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Effect of extreme temperatures on battery charging and performance of electric vehicles
Abstract
Extreme temperatures pose several limitations to electric vehicle (EV) performance and charging. To investigate these effects, we combine a hybrid artificial neural network-empirical Li-ion battery model with a lumped capacitance EV thermal model to study how temperature will affect the performance of an EV fleet. We find that at −10 °C, the self-weighted mean battery charging power (SWMCP) decreases by 15% compared to standard 20 °C temperature. Active battery thermal management (BTM) during parking can improve SWMCP for individual vehicles, especially if vehicles are charged both at home and at workplace; the median SWMCP is increased by over 30%. Efficiency (km/kWh) of the vehicle fleet is maximized when ambient temperature is close to 20 °C. At low (−10 °C) and high (+40 °C) ambient temperatures, cabin preconditioning and BTM during parking can improve the median efficiency by 8% and 9%, respectively. At −10 °C, preconditioning and BTM during parking can also improve the fleet SOC by 3–6%-units, but this also introduces a “base” load of around 140 W per vehicle. Finally, we observe that the utility of the fleet can be increased by 5%-units by adding 3.6 kW chargers to workplaces, but further improved charging infrastructure would bring little additional benefit.
... When hundreds of cells are linked together in series or parallel, it may be considerably worse [11]. The heat conditions in which batteries run have a profound impact on their electric capability and aging features [12]: High temperatures result in progressive energy density, however they also rush the battery's degradation [13,14]. Low temperatures impair the battery's charging/discharging efficiency by enhancing internal resistance [15]. ...
... The necessity of studying the TMS of LIBs is evident from the above literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] survey, and it is backed up by extensive research in this area. TMS techniques can be categorized into two types namely active techniques and passive techniques. ...
Lithium-ion batteries (LIBs) are becoming gradually common in our everyday lives, associated with the rapid
growth of electric vehicles (EVs) as well as hybrid vehicles (HVs). The thermal performance of a battery pack has
a significant impact on its stability, aging, and durability. Hence the thermal management system (TMS) of
battery packs for EVs is one of the prominent research areas in recent years. In this study, bibliometric analysis
has been conducted by using the Scopus database between the years 2000 and 2021 to assess scientific articles in
the field of TMS of EV’s battery packs. After two iterations, we have found 983 articles relevant to this topic.
Based on the analytical findings, the TMS of LIBs has been rapidly expanding, and significant outcomes will
remain to increase in the future. Only a few countries have shown their significant role in this area. Among them,
China seems to be the active participation country in this research field. Tsinghua University in China and
Ontario Tech University in Canada are the most renowned Universities which produce more number of individual
as well as collaborative research and review articles. In comparison to other journals, SAE Technical
Papers has published highest number of articles. There are nearly 15 major funding agencies that sponsored the
fund for the project in this area. In that, 52 projects have been funded by the National Natural Science Foundation
of China. Furthermore, a quantitative analysis has been conducted on the development trend in TMS of
LIBs and will serve as an established resource for budding scholars who are interested in this research area.
... The overheat of battery cells would lead to accelerated degradation, capacity fading, or even full damage of the cells [48]. Due to the deviations of the temperature control accuracy of EV BTMS (around ±1 K) and the high power output requirement of the commercial EVs (more uniform cell temperatures could deliver more stable and accurate power output since the battery output performance is significantly related to the battery temperature [49]), the maximum Li-ion battery cell operating temperature should be close to 25 °C to deliver the best output performance and maintain the longest service lifetime [50,51]. Although 25 °C is the ideal operating temperature for Li-ion batteries, the cooling capacities of both active and passive air-cooling BTMSs heavily rely on the ambient temperatures. ...
... The overheat of battery cells would lead to accelerated degradation, capacity fading, or even full damage of the cells [48]. Due to the deviations of the temperature control accuracy of EV BTMS (around ±1 K) and the high power output requirement of the commercial EVs (more uniform cell temperatures could deliver more stable and accurate power output since the battery output performance is significantly related to the battery temperature [49]), the maximum Li-ion battery cell operating temperature should be close to 25 • C to deliver the best output performance and maintain the longest service lifetime [50,51]. Although 25 • C is the ideal operating temperature for Li-ion batteries, the cooling capacities of both active and passive air-cooling BTMSs heavily rely on the ambient temperatures. ...
An air-cooling battery thermal management system is a reliable and cost-effective system to control the operating temperatures of the electric vehicle battery pack within an ideal range. Different from most designs of the rectangular battery pack in previous research, this one proposed a novel isosceles trapezoid layout to improve system heat dissipations. The simulation results showed that the trapezoid design delivered better cooling performances than the rectangular one with a maximum temperature reduction of 0.9 °C and maximum temperature difference reduction of 1.17 °C at the inlet air flow rate of 60 L/s. Moreover, the cooling performance was further boosted by an aluminum heat spreader. The boosted design delivers an average Max T (32.95 °C) and an average ΔT (3.10 °C) at five different flow rates, which are 8.8% and 66.1% lower the one without the spreader (35.85 °C and 5.15 °C). Compared with the rectangular design without the spreader, the average Max T and ΔT of the boosted trapezoid design are reduced by 10.4% and 91.9% in addition to a space-saving of about 5.26%.
... Battery performance decreases when used at temperatures greater than 45°C [135] due to the reduction of the porosity and pore connectivity at high temperatures which subsequently leads to a decrease in the uniformity of the battery current [136]. However, a very cold operating temperature also has the ability to cause a significant decrease in battery performance [137] as indicated by the findings of Lindgren and Lund that the charging capacity of a battery can reduce by an average of 15% when charged at 10°C [138]. This means it is important to manage the battery towards ensuring it is maintained at an optimal operating temperature of 20°C [138] and Dubey, Pulugundla, and Srouji found immersion cooling to be better for this purpose compared to Cold-Plate Based Cooling [78]. ...
... However, a very cold operating temperature also has the ability to cause a significant decrease in battery performance [137] as indicated by the findings of Lindgren and Lund that the charging capacity of a battery can reduce by an average of 15% when charged at 10°C [138]. This means it is important to manage the battery towards ensuring it is maintained at an optimal operating temperature of 20°C [138] and Dubey, Pulugundla, and Srouji found immersion cooling to be better for this purpose compared to Cold-Plate Based Cooling [78]. Immersion cooling was also observed to have the ability to cool the entire cell surface and improve temperature uniformity by reducing the local heating effect on the positive and negative electrodes compared to indirect cooling [84]. ...
The world's energy consumption shows an increasing trend. Unfortunately, it is still dominated by the use of fossil energy. This condition results in concerns that an energy crisis will occur due to running out of non-renewable energy and an environmental crisis of global warming. One of the technology sectors that consumes a lot of energy is computer servers and data centers. This technology sector is growing rapidly due to the increasingly complex computing and larger data. One of the main causes of inefficiency in this technology is a decrease in performance due to an increase in temperature. Therefore, effective and efficient technology is needed to overcome the increasing operating temperature of computer servers and data centers. One of the most prominent cooling technologies to solve this problem is immersion colling. This method has developed in various types with their respective advantages and disadvantages according to application needs. Therefore, review literature is needed to comprehensively discuss the development of immersion cooling technology from the past until now and projected developments in the future. In more detail, this paper comprehensively compiles the latest findings of immersion cooling technology which includes an overview of the cooling system, history, implementation, construction type, and advantages. Ó 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
... Another limitation of this work is the use of the battery in extreme temperature conditions (T < 0 • C or T > 55 • C). In [29], for example, it is observed that at a temperature of −10 • C, the average SoC in an electric vehicle is incremented by 3-6% units every hour, because a larger charge current is accepted at the same voltage. ...
... This number was defined using the idea of stagnation, that is, when no significant improvement of the population was observed after several consecutive generations, that is, when the average aptitude or the best individual did not improve anymore or when the aptitudes of the individuals in a population became very similar. The best individual in the population after this optimization process contains information from: σ best = 2.4203 (28) η best = 4.3807 (29) When adjusting the parameters of the ANN models, with the exception of the ANN MCC,AG , carried out by the error backpropagation algorithm, it was empirically verified that the best results were found when defining the learning coefficient η with the value equal to 1. A low learning rate tends to increase the model's convergence time, since the adjustment of ANN parameters (Weights and Bias) is performed in a more subtle way. ...
Nowadays, electric vehicles have gained great popularity due to their performance and efficiency. Investment in the development of this new technology is justified by increased consciousness of the environmental impacts caused by combustion vehicles such as greenhouse gas emissions, which have contributed to global warming as well as the depletion of non-oil renewable energy source. The lithium-ion battery is an appropriate choice for electric vehicles (EVs) due to its promising features of high voltage, high energy density, low self-discharge, and long life cycles. In this context, State of Charge (SoC) is one of the vital parameters of the battery management system (BMS). Nevertheless, because the discharge and charging of battery cells requires complicated chemical operations, it is therefore hard to determine the state of charge of the battery cell. This paper analyses the application of Artificial Neural Networks (ANNs) in the estimation of the SoC of lithium batteries using the NASA’s research center dataset. Normally, the learning of these networks is performed by some method based on a gradient, having the mean squared error as a cost function. This paper evaluates the substitution of this traditional function by a measure of similarity of the Information Theory, called the Maximum Correntropy Criterion (MCC). This measure of similarity allows statistical moments of a higher order to be considered during the training process. For this reason, it becomes more appropriate for non-Gaussian error distributions and makes training less sensitive to the presence of outliers. However, this can only be achieved by properly adjusting the width of the Gaussian kernel of the correntropy. The proper tuning of this parameter is done using adaptive strategies and genetic algorithms. The proposed identification model was developed using information for training and validation, using a dataset made available in a online repository maintained by NASA’s research center. The obtained results demonstrate that the use of correntropy, as a cost function in the error backpropagation algorithm, makes the identification procedure using ANN networks more robust when compared to the traditional Mean Squared Error.
... At À20 C, both voltages and state of discharges (SoDs) were about 20% lower than those at 45 C. At À40 C, the power density and energy density of Panasonic 18,650 Li-ion batteries were only about 1.25% and 5% of those at 25 C (Nagasubramanian, 2001). Lindgren et al. (Lindgren and Lund, 2016) reported that a Li-ion battery's self-weighted mean charging power (SWMCP) at À10 C could only reach about 85% of that at 20 C. Preheating strategy is a common solution to solve the low-temperature issue. Li et al. (Li et al., 2021c) polarized the cells by pulse currents to provide rapid heating at low temperatures. ...
... Wright et al. (Wright et al., 2003) tested 18,650 cells at 45 C and displayed a faster capacity fade rate proportional to the square root of total cycling time compared with a slower linear fade rate at 25 C. As the cycling number increases, degradations would be more rapid with rising temperature (>40 C) while capacity would decrease contrarily with higher temperature (Lindgren and Lund, 2016). Waldmann et al. (Waldmann et al., 2014) discovered that 25 C was the optimal temperature for a longer cycle life of Li x Ni 1/3 Mn 1/3 Co 1/3 O 2 /Li y Mn 2 O 4 blended cathodegraphite/carbon 18,650 battery. ...
Vehicle electrification has always been a hot topic and gradually become a major role in the automobile manufacturing industry over the last two decades. This paper presented comprehensive discussions and insightful evaluations of both conventional electric vehicle (EV) batteries (such as lead-acid, nickel-based, and lithium-ion batteries, etc.) and the state-of-the-art battery technologies (such as all-solid-state, silicon-based, lithium-sulfur, and metal-air batteries, etc.). Battery major component materials, operating characteristics, theoretical models, manufacturing processes, and end-of-life management were thoroughly reviewed. Different from other reviews focusing on theoretical studies, this review emphasized the key aspects of battery technologies, commercial applications, and lifecycle management. Useful battery managing technologies such as health prediction, charging and discharging, as well as thermal runaway prevention were thoroughly discussed. Two novel hexagon radar charts of all-round evaluations of most reigning and potential EV battery technologies were created to predict the development trend of the EV battery technologies. It showed that lithium-ion batteries (3.9 points) would be still the dominant product for the current commercial EV power battery market in a short term. However, some cutting-edge technologies such as an all-solid-state battery (3.55 points) and silicon-based battery (3.3 points) are highly likely to be the next-generation EV onboard batteries with both higher specific power and better safety performance.
... In the harsh conditions of −20 • C, the battery capacity drops to 72% after only eight cycles. In contrast, at higher temperatures (>+40 • C), the charging capacity increases and the internal resistance decreases further, compared with charging at room temperature [57]. ...
... battery capacity drops to 72% after only eight cycles. In contrast, at higher temperatur (>+40 °C), the charging capacity increases and the internal resistance decreases furthe compared with charging at room temperature [57]. ...
Understanding the aging mechanism for lithium-ion batteries (LiBs) is crucial for optimizing the battery operation in real-life applications. This article gives a systematic description of the LiBs aging in real-life electric vehicle (EV) applications. First, the characteristics of the common EVs and the lithium-ion chemistries used in these applications are described. The battery operation in EVs is then classified into three modes: charging, standby, and driving, which are subsequently described. Finally, the aging behavior of LiBs in the actual charging, standby, and driving modes are reviewed, and the influence of different working conditions are considered. The degradation mechanisms of cathode, electrolyte, and anode during those processes are also discussed. Thus, a systematic analysis of the aging mechanisms of LiBs in real-life EV applications is achieved, providing practical guidance, methods to prolong the battery life for users, battery designers, vehicle manufacturers, and material recovery companies.
... Different models have been used to study the thermal behavior of lithium-ion cells like the lumped capacitance thermal approach, equivalent circuit thermal models, numerical models, and analytical models [32]. Although, the lumped capacitance thermal approach [33,34] and the equivalent circuit thermal models [35,36] are simple and computationally efficient, they are less accurate in comparison to the other methods. However, the electro-thermal models coupled by numerical methods like CFD [37], FEM [38] and Lattice-Boltzmann [39] provide highly accurate results but such methods have complex formulations and high computational costs. ...
... Eqs. (34) and (37) are normalization integrals to satisfy the orthogonality condition. Now, the temperature rise in the cell is calculable by use of the following inversion formula, ...
Lithium-ion batteries have a high energy content, which makes them a great option for mobile storage applications. However, there are some serious concerns regarding their performance in terms of uncontrolled overheating. In this study, an analytical thermal model is developed based on the integral transform technique to predict the temperature field in a cylindrical lithium-ion cell. The temperature rise and the thermal gradient, as the significant parameters for the safety and performance assessment of lithium-ion batteries, are investigated for the lithium-ion cell. Moreover, the thermal behavior of the lithium-ion cell is comprehensively studied for different thicknesses of the component layers. It is found that the optimum thickness of the positive active material, the negative active material, the positive current collector, and the negative current collector for the efficient thermal operation of the lithium-ion cell is 180, 34, 21, and 20 μm, respectively. Furthermore, the performance of the optimized jelly-roll is assessed for the different types of cylindrical lithium-ion cells. The results indicate that the 21700 cell has the best thermal performance for use in high charge/discharge applications.
... When the battery is at a relatively high operating temperature, it can increase the rate of internal side reactions, such as the rate of SEI decomposition and regeneration. It leads to an accelerated rate of irreversible loss of active material and triggers a progressive fault in which the battery capacity fades quickly [60,61]. In the charging and discharging process, if the battery working environment temperature is too high and the heat production rate is significantly higher than the heat dissipation rate, the lithium-ion battery may have different degrees of expansion. ...
The battery system, as the core energy storage device of new energy vehicles, faces increasing safety issues and threats. An accurate and robust fault diagnosis technique is crucial to guarantee the safe, reliable, and robust operation of lithium-ion batteries. However, in battery systems, various faults are difficult to diagnose and isolate due to their similar features and internal coupling relationships. In this paper, the current research of advanced battery system fault diagnosis technology is reviewed. Firstly, the existing types of battery faults are introduced in detail, where cell faults include progressive and sudden faults, and system faults include a sensor, management system, and connection component faults. Then, the fault mechanisms are described, including overcharge, overdischarge, overheat, overcool, large rate charge and discharge, and inconsistency. The existing fault diagnosis methods are divided into four main types. The current research and development of model-based, data-driven, knowledge-based, and statistical analysis-based methods for fault diagnosis are summarized. Finally, the future development trend of battery fault diagnosis technology is prospected. This paper provides a comprehensive insight into the fault and defect diagnosis of lithium-ion batteries for electric vehicles, aiming to promote the further development of new energy vehicles.
... The electric vehicle driving range is rated under specific driving conditions [7][8][9][10][11], which do not include temperature variations during rating tests. The general effects of temperature on the driving range of electric vehicles have been widely studied and characterized [12][13][14][15][16], and more specifically, the seasonal variation in temperature has been studied and characterized [17][18][19][20]. Nevertheless, the sudden changes in ambient temperature caused by variable climatic conditions, despite the recent advances in this field, have not yet been sufficiently studied and analyzed [21,22]. ...
The goal of this paper is the evaluation of lithium-ion batteries that power electric vehicles (EVs) under variable climatic conditions to determine how the driving range of a vehicle is modified because of changes in battery performance caused by the variability of environmental conditions. The influence of sudden changes in ambient temperature on the performance of the battery that powers electric vehicles has been studied and analyzed. The study is focused on how trips across geographical zones with different climates affect the autonomy of an electric vehicle’s battery, and thus the driving range of the electric vehicle (EV). A model has been developed to reproduce on a laboratory scale the real conditions to which EVs are subject when circulating under fluctuating temperatures, which force the battery to operate in a transient or non-steady state. A simulation has been run for different climatic conditions to evaluate the performance of the battery and the driving range of the electric vehicle under variable operating conditions. A laboratory prototype has been designed and built to validate the modeling and to adjust the theoretical approach to experimental values through the corresponding correction factor in case significant deviations occur. The model has been validated for a simulated route that reproduces a real driving trip for specific geographical areas. The model indicates that there is a shortening in the global driving range of 43.5 km over a trip distance of 538 km, which means there is a reduction of 8%. The simulation has been applied to a specific geographical area in the nearby of the city of Lyon (France), for a temperature gap of 39 °C, from −6 °C to 33 °C, but can also be used for almost any other zones, although the reduction in driving range may vary because of specific climate conditions.
... Also, there is only a slight decrease in the discharging capacity in the temperature range of 20 • C to 40 • C. The variation in discharging capacity between 20 • C and 40 • C, especially for a 0.5 C discharging rate, is just 0.133 Ah, or 4.9% of the claimed capacity of 2.7 Ah battery cell. It is worth noting that higher temperature (40 • C in this study) can cause fast battery capacity deterioration as the number of cycles rises, despite the fact that the available capacity of Li-ion batteries increases at higher temperatures [132,133,134]. ...
The lithium-ion batteries used in electric vehicles have a shorter lifespan than other vehicle components, and the degradation mechanism inside these batteries reduces their life even more. Battery degradation is considered a significant issue in battery research and can increase the vehicle’s reliability and economic concerns. This study highlights the degradation mechanisms in lithium-ion batteries. The aging mechanism inside a battery cannot be eliminated but can be minimized depending on the vehicle’s operating conditions. Different operating conditions affect the aging mechanism differently. Knowing the factors and how they impact battery capacity is crucial for minimizing degradation. This paper explains the detailed degradation mechanism inside the battery first. Then, the major factors responsible for the degradation and their effects on the battery during the operation of electric vehicles are discussed. Also, the different techniques used to model the degradation of a battery and predict its remaining life are explained in-depth, along with the techniques to abate the aging process. Finally, this study focuses on the research gaps, difficulties in predicting the lifetime, and reducing the degradation mechanism of a battery used in electric vehicles.
... Insufficient heat dissipation in the battery module results in a high battery temperature, which affects the lifetime and safety of the battery. As such, it is important to design a high-quality BTMS (battery thermal management system) to ensure that the temperature of the battery module remains stable [10][11][12]. ...
In this paper, the thermal management of a battery module with a novel liquid-cooled shell structure is investigated under high charge/discharge rates and thermal runaway conditions. The module consists of 4 × 5 cylindrical batteries embedded in a liquid-cooled aluminum shell with multiple flow channels. The battery module thermal management and the suppression of thermal propagation were experimentally examined. The temperature rise of the battery in the discharging process is significantly greater than that in the charging phase. As the coolant flow speed increases, the maximum temperature of the battery module decreases slightly, while the temperature difference remains at the same level, at the expense of a much-increased pressure drop. With the presented liquid-cooled shell, the suppression of thermal propagation was investigated for both internal and corner battery thermal runaway. It is found that the temperature of the adjacent battery can be maintained at under 70 °C, indicating that the propagation of thermal runaway can be successfully suppressed by heat dissipation through the surrounding liquid flow. In addition, the electrically induced thermal profile along the battery interconnection was identified through thermal imaging. Hot spots were found on the confluence busbars of the batteries in series connection. In order to improve the safety of battery modules, a parallel battery connection in the battery module is recommended, which can reduce the busbar temperature by 4.86 °C, as determined through numerical simulations. Experimental measurements were also conducted to verify the simulation results.
... The pulse charge/discharge capability was investigated in detail [5][6]. Meanwhile, Lindgren et al. studied the charging and heat generation characteristics of the lithium battery at ambient temperatures ranging from -10℃ to 40℃ [7][8]. Ma et al. [9] conducted a comprehensive review of the effects of temperature on lithium-ion batteries. ...
The paper studies the surface temperature rise and pulse charge/discharge capacity of batteries with different charge/discharge rates under different temperature conditions. The discharge performance and charge rate characteristics of an automotive lithium-ion battery were investigated. The battery discharge performance tests were conducted under different ambient temperatures and different charge rate conditions to obtain the battery charge voltage curve, charge capacity, and the temperature change pattern of the external surface of the battery during the charging process. Also, internal resistances of the battery cell were measured using the hybrid pulse power characteristic (HPPC). Experimental results show that the battery’s 10% to 90% DOD range shows the excellent pulse charging and discharging ability of the battery, and the temperature change of the battery is much higher at the beginning of the charging process and the end of the discharging process due to the influence of internal resistance.
... Next, the climatic conditions and the nature of the environment significantly affect the range of an EV. The range decreases at extremely low and high environmental temperatures (Lindgren & Lund, 2016;Vatanparvar & Faruque, 2018;Argue, 2021), and according to Vatanparvar and Faruque (2018) the optimal temperature for an EV is 21.5°C. Manufacturers attempt to limit this problem by using different cooling or heating systems for the batteries (Ji & Wang, 2013;Jaguemont, boulon & Dubé, 2016;Geotab, 2021;MAHLE, 2021). ...
Research and development help improve the reliability of EVs range, battery capacity, and trouble-free charging (or service). These factors affect consumers’ interest in EVs. The quality of EV use can be supported by a modern technology called Energy Assistant (EA). The task of EA is to inform the driver about the current range, the necessity to recharge the batteries, and so on to avoid a critical situation. The main aim of this article was to investigate factors and input parameters for the proposal of EA. The Edison EV designed at the University of Žilina, was used for experimental work under real conditions and in an accredited lab with MAHA equipment.
... (25)-(32). The key operating equations for buck mode are from eq. (25)-(28), whereas boost mode is from Eq. (29)-(32).EV battery voltage across the dc link is obtained as current is obtained by integrating Eq.(25), and the complete voltage discharging cycle is given by Link is input to the system, which is represented as VEV,B+VL0 = VDC, ...
Transportation is currently advancing towards Electric Vehicle (EV) technology. This paper presents a brief and systematic analysis of the real-time issues obtained in Electric Vehicles (EVs) due to the various ranges of energy storage devices. In general, EV energy management system is integrated with power electronic circuits for effective power conversion and reliable operation. Some issues are addressed while using the batteries in EV systems such as charging time, efficiency of battery, and raw materials. Not only battery issues but also real-time non-technical issues and operational issues are also discussed in this paper. During energy conversion with power electronic circuits, the system attains extreme temperature levels which in turn reduces the performance of the system. To maintain an optimum temperature level, it is important to study the temperature effect of the system at the most prior levels. Due to the adaption of power electronic components, some extent of noise is generated, technically treated as Electromagnetic Interference (EMI), as system capacity increases the EMI content also improved proportionately. Therefore, to mitigate the EMI effect, the low pass filter-based EMI filter is introduced in the system such that the noise level is suppressed. Bidirectional Charging System (BCS) is one of the emerging technologies in EV to obtain autonomous power supply systems in the form of Vehicle to Grid (V2G), Grid to Vehicle (G2V), and Vehicle to Load (V2L). To know the behaviour of BCS the proposed RNN controller is employed and is compared with ANN bidirectional charging model. BCS charging system with RNN controller has better dynamic response to exchange the power via DC/DC converter and AC/DC converter as compared to ANN controller.
... Verma et al. studied the battery pack temperature at different ambient temperatures (21 and 50 °C) by using capric acid as PCM [24]. The effect of distinct thickness of PCM (3,7,9, and 12 mm) on the battery temperature was investigated in which the 3 mm thickness of the PCM layer showed the best cooling performance. Zhang et al. prepared the composite PCM using kaolin, expanded graphite, and paraffin to manage the thermal features of a rectangular battery [25]. ...
The influence of discharge rate, ambient temperature, and phase change material on the maximum temperature and the highest temperature difference was investigated. The maximum temperature of the battery was tested with and without phase change material under extreme discharge rates (4C and 5C) and ambient temperatures (310 K and 320 K). Results showed that a phase change material reduced the maximum temperature from 327.94 K to 306.45 K for a 14.6 Ah lithium-ion battery discharged at 5C-rate and 320 K. Quantitatively determined parameter effects revealed that the PCM parameter considerably had a remarkable influence on maximum temperature compared to discharge rate and ambient temperature. Moreover, the influence of ambient temperature on the maximum temperature was approximately 2.5 times greater than the C-rate, while the influence of ambient temperature on the highest temperature difference was approximately 50 times greater than the C-rate. The quantified parameter effects can be used to improve the phase change material-battery cooling system.
... This is in good agreement with the results of a simulation for an electric car fleet, with the highest efficiency at 20 • C. At −10 • C the efficiency decreased by 16%. [2]. Energy consumption experiments were carried out with the internal electric heater of the car itself, a Nissan Leaf as well, and an external fuel-fired heater [3]. ...
At low temperatures lithium-ion batteries have a low performance and the usable capacity is reduced. Furthermore, specific aging processes such as lithium plating are accelerated. Aside from that, charging at temperatures below 0∘C can lead to safety issues. Preheating of the Li-Ion cells can reduce the previously mentioned problems. Most of the known external or internal heating methods need a lot of space and reduce the overall energy density of a battery. In this work a high frequency heater is presented, which in contrast to most other high frequency heaters, does not require an inductance as an additional component which can reduce costs and space. Only two semiconductor switches and a capacitor are needed. Apart from that, it can be easily implemented in the battery management system within a battery disconnection unit with semiconductor switches. Measurements of the high frequency heater show heating rates up to 38K/min for an 18650 cell. For a temperature rise of 20K the presented heater only requires 3.5% of the nominal cell capacity. After more than 1800 heating sequences from −10∘C to 10∘C, a capacity fading of 15 % was observed.
... The public acceptance following the attention given to the matter by private transportation players can be interpreted as the same trend in the industry [4]. One of the candidates in the spotlight as the power source is lithium-ion batteries (LIBs) because of their enormous benefits such as relatively low cost and self-discharge rate, long life cycle, and high power and energy density [5][6][7]. However, targeted, comprehensive studies are needed to deal with problems originating from electrochemical reactions and the corresponding generated heat in a LIB pack, such as capacity degradation [8] and thermal runaway [9][10][11]. ...
An innovative liquid cooling system that contains stair and wavy channels by alumina nanofluid with copper sheath is numerically analyzed to improve the battery thermal management system's temperature distribution and cooling capacity during discharge/charge processes. The effects of the charge/discharge current rate, alumina nanofluid coolant, inflow velocity, ambient/fluid inflow temperature, stair channel, interfacing regions between adjacent LIBs, and the contact regions between LIBs and wavy channel shell on the thermal efficiency of the battery module was investigated. Based on the outcomes, it is concluded that the addition of coolant alumina nanofluid with a 2% volume fraction notably reduces the highest temperature and temperature non-uniformity across the battery module. Simulation results illustrated that by using a 2% volume fraction of Alumina nanofluid, the peak temperature and temperature difference for the discharge process decreased 1.2 °C and 0.4 °C, compared to DI water, respectively. In addition, increasing the coolant inlet velocity led to a decrease in maximum temperature and temperature difference. Different ambient/fluid inflow temperatures of the battery module are evaluated to simulate different weather climates. An innovative design using the stair channel cooling is finally developed for the LIB thermal management system. Comparing the two stair and straight channels, it is accomplished that the temperature non-uniformity for the stair-type channel is almost reduced by 0.19 °C and 0.22 °C for the discharge and charge processes, respectively.
... In extreme cases, the high temperature even causes a series of chemical side reactions inside the battery, eventually leading to catastrophic accidents such as fire and explosion of the whole battery pack or even the entire vehicle [12]. In addition, the temperature difference between cells will reduce the performance of the battery pack, which is often determined by the battery with the worst performance [13][14][15][16]. ...
Electric vehicles are inevitably affected by vibration when driving. However, the influence of vibration on the thermal field is neglected in most theoretical and experimental studies on battery thermal management systems (BTMS) in the current literature. Due to the addition of high thermal conductivity elements such as carbon nanoparticles and graphene into pure phase change material (PCM), the heat transfer properties of these composite phase change materials (CPCM) differ significantly from those of pure PCM. The present work focuses on the influence of mechanical vibration on the BTMS based on CPCM. A series of experiments are carried out for the BTMS based on pure paraffin and three CPCM with a mass fraction of expanded graphite or graphene from 0 to 20 % under the vibration amplitudes of 2–4 mm and frequencies of 10–30 Hz. The results show that the small vibration amplitude is beneficial to strengthening the heat transfer of CPCM and lowering the battery's operating temperature. Besides, the mechanical vibration can accelerate the dispersion and collision of the high thermal conductivity particles in CPCM, thus improving the heat dissipation efficiency of the BTMS. It can significantly prolong the latent heat utilization time of the CPCM and then extend the period when the battery pack is in the suitable working temperature range. However, the too high or too low vibration frequency is not conducive to enhancing heat transfer. The 20 Hz is found to be an optimal vibration frequency. And the vibration has the best cooling effect on the CPCM with 20 % expanded graphite among the three CPCMs considered in this study. Finally, the grey relational analysis is applied to investigate the combined effects of three factors. It is determined that the impact ranking from the largest to the smallest is vibration frequency, the composition of CPCM, and vibration amplitude.
... To achieve this, a simplified thermal model of the passenger cabin, based on experimentally obtained warm-up and cool-down time constants, was used. Regarding short trips, [9] quantified a gain of 8% of energy efficiency for a fleet of BEVs at -10 °C. This gain included cabin preheating and battery thermal management. ...
The driving range of battery electric vehicles (BEVs) is greatly influenced by ambient conditions, especially at low temperatures. To address this, the battery and the passenger cabin can be preheated using energy from the electric grid. This is regarded as a strategy to reduce the energy consumption of these vehicles in winter. For long trips, preheating can indeed be translated into a slight increase in the driving range. However, for short trips, the amount of energy saved from the battery does not outweigh the additional energy demanded from the grid. This article aims to quantify the increase of the driving range and of the total energy consumption with preheating. To do this, a simulation of the interconnection of the main subsystems of a BEV is used. The results for an ambient temperature of –10 °C, 45 min of preheating, and a normalized extra-urban driving cycle show an increase of 8.5 km of the driving range for a long trip and an increase of 17% of the total energy consumption for a 1-h trip.
... In addition, different factors that affect EV energy consumption were also discussed. We can cite the road gradient effect [23,24] which has a linear impact on fuel usage, and the effect of ambient temperature [25,26] affecting EV battery capacity linearly between 15C and 20C. An approach that considered both geographical and temperature factors was proposed in [27], estimating optimal routes and their duration. ...
Electric Vehicles (EVs) are regarded to be among the most environmentally and economically efficient transportation solutions. However, barriers and range limitations hinder this technology’s progress and deployment. In this paper, we examine EV route planning to derive optimal routes considering energy consumption by analyzing historical trajectory data. More specifically, we propose a novel approach for EV route planning that considers real-time traffic incidents, road topology, charging station locations during battery failure, and finally, traffic flow prediction extracted from historical trajectory data to generate energy maps. Our approach consists of four phases: the off-line phase which aims to build the energy graph, the application of the A* algorithm to deliver the optimal EV path, the NEAT trajectory clustering which aims to produce dense trajectory clusters for a given period of the day, and finally, the on-line phase based on our algorithm to plan an optimal EV path based on real traffic incidents, dense trajectory clusters, road topology information, vehicle characteristics, and charging station locations. We set up experiments on real cases to establish the optimal route for electric cars, demonstrating the effectiveness and efficiency of our proposed algorithm.
... Han et al. (2016) integrating a differential equation that provides an EV's instantaneous power (Yi and Bauer, 2016). While some papers zoomed into the impact of temperature on the charging process (Lindgren and Lund, 2016) and (Motoaki et al., 2018), this topic remains open for further researches. ...
Since road transport accounts for a high share of global Greenhouse Gas (GHG) emissions, Electric Vehicles (EVs), including Battery EVs (BEVs) and Plug-in Hybrid EVs (PHEVs), offer a promising alternative to replace fossil-fuel dependent Internal Combustion Engine Vehicles (ICEVs), with a low-or zero-emission transport solution. While this technology's adoption has been rapidly increasing over the last decade, its market share remains restrained by socio-techno-economic barriers in most countries. Overcoming these barriers is an essential step into a massive purchasing activity of electric vehicles. This dissertation aims at investigating innovative business models by examining the transactions within the members of the EV ecosystem. This dissertation deals with future and multidisciplinary research in economics, management, and engineering sciences on decarbonised car systems' future. First, this thesis addresses the main charging infrastructure deployment barrier by presenting a literature review about the deployment methodologies and pointing out the neglected set of parameters that present our research gaps. Second, it investigates the trade-offs between the different-power charging infrastructure and the BEV owner by considering: BEV investment and range, public infrastructure availability, at-home charger availability, and the adaptability between charger and BEV in terms of technical constraints. It also concludes with the most suitable BEV battery capacity for urban and rural needs, and the installation of fast chargers. Next, it analyses the influence of socio-demographic, economic, and technical factors on electric vehicle adoption in French departments, using mixed-effects regression. This chapter points out policy recommendations to accelerate the electric mobility transition. Finally, this thesis analyses the influence of the Regulation number (EU)2019/631 on the automotive manufacturers’ portfolio, and compares the results with the directive of the European Commission.
... Han et al. (2016) integrating a differential equation that provides an EV's instantaneous power (Yi and Bauer, 2016). While some papers zoomed into the impact of temperature on the charging process (Lindgren and Lund, 2016) and (Motoaki et al., 2018), this topic remains open for further researches. ...
Since road transport accounts for a high share of global Greenhouse Gas (GHG) emissions, Electric Vehicles (EVs), including Battery EVs (BEVs) and Plug-in Hybrid EVs (PHEVs), offer a promising alternative to replace fossil-fuel dependent Internal Combustion Engine Vehicles (ICEVs), with a low-or zero-emission transport solution. While this technology's adoption has been rapidly increasing over the last decade, its market share remains restrained by socio-techno-economic barriers in most countries. Overcoming these barriers is an essential step into a massive purchasing activity of electric vehicles. This dissertation aims at investigating innovative business models by examining the transactions within the members of the EV ecosystem. This dissertation deals with future and multidisciplinary research in economics, management, and engineering sciences on decarbonised car systems' future. First, this thesis addresses the main charging infrastructure deployment barrier by presenting a literature review about the deployment methodologies and pointing out the neglected set of parameters that present our research gaps. Second, it investigates the trade-offs between the different-power charging infrastructure and the BEV owner by considering: BEV investment and range, public infrastructure availability, at-home charger availability, and the adaptability between charger and BEV in terms of technical constraints. It also concludes with the most suitable BEV battery capacity for urban and rural needs, and the installation of fast chargers. Next, it analyses the influence of socio-demographic, economic, and technical factors on electric vehicle adoption in French departments, using mixed-effects regression. This chapter points out policy recommendations to accelerate the electric mobility transition. Finally, this thesis analyses the influence of the Regulation number (EU)2019/631 on the automotive manufacturers’ portfolio, and compares the results with the directive of the European Commission.
... A recent study from Idaho National Laboratory (INL) reported that the SOC of a 30-min DC fast chargers (DCFC) could drop by as large as 36% from warm temperature (25 • C) to cold temperature (0 • C) (Motoaki et al. 2018), indicating the sensitivity of EVs routing performance in cold regions. Further, a number of relevant recent studies demonstrate that the performance of the Lithium-ion (Li-ion) battery is sensitive to the ambient weather (e.g., Dubarry et al. 2012;Ji et al. 2013;Jaguemont et al. 2016;Lindgren and Lund 2016). Although very relevant, the temperature effect on EVs fast-charging is not considered and extensively examined. ...
This study investigates how the location-routing decisions of the electric vehicle (EV) DC Fast Charging (DCFC) charging stations are impacted by the ambient temperature.Electric vehicles are expected to contribute significantly to the delivery mission of logistic companies in the future. In an EV delivery logistics network equipped with DCFC stations, this study investigates how the location strategy of DCFC charging stations and the routing plan of a fleet of EVs are impacted by the ambient temperature. We formulated this problem as a mixed-integer linear programming model that captures the realistic charging behavior of the DCFC’s in association with the ambient temperature and their subsequent impact on the EV charging station location and routing decisions. Two innovative heuristics are proposed to solve this challenging model in a realistic test setting, namely, the two-phase Tabu Search-modified Clarke and Wright algorithm and the Sweep-based Iterative Greedy Adaptive Large Neighborhood algorithm. We use Fargo city in North Dakota as a testbed to visualize and validate the algorithm performances. The results clearly indicate that the EV DCFC charging station location decisions are highly sensitive to the ambient temperature, the charging time, and the initial state-of-charge. The results provide numerous managerial insights for decision-makers to efficiently design and manage the DCFC EV logistic network for cities that suffer from high-temperature fluctuations.
... The ambient temperature can also greatly affect the charged 310 capacity, as the underlying electrochemical reactions inside 311 EV batteries are highly sensitive to extreme temperatures [34]. ...
Enabling charging capacity abnormality diagnosis is essential for ensuring battery operation safety in electric vehicle (EV) applications. In this paper, a data-driven method is proposed for battery charging capacity diagnosis based on massive real-world EV operating data. Using the charging rate, temperature, State-of-Charge and accumulated driving mileage as the inputs, a tree-based prediction model is developed with a polynomial feature combination used for model training. A statistics-based method is then used to diagnose battery charging capacity abnormity by analyzing the error distribution of large sets of data. The proposed tree-based prediction model is compared with other state-of-the-art methods and is shown to have the highest prediction accuracy. The holistic diagnosis scheme is verified using unseen data.
... 3) Another external factor that can have a significant influence on charge speed is the temperature of the battery [58][59][60]. A battery works optimally if the temperature is not too high and not too low and in practice, this is usually between 15 and 35 ° [61]. ...
Road transport electrification is essential for meeting the European Union's goals of decarbonization and climate change. In this context, an Ultra-Fast Charging (UFC) system is deemed necessary to facilitate the massive penetration of Electric Vehicles (EVs) on the market; particularly as medium-long distance travels are concerned. Anyway, an ultra-fast charging infrastructure represents the most critical point as regards hardware technology, grid-related issues, and financial sustainability. Thus far, this paper presents an impact analysis of a fast-charging station on the grid in terms of power consumption, obtained by the Monte Carlo simulation. Simulation results show that it is not economical convenient size the assumed ultra-fast charging station for the maximum possible power also considering its high impact on the grid. In view of the results obtained from the impact analysis, the last part of the paper focuses on finding a method to reduce the power installed for the DC/DC stage while keeping the possibility for the electric vehicle to charge at their maximum power. To achieve this goal a modular approach is proposed. Finally, two different modular architectures are presented and compared. In both the solutions, the probability of having EVs charging at limited power is less than 5%.
The number of EVs is increasing globally. In cold climates, it is generally recommended to use electricity from the grid to preheat the EV cabin before using the car, to extend driving ranges, to ensure comfort, and for safety. A majority of such preheating sessions are happening in the morning hours during the winter, when there is also a high demand for other energy use. It is thus important to understand the power loads for grid-connected preheating of EV cabins. This work presents an experimental study, with 51 preheating sessions of five typical EV models during different outdoor temperatures. The results of the study showed that during the preheating sessions, most of the EVs had a power use of between 3 and 8 kW initially, which was reduced to about 2 to 4 kW after a 10 to 20 min initial period. For most of the sessions, the preheating lasted between 15 and 45 min. The preheating energy use was found to be up to 2 kWh for most EVs, with a maximum of 5 kWh. Multiple linear regression models were developed, to investigate the relationship between various variables and the energy use for preheating. Finally, hourly energy loads for EV cabin preheating were compared to other energy loads in apartment buildings. The power and energy loads for preheating EV cabins are affected by a number of parameters, such as the specific EV, charge point, preheating duration, temperature levels, and user habits.
This review of the literature explores the potentials of liquid micro-/mini-channels to reduce operating temperatures and make temperature distributions more uniform in batteries. First, a classification and an overview of the various methods of battery thermal management are presented. Then, different types of lithium-ion batteries and their advantages and disadvantages are introduced, and the components of batteries are described in detail. The studies conducted on the performance of micro-/mini-channels for cooling all types of rectangular and cylindrical batteries are reviewed, and the key finding of these studies is presented. It is shown that, in general, using counterflow configuration creates a rather uniform temperature distribution in the battery cell and keeps the maximum temperature difference below \(5^\circ \mathrm{C}\). The lowest battery maximum temperature is obtained for parallel and counterflow configurations in the straight and U-turn channels, respectively. In a parallel configuration, the peak point of the battery temperature is in the outlet area. However, in the counter-flow configuration, it occurs in the central region of the battery module. The survey of the literature further reveals that proper channel paths and flow configurations keep the battery maximum temperature within the safe range of \(25\,^\circ {\text{C}} < T_{\max } < 40\,^\circ {\text{C}}\). For such flow configurations, the pressure drop remains minimally affected.
Although great progress has been made in new electrolytes for lithium metal batteries (LMBs), the intrinsic relationship between electrolyte composition and cell performance remains unclear due to the lack of valid quantization method. Here, we proposed the concept of negative center of electrostatic potential (NCESP) and Mayer bond order (MBO) to describe solvent capability, which highly relate to solvation structure and oxidation potential, respectively. Based on established principles, the selected electrolyte with 1.7 M LiFSI in methoxytrimethylsilane (MOTMS)/ (trifluoromethyl)trimethylsilane (TFMTMS) shows unique hyperconjugation nature to stabilize both Li anode and high‐voltage cathode. The 4.6 V 30 μm Li||4.5 mAh cm‐2 lithium cobalt oxide (LCO) (low N/P ratio of 1.3) cell with our electrolyte shows stable cycling with 91% capacity retention over 200 cycles. The bottom‐up design concept of electrolyte opens up a general strategy for advancing high‐voltage LMBs.
Although great progress has been made in new electrolytes for lithium metal batteries (LMBs), the intrinsic relationship between electrolyte composition and cell performance remains unclear due to the lack of valid quantization method. Here, we proposed the concept of negative center of electrostatic potential (NCESP) and Mayer bond order (MBO) to describe solvent capability, which highly relate to solvation structure and oxidation potential, respectively. Based on established principles, the selected electrolyte with 1.7 M LiFSI in methoxytrimethylsilane (MOTMS)/ (trifluoromethyl)trimethylsilane (TFMTMS) shows unique hyperconjugation nature to stabilize both Li anode and high-voltage cathode. The 4.6 V 30 μm Li||4.5 mAh cm-2 lithium cobalt oxide (LCO) (low N/P ratio of 1.3) cell with our electrolyte shows stable cycling with 91% capacity retention over 200 cycles. The bottom-up design concept of electrolyte opens up a general strategy for advancing high-voltage LMBs.
Electric vehicles are a means of reducing CO2 emissions in transportation. However, the sustainability of electric vehicle batteries is affected by battery health degradation, which decreases their overall lifetime. This results in a substantial amount of depleted batteries due to replacements. Users have a major impact on battery health degradation through their actions while handling electric vehicles, such as the use of fast charging. To mitigate this problem, this article presents a methodology to generate user guidance for battery-friendly actions in the upcoming use. Therefore, we first identify general recommendations from related work and combine them with the vehicle context in order to define context-aware recommendations in the form of if–then rules. These context-aware recommendations are then used to generate user advice. Second, the article covers how to predict the vehicle context in order to determine necessary recommendations. Third, a prescriptive recommendation system architecture is proposed, which takes vehicle context information, and produces user guidance. Finally, we test the proposed architecture using fuzzy logic as decision system. Overall, the architecture provides satisfactory user advice.
Electric vehicle (EV) performance in terms of the available driving range per charge and the energy consumption rate continuously degrades during its service life. Quantitative assessments of EV performance degradation play an important role in EV residual value analysis, battery management, and battery recycling. However, EV performance degradation is highly sensitive to both ambient temperature and battery aging states; coupled factors make its quantification challenging. Here, a novel big data-driven decoupling framework is proposed to investigate the partial relationships between EV performance degradation and each individual variable (e.g., temperature and total driving distances). The core innovation involves the decoupling process that can enable real-world and large-scale degradation assessments. The basic functionality of the decoupling is achieved by an iterative learning framework where different machine learning-based models can communicate with each other. It achieves the advantages of unsupervised training and high performance; the mean absolute error can be controlled less than 0.1 in the model validation of EV ranges. Its effectiveness is verified using different real-world EV datasets. By utilizing the framework, the changes in the range and energy consumption of EVs across 10 urban areas in China are assessed. The results show that the range and energy consumption rate of EVs are more greatly influenced by ambient temperature than by battery aging. Less consideration of variable decoupling may yield misleading results in EV performance analysis. Our proposed framework opens avenues for quantifying EV performance degradation via real-world EV data, which is critical to onboard and cloud-based EV research.
For a sustainable future, electric vehicles (EVs) are expected to offer a superior alternative to conventional fossil fuel-based vehicles. However, the performance of lithium-ion batteries used in EVs is known to deteriorate at low temperatures. Hence, preheating of EV batteries becomes imperative in cold climates. In the present paper, a potassium carbonate salt hydrate-based Thermochemical Energy Storage System (TESS) is proposed for battery preheating. The Energy Storage Bed (ESB) is a reactor of this system in which hydration-dehydration reactions take place. The ESB is envisioned as a modular design that presents the advantage of scalability for different EV variants and ambient conditions. A 2-D model of ESB is developed and its performance is analysed by various parametric studies. The proposed TESS achieves cycle efficiency of 47.1 %, volumetric energy storage density of 153.4 kWh m⁻³, and specific heating power of 88.6 W kg⁻¹. Further, the analysis is extended to study the temporal variation in the battery temperature of an electric car by the addition of the proposed TESS to the existing heating architecture. The effect of various parameters associated with the heating circuit, on the evolution of battery temperature is also studied. Using the proposed TESS, a heating rate of 0.43 °C min⁻¹ can be achieved for battery preheating of a typical electric passenger car.
In order to reduce the temperature of the battery and improve its thermal safety during use, this paper tentatively designs an oil-immersed battery thermal management system to validate the feasibility of the insulating oil on cooling the discharging battery. Through a series of experiments, the dielectric property of the transformer oil and silicon oil is further confirmed, and their cooling effectiveness is superior to the natural convection. Specifically, the insulating oils not only control the battery temperature below 35 ℃, but also thermally homogenize the battery with temperature differences below 3 ℃ in the discharge rate ranging from 1 to 2 C. Furthermore, both the cooling and homogenization performances demonstrate that the transformer oil is better than silicon oil. This work experimentally demonstrates the performance of silicon oil and transformer oil for cooling the battery, and the feasibility of self-designed oil-immersed battery cooling system is also verified, which can provide effective guidance for the development of higher-efficient direct-contact liquid cooling system in the future.
Cell voltage inconsistency of a battery pack is the main problem of the Electric Vehicle (EV) battery system, which will affect the performance of the battery and the safe operation of electric vehicles. In real-world vehicle operation, accurate fault diagnosis and timely prediction are the key factors for EV. In this paper, real-world driving data is collected from twenty all-electric buses for many years and divided into three driving fragments to analyze cell voltage inconsistency and summarize the voltage characteristics of the cell when an inconsistency fault occurred. A fault diagnosis method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed for timely localization of the abnormal battery cell. It is found that the DBSCAN clustering algorithm has shown better effectiveness and accuracy as compared to K-means to locate irregular battery cells. A fault prediction method based on the Least-Square Support Vector Regression (LS-SVR) is developed to predict the change of the monomer voltage. The experimental comparison show that LS-SVR has better prediction accuracy than ordinary Support Vector Regression (SVR), and it can make short-term predictions based on the voltage difference and monomer voltage value for cell consistency failures and over/under voltage faults.
Lithium-ion battery (LIB) degradation is often characterized at three distinct levels: mechanisms, modes, and metrics. Recent trends in diagnostics and prognostics have been heavily influenced by machine learning (ML). This review not only provides a unique multi-level perspective on characterizing LIB degradation, but also highlights the role of ML in achieving higher accuracies with accelerated computation times. We survey the state-of-the-art in degradation research and show that existing techniques lay the foundations for a unified ML method – a single tool for characterizing degradation at multiple levels. This could inform optimal management of lithium-ion systems, thus extending lifetimes and reducing costs. We propose a framework for the hypothesized technique using pulse injection, digital-twinning, and neural networks, and identify the challenges and future trends in degradation research.
With the wide application of supercapacitors, it has been recognized that the drop of energy storage capacity due to cold environmental temperatures limits their utilization efficiency. In this study, a binder-free Ni/Co-layered double hydroxide (LDH)-based high-performance energy storage device is constructed on Ni foam by in situ electrochemically triggered MOF hydrolysis, which exhibits a remarkably enhanced capacity under solar irradiation. Through electrochemically controlled hydrolysis, the ligands in MOFs are replaced by OH–, and the obtained NiCo-LDH retains the original hierarchical porous structure of the MOFs. Benefitting from the sufficient oxygen vacancies and large surface area, the NiCo-LDH electrode shows a high capacity of 5.4 C cm–2 at 1.25 mA cm–2, which is 64.3 times higher than that of the MOF template. Importantly, the excellent photothermal conversion ability (temperature increase of 52.9°C within only 30 s) under solar irradiation increases the capacity to 226.0% even at sub-freezing temperature. Furthermore, the asymmetric supercapacitor fabricated using NiCo-LDH delivers an energy density increase of 329.2% in a low-temperature environment (−4°C) after 15 min solar-light irradiation.
Dans la littérature scientifique, la consommation d'énergie des véhicules électriques à batterie est généralement estimée en ne considérant que le sous-système de traction. De plus, cette approche ne peut pas prendre en compte tous les impacts des conditions climatiques sur la consommation (et sur l’autonomie). En effet, les conditions ambiantes ont un impact sur les performances des batteries Li-ion, sur la puissance nécessaire pour la traction et sur la consommation d'énergie du sous-système de confort. Le produit combiné de ces effets peut réduire considérablement l'autonomie de ces véhicules. Ce fait constitue une contrainte au développement des véhicules électriques sur le marché automobile, notamment dans des régions aux hivers très rigoureux (exemple du Canada). Dans ce cadre, une collaboration entre l'Université de Lille (France) et l'Université du Québec à Trois-Rivières (Canada) a été établie pour étudier, entre autres, l'impact des conditions climatiques sur la consommation d'énergie des véhicules électriques.L’objectif de cette thèse en cotutelle est de développer un outil de simulation flexible d’un véhicule électrique dans son environnement. L’outil doit permettre d’étudier les performances énergétiques de ces véhicules sur une large plage de conditions climatiques. Pour atteindre cet objectif, une interconnexion des modèles de différents domaines de la physique (électrique, mécanique et thermique) est réalisée, en suivant une approche systémique. Les modèles considérés correspondent aux sous-systèmes de traction, de confort thermique et de stockage (avec le chargeur de batteries). Le formalisme Représentation Énergétique Macroscopique est utilisé pour organiser et interconnecter de façon causale les modèles des différents sous-systèmes, ainsi que de l’environnement du véhicule. Les facteurs ambiants considérés dans l’outil sont la température, l’humidité relative et les conditions du ciel. De plus, la validité de l’outil est vérifiée avec des tests expérimentaux sur une voiture réelle entre -5 °C et 30 °C.La pertinence de l’outil est mise en évidence avec la réalisation de trois études sur la consommation d’énergie d’un véhicule électrique sous différents profils de conduite et conditions ambiantes. La première étude est l’estimation de l’impact énergétique individuel des conditions ambiantes considérées sur chaque sous-système d’un véhicule électrique. La deuxième étude est l’estimation de la variation annuelle de la consommation d’énergie dans un climat canadien. Enfin, la dernière étude est une évaluation des avantages énergétiques du préchauffage en hiver.
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.
Electric vehicle thermal management system (EVTMS) and its corresponding control strategy are essential to ensure the battery performance at low temperatures. Due to the complexity of the low-temperature fast charging process, it is necessary to comprehensively consider the battery heating and charging performance when developing the heating strategy. However, most studies only focus on the charging time. In this context, an EVTMS is investigated in this paper, and the simulation models are established. In addition, the battery heating and charging performance under low-temperature fast charging conditions are analyzed by experiment and simulation. An improved fast charging protocol for reducing the battery-lifetime degradation is proposed. Moreover, an EVTMS multi-stage heating control strategy is developed to realize the cooperative control with the improved charging strategy. Taking dual crucial yet conflicting factors, heating energy consumption and charging time, as the optimization objectives, the proposed EVTMS control strategy is optimized using genetic algorithm. And an optimal heating control strategy is obtained. The results show that compared with the strategy only considering the charging time, the optimal strategy can reduce the heating energy consumption by 11.61% and increase the charging efficiency by 1.2%, only with the increase of the charging time by 2.98 min.
The use of lithium-ion batteries has increased in recent years with the development of industries such as automobiles. To achieve the desired voltage and current, it is necessary to place a large number of these batteries in series and in parallel with each other. One of the disadvantages of this type of battery is its operating temperature. Placing the batteries next to each other due to the heat generated by the batteries can cause problems such as explosions. Therefore, it is necessary to measure the temperature of the battery pack regularly and use cooling systems if necessary. The present study investigates the numerical and experimental transfer of natural heat transfer and estimates the increase in temperature at different discharge rates in a lithium battery pack consisting of 3 stage. In order to validate the numerical results, after the battery was fully charged and its temperature was homogenized, the battery pack was discharged at a rate of 0.2C and its results were recorded. The results showed that the numerical mode error is less than 5%. The battery pack was also simulated for two other discharge rates. From the simulation results, it can be seen that with increasing discharge rate, the rate of increase of battery pack temperature increases, so that at discharge rates higher than 1C, cooling systems should be used.
A hybrid battery thermal management system (BTMS) with phase change material (PCM) coupled cooling plate arrayed in the manner of honeycomb is proposed for the cylindrical lithium battery pack in this paper. Paraffin wax and glycol are used as PCM and cooling liquid, respectively. 3D numerical simulations are carried out to analyze temperature characteristics of the system, coupling characteristics of PCM and cooling plate, mass flow rate of cooling liquid and channel number of cooling plate on the heat dissipation of battery pack. Results show that the temperature distribution of the battery pack has good uniformity, which is the most prominent advantage of the proposed system. The coupling of PCM and cooling plate greatly improve heat dissipation effect. Increasing mass flow rate of cooling liquid can significantly improve heat dissipation efficiency, but the maximum temperature difference (ΔTmax) of battery cell also increases. However, the influence of channel number of cooling plate on heat dissipation efficiency is lower than that of mass flow rate. Adding one channel can reduce the battery temperature by 2K, but it has little effect on ΔTmax. This study may provide effective guidance for the design of the PCM coupled liquid cooling BTMS.
This work evaluates the impacts of ambient temperature and trip characteristics on the energy consumption of an electric vehicle (EV) during road tests. The trip characteristics are here defined by the driving distance, stop time percentage and average vehicle speed. The analysis uses data collected from real-world driving of an EV in one of the most populous metropolitan regions in the UK for almost four years, using a dedicated monitoring software for real-time vehicle data processing. The results reveal that the EV specific energy consumption (SEC) increases under operation at low temperature, also showing a larger scatter. Significant changes in SEC are linked to auxiliary energy demand and trip characteristics, especially under cold temperatures. Trips complying with a real-world driving test procedure produced lower SEC than random trips at cold temperatures but showed closed values at moderate temperatures. At both cold and moderate temperature conditions the EV presented lower SEC for urban driving, in comparison with rural and motorway operation, confirming its adequacy for application in metropolitan areas. Urban EV operation at low temperatures from 0°C to 15°C has a trip range 28% lower than driving at moderate temperatures from 15°C to 25°C.
To analyze the dual-use characteristics of outdoor microchannel heat exchangers in electric vehicle heat pump systems, numerical calculation models of the heat transfer and pressure drop under different states were analyzed and selected. The heat transfer and flow characteristics of the heat exchanger were simulated and experimentally studied. Additionally, the heat transfer and flow performance of the heat exchanger under different wind velocities and process conditions were discussed, and the temperature uniformity on the surface was analyzed. The simulation results show that the calculated value and the experimental error of the heat exchanger performance are within 9%–15%. Regarding the refrigerant side, the number of internal holes in the flat tube and the aspect ratio of the internal holes should be appropriately selected according to the heat transfer effect and pressure drop based on the simulation. In addition, regarding the air side, the proper fin spacing and opening angle can improve the comprehensive performance. The experimental results for the heat pump system show that the comprehensive performance of a two-pass heat exchanger is better than a three-pass heat exchanger. Under low-temperature heating conditions, the temperature uniformity coefficient of the two-pass heat exchanger is 12% lower than that of the three-pass heat exchanger; thus, the two-pass heat exchanger exhibits a better heat exchange effect. It was found through experiments that a proper increase in the compressor speed can reduce the temperature uniformity coefficient when the outdoor heat exchanger is employed as an evaporator, thus making the refrigerant distribution more uniform.
Experimental study was carried out to quantify the influence of temperature and different C-rate of discharge on in-house fabricated Lithium-ion (Li-ion) cell. 30Ah Li-ion cell is made of Lithium Iron Phosphate (LFP) cathode and Meso Carbon Micro Beads (MCMB) anode in prismatic configuration. Capability of Li-ion cell is defined by discharge capacity, voltage & power at different C-rate of discharge. Influence of 4 different current rates (C/5, C/2, 1C & 2C) at 5 different temperatures (−20, 0, 20, 40 and 60 °C) were studied. High discharge rate increases current density of cell which affect mass transport at electrode surface and electrolyte. Increased ohmic and concentration polarization at high rate of discharge decrease the original capacity. Average discharge voltage of cell is reduced gradually as operating temperature drop to below 20 °C. Electrochemical Impedance (EI) were measured on Li-ion cell in different frequency domain at different temperatures (−20, 0, 20 and 60 °C). The obtained impedance spectra were examined with an equivalent circuit using Zman software. The ohmic and charge transfer resistance displayed a solid dependence with respect to temperature.
Significance
The surging demand for battery resources and energy from EVs signifies a need to reassess the real-world battery utilization and energy consumption of urban EVs. In this work, we incorporate unique and previously unavailable datasets of urban-scale EV operation to better understand the battery utilization and energy consumption of large-scale EV utilization. High-resolution operating data of EVs across multiple regions are collected and integrated with vehicle feature data as the input for modeling. Accordingly, we expose several issues that have nonnegligible impacts on battery resources and the urban power supply, such as low battery utilization and seasonal changes in the energy consumption of EVs. The provided models and data can be extensively utilized for further EV-related resource and energy investigations.
The rapid development of information and communication technologies (ICTs) has provided rich resources for spatio-temporal data mining and knowledge discovery in modern societies. Previous research has focused on understanding aggregated urban mobility patterns based on mobile phone datasets, such as extracting activity hotspots and clusters. In this paper, we aim to go one step further from identifying aggregated mobility patterns. Using hourly time series we extract and represent the dynamic mobility patterns in different urban areas. A Dynamic Time Warping (DTW) algorithm is applied to measure the similarity between these time series, which also provides input for classifying different urban areas based on their mobility patterns. In addition, we investigate the outlier urban areas identified through abnormal mobility patterns. The results can be utilized by researchers and policy makers to understand the dynamic nature of different urban areas, as well as updating environmental and transportation policies.
Understanding and tracking battery degradation mechanisms and adapting its operation have become a necessity in order to enhance battery durability. A novel use of differential thermal voltammetry (DTV) is presented as an in-situ state-of-health (SOH) estimator for lithium-ion batteries.
Accelerated ageing experiments were carried on 5Ah commercial lithium-ion polymer cells operated and stored at different temperature and loading conditions. The cells were analysed regularly with various existing in-situ diagnosis methods and the novel DTV technique to determine their SOH. The diagnosis results were used collectively to elaborate the degradation mechanisms inside the cells. The DTV spectra were decoupled into individual peaks, which each represent particular phases in the negative and positive electrode combined. The peak parameters were used to quantitatively analyse the battery SOH.
A different cell of the same chemistry with unknown degradation history was then analysed to explore how the cell degraded. The DTV technique was able to diagnose the cell degradation without relying on supporting results from other methods nor previous cycling data.
In this paper we present a visual analytics approach for deriving spatio-temporal patterns of collective human mobility from a vast mobile network traffic data set. More than 88 million movements between pairs of radio cells-so-called handovers-served as a proxy for more than two months of mobility within four urban test areas in Northern Italy. In contrast to previous work, our approach relies entirely on visualization and mapping techniques, implemented in several software applications. We purposefully avoid statistical or probabilistic modeling and, nonetheless, reveal characteristic and exceptional mobility patterns. The results show, for example, surprising similarities and symmetries amongst the total mobility and people flows between the test areas. Moreover, the exceptional patterns detected can be associated to real-world events such as soccer matches. We conclude that the visual analytics approach presented can shed new light on large-scale collective urban mobility behavior and thus helps to better understand the "pulse" of dynamic urban systems.
Compared with other commonly used batteries, lithium-ion batteries are featured by high energy density, high power density, long service life and environmental friendliness and thus have found wide application in the area of consumer electronics. However, lithium-ion batteries for vehicles have high capacity and large serial-parallel numbers, which, coupled with such problems as safety, durability, uniformity and cost, imposes limitations on the wide application of lithium-ion batteries in the vehicle. The narrow area in which lithium-ion batteries operate with safety and reliability necessitates the effective control and management of battery management system. This present paper, through the analysis of literature and in combination with our practical experience, gives a brief introduction to the composition of the battery management system (BMS) and its key issues such as battery cell voltage measurement, battery states estimation, battery uniformity and equalization, battery fault diagnosis and so on, in the hope of providing some inspirations to the design and research of the battery management system.
The performance of a hybrid electric vehicle (HEV) depends strongly on the performance of its high-voltage battery pack, which is influenced by temperature. We have been working on thermal management of batteries in HEVs, including cooling and heating issues. In cold temperatures, batteries perform poorly because of high internal resistance; the vehicle may start slowly. The battery may need to be preheated by heating the internal core, heating the external of a module with electric heaters or a hot fluid, or heating around each cell in a module with electric heaters or a hot fluid. We used finite element thermal analysis to analyze the transient thermal behavior of a typical battery for each preheating method and compared the energy required to heat the battery. Heating the internal core with alternating current (AC) through battery terminals was the most effective and energy-efficient method. Although direct current (DC) can heat the battery, it may damage the battery. We found that 100 Amp, 60 Hz AC heating was effective for warming up a non-operating 16 Amp-h lead acid battery at -40°C to deliver an acceptable performance. However, 60 Hz AC heating is good for electric vehicle applications. For HEV applications, higher frequency currents must be used for smaller and lighter power electronics and for an on-board generator. We have tested the feasibility of a high frequency heater circuit for on-board vehicle use. Preliminary results have shown that applying a 60 A, 10 kHz current to a nickel metal hydride pack initially at -20°C restored the battery performance close to its +25°C performance in less than 3 minutes. This paper provides an overview of battery thermal management progress for HEVs, the results of finite element thermal analysis, and experimental results of AC heating of batteries.
Assuring the right temperature in battery compartments of an electric or hybrid vehicle is crucial for the safe operation and the achievement of optimal performance of the batteries. This paper is about the design, fabrication, and testing of a novel system for thermal management for electric/hybrid vehicles. This system is based on Peltier-effect heat pumps. The experiment results show the applicability of this type of technology for the thermal management for this type of vehicles.
We describe an advanced lithium-ion battery model for system-level analyses such as electric vehicle fleet simulation or distributed energy storage applications. The model combines an empirical multi-parameter model and an artificial neural network with particular emphasis on thermal effects such as battery internal heating. The model is fast and can accurately describe constant current charging and discharging of a battery cell at a variety of ambient temperatures. Comparison to a commonly used linear kilowatt-hour counter battery model indicates that a linear model overestimates the usable capacity of a battery at low temperatures. We highlight the importance of including internal heating in a battery model at low temperatures, as more capacity is available when internal heating is taken into account.
Plug-in electric vehicles (PEV) are emerging as an efficient and sustainable alternative for private and public road transportation. From the point of view of electric grids, PEVs are currently considered as simple loads due to their low market penetration. However, as the PEV fleet grows, implementation of an intelligent management system will be necessary in order to avoid large capital expenditures in network reinforcements and negative effects on electric distribution networks, such as: voltage deviations, transformers and lines saturations, increase of electrical losses, etc. These issues may jeopardize the safety and reliability of the grid. As a consequence, this topic has been researched in many papers where a wide range of solutions have been proposed. This paper presents a review of different strategies, algorithms and methods to implement a smart charging control system. Also significant projects around the world about PEVs integration are presented. Finally, on the basis of this review, main findings and some recommendations are presented.
Large penetration of electric vehicles (EVs) can have a negative impact on the power grid, e.g., increased peak load and losses, that can be largely mitigated using coordinated charging strategies. In addition to shifting the charging process to the night valley when the electricity price is lower, this paper explicitly considers the EV owner convenience that can be mainly characterized by a desired state of charge at the departure time. To this end, the EV charging procedure is defined as an uninterruptible process that happens at a given discrete charging rate, and the coordinated charging is formulated as a scheduling problem. The Scalable Real-Time Greedy (S-RTG) algorithm is proposed to schedule a large population of EVs in a decentralized fashion, explicitly considering the EV owner criteria. Unlike majority of existing approaches, the S-RTG algorithm does not rely on iterative procedures, and does not require heavy computations, broadcast messages, or extensive bi-directional communications. Instead, the proposed algorithm schedules one EV at a time with simple computations, only once (i.e., at the time the EV connects to the grid), and only requires low-speed communication capability making it suitable for real-time implementation. Numerical simulations with significant EVs penetration and comparative analysis with scheduling policies demonstrate the effectiveness of the proposed algorithm.
The electrification of transportation is seen as one of the solutions to challenges such as global warming, sustainability, and geopolitical concerns on the availability of oil. From the perspective of power systems, an introduction of plug‐in electric vehicles presents many challenges but also opportunities to the operation and planning of power systems. On the one hand, if vehicles are considered regular loads without flexibility, uncontrolled charging can lead to problems at different network levels endangering secure operation of installed assets. However, with direct or indirect control approaches the charging of vehicles can be managed in a desirable way, e.g., shifted to low‐load hours. Furthermore, vehicles can be used as distributed storage resources to contribute to ancillary services for the system, such as frequency regulation and peak‐shaving power or help integrate fluctuating renewable resources. All these modes of operation need appropriate regulatory frameworks and market design if the flexibility of the vehicles is to be capitalized. In most of the proposed approaches, a so‐called aggregator could be in charge of directly or indirectly controlling the charging of vehicles and serve as an interface with other entities such as the transmission system operator or energy service providers. However, fully decentralized schemes without an aggregator are also conceivable, for instance, to provide primary frequency control. Communication also plays a key role, as in most of the control schemes a significant amount of information needs to be transmitted between vehicles and control entities. The management of electric vehicles as distributed resources fits well in the paradigm of smart grids, where an advanced use of communication technologies and metering infrastructure, increased controllability and load flexibility, and a larger share of fluctuating and distributed resources are foreseen.
This article is categorized under: Energy Infrastructure > Economics and Policy
Energy Systems Economics > Systems and Infrastructure
Energy Systems Analysis > Systems and Infrastructure
Electric vehicles (EVs) and renewable energy sources offer the potential to substantially decrease carbon emissions from both the transportation and power generation sectors of the economy. Mass adoption of EVs will have a number of impacts and benefits, including the ability to assist in the integration of renewable energy into existing electric grids. This paper reviews the current literature on EVs, the electric grid, and renewable energy integration. Key methods and assumptions of the literature are discussed. The economic, environmental and grid impacts of EVs are reviewed. Numerous studies assessing the ability of EVs to integrate renewable energy sources are assessed; the literature indicates that EVs can significantly reduce the amount of excess renewable energy produced in an electric system. Studies on wind–EV interaction are much more detailed than those on solar photovoltaics (PV) and EVs. The paper concludes with recommendations for future research.
Battery electric vehicles (BEVs) offer the potential to reduce both oil imports and greenhouse gas emissions, but have a limited utility that is affected by driver aggression and effects of climate—both directly on battery temperature and indirectly through the loads of cabin and battery thermal management systems. Utility is further affected as the battery wears through life in response to travel patterns, climate, and other factors. In this paper we apply the National Renewable Energy Laboratory's Battery Lifetime Analysis and Simulation Tool for Vehicles (BLAST-V) to examine the sensitivity of BEV utility to driver aggression and climate effects over the life of the vehicle. We find the primary challenge to cold-climate BEV operation to be inefficient cabin heating systems, and to hot-climate BEV operation to be high peak on-road battery temperatures and excessive battery degradation. Active cooling systems appear necessary to manage peak battery temperatures of aggressive, hot-climate drivers, which can then be employed to maximize thru-life vehicle utility.
This paper reviews the current status and implementation of battery chargers, charging power levels, and infrastructure for plug-in electric vehicles and hybrids. Charger systems are categorized into off-board and on-board types with unidirectional or bidirectional power flow. Unidirectional charging limits hardware requirements and simplifies interconnection issues. Bidirectional charging supports battery energy injection back to the grid. Typical on-board chargers restrict power because of weight, space, and cost constraints. They can be integrated with the electric drive to avoid these problems. The availability of charging infrastructure reduces on-board energy storage requirements and costs. On-board charger systems can be conductive or inductive. An off-board charger can be designed for high charging rates and is less constrained by size and weight. Level 1 (convenience), Level 2 (primary), and Level 3 (fast) power levels are discussed. Future aspects such as roadbed charging are presented. Various power level chargers and infrastructure configurations are presented, compared, and evaluated based on amount of power, charging time and location, cost, equipment, and other factors.
Battery electric vehicles (BEVs) offer the potential to reduce both oil imports and greenhouse gas emissions, but have a limited utility due to factors including driver range anxiety and access to charging infrastructure. In this paper we apply NREL's Battery Lifetime Analysis and Simulation Tool for Vehicles (BLAST-V) to examine the sensitivity of BEV utility to range anxiety and different charging infrastructure scenarios, including variable time schedules, power levels, and locations (home, work, and public installations). We find that the effects of range anxiety can be significant, but are reduced with access to additional charging infrastructure. We also find that (1) increasing home charging power above that provided by a common 15 A, 120 V circuit offers little added utility, (2) workplace charging offers significant utility benefits to select high mileage commuters, and (3) broadly available public charging can bring many lower mileage drivers to near-100% utility while strongly increasing the achieved miles of high mileage drivers.
Time-dependent, thermal behaviour of a lithium-ion (Li-ion) polymer cell has been modelled for electric vehicle (EV) drive cycles with a view to developing an effective battery thermal management system. The fully coupled, three-dimensional transient electro-thermal model has been implemented based on a finite volume method. To support the numerical study, a high energy density Li-ion polymer pouch cell was tested in a climatic chamber for electric load cycles consisting of various charge and discharge rates, and a good agreement was found between the model predictions and the experimental data. The cell-level thermal behaviour under stressful conditions such as high power draw and high ambient temperature was predicted with the model. A significant temperature increase was observed in the stressful condition, corresponding to a repeated acceleration and deceleration, indicating that an effective battery thermal management system would be required to maintain the optimal cell performance and also to achieve a full battery lifesapn.
Battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) are often labeled “green”, implying that they will significantly reduce greenhouse gas (GHG) emissions. But actual GHG reductions will depend on two factors: the number of electric vehicles that can be sold to Americans that are fond of driving large vehicles long distances, and the GHGs emitted by the electrical power plants that charge the EV batteries. This article evaluates the maximum potential of EVs to cut GHG emissions and oil consumption in the U.S. and compares them with the GHG and oil reduction potential of hydrogen-powered fuel cell electric vehicles. Even if all US light duty vehicles (LDVs) (cars and trucks) were replaced by a combination of battery EVs for small vehicles and plug-in hybrids for all other LDVs, then GHGs could at most be reduced by 25% and oil consumption could be reduced by less than 67%. But if all LDVs in the U.S. were replaced by fuel cell electric vehicles powered by hydrogen made from natural gas, then GHGs would be immediately reduced by 44% and oil consumption by nearly 100%.
Electric vehicles have recently been gaining increasing worldwide interest as a promising potential long-term solution to sustainable personal mobility; in particular, battery electric vehicles (BEVs) offer zero tailpipe emissions. However, their true ability to contribute to greenhouse gas (GHG) emissions reductions can only be properly assessed by comparing a life cycle assessment of their GHG emissions with a similar assessment for conventional internal combustion vehicles (ICVs). This paper presents an analysis for vehicles typically expected to be introduced in 2015 in two example markets (the UK and California), taking into account the impact of three important factors: Like-for-like vehicle comparison and effect of real-world driving conditions. Accounting for the GHG emissions associated with meeting the additional electricity demand for charging the batteries. GHG emissions associated with vehicle manufacture, disposal, etc. This work demonstrates that all of these factors are important and emphasises that it is therefore crucial to clearly define the context when presenting conclusions about the relative GHG performance of BEVs and ICVs - such relative performance depends on a wide range of factors, including the marginal regional grid GHG intensity, vehicle size, driving pattern, loading, etc.
Plug-in hybrid electric vehicles (PHEVs) are the next big thing in the electric transportation market. While much work has been done to detail what economic costs and benefits PHEVs will have on consumers and producers alike, it seems that it is also important to understand what impact PHEVs will have on distribution networks nationwide. This paper finds that the impact of PHEVs on the distribution network can be determined using the following aspects of PHEVs: driving patterns, charging characteristics, charge timing, and vehicle penetration. The impacts that these aspects of PHEVs will have on distribution networks have been measured and calculated by multiple authors in different locations using many different tools that range from analytical techniques to simulations and beyond. While much work has already been completed in this area, there is still much to do. Areas left for improvement and future work will include adding more stochasticity into models as well as computing and analyzing reliability indices with respect to distribution networks.
The scarce supply of fossil fuel in the mere future has driven the development of electric vehicles (EV) worldwide. Plug-in connectors have been commonly proposed for EV charging, however, these systems have disadvantages such as safety, esthete, and operation in snow. Therefore, a new method to inductively charge the vehicle without any physical contact has been proposed. This paper presents a state of the art literature review on the recent advancements of Inductive Power Transfer (IPT) technology used in EV charging. A possible future technology to solve the inherent range anxiety problem is also presented using roadway electrification and in-motion power transfer concepts.
For the past few years, plug-in electric vehicle (PEV) technology development has gained immense popularity. Recent studies show that if PEVs displaced half of all vehicles on the road, they would require only an 8% increase in electricity generation. Results similar to this help encourage the continuing development of PEVs. Despite this small increase in generation, uncontrolled charging, especially during on-peak summer hours, could overload the current power grid. This paper provides a narrative literature survey of the development and impact of PEVs. Subjects cover PEV industry trends, charge and discharge scenarios, and impacts on distribution systems. Some concluding remarks are summarized.
Battery management systems in hybrid electric vehicle battery packs must estimate values descriptive of the pack’s present operating condition. These include: battery state of charge, power fade, capacity fade, and instantaneous available power. The estimation mechanism must adapt to changing cell characteristics as cells age and therefore provide accurate estimates over the lifetime of the pack.In a series of three papers, we propose a method, based on extended Kalman filtering (EKF), that is able to accomplish these goals on a lithium ion polymer battery pack. We expect that it will also work well on other battery chemistries. These papers cover the required mathematical background, cell modeling and system identification requirements, and the final solution, together with results.In order to use EKF to estimate the desired quantities, we first require a mathematical model that can accurately capture the dynamics of a cell. In this paper we “evolve” a suitable model from one that is very primitive to one that is more advanced and works well in practice. The final model includes terms that describe the dynamic contributions due to open-circuit voltage, ohmic loss, polarization time constants, electro-chemical hysteresis, and the effects of temperature. We also give a means, based on EKF, whereby the constant model parameters may be determined from cell test data. Results are presented that demonstrate it is possible to achieve root-mean-squared modeling error smaller than the level of quantization error expected in an implementation.
How green is that electric car? Depends on where you plug it in
Sähköautot ja talvi (Electric cars and winter)
- T Kupiainen
T. Kupiainen, Sähköautot ja talvi (Electric cars and winter), 2013. http://winteve.fi/wp-
content/uploads/2013/05/S%C3%A4hk%C3%B6auto-ja-talvi.pdf.
Energy Management in Households with Coupled Photovoltaics and Electric Vehicles
- T Rasku
T. Rasku, Energy Management in Households with Coupled Photovoltaics and Electric Vehicles, Aalto
University School of Science, 2015. http://urn.fi/URN:NBN:fi:aalto-201511205225.
Electric Vehicles Out in the Cold
- K Bullis
K. Bullis, Electric Vehicles Out in the Cold, MIT Technology Review. (2013) 1.
http://www.technologyreview.com/news/522496/electric-vehicles-out-in-the-cold/
(accessed
January 5, 2016).
- A Santos
- N Mcguckin
- H Y Nakamoto
- D Gray
- S Liss
A. Santos, N. McGuckin, H.Y. Nakamoto, D. Gray, S. Liss, 2009 National Household Travel Survey:
Summary of Travel Trends, 2011. http://nhts.ornl.gov/2009/pub/stt.pdf.
The role of electric vehicles in smart grids, Wiley Interdisciplinary Reviews: Energy and Environment
- M D Galus
- M G Vayá
- T Krause
- G Andersson
M.D. Galus, M.G. Vayá, T. Krause, G. Andersson, The role of electric vehicles in smart grids, Wiley
Interdisciplinary Reviews: Energy and Environment. 2 (2013) 384–400. doi:10.1002/wene.56.
Design and Analysis of Large Lithium-ion Battery Systems
- S Santhanagopalan
- K Smith
- J Neubauer
- G.-H Kim
- A Pesaran
- M Keyser
S. Santhanagopalan, K. Smith, J. Neubauer, G.-H. Kim, A. Pesaran, M. Keyser, Design and Analysis of
Large Lithium-ion Battery Systems, 1st ed., Artech House, 2014.
Finnish national travel survey
- The Finnish
- Transport Agency
- Wsp Finland
- Oy
The Finnish Transport Agency, WSP Finland Oy, Henkilöliikennetutkimus 2010-2011 (Finnish national
travel survey 2010-2011), Helsinki, Finland, 2012. http://www.hlt.fi/.
An introduction to secondary batteries
- T B Reddy
T.B. Reddy, An introduction to secondary batteries, in: T.B. Reddy (Ed.), Linden's Handbook of
Batteries, 4th ed., McGraw-Hill, 2011: pp. 1-20.
Shades of Green: Electric Cars' Carbon Emissions Around the Globe
- L Wilson
L. Wilson, Shades of Green: Electric Cars' Carbon Emissions Around the Globe, 2013.
http://shrinkthatfootprint.com/wp-content/uploads/2013/02/Shades-of-Green-Full-Report.pdf.
The impact of plug-in hybrid electric vehicles on distribution networks: A review and outlook
- R C Green
- L Wang
- M Alam
R.C. Green, L. Wang, M. Alam, The impact of plug-in hybrid electric vehicles on distribution networks:
A review and outlook, Renewable and Sustainable Energy Reviews. 15 (2011) 544-553.
doi:10.1016/j.rser.2010.08.015.
Batteries for Electric and Hybrid Vehicles
- D A Corrigan
- A Masias
D.A. Corrigan, A. Masias, Batteries for Electric and Hybrid Vehicles, in: T.B. Reddy (Ed.), Linden's
Handbook of Batteries, 4th ed., McGraw-Hill, 2011: pp. 1-48.
Linden's Handbook of Batteries
- J Dahn
- G M Ehrlich
J. Dahn, G.M. Ehrlich, Lithium-ion batteries, in: T.B. Reddy (Ed.), Linden's Handbook of Batteries, 4th
ed., McGraw-Hill, 2011: pp. 1-79.
National Household Travel Survey: Summary of Travel Trends
- A Santos
- N Mcguckin
- H Y Nakamoto
- D Gray
- S Liss
A. Santos, N. McGuckin, H.Y. Nakamoto, D. Gray, S. Liss, 2009 National Household Travel Survey:
Summary of Travel Trends, 2011. http://nhts.ornl.gov/2009/pub/stt.pdf.