaction. On the other hand, low SOC causes corrosion of anode copper current collectors and structural disordering of cathode active material dramatically, leading to accelerated aging of LIB [97]. ...
... Further, batteries exhibit different aging behavior at the same operating temperatures with different SOC levels [44]. It is concluded that the cycling between 45% and 55% SOC has the smallest effect on both capacity loss and resistance increase [96]. ...
... The SOH is quantified using two metrics, namely the capacity fade and power fade. Capacity fade is defined as the ratio of current capacity to the nominal/rated capacity (C rated ) of the battery [96]. The resistance rise (R 0inc ) is used as the indicator of power fade and is expressed as ...
Article
Battery management system (BMS) is an integral part of the Lithium-ion battery (LIB) for safe operation and power management. The advanced BMSs also provide state of charge (SOC) and state of health (SOH) information. Accurate estimation of the SOC and SOH from a sparse set of input and output measurements (voltage, current, and surface temperature) is challenging due to the internal inter-related complex electrochemical side reactions. Several factors, such as charge/discharge rate, operating temperature, internal aging, abnormal charging-discharging cycles, and internal faults, adversely affect the LIB's health. To aid the development of intelligent and robust BMS with the capability of health-conscious decision making, a deep understanding of the internal degradation mechanisms and the effect of external degradation-inducing factors are of primary importance. This paper presents an in-depth review of internal and external degradation mechanisms at both anode and cathode of LIB with their corresponding mathematical models and correlation with SOH metrics (capacity and power fade). Different electrochemical models integrated with the internal degradation mechanisms and their governing equations are discussed and summarized. The effects of the external aging factors on capacity and power fade and the dominant degradation mechanism under cycling and stored conditions are also reviewed and tabulated for quick reference. Recent developments in BMS's capabilities for SOH estimation using advanced and intelligent algorithms under various internal degradation conditions are also presented. Finally, the challenges in modeling, estimation of SOH, and several future research directions for developing self-learning and smart BMS are provided.
... The critical area where the side reactions take place is the interface between electrolyte and electrodes. Particularly, the main aging process happens at the carbon electrodeelectrolyte interface, with the development of the so-called Solid Electrolyte Interphase (SEI) [16][17][18]. The typical graphite anode works outside the electrolyte stability window, so, during the first cycles of a new cell, a reaction between intercalated Li+ and the electrolyte solution creates this SEI, forming a protective layer that prevents further degradation of the graphite anode and allows a stable operation, as SEI is permeable to Li+. ...
... The solvent particles may also interact with the electrode causing graphite exfoliation, which results in loss of active material and gas formation [16]. It is important to state that lithium titanium oxide (LTO) cells do not face any SEI formation, with batteries that have shown lower capacity fade and longer lifetime compared to the graphite-based one [17]. The price to pay is a lower energy density for LTO batteries, due to the lower voltage for the single cell. ...
... In this work, semiempirical models have been developed to estimate the aging of the battery pack modeled in Section 4. The semiempirical models use experimental data to determine the variables of the chemical equations used to describe the reactions of interest at a macroscopic level. Among them, the Arrhenius Equation (6) is very often used when describing the reaction rate k of SEI formation, which is the main aging phenomena, and its temperature dependence [15][16][17][18]. ...
Article
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The growing interest in Electrical Vehicles (EVs) opens new possibilities in the use of Li-ion batteries in order to provide ancillary grid services while they are plugged to recharging stations. Indeed, Vehicle-to-Grid (V2G), Vehicle-to-Building (V2B), Vehicle-to-Home (V2H) as well as Vehicle-to-Vehicle (V2V) services can be carried out depending on the particular installation and on the connection to the distribution grid of the considered recharging station. Even if these are interesting and challenging opportunities, the additional charging/discharging cycles necessary to provide these services could decrease the expected life of EV batteries. For this reason, it is of paramount importance to study and develop reliable models of the batteries, which take the aging phenomena affecting the reliability of the Li-ion cells into account to evaluate the best charging/discharging strategy and the economic revenues. To this aim, this paper focuses on a battery pack made up with Li-ion nickel–manganese–cobalt (NMC) cells and proposes a semiempirical Electrothermal Aging Model, which accounts for both calendar and cycle aging. This modeling phase is supported by several experimental data recorded for many charge and discharge cycles at different C-rates and for several temperatures. Thus, it is possible to analyze and compare scenarios considering V2G services or not. Results show that the considered battery is subjected to a life reduction of about 2 years, which is a consequence of the increased Ah charge throughput, which moves from 120,000 Ah over 10 years (scenario without V2G services) to almost 230,000 Ah over 8 years (scenario with V2G services).
... The application to changing stress factors is outside the scope of many publications that develop empirical aging models [5,8,[11][12][13][14][15][16]. Other publications show model results during changing stress factors, however without specifying which method was used to model these cases [17][18][19]. A third group of publications specify how to apply their aging functions to switching stress factors [20][21][22][23][24][25]. ...
... A widely adapted analytic function for capacity fade in empirical aging models is a power law function with a square-root dependence of time for calendar aging. This is motivated by the solid electrolyte interphase (SEI) growth, which reduces the capacity fade rate with increasing SEI thickness [8,13,17]. Attia et al. [37] however suggest that the understanding of SEI growth kinetics is insufficient for this motivation [38] and highlight that the generalized power law, i.e. time or charge-throughput to the power of ∈ ]0, 1[ outperforms the = 0.5 assumption. The generalized power law ...
Article
Aging models are important tools to optimize the application of lithium-ion batteries. Usually, aging models are parameterized at constant storage or cycling conditions, whereas during application, storage and cycling conditions can change. In the literature, two different methods for modeling capacity fade during such dynamic operation are proposed. These methods use either the cumulated charge-throughput (CCT-method) or the current capacity (CAP-method) as reference points, when aging conditions are changing. In this work, we show that the CAP-method models capacity fade more accurately when applied to dynamic cyclic aging tests with periodically changing mean state-of-charge, depth-of-discharge, ambient temperature and discharge rates for a commercial NCA cell with a silicon-doped graphite anode. However, in cases where the difference between actual and reference charge-throughput of the CAP-method becomes large, the capacity gradient is modeled more accurately with the CCT-method. Because the relative capacity fade error of the CAP-method is small at <6%, we assume that capacity fade behaves path-independently for the dynamic cyclic aging tests since the CAP-method assumes path independence through history independence. Moreover, because the measured capacity fade is non-commutative, which is sometimes labeled path-dependent, we recommend to not consider non-commutative capacity fade as a definitive sign of path-dependent degradation.
... Most studies have suggested that battery capacity fading occurs due to charging cycles, where the magnitude of loss is dependent on the cycle number and depth of discharge (DoD) [28,29]. In summary, the effects of high DoDs intensify volumetric expansion and stress the battery electrodes, resulting in severe changes in structure and volume that cause capacity and power loss [27]. ...
... A single charging/discharging cycle per day and a constant battery replacement cost are not suitable for reality, nor is a fixed lifespan. These assumptions are not valid if the battery is cycled more than once per day, as more frequent cycling increases the rate at which the battery cells degrade and hasten the time at which they need to be replaced [29]. Figure 3 explains the degradation cost evolution, which indicates that the higher it is, the higher the preference for discharging, which tends to be reduced with DoD increment. ...
Article
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Advances in communication technologies and protocols among vehicles, charging stations, and controllers have enabled the application of scheduling techniques to prioritize EV fleet charging. From the perspective of users, residential EV charging must particularly address cost-effective solutions to use energy more efficiently and preserve the lifetime of the battery—the most expensive element of an EV. Considering this matter, this research addresses a residential EV charging scheduling model including battery degradation aspects when discharging. Due to the non-linear characteristics of charging and battery degradation, we consider a mixed integer non-linearly constrained formulation with the aim of scheduling the charging and discharging of EVs to satisfy the following goals: prioritizing charging, reducing charging costs and battery degradation, and limiting the power demand requested to the distribution transformer. The results shows that, when EVs are discharged before charging up within a specific state-of-charge range, degradation can be reduced by 5.3%. All charging requests are completed before the next-day departure time, with 16.35% cost reduction achieved by scheduling charging during lower tariff prices, in addition to prevention of overloading of the distribution transformer.
... The simulations of this study consider temperatures of À10 C to þ10 C. Many aging models are only valid in the higher temperature range [48] or do not consider the possible influence of lithium plating on aging [49]. The model of Wang et al. [16] is validated between 10 C and 46 C and is extrapolated to lower temperature ranges. ...
... A limitation of the model to be noted is the absence of validation for dynamic cycles, as in Refs. [48,49]. Nevertheless, the dynamics is considered via the changing stress factors. ...
Article
Short range remains a major disadvantage of battery electric vehicles compared to vehicles that have combustion engines. Range reductions also result from low ambient temperatures and from battery aging. By varying power of the heating system depending on the highly fluctuating propulsion power and using electrothermal recuperation, range losses can be minimized. This paper focuses on the development and comparison of strategies to control the vehicle's heating circuit. Rule-based, fuzzy logic, and optimization-based strategies are designed and implemented in a validated BMW i3 simulation model. The results show that fuzzy logic leads to the greatest improvement of range and battery lifetime. Compared to the standard strategy, range can be increased by 14% at −10 °C and by 6.5% at 0 °C. The current throughput during recuperation is reduced the most by a rule-based strategy that prioritizes the heater. For discharging, fuzzy logic can reduce the current throughput by a maximum of 11%, which leads to a capacity fade reduction of 4.3%. Since air mass is controlled separately, the cabin temperature remains almost constant, thus maintaining the comfort of the user.
... The aging process of a battery can be monitored by performing charging and discharging cycles while measuring the electrochemical characteristics, e.g., impedance and capacity [8][9][10][11][12][13]. ...
Article
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Lithium-ion batteries have good performance and environmentally friendly characteristics, so they have great potential. However, lithium-ion batteries will age to varying degrees during use, and the process is irreversible. There are many aging mechanisms of lithium batteries. In order to better verify the internal changes of lithium batteries when they are aging, post-mortem analysis has been greatly developed. In this article, we summarized the electrical properties analysis and post-mortem analysis of lithium batteries developed in recent years and compared the advantages of varieties of both destructive and non-destructive methods, for example, open-circuit-voltage curve-based analysis, scanning electron microscopy, transmission electron microscopy, atomic force microscopy, X-ray photoelectron spectroscopy and X-ray diffraction. On this basis, new ideas could be proposed for predicting and diagnosing the aging degree of lithium batteries, at the same time, further implementation of these technologies will support battery life control strategies and battery design.
... The advantage of physico-chemical models is the possibility of mapping changes taking place in each part of a LIC as a consequence of cycling, however, the identification of their parameters and implementation are highly complicated. A frequent approach among researchers is the use of methods based on regression [16,36,48,53,58]. In such models, in the majority of cases, the effect of selected cycling parameters on LIC degradation is analysed. ...
Article
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The paper deals with the subject of the prediction of useful energy during the cycling of a lithium-ion cell (LIC), using machine learning-based techniques. It was demonstrated that depending on the combination of cycling parameters, the useful energy (RUE c) that can be transferred during a full cycle is variable, and also three different types of evolution of changes in RUE c were identified. The paper presents a new non-parametric RUE c prediction model based on Gaussian process regression. It was proven that the proposed methodology enables the RUE c prediction for LICs discharged, above the depth of discharge, at a level of 70% with an acceptable error, which is confirmed for new load profiles. Furthermore, techniques associated with explainable artificial intelligence were applied to determine the significance of model input parameters-the variable importance method-and to determine the quantitative effect of individual model parameters (their reciprocal interaction) on RUE c-the accumulated local effects model of the first and second order. Highlights Abstract A new non-parametric useful energy model for • long-term prediction was developed. Developed model takes into account the lifetime • degradation of the cell. Identification of three types of RUEc evolution • over exploitation period of the cells. XAI techniques were used to quantify effect of • model parameters on RUEc. The proposed methodology can be applied to • electrochemical cells of other types. Burzyński D. Useful energy prediction model of a Lithium-ion cell operating on various duty cycles. Eksploatacja i Niezawodnosc-Maintenance and Reliability 2022; 24 (2): 317-328, http://doi.org/10.17531/ein.2022.2.13. Article citation info: cycle life modelling, lithium-ion battery, machine learning, predictive models, useful energy prediction. Keywords This is an open access article under the CC BY license
... Lead-acid batteries are used for uninterruptible power supply as the batteries are on standby most of the time at high states of charge. At high states of charge, however, lithium-ion batteries exhibit accelerated aging [20]. This is not the case with lead-acid batteries, which is why long lifetimes can be expected in this application in particular. ...
Preprint
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With this update of our previous publications, we publish the latest data of the market development of battery storage systems (BSS) in Germany, one of the leading storage markets worldwide. For the analyses of both stationary and mobile storage markets, we use the public registrations of the German Federal Network Agency and the Federal Motor Transport Authority, our own databases from the monitoring of subsidy programs, and bilateral exchange with large companies. In comparison to 2020, the market for home storage systems (HSS) grew by 50% in terms of battery energy in 2021 and is by far the largest stationary storage market in Germany. We estimate that about 145,000 HSS (1.27 GWh / 0.73 GW) were installed solely in 2021. The average specific price for a medium-sized HSS was about 1,000 EUR/kWh in 2021, showing a price decrease of 8% from 2020 to 2021. The emerging market for industrial storage systems (ISS) grew by 15% in 2021, with a total of 900 ISS (0.06 GWh / 0.03 GW) installed, although industrial PV installations decreased. The market growth for large-scale storage systems (LSS) remains on a relatively low level with 11 LSS (0.03 GWh / 0.04 GW) commissioned. The market for battery electric vehicles (EV) doubled in 2021 and new registrations raised to 680,000 EV (22.45 GWh / 31.18 GW). The public charging infrastructure could not follow this growth and grew linearly with about 11,700 new installations to 50,000 charging points, most of which are up to 22 kW. The number of EV per charging point grew from 10 in 2018 to 25 in 2021. In total, we estimate that over 430,000 stationary BSS with a battery energy of 4.46 GWh and a power of 2.64 GW and 1,270,000 EV with a battery energy of 39.59 GWh, a DC charging power of 51.84 GW, and an AC charging power of 7.71 GW were operated in Germany by the end of 2021.
... It is influenced by the storage temperature, where a low temperature is beneficial and the SOC during the storage, which also should be low, but mainly on the time itself [63]. The cycle aging refers to how many cycles the battery has experienced and is mainly influenced by the amount for charge throughput, but is also sensitive to the temperature and depth of discharge (DOD) [61]. Both aging effects are superimposed and cause the capacity to decline and the internal resistance to increase. ...
Thesis
Tolerances within static interconnected cells in automotive battery packs are limiting the overall usable capacity. This dissertation investigates the cascaded H-Bridges (CHB) inverter as an alternative interconnection. Actual cell variations are reviewed, a control strategy is defined, the most suitable state estimator is identified, and the inverter configuration is optimized. The actual capacity gain is limited for high quality cells. However, the efficiency is increased by 5.3 % and total vehicle costs are reduced by USD 2,647. Cells with increased tolerances and therefore potentially decreased costs are enabled.
... The model distinguishes between calendric and cyclic aging as two separate model parts. The aging results from both model parts are computed according to Eq. (1) because the underlying aging mechanisms accumulate [19]. ...
Article
Utility-scale Battery Energy Storage Systems (BESS) are becoming increasingly important for the transition to large shares of renewable energy sources in the electricity grid. Hybrid battery storage systems are an interesting option to increase the profitability of BESS by combining low-cost battery technologies with more expensive, but also more efficient and robust ones. In theory, such a hybrid system can be cheaper than a single-technology system by leveraging synergy effects while still satisfying the requirements of a given application. An energy management system, which allocates the setpoint power of the BESS to the individual battery technologies, is crucial for taking advantage of the hybrid system layout. This paper details an analysis of different energy management algorithms for hybrid BESS using the example of a real-world project called M5BAT and compares the latter to alternative BESS layouts. Besides two heuristic algorithms, a non-predictive optimization and a predictive optimization are developed. Models comprising the electrical, thermal and aging behavior of the considered BESS components are introduced. Simulations of the operation of the BESS providing frequency containment reserve are conducted for determining the operating costs. The performance of the algorithms is evaluated based on the simulation results. Comparing the results shows significant differences in the operating costs between the algorithms, especially when optimized to reduce operating costs. The economic advantage of hybrid BESS is validated by additional simulations of a virtual hybrid BESS and a virtual single-technology BESS. Although the layout has not been optimized in terms of individual sizing of the different battery technologies, the hybrid BESS show a considerable advantage over the single-technology BESS. Highlights -Comparative study of energy management algorithms for power allocation in hybrid battery energy storage systems. -Development of heuristic power allocation algorithms and optimization approaches for cost-optimal operation. -Modeling of all relevant components and effects that contribute to the operating costs. -Simulations based on frequency-containment reserve operation of a real-world system. -Evaluation of the advantage of hybrid over mono-technology battery energy storage.
... The research approaches in battery health are mainly divided into model-based methods and data-driven approaches. Model-based approaches describe the internal dynamics of the battery at different scales through mathematical models (e.g., empirical models [8,10], equivalent circuit models [11][12][13], and electrochemical models [14][15][16]), while determining the balance between the complexity of the battery model and the prediction accuracy is still a complex problem that needs to be further addressed. In contrast, data-driven approaches based on machine learning algorithms are dedicated to mining the close relationship between battery aging data and the state of health in the laboratory or under actual operating conditions, without the need to construct mathematical models. ...
Article
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Accurate health prognostics of lithium-ion battery packs play a crucial role in timely maintenance and avoiding potential safety accidents in energy storage. To rapidly evaluate the health of newly developed battery packs, a method for predicting the future health of the battery pack using the aging data of the battery cells for their entire lifecycles and with the early cycling data of the battery pack is proposed. Firstly, health indicators (HIs) are extracted from the experimental data, and high correlations between the extracted HIs and the capacity are verified by the Pearson correlation analysis method. To predict the future health of the battery pack based on the HIs, degradation models of HIs are constructed by using an exponential function, long short-term memory network, and their weighted fusion. The future HIs of the battery pack are predicted according to the fusion degradation model. Then, based on the Gaussian process regression algorithm and battery pack data, a data-driven model is constructed to predict the health of the battery pack. Finally, the proposed method is validated with a series-connected battery pack with fifteen 100 Ah lithium iron phosphate battery cells. The mean absolute error and root mean square error of the health prediction of the battery pack are 7.17% and 7.81%, respectively, indicating that the proposed method has satisfactory accuracy.
... The annualised life cycle cost (ALCC) is calculated using the actual BESS life from simulation results instead of a constant life given by the manufacturer. The end of life condition for the battery is assumed to be a 20% degradation from its initial capacity (Schmalstieg et al., 2014). These simulation results can be used to decide the BESS size and reduce the cost of energy generation. ...
Article
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Battery energy storage system (BESS) is a crucial part of standalone renewable hybrid power systems. Dynamic battery degradation analysis and life prediction are essential for better techno-economic estimation of standalone PV-wind battery hybrid power systems. With this viewpoint, this paper aims to study battery degradation using a physics-based pseudo-two-dimensional (P2D) thermal battery model integrated with renewable PV-wind hybrid power systems and investigates the impact of BESS size variation on its degradation and its effect on the energy generation costs A power management and control strategy is developed to ensure continuous power flow with two regulation modes; (a) maximum power point tracking and (b) controlled power generation. Yearly real-world load data, operating, and ambient conditions are used to study five different percentage mixes of PV and wind power generation scenarios. For example, a mix of 70% PV-30% wind, 350 kWh BESS is needed (base case) based on the demand. The yearly degradation rate for this case is calculated to be 3.80%. The degradation rates vary from 3.80 to 2.33% per year for every 10% increment in the BESS size from the base case. Performing a techno-economic analysis reveals that the least energy generation cost is achieved when increasing the BESS size by 20%. This increases BESS life from 5.3 years to 7.3 years and reduces the generation cost from 35.19 ₹ kWh⁻¹ (0.482 $ kWh⁻¹) to 34.34 ₹ kWh⁻¹ (0.470 $ kWh⁻¹). These results provide essential insights to analyse the impact of BESS sizing on degradation and energy generation cost in a standalone PV-wind battery hybrid power system framework. The oversized BESS provides extended life and reduces the energy generation cost for a standalone PV-wind-battery hybrid power system.
... Cycle aging of BESS is affected by kinetic factors related to charging and discharging cycle operating parameters including charging/discharging rate, depth of cycle, cycle number, and operating temperature [21]- [24]. Further, cycle aging is primarily affected by the cycle number and the depth of cycle [25], [26]. Incorporating an accurate battery cycle aging estimation method into BESSs short-term operational decisions is important to improve the longevity and profitability of the battery. ...
Article
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Battery energy storage systems (BESSs) have been widely used in power grids to improve their flexibility and reliability. However, the inevitable battery life degradation is the main cost in BESS operations. Thus, an accurate estimation of battery aging cost is strongly needed to cover the actual cost of BESSs. The existing models of battery life degradation either are not fully accurate to estimate the actual cost or are not solved easily because of their computation nonlinearity. In this paper, a piece-wise linear battery aging cost model with an accurate estimate of battery life degradation for BESSs is proposed to extend battery life and improve battery profits. In our method, the widely-used Arrhenius law is modified to quantify the battery life degradation affected by the depth of cycle. Further, a nonlinear battery cycle aging cost model is developed by finding the derivative of battery life degradation with respect to discharging power, which indicates the battery life degradation rate due to depth of cycle. To reduce the complexity of computation, a piece-wise linearization method is proposed to simplify the battery cycle aging cost model. Finally, the cycle aging cost model with an accurate estimation of battery life degradation is applied to the optimization dispatch in the day-ahead energy and auxiliary service market. The results show that the error of estimating the battery cycle aging cost of BESSs is less than 5% under proper piece-wise segment numbers. The profits are increased by 27% and the battery life is extended by 11% than the fixed cost method.
... Researchers have tried to gather the benefits of both model types by replacing the thermal part of the 1D electro-thermal model [31] with a reduced-order model of the 3D CFD battery model for temperature calculations (for quick computation), while the electrical properties of the battery are achieved by the 1D electrical part [32]. Thus, an optimal, robust, and precise electro-thermal model can be an integral part of the whole modeling framework where lifetime, safety, and reliability sections work together providing feedback to each other [33]. ...
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The automotive energy storage market is currently dominated by the existing Li-ion technologies that are likely to continue in the future. Thus, the on-road electric (and hybrid) vehicles running on the Li-ion battery systems require critical diagnosis considering crucial battery aging. This work aims to provide a guideline for pack-level lifetime model development that could facilitate battery maintenance, ensuring a safe and reliable operational lifespan. The first of the twofold approach is a cell-level empirical lifetime model that is developed from a lab-level aging dataset of commercial LTO cells. The model is validated with an exhaustive sub-urban realistic driving cycle yielding a root-mean-square error of 0.45. The model is then extended to a 144S1P modular architecture for pack-level simulation. The second step provides the pack electro-thermal simulation results that are upscaled from a cell-level and validated 1D electrical model coupled with a 3D thermal model. The combined simulation framework is online applicable and considers the relevant aspects into account in predicting the battery system’s lifetime that results in over 350,000 km of suburban driving. This robust tool is a collaborative research outcome from two Horizon2020 EU projects—GHOST and Vision xEV, showcasing outstanding cell-level battery modeling accuracies.
... Following this argumentation and assuming a similar trend, the battery can be expected to achieve a mileage and operating time far over the manufacturer warranty of 160,000 km and 8 years up to a SOH of 70%, with only approximately 8 % of capacity loss for the dynamic highway scenario up to the warranty limit, if operated under realistic conditions and according to the manufacturer guidelines. An early knee-point of the aging course with automotive A-grade cells, as measured with C-grade cells within the study of Baumh€ ofer et al. [69], is unlikely, which is why a trend of the capacity decay proportional to ffiffiffiffi Q p and, thus, ffiffiffiffiffiffiffiffi EFC p can be expected until 70% SOH, already visible in Fig. 17(b) and reported by previous studies in the field [74,75]. To support these assumptions with ground-truth data and exact figures, aging experiments are being conducted beyond the scope of this article and will be analyzed in a follow-up article, as stated in the previous section. ...
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With the rise of battery electric vehicles to mass production, many technical improvements have been realized to drastically increase the electric range, efficiency, and sustainability. However, insights into those valuable state-of-the-art solutions are usually not shared with researchers due to the strict non-disclosure policies of electric vehicle manufacturers. Many studies, therefore, rely on assumptions, best-guess estimates, or insider knowledge. This article presents an in-depth multi-scale analysis of the electric powertrain characteristics of a Volkswagen ID.3 Pro Performance. The focus is set on the range, power, and lifetime perceivable by the user. Thorough experimental tests are performed from the battery cell to vehicle level, following the energy conversion from source to sink. Energy densities are determined at all levels and the absolute electric range is quantified under varying operating conditions. Power capability is evaluated at cell level by quantifying the battery cell and pack performance with current rate tests in charge/discharge scenarios and impedance determination, as well as by determining powertrain energy conversion efficiency with in-vehicle measurements. Moreover, accelerated aging tests of the lithium-ion battery cells are performed with close to real-world conditions and projected to vehicle level, demonstrating that the lithium-ion battery pack achieves mileages outperforming the warranty information of the manufacturer under real-world operation. Overall, the results provide valuable insights into the current state of the art and can serve as a reference for automotive engineering in academia. Over 10 GB of lithium-ion battery cell, pack, and overall powertrain measurement data from the lab and real-world environment is available as open source alongside the article.
... Two main approaches exist to predict capacity fade in lithium-ion batteries. On the one hand, (semi-)empirical approaches based on simplified physical equations or data driven methods generate a precise aging quantification from large datasets [1][2][3][4][5][6] . However, obtaining the necessary datasets is costly and the resulting models can not easily be extrapolated or adapted to new cell chemistries. ...
Preprint
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Accurately predicting aging of lithium-ion batteries would help to prolong their lifespan, but remains a challenge owing to the complexity and interrelation of different aging mechanisms. As a result, aging prediction often relies on empirical or data-driven approaches, which obtain their performance from analyzing large datasets. However, these datasets are expensive to generate and the models are agnostic of the underlying physics and thus difficult to extrapolate to new conditions. In this article, a physical model is used to predict capacity fade caused by solid-electrolyte interphase (SEI) growth in 62 automotive cells, aged with 28 different protocols. Three protocols parametrize the time, current and temperature dependence of the model, the state of charge dependence results from the anode's open circuit voltage curve. The model validation with the remaining 25 protocols shows a high predictivity with a root-mean squared error of 1.28%. A case study with the so-validated model shows that the operating window, i.e. maximum and minimum state of charge, has the largest impact on SEI growth, while the influence of the applied current is almost negligible. Thereby the presented model is a promising approach to better understand, quantify and predict aging of lithium-ion batteries.
... These factors are, e.g. temperature, storage voltages for calendar ageing as well as cycle depth, state-of-charge (SOC) range, mechanical pressure, current rate and charge throughput for (charge/discharge) cycle ageing [8,9]. The mentioned stress factors influence ageing in electrodes and electrolytes. ...
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The testing of battery cells is a long and expensive process, and hence understanding how large a test set needs to be is very useful. This work proposes an automated methodology to estimate the smallest sample size of cells required to capture the cell-to-cell variability seen in a larger population. We define cell-to-cell variation based on the slopes of a linear regression model applied to capacity fade curves. Our methodology determines a sample size which estimates this variability within user specified requirements on precision and confidence. The sample size is found using the distributional properties of the slopes under a normality assumption, and an implementation of the approach is available on GitHub. For the five datasets in the study, we find that a sample size of 8-10 cells (at a prespecified precision and confidence) captures the cell-to-cell variability of the larger datasets. We show that prior testing knowledge can be leveraged with machine learning models to operationally optimise the design of new cell-testing, leading up to a 75% reduction in experimental costs.
... Fully data driven battery aging models differ significantly from physics-based models because they rely entirely on experimental or real-time feedback data. Simple data-driven models implement generic data fitting equations such as an empirical aging law [18], or the Arrhenius law equation [19]. These simple models follow rudimentary calibration techniques and result in easy implementation with a minimal number of calibration parameters. ...
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A growing interest in the study of aging related phenomena in lithium-ion batteries is propelled by the increasing utilization of energy storage systems in electric vehicles and in buildings as stationery energy accumulators paired with renewable energy sources. This paper proposes a mixed-degradation model approach that combines the benefits of a semi-empirical approach with that of a physics-based model. This enables easy calibration for different battery chemistries, the ability to extrapolate when necessary, and is computationally efficient enough to be coupled with real-time running control systems. To demonstrate the effectiveness of the proposed approach, the effect of two different control strategies in a smart home energy management system is demonstrated on the aging of a Lithium iron phosphate (LFP) battery.
... SLBESS in this application usually operates at a lower DOD. Nevertheless, the experiments in [128] indicate that the cycling around a middle SOC level will lead to less aging, which can help extend the service life of SLBESSs. In the PV application, the SLBESSs connected with PV panels work in a larger DOD due to the daily variation of solar radiation. ...
... The truck has access to 700 kW during the charging break and 50 kW overnight. The battery model uses the parametrization by Schmalstieg et al. [9] that was scaled to reflect the cycle life of status quo automotive batteries [10]. The required battery size of the truck to operate without any limitations until the battery end of life was iteratively determined to be 718 kWh battery. ...
Conference Paper
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Sizing a battery thermal management system (BTMS) for worst-case scenarios ensures that the battery is always operated in the preferred temperature window, but requires large, heavy, expensive cooling systems, and may cause large temperature differences within and between the cells. A control strategy that activates the cooling system at low cell temperatures minimizes battery aging, but also increases the cooling system energy consumption. To find the optimal installed cooling power and control strategy, we apply a previously developed method for the techno-economic design of battery thermal management systems to a battery-electric long-haul truck. Results show that the cost-optimal installed thermal cooling power is 85 % lower than the peak ohmic losses. The cost-optimal cooling strategy activates the cooling system when the cell temperature exceeds 25°C, which is lower than the cooling thresholds observed in electric passenger cars. The control strategy has a larger impact on the system cost than the installed cooling power.
... In order to estimate the battery lifetime and the required battery replacement frequency, a basic battery aging model is developed in an approach similar to Ploehn et al. (2004); Schmalstieg et al. (2014). Note that the battery life span depends on the discharge/charge cycle, battery voltage, temperature, and C-rate. ...
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The high cost of battery electric buses (BEB), driven by battery and infrastructure costs, is a key factor limiting their market penetration. These costs are determined by the choice of battery size and charging infrastructure. In addition, the electrification of public transit buses raises questions regarding the fleets’ punctuality toward their pre-defined schedules, due to the possible schedule delays resulting from BEB charging. In this context, this paper presents a methodology to assess the techno-economic performance of BEB fleets for different battery sizes and charging infrastructures and strategies. The proposed methodology is based on a detailed total cost of ownership (TCO) model considering purchase, operation, maintenance, and infrastructure costs. In addition, a punctuality index (PI) is introduced to quantify schedule delays due to BEB charging events during the day. A case study is used to illustrate the model and results show that overnight charging has the highest TCO, 13% higher than the TCO observed during end-line or opportunity charging. Moreover, a clear trade-off is observed between TCO and BEB punctuality to their schedule. Results show that there is significant room to reduce the TCO of BEB while respecting their operational constraints, by co-optimizing the battery size and charging infrastructure.
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This study analyses and presents a new ramp-rate control algorithm for smoothing PV power fluctuations, designed to address three fundamental objectives: to reduce battery cycling, to meet minimum storage requirements and to be able to operate, without ramp-rate violations, with real publicly-available forecasting. The algorithm was compared to three benchmark methods and, as a performance limit, also to a hypothetical perfect prediction. Different performance variables were analyzed for all the strategies within a restricted ramp-rate constraint (2%/min): minimum storage requirement, battery power distributions, throughput energy, state of charge (SOC) distributions, degradation (calendar and cycling), expected battery lifespan and levelized cost of energy (LCOE). The proposal proves to be the most cost-effective smoothing technique and the simulation results show that its performance is comparable to the obtained with the use of an assumed perfect prediction.
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Considering the variabilities among each cell especially during the battery accelerated decay period, the parameterized empirical model is doubtful for predicting the Lithium-ion (Li-ion) battery Remaining Useful Life (RUL). Thus, an Empirical-Data Hybrid Driven Approach (EDHDA) is proposed to utilize both the prior knowledge and the historical dataset for the lifetime prediction of the Li-ion battery under capacity diving conditions. A polynomial-based model is firstly proposed to provide the basic accuracy for the EDHDA. Meanwhile, an improved Gaussian Process Regression (GPR) with a partial charging voltage profile is designed to make full use of the operational dataset. The EDHDA is then established with a dual Particle Filter (PF) framework combining the advantages of the above two methods. In this way, accurate estimations of the current capacity can be obtained by fusing the two models, even under capacity diving conditions. The parameters of the empirical model can also be updated according to the fused capacity to obtain accurate RUL predictions with uncertainty levels. Experimental results show that the proposed EDHDA has a high RUL prediction accuracy under capacity diving even with limited data.
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This research proposes a methodological framework that effectively and efficiently identifies Pareto-optimal solutions of power flow control strategies (PFCSs) in heterogeneous battery energy storage systems (BESSs) and aims at achieving operational decision-making. The economic and ecological benefit of a BESS is strongly dependent on the efficiency of its operation. However, rapid changes in battery technology significantly impact the incorporation of an appropriate PFCS. Here, the Hyper Space Exploration (HSE) methodology is applied to the validated simulation environment of a BESS for the purpose of quantifying trade-offs in various use cases and system configurations. Simulations are carried out to analyze the target indicators performance, efficiency, and service life relating to the applied PFCS in a peak shaving application scenario. The results illustrate the correlations between the design variables, use case variables, and target indicators. A positive correlation is observed between the target indicators performance and efficiency, whereas a negative correlation is found between the criterion of service life and the other target indicators. By analyzing them systematically, the methodological framework further enhances understanding the trade-offs between the target indicators. The framework also applies to other PFCSs, use cases, and target indicators.
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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.
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The monitoring and prediction of the health status and the end of life of batteries during the actual operation plays a key role in the battery safety management. However, although many related studies have achieved exciting results, there are few systematic and comprehensive reviews on these prediction methods. In this paper, the current prediction models of remaining useful life of lithium‐ion batteries are divided into mechanism‐based models, semi‐empirical models and data‐driven models. Their advantages, technical obstacles, improvement methods and prediction performance are summarized, and the latest research results are shown by comparison. We highlight that the fusion models of convolution neural network, long short term memory network and so on, which have great practical application prospects because of their outstanding computing efficiency and strong modeling ability. Finally, we look forward to the future work in simplifying the model and improving its interpretability. In this paper, the research progress of mechanism‐based model, semi‐empirical model and data‐driven model in the prediction of health state and remaining useful life of lithium‐ion battery are reviewed, and the advantages, limitations and improved methods of the three models are compared. This review contributes to an in‐depth understanding of the mechanism, quantitative methods and prediction methods of battery life aging, and provides an important reference for the further development of high‐accuracy prediction models.
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To widely commercialize electric vehicles more efforts for their life improvement seem extremely inevitable. Thermal conditions can have profound and nonlinear effects on the degradation rate of an electric vehicle battery pack as well as its performance and safety level. In the current study, both cycle life and calendar life of a commercial LiFePO4 cell are investigated experimentally by means of capacity fading and resistance increment evaluation for 4 different thermal conditions from extremely cold condition of − 20 °C—which is not well studied in the literature—till hot condition of 55 °C. The calendar life tests show that the best condition for storing cells is at 5 °C and 50% SOC and the cycle life tests demonstrate that the best operating temperature is 25 °C based on the dynamic stress test discharge/charge profile (a test profile for electric vehicles). It is also found that the capacity fading and resistance increment at a high temperature such as 50 °C are destructively significant. The presented curves in this paper can also serve as an aging data source for further work on battery lifetime modeling and diagnostics. The role of temperature on the degradation level is also discovered via scanning electron microscopy.
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60 large format automotive grade lithium-ion pouch cells with graphite/NMC chemistry are tested following a design of experiment. Realistic driving profiles resembling a plugin-hybrid electric vehicle are used with variation of five aging factors: temperature, maximum and minimum state of charge, charging power, and the ratio of charge depleting vs. charge sustaining cycling. Capacity fade is cleaned from calendar aging and multivariate stepwise linear regression is used to parameterize an empirical model of cyclic capacity fade. Temperature and the ratio between charge depleting and charge sustaining cycling show the biggest impact on cyclic aging, whereas charging power has little effect in the chosen range of aging conditions. The importance of considering interdependencies between aging factors for modeling is pointed out, major interdependencies are found between the factors temperature and charging power and between minimum and maximum state of charge. Leave-one-out cross validation is used to show the capability of the comparatively simple model approach to predict cyclic aging within the tested range.
Thesis
Initiatives to decrease emissions from the transport sector are increasing worldwide by seeking alternative technologies to replace oil-based mobility. Battery Electric Buses (BEB) present a promising solution thanks to their high energy efficiency, low greenhouse gas emissions and the absence of local pollutant emissions. However, this technology still faces many challenges, especially its high total cost of ownership (TCO) and other operational factors such as the limited bus driving range, the high energy refueling time, and the required charging technologies and strategies. In this context, this thesis presents a systematic methodology that aims at developing solutions to help overcoming these challenges by providing optimal battery sizing and charging strategy for BEB. First, a comprehensive multi-physical bus energy model is developed to evaluate its energy needs considering all the energy systems encountered within. The energy consumption of the bus is then evaluated at a variety of operating conditions. Then, a techno-economic model of an entire bus line is developed in order to assess the impact of different battery sizing and charging strategies on the costs and operation of BEB. A TCO model is introduced considering the BEB unit costs, battery purchase and replacement costs, energy and power costs, infrastructure, and maintenance costs. A case study in Paris city is presented and the analysis reveals the resulting tradeoff between the TCO and BEB schedule disruptions and delays as function of different battery sizes and charging strategies. A methodology to minimize the TCO of BEB deployment is presented providing the optimal battery sizing and charging strategy for BEB, while respecting the BEB operation constraints. The methodology is a 2-step optimization algorithm that utilizes both Dynamic programming and Genetic Algorithm optimization routines. The results show that the proposed methodology could reduce the BEB TCO between 15-25% compared to the currently adopted approaches to deploy BEB.
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In-orbit satellite REIMEI, developed by the Japan Aerospace Exploration Agency, has been relying on off-the-shelf Li-ion batteries since its launch in 2005. The performance and durability of Li-ion batteries is impacted by various degradation mechanisms, one of which is the growth of the solid-electrolyte interphase (SEI). In this article, we analyse the REIMEI battery and parameterize a full-cell model with electrochemical cycling data, computer tomography images, and capacity fading experiments using image processing and surrogate optimization. We integrate a recent model for SEI growth into a full-cell model and simulate the degradation of batteries during cycling. To validate our model, we use experimental and in-flight data of the satellite batteries. Our combination of SEI growth model and microstructure-resolved 3D simulation shows, for the first time, experimentally observed inhomogeneities in the SEI thickness throughout the negative electrode for the degraded cells.
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Li-ion batteries lose both capacity and power over time due to calendar aging caused by slow parasitic processes that consume Li+ ions. Studying and mitigating these processes is traditionally an equally slow venture, which is especially taxing for the validation of new active materials and electrolyte additives. Here, we evaluate whether potentiostatic holds can be used to accelerate the diagnosis of Li+ loss during calendar aging. The technique is based on the idea that, under the right conditions, the current measured as the cell voltage is held constant can be correlated with the instantaneous rate of side reactions. Thus, in principle, these measurements could capture the rate of capacity fade in real time. In practice, we show that this method is incapable of quantitatively forecasting calendar aging trends. Instead, our study demonstrates that potentiostatic holds can be applied for initial qualitative screening of systems that exhibit promising long-term stability, which can be useful to shrink the parameter space for calendar aging studies. By facilitating the identification of improved formulations, this approach can help accelerate innovation in the battery industry.
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Accurately predicting aging of lithium-ion batteries would help to prolong their lifespan, but remains a challenge owing to the complexity and interrelation of different aging mechanisms. As a result, aging prediction often relies on empirical or data-driven approaches, which obtain their performance from analyzing large datasets. However, these datasets are expensive to generate and the models are agnostic of the underlying physics and thus difficult to extrapolate to new conditions. In this article, a physical model is used to predict capacity fade caused by solid-electrolyte interphase (SEI) growth in 62 automotive cells, aged with 28 different protocols. Three protocols parametrize the time, current and temperature dependence of the model, the state of charge dependence results from the anode’s open circuit voltage curve. The model validation with the remaining 25 protocols shows a high predictivity with a root-mean squared error of 1.28%. A case study with the so-validated model shows that the operating window, i.e. maximum and minimum state of charge, has the largest impact on SEI growth, while the influence of the applied current is almost negligible. Thereby the presented model is a promising approach to better understand, quantify and predict aging of lithium-ion batteries.
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To prevent probable battery failures and ensure safety, battery state of health evaluation is a critical step. This study lays out a coherent literature review on battery health estimation techniques to assist the research community with helpful information. Various techniques are systematically classified into respective groups and subgroups for easier understanding and follow-up. This study addresses the advantages and limitations of those techniques, along with their precision and application complexity. Furthermore, the procedures are briefly discussed on the premise of cost, computational effort, the requirement of sophisticated equipment, and their adaptability to various battery chemistries. Lastly, it draws the reader's attention towards a probable futuristic battery management architecture that may dictate the next decade's research efforts.
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Utility-scale Battery Energy Storage Systems (BESS) are becoming increasingly important for the transition to large shares of renewable energy sources in the electricity grid. Hybrid battery storage systems are an interesting option to increase the profitability of BESS by combining low-cost battery technologies with more expensive, but also more efficient and robust ones. In theory, such a hybrid system can be cheaper than a single-technology system by leveraging synergy effects while still satisfying the requirements of a given application. An energy management system, which allocates the setpoint power of the BESS to the individual battery technologies, is crucial for taking advantage of the hybrid system layout. This paper presents a detailed analysis of different energy management algorithms for hybrid BESS using the example of the real-world M5BAT project and compares that to alternative BESS layouts. Besides two heuristic algorithms, a non-predictive optimization and a predictive optimization are developed. Models comprising the electrical, thermal and aging behavior of the considered BESS components are introduced. Simulations of the operation of the BESS providing frequency containment reserve are conducted for determining the operating costs. The performance of the algorithms is evaluated on the basis of the simulation results. The comparison of the results shows significant differences of the operating costs between the algorithms, especially in the case of the optimizations, which achieve notably less operating costs. The economic advantage of hybrid BESS is validated by additional simulations of a fictional hybrid BESS and a fictional single-technology BESS. Although the layout has not been optimized in terms of individual sizing of the different battery technologies, the hybrid BESS show a considerable advantage over the single-technology BESS. Keywords:-Hybrid battery energy storage systems-Energy management-Frequency containment reserve-Optimization-BESS component models Highlights-Hybrid BESS model-Energy management based on an optimization approach-Comparison with heuristic energy management approaches-Based on real-world system
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Battery life is of critical importance for the reliable and economical operation of electric vehicles (EVs). Normal aging accounts for more than 80% of the battery available cycle range. Accurate and robust battery life models of normal aging are essential for battery health management systems and life evaluation before accelerated aging. Capacity recovery, test errors and accelerated aging all affect life model building during normal aging. Therefore, this paper proposes an improved life model based on wavelet transform (WT) signal processing to accurately predict the decline trend of the battery in the normal aging stage. In this paper, the capacity recovery, test noise and capacity diving in the aging trend are effectively removed by wavelet transform. We obtained an optimized life model through the analysis of the model structure and the analysis of the parameter sensitivity of the life model. The particle swarm algorithm (PSO) is employed to identify the parameters of the empirical models with the normal aging data extracted by the WT. Through verification, it is found that the modified cycle life model proposed in this paper can accurately predict the normal aging trajectory of batteries under different discharge rates and temperatures. The prediction error of the improved life model for normal aging is 1.09%.
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Superimposed Alternating Current (AC) imposed by electric machines and power electronics components in renewable energy systems and electric vehicles (EVs) exposes Li-ion batteries (LIBs) to high-amplitude AC harmonics (i.e., ripple currents). These AC perturbations make LIBs experience shallow charge-discharge cycles or microcycles, resulting in significantly increased battery charge throughput. Recently high amplitude AC-only waveforms have also attracted significant attention as they can be used to warm up/preheat EV batteries in low-temperature environments. To understand what happens when the amplitude of AC-bearing duty cycles is pushed to higher levels, beyond the recommended constant current value set by the manufacturer, a set of fresh LiFePO4 (LFP) cells were subjected to high-amplitude AC-only profiles at low and high frequencies for extended periods. Subsequently, LFP cells were cycled on dynamic current profiles (superimposed AC profiles) for ∼ 200 days, representing real situations encountered by LIBs in renewable energy and electromobility systems. Long-term aging results revealed that as long as the AC current induces a voltage oscillation that remains below the calculated threshold polarization overpotential, the cycle-life of LFP cells is not significantly affected for AC duties as high as the recommended constant current values. Furthermore, for high-frequency AC, a significant amount of microcycling is effectively filtered out by the battery’s double-layer capacitance. However, for low-frequency superimposed AC, for which most of the AC current flows through the charge-transfer resistance, higher charge throughput induced by microcycles causes accelerated degradation.
Chapter
This chapter focuses on the data science-based management for another three key parts during battery operations including the battery ageing/lifetime prognostics, battery fault diagnosis, and battery charging. For these three key parts, their fundamentals are first given, followed by the case studies of deriving various data science-based solutions to benefit their related operation management.
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Micro short circuit (MSC) in Li-ion batteries is characterized by slow development, and usually, MSC fault does not cause significant voltage fluctuations in the early stage. Therefore, early and accurate identification of MSC-faulty batteries is difficult. This paper proposes an MSC fault diagnosis method based on the evolution of the battery charging voltage ranking within multiple charging sections. The ageing trajectory of parameters with significant contributions to the battery terminal voltage is analyzed and proposes the hypothesis of constant battery charging voltage ranking. The battery data are pre-processed using wavelet denoising, and the 3-σ criterion performs the first layer of voltage anomaly diagnosis. The cell charging voltage is ranked horizontally at each sampling point to obtain a median ranking value representing the cell's voltage ranking level in that charging section. The voltage ranking variation factor is constructed, and the 3-σ criterion is used again to detect fault cells with abnormal voltage ranking variation. The method's effectiveness is verified using the actual faulty vehicle data collected. The results show that the method can accurately identify MSC faulty cells despite the absence of voltage anomalies and apparent inconsistencies in the faulty cells.
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Due to the increasing importance of lithium-ion batteries in electric vehicle and renewable energy applications, battery aging is a subject of intense research. Although many laboratory experiments are performed under well-controlled static conditions, batteries are stored and operated under varying conditions of temperature and state of charge in their real-life performance, so that a suitable model for predicting the effects of calendar aging in lithium-ion batteries with dynamic conditions is highly desirable. In this paper, we review previous models to calculate capacity loss due to calendar aging under variable temperature and state-of-charge conditions according to experimentally observed power-law behavior, and propose a novel model based on fractional calculus. To validate the new model, we compare its predictions with experimental results showing that it can reproduce the non-monotonic behavior that is observed when the state of charge or the temperature change significantly. This is an interesting application of fractional calculus since this characteristic is not obtained with non-fractional models.
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The paper provides a comprehensive battery storage modeling approach, which accounts for operation- and degradation-aware characteristics and can be used in optimization problem formulations. Particularly, Mixed-Integer Linear Programming (MILP) compatible models have been developed for the lithium iron phosphate (LiFePO4) battery storage using the Special Order Sets 2 to represent the nonlinear characteristics, including efficiency, internal resistance growth, and capacity fade. Such formulation can be used in problems related to various applications, i.e., power systems, smart grid, and vehicular applications, and it allows finding the globally optimal solution using off-the-shelf academic and commercial solvers. In the numerical study, the proposed modeling approach has been applied to realistic scenarios of peak-shaving, where the importance of considering the developed models is explicitly demonstrated. Operation- and degradation-aware techno-economic analysis showed that the optimal battery capacity is driven by operating rather than service requirements. Particularly, a considerable battery over-sizing becomes economically feasible when the battery storage is used more extensively. Another finding suggests that to achieve the maximum value from battery storage, its operation strategy needs to be significantly modified during the course of its lifetime. In the scenarios considered, the charging time gradually increased from four to seven hours, while the average SoC decreased by 20%. Such an adaptable scheduling results in reduced battery degradation and a longer lifetime, which may provide as much as 12.1% of savings in the battery storage system project.
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Cells were life-cycled cells using profiles with a 3, 6, or 9% change in state of charge (DSOC) at 40, 50, 60, and 70 8C. From the voltage response of the cells to the life-cycle profile at each temperature, we separated the overall impedance rise into two simpler terms, R o (ohmic) and R p (polarization), using an equivalent circuit model. The R o data tend to follow the expected trends (40 > 50 > 60 > 70 8C). Although the R p data trends show that R p can either decrease or increase asymptotically with time, the overall temperature-dependent behavior is similar to that of R o . We illustrate the types of processes that can occur in one lithium-ion cell chemistry. Based on the initial rates, the processes are complex. The R o term dominates the observable cell impedance, but R p adds a non-trivial contribution. # 2002 Elsevier Science B.V. All rights reserved.
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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.
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This paper presents the test results and analysis of the power and capacity fade resulting from the cycle-life testing using PNGV (now referred to as FreedomCAR) test protocols at 25 and 45 °C of 18650-size Li-ion batteries developed by the US Department of Energy sponsored Advanced Technology Development (ATD) Program. Two cell chemistries were studied, a Baseline chemistry that had a cathode composition of LiNi0.8Co0.15Al0.05O2 with binders, that was cycle-life tested at 25 and 45 °C, and a Variant C chemistry with a cathode composition of LiNi0.8Co0.10Al0.10O2 with binders, that was tested only at 45 °C. The 300 Wh power, and % power fade were determined as a function of test time, i.e. the number of test cycles for up to 44 weeks (369,600 test cycles) for the Baseline cells, and for 24 weeks (201,600 test cycles) for the Variant C cells. The C/1 and C/25 discharge capacity and capacity fade were also determined during the course of these studies. The results of this study indicate that the 300 Wh power for the Baseline cells tested at 25 °C (up to 44 weeks of testing) decreased as a linear function of test time. The % power fade for these cells increased as a linear function of test time. The Baseline cells tested at 45 °C (up to 44 weeks of testing) displayed a decrease in their power proportional to the square root of the test time, with a faster rate of decrease of the power occurring at ∼28 weeks of testing. The % power fade for these cells also increased as the square root of the test time, and exhibited an increase in the % power fade rate at ∼28 weeks of testing. The 45 °C tested Baseline cells’ power decreased, and their % power fade increased at a greater rate than the 25 °C tested Baseline cells. The power fade was greater for the Variant C cells. The power of the Variant C cells (tested at 45 °C) decreased as the square root of the test time, and their % power fade was also found to be a function of the square root of the test time (up to 24 weeks of testing), i.e. the rate of decrease in the power and the increase in the % power fade rate was greater for the Variant C cells than for the Baseline cells also tested at 45 °C. The C/1 and C/25 Ah capacities of the Baseline cells tested at 25 and 45 °C were determined to be a function of the square root of the cycle time (i.e. number of test cycles) for test times up to 44 weeks. The capacity fade was greater at 45 °C than at 25 °C. Similarly, the C/1 and C/25 charge capacities of the Variant C cells were found to be a function of the square root of the test time (up to 24 weeks of testing). The C/1 and C/25 charge capacities decreased as a function of test time and the rate of decrease was smaller for the Variant C cells as compared to the Baseline cells over comparable test times (24 weeks).
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Battery life is an important, yet technically challenging, issue for battery development and application. Adequately estimating battery life requires a significant amount of testing and modeling effort to validate the results. Integrated battery testing and modeling is quite feasible today to simulate battery performance, and therefore applicable to predict its life. A relatively simple equivalent-circuit model (ECM) is used in this work to show that such an integrated approach can actually lead to a high-fidelity simulation of a lithium-ion cell's performance and life. The methodology to model the cell's capacity fade during thermal aging is described to illustrate its applicability to battery calendar life prediction.
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The dashed lines show the calendar part of the aging. Three profiles were tested at three temperature levels. Both capacity and resistance are shown for each test
  • Fig
Fig. 19. Comparison between measured data (circles) and simulation results (lines). The dashed lines show the calendar part of the aging. Three profiles were tested at three temperature levels. Both capacity and resistance are shown for each test.