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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.
... One of the main factors that affect battery performance in electric vehicles is temperature. Cold temperatures increase charging time and decrease acceleration due to an increase in internal resistance [1]. Cabin preconditioning and battery thermal management, such as resistive heating, can mitigate these effects [1]. ...
... Cold temperatures increase charging time and decrease acceleration due to an increase in internal resistance [1]. Cabin preconditioning and battery thermal management, such as resistive heating, can mitigate these effects [1]. Hot temperatures increase battery degradation and charging time and decrease the vehicle range [1]. ...
... Cabin preconditioning and battery thermal management, such as resistive heating, can mitigate these effects [1]. Hot temperatures increase battery degradation and charging time and decrease the vehicle range [1]. There are several methods to cool vehicle batteries, these include using cooling tubes, micro-channelled cooling plates, and single and two-phase immersion coolers [2], [3]. ...
Two-phase immersion cooling has the potential to maintain electric vehicle batteries at uniform temperatures thus offering effective battery thermal management. In this study, the effects of changing the refrigerant used, and varying the space between each battery cell were investigated using Computational Fluid Dynamics (CFD) simulations. Results showed that all used refrigerants achieved similar temperature distribution, but R245fa achieved the highest power from cooling 27,000 battery cells, the maximum number used in Volvo lorries, by enabling the production of electricity using organic Rankine cycle.
... Typically, Electric vehicles have larger batteries compared to other batteries in use and it takes longer duration to charge it (Verma et al., 2018). This shows that the Electric vehicles purely depends on their loaded batteries (Lindgren and Lund, 2016;Liu, et al., 2019). Even though, considering the longer duration of charging also, the distance covered by the Electric vehicles is comparatively lesser than conventional vehicles. ...
... The possibility of an explosion is slight; though, contemporary EV battery systems include several safety precautions including thermal runaway protection. To further assure their safety, EVs go through extensive testing and certification procedures (Lindgren et al., 2016). Battery explosions in EVs can nevertheless happen despite the very low danger owing to a variety of reasons, including manufacturing flaws, poor charging, or physical damage to the battery. ...
To combat emissions from transportation sector, India is transitioning to a “zero or low carbon emission” transportation paradigm by encouraging the use of hydrogen fuel cars and electric vehicles (EVs). The study aims to investigate the challenges impeding the adoption of electric vehicles and to understand the causal relations among them. The novel Grey Influence Analysis (GINA) methodology has been utilised to study the causal relations among the challenges and managers and practitioners are benefitted as they are able to identify the most important challenges in the adoption of EVs in India and act accordingly. The findings of a survey-based study show that lack of charging infrastructure comes in second place and the reliability and lifespan of EV components come in first place, when evaluating the overall or total influence scores. The third place among the most important drivers is occupied by lack of servicing/workshops, low awareness of battery characteristics, high upfront cost, and fear of battery explosions. On detailed analysis of the driving forces for adoption, it is apparent that the reliability and lifespan of EV components is the most important driving factor for adoption; hence, policy makers and practitioners can focus on this for augmenting the adoption rates.
... Factors that dissuade consumers from buying EVs are range anxiety [23], long charging times [24], a lack of charging infrastructure [23], high purchase prices [22], less concern for environmental protection [25], increased use of individual transport [22], a lack of trust [21], lack of information [21], a large number of charging events compared to refuelling events [15], unwillingness of consumers to pay substantial premiums [26], safety concerns while charging [27], unknown or lesser resale value [27], consumer confidence [28], environmental issues due to battery disposal [29], uncleanliness of the electric grid [30], and the absence of an established used car market [29]. Additionally, especially in countries like Finland [31] due to harsh winter conditions, the batteries of electric vehicles are discharged more quickly because of the low temperatures [31], thereby affecting the range of the vehicle [32,33]. ...
... Factors that dissuade consumers from buying EVs are range anxiety [23], long charging times [24], a lack of charging infrastructure [23], high purchase prices [22], less concern for environmental protection [25], increased use of individual transport [22], a lack of trust [21], lack of information [21], a large number of charging events compared to refuelling events [15], unwillingness of consumers to pay substantial premiums [26], safety concerns while charging [27], unknown or lesser resale value [27], consumer confidence [28], environmental issues due to battery disposal [29], uncleanliness of the electric grid [30], and the absence of an established used car market [29]. Additionally, especially in countries like Finland [31] due to harsh winter conditions, the batteries of electric vehicles are discharged more quickly because of the low temperatures [31], thereby affecting the range of the vehicle [32,33]. ...
Greenhouse gas (GHG) emissions from transport contribute significantly to climate change. Some of the transport policies with the greatest potential to mitigate climate change are related to zero-emission vehicles. This study aimed to analyse the different factors, and their importance, influencing purchase decisions for battery electric vehicles (BEV). Experts’ perceptions were collected with a Delphi study consisting of a two-round survey to assess factors that would increase the probability of a petrol- or diesel-car owner purchasing a BEV in Finland in the year 2025. Increasing the possibilities for home charging and the provision of a purchase subsidy were seen as the most important factors. Public fast charging and the difference in use costs between current technology vehicles and BEVs were also recognised as important factors. Existing systems of financial instruments and policies must be constantly evaluated and updated due to the evolving BEV industry.
... At low temperatures, the ion transport capacity in the bulk electrode, electrolyte, and electrode/electrolyte interfaces is blocked, which significantly lowers the redox reaction rate. [37][38][39][40] Hence, the energy and power capability of the capacitor are lower in accordance with the low-temperature (0°C) EIS results (Figure S17c Supporting Information). ...
... The increasing temperature of devices fasten the ions movement and improve ionic conductivity in electrolyte, thus effectively reducing the intrinsic resistance of devices. [37][38][39][40] Moreover, the redox reaction rate also accelerated by the improved ions diffusion. [40] This demonstrates that controlling the temperature is vital for the electrochemical reaction process. ...
Large‐capacity energy storage devices are attracting widespread research attention. However, the decreased capacity of these devices due to cold weather is a huge obstacle for their practical use. In this study, an electrochemical self‐adaptive reconstructed CuxS/Cu(OH)2‐based symmetric energy storage device is proposed. This device provides a satisfactorily enhanced photothermal capacity under solar irradiation. After electrochemical reconstruction treatment, the morphological structure is rearranged and the CuxS component is partially converted to electrochemically active Cu(OH)2 with the introduction of a large number of active sites. The resulting CuxS/Cu(OH)2 electrode provides a significant capacitance of 115.2 F cm⁻² at 5 mA cm⁻². More importantly, its wide working potential range and superior photo‐to‐thermal conversion ability endow CuxS/Cu(OH)2 with superb performance as full‐purpose photothermally enhanced capacitance electrodes. Under solar irradiation, the surface temperature of CuxS/Cu(OH)2 is elevated by 76.6 °C in only 30 s, and the capacitance is boosted to 230.4% of the original capacitance at a low temperature. Furthermore, the assembled symmetric energy storage device also delivers a photothermal capacitance enhancement of 200.3% under 15 min solar irradiation.
... Another relevant work is [50], which simulated how outdoor temperatures affect battery charging and performance of EVs. Their model included 212 EVs with maximum 3.6 kW charging power, maximum 4.0 kW cabin heater power (COP 2.5), and 0.3 kW battery heater power (COP 1). ...
... There exist some experimental studies with power data for EV heating sessions [37,39,40,46,47], but these studies focused on improving the HVAC systems in EVs and were not seen in relation with energy loads in buildings or the grid. The few studies analysing how EV preheating loads may impact the distribution grid [48][49][50], were based on simulations. To validate and improve models and simulations, access to real-world data is a significant factor [51]. ...
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.
... In low temperature environments, EVs can have reduced range and increased charging time, often necessitating the use of heaters to improve battery efficiency. 7,8 To increase adoption of batteries in these extreme conditions, it is crucial to understand the effect of electrode shape on the cell resistance, in particular at low temperature, and on the mechanical stress that develops from electrode expansion due to intercalation. ...
Rechargeable batteries that incorporate shaped three-dimensional electrodes have been shown to have increased power and energy densities when compared to a conventional geometry, i.e. a planar cathode and anode that sandwich an electrolyte. Electrodes can be shaped to enable a higher active material loading, while keeping ion transport distances small. However, the relationship between electrical and mechanical performance of shaped electrodes remains poorly understood. Many electrode designs have been explored, where the electrodes are individually shaped or intertwined, and advances in manufacturing and shape/topology optimization have made such designs a reality. Here, we explore sinusoidal half cells and interdigitated full cells. First, we use a simple electrostatics model to understand the cell resistance as a function of shape. We focus on low-temperature conditions, where the electrolyte conductivity decreases relative to that of the electrode; here, LiPF6 EC:DMC electrolyte and MnO2 electrode are considered. Next, we use a chemo-mechanics model to examine the stress that arises due to intercalation-driven volume expansion. We show that shaped electrodes provide a significant reduction in resistance in low-temperature conditions, however, they exhibit unfavorable stress concentrations. Overall, we find that the fully interdigitated electrodes may provide the best balance with respect to this resistance-stress trade-off.
... The paper [29] lacks an impact assessment of EVs for hot conditions. In [30] for temperatures 30 • C and 40 • C charging time increases by 15-31% as compared to the optimal temperature due to an increase in HVAC (Heating Ventailing Air conditioning) and BTM (Battery Thermal Management) load during driving which compensated at charging. Moreover, the driving efficiency decreases at 40 • C by 25% which requires frequent charging. ...
The integration of electric vehicles (EVs) is rapidly growing compared to conventional vehicles in Qatar. To assess how these electric vehicles will impact Qatar’s distribution network, it is necessary to accurately model EV loads. However, EV loads exhibit uncertainties due to driving behaviour in charging time, state of charge (SOC), number of trips, and distance travelled. This necessitates the development of a probabilistic model. The Monte-Carlo method is employed to predict EV charging profiles probabilistically. The generated EV load profiles are assigned to different sectors and compared with the base case voltage profile curve. The IEEE-33 bus system is utilized to evaluate EV impacts considering the load pattern of Qatar. EV load profile generation is performed using MATLAB software, and impact assessment is conducted in DIgSILENT software. The results indicate that following EV integration, the system’s voltage profile experiences drops in the early morning and afternoon. A proposed charging scheme (R2), coupled with the integration of solar PV into the system, can mitigate this voltage drop issue. The implementation of the proposed reward charging scheme improves system performance in terms of the voltage profile, ensuring grid resilience.
... However, there are many scientific achievements in the field of the temperature impact on BEVs. Research by Lindgren and Lund [69] indicates additional important parameters, such as the preliminary preparation and heating of vehicle cabins, which influence the optimization of fleet utilization. Detailed results of work related to cabin conditioning energy consumption of EVs range were presented by Kambly and Bradley [70], taking into account several factors of geographical and temporal differences, such as local humidity, duration and thermal soak, local solar radiation, and local ambient temperature. ...
Citation: Cieśla, M.; Nowakowski, P.; Wala, M. The Impact of Variable Ambient Temperatures on the Energy Efficiency and Performance of Electric Vehicles during Waste Collection. Energies 2024, 17, 4228. https:// Abstract: The market for electric cars (EVs) is growing quickly, which has led to a diversity of models and significant technological advancements, particularly in the areas of energy management, charging, range, and batteries. A thorough analysis of the scientific literature was conducted to determine the operational and technical parameters of EVs' performance and energy efficiency, as well as the factors that influence them. This article addresses the knowledge gap on the analysis of ambient temperature-related parameters' effects on electric garbage trucks operating in particular urban traffic conditions for selective waste collection. To optimize vehicle routes, a computational model based on the Vehicle Routing Problem was used, including the Ant Colony Optimization algorithm, considering not only the load capacity of garbage trucks but also their driving range, depending on the ambient temperature. The results show that the median value of collected bulky waste for electric waste collection vans, depending on the ambient temperature, per route is 7.1 kg/km and 220 kg/h. At a temperature of −10 • C, the number of points served by EVs is 40-64% of the number of points served by conventional vehicles. Waste collection using EVs can be carried out over short distances of up to 150 km, which constitutes 95% of the optimized routes in the analyzed case study. The research contributed to the optimal and energy-efficient use of EVs in variable temperature conditions.
... Extreme cold temperatures impact battery performance, and slow chemical reactions lead to poor ionic conductivity in the electrolytes and cause a reduction in the energy and power capability. Lindgren and Lund [9] discussed the effect of ambient temperatures on battery state of charge (SOC), power consumption, charging time, and charging power. At extreme temperatures, battery thermal management (BTM) and preconditioning of cabin temperature are crucial. ...
The widespread adoption of EVs will pose challenges to the planning and operation of the electric power grid if their charging is not coordinated and controlled. Therefore, the projection of EV load growth and hosting capacity analysis of EV charging infrastructure is critical for the power distribution grid. This study has investigated EV load projection in the state of North Dakota based on EV sales information, daily travel data, and temperature variations in North Dakota and investigated the load hosting capacity of a residential distribution system. An incremental hosting capacity analysis (HCA) was performed, and results show that the delayed/midnight charging strategy performs significantly better than the immediate/uncontrolled charging of EVs in the residential distribution grid. The addition of distributed generators with storage systems also improves the overall hosting capacity of the distribution network.
... However, the progress of electric vehicle development is hindered by two key factors: limited range and environmental adaptability [4]. As the largest energy-consuming auxiliary system in an EV, reducing the energy consumption of the air-conditioning system plays a crucial role in improving its range [5]. The air conditioning system relies entirely on battery power; hence, it becomes the most energy-intensive auxiliary system in an EV. ...
This study constructed an experimental bench for a R134a heat pump air conditioning system in a commercial electric vehicle. Based on the results from the experimental bench, the simulation model of the heat pump air conditioning system developed by AMESim was calibrated, and its reliability was verified. As the compressor and electronic expansion valve (EXV) are essential control components of the heat pump air conditioning system, simulations were conducted to investigate the impact of EXV opening on performance parameters such as refrigerating capacity, subcooling, superheat compressor power consumption, and Coefficient of Performance (COP) at different compressor speeds. The findings indicate that EXV opening significantly affects system subcooling and COP, and higher COP can be obtained by controlling the subcooling of the system through the EXV.
... In fact, temperature changes can significantly influence the electrochemical processes occurring inside an LIB [9]. These in turn affect its capacity, internal impedance, maximum charge-discharge rates, overall efficiency and operational life-span [3,[10][11][12][13]. ...
In this study, the performances of a pouch Li-ion battery (LIB) with respect to temperature, pressure and discharge-rate variation are measured. A sensitivity study has been conducted with three temperatures (5 °C, 25 °C, 45 °C), four pressures (0.2 MPa, 0.5 MPa, 0.8 MPa, 1.2 MPa) and three electrical discharge rates (0.5 C, 1.5 C, 3.0 C). Electrochemical processes and overall efficiency are significantly affected by temperature and pressure, influencing capacity and charge–discharge rates. In previous studies, temperature and pressure were not controlled simultaneously due to technological limitations. A novel test bench was developed to investigate these influences by controlling the surface temperature and mechanical pressure on a pouch LIB during electrical charging and discharging. This test rig permits an accurate assessment of mechanical, thermal and electrical parameters, while decoupling thermal and mechanical influences during electrical operation. The results of the study confirm what has been found in the literature: an increase in pressure leads to a decrease in performance, while an increase in temperature leads to an increase in performance. However, the extent to which the pressure impacts performance is determined by the temperature and the applied electrical discharge rate. At 5 °C and 0.5 C, an increase in pressure from 0.2 MPa to 1.2 MPa results in a 5.84% decrease in discharged capacity. At 45 °C the discharge capacity decreases by 2.17%. Regarding the impact of the temperature, at discharge rate of 0.5 C, with an applied pressure of 0.2 MPa, an increase in temperature from 25 °C to 45 °C results in an increase of 4.27% in discharged capacity. The impact on performance varies significantly at different C-rates. Under the same pressure (0.2 MPa) and temperature variation (from 25 °C to 45 °C), increasing the electrical discharge rate to 1.5 C results in a 43.04% increase in discharged capacity. The interplay between temperature, pressure and C-rate has a significant, non-linear impact on performance. This suggests that the characterisation of an LIB would require the active control of both temperature and pressure during electrical operation.
... Based on the work done in the literature by Ngo (2019), the impact of the temperature on ridership is estimated as follows: For each 10 • C decrease in the ambient temperature, the transit ridership will increase by approximately 2%. Therefore, linear interpolation is applied to approximate the transit ridership in each of the weather intervals (Lindgren and Lund, 2016). The calculation is similar to what is performed to obtain the drain rate "C D " in different weather conditions. ...
... Penggunaan baterai secara terus menerus, dapat menyebabkan pengurangan kapasitas dan penurunan performa (Fajrianingrum, dkk, 2022). Baterai yang rusak dapat disebabkan oleh penggunaan yang tidak optimal, dimana kapasitas baterai dapat menjadi kosong atau terlalu penuh, sehingga mengakibatkan baterai menjadi terlalu panas (overheating) serta dapat mengurangi umur baterai (Lindgren & Lund, 2016). Oleh karena itu, penting untuk menjaga baterai dari masalah seperti overcharge, undercharge (Hauck & Kurrat, 2018), overcurrent, short circuit (Ouyang, dkk, 2018), serta variasi suhu (Łebkowski, 2017), agar baterai dapat bertahan lama. ...
ABSTRAKPenggunaan panel surya sebagai sumber energi terbarukan membutuhkan baterai sebagai tempat penyimpanan energi. Penggunaan baterai secara terus menerus, dapat menyebabkan pengurangan kapasitas dan penurunan performa. Untuk mengatasi permasalahan tersebut, diperlukan sistem estimasi nilai State of Charge (SOC) pada baterai yang berfungsi untuk mengontrol kondisi charge, agar performa baterai tetap optimal. Pada penelitian dikembangan suatu sistem estimasi SOC pada baterai jenis lead acid, dengan metode algoritma Elman Recurrent Neural Network (ERNN). Keunggulan yang terkait dengan metode ERNN meliputi proses iterasi menjadi lebih cepat, peningkatan kecepatan pembaruan parameter, dan pencapaian konvergensi yang lebih cepat. Hasil dari penelitian estimasi SOC pada baterai lead acid 12V, 12Ah dengan menggunakan algoritma ERNN sebesar 0.101% sedangkan dengan algoritma Feedforward Backpropagation sebesar 0.767%. Sehingga dapat disimpulkan bahwa algoritma ERNN lebih efisien dalam mengestimasi nilai SOC pada baterai lead acid.Kata kunci: Baterai, Elman Recurrent Neural Network, Panel Surya, State of Charge; Lead Acid ABSTRACTUsing solar panels as a renewable energy source requires batteries as energy storage. Continuous use of batteries can result in reduced capacity and performance degradation. Based on these problems, a State of Charge (SOC) estimation system is needed for the battery to control charge conditions so that battery performance remains optimal. In this research, a SOC estimation system was developed for lead acid battery using the Elman Recurrent Neural Network (ERNN) algorithm. The advantage of the ERNN method is that the iteration process is faster, the parameter update speed is increased, and convergence is faster. The results of the SOC estimation for a 12V, 12Ah lead acid battery using the ERNN algorithm were 0.101%, while the Feedforward Backpropagation algorithm resulted in 0.767%. The ERNN algorithm is more efficient in estimating the SOC value of a lead acid battery.Keywords: Battery, Elman Recurrent Neural Network, Solar Panel, State of Charge, Lead Acid
... 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.
In the United States, the drive towards electric vehicle (EV) adoption seeks to reduce greenhouse gas emission and achieve the national goal of net-zero emissions by 2050. The significant contribution of heavy-duty semi-trailer trucks in transport-related carbon emissions and energy use motivate the environmental benefits of their electrification. The electrification of heavy-duty semi-trailer trucks, which significantly contribute to tailpipe emissions, presents an opportunity to mitigate these impacts while improving overall air quality.
To facilitate the shift to electrification, this study presents a methodology for identifying locations for Electric Truck Charging Stations (ETCS), utilizing data-driven approaches applicable to electric truck deployment needs. A tiered site selection strategy for ETCS sites ensures access coverage, identifying eligible ETCS sites and emphasizing arterials and interstates' significance. Comparison with existing truck parking facilities highlights the need for strategic ETCS placement. In addition, the study investigates the impact of advancements in battery technology on infrastructure requirements, analyzing how improved travel ranges may affect the distribution and number of charging stations required. Finally, regression analysis shows an inverse relation between travel range enhancements and ETCS demand, providing insights for future infrastructure planning that balances technological advancements with efficient charging network development.
div class="section abstract"> This study leverages the temperature impact data obtained from the battery systems of airworthiness-certified fixed-wing electric aircraft to predict and correct the performance of eVTOL battery systems under various temperature conditions. Due to the lack of airworthiness-certified eVTOL models, it is challenging to directly test battery system parameters under temperature variations. However, using data from Ma Xin's team's production batteries tested on certified fixed-wing electric aircraft, we can accurately measure the effects of temperature changes. The capacity retention data at temperatures of -40°C, -20°C, -10°C, 0°C, 0°C, 25°C, 35°C, 45°C, 55°Care 78.14%, 83.3%, 84.1%, 88.1%, 92.3%, 100.0%, 102.0%, 103.9%, 104.6%. These quantified results provide a basis for modeling and experimental validation of eVTOL battery systems, ensuring their performance and safety across a wide range of temperatures. Although there are some research of battery system of eVtol in room temperature, the data and research of impact of various temperature on battery systems of eVTOLin this article is not published before.
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Reduction of the driving range under either cold temperature or real-world running condition has become the biggest challenge for battery electric vehicle (BEV). In this paper, a simulation platform that combines a kinematics model, a thermal management model, and extracted typical running conditions has been established to estimate the energy flows inside the electric vehicle under cold temperature and real-world running condition. Three vehicles have been selected to validate the accuracy of the simulation platform, giving an accuracy between 90.6% and 96.6% according to different running conditions. Under highway running condition, the driving range could be reduced by 54%. Under urban running condition, when the environment temperature drops down to −20°C, the driving range is only 49.1% of that under 20°C. In addition, there could be a 4.4% increase in driving range if the target cabin temperature could be decreased from 28°C to 20°C. According to simulation, the application of motor waste heat recovery, internal gas recirculation, and heat pump, could increase the driving range at −7°C under urban running condition by 3.5%, 2.9%, and 3.9%, indicating a 10.3% improvement in total. This has been validated via experimental test after implementing these three approaches onto the test vehicle.
Numerous explorations have been made with the goal of achieving “carbon neutrality.” Among them, lithium-ion batteries are an effective and efficient way to achieve this goal. Therefore, it is very important to predict and prevent the possible failure of lithium-ion batteries. At present, the analysis and prediction methods for battery failure are mainly divided into three categories: data-driven, model-based, and threshold-based. The three methods have different characteristics and limitations due to their different mechanisms. This paper first introduces the types and principles of battery faults. Then, the parameter selection in the process of fault diagnosis is described. Subsequently, the latest research progress of three kinds of fault diagnosis methods is summarized, which is conducive to promoting the development of battery fault diagnosis. Finally, this review provides valuable insights and guidance for future research endeavors.
The development of public charging infrastructure has become an important component of smart city construction and energy conservation and emission reduction. As the country with the largest number of electric vehicles in the world, China's experience in the development of public charging infrastructure is of demonstrative significance globally. This article constructs a comprehensive evaluation index of the development level of public charging infrastructure based on provincial panel data from 2018 to 2023 and measures it using the entropy weight-TOPSIS method. By analyzing the coupling coordination degree between the development level of public charging infrastructure and regional resilience, spatial and temporal differences and evolutionary characteristics are explored. Finally, the factors influencing the development are analyzed using a spatial Durbin model with mixed fixed effects. The results show that: 1) The development level of public charging infrastructure in China has been increasing year by year, but regional disparities are gradually widening, with an increasing probability of downward transfer of coupling coordination status; 2) The coupling coordination degree in most regions of China has reached a primary coordination level or above, and there is a significant spatial agglomeration relationship, with high-value agglomeration areas in the eastern region showing a significant spillover effect in the east-west direction; 3) The development level of public charging infrastructure is influenced by various factors, among which economic development level, power supply capacity, government budget expenditure, urbanization process, pollutant emissions, and environmental temperature are key driving factors. This study analyzes the spatial and temporal pattern evolution of public charging infrastructure in China and its influencing factors, providing decision-making references for the development of global electrified transportation.
Developing electrolytes for Li metal batteries capable of operating under extreme conditions is a significant challenge and is often hindered by the absence of systematic solvent screening studies. In this...
In this paper, the thermal performance of a new liquid-cooled shell structure for battery modules is investigated by numerical simulation. The module consists of 4 × 5 cylindrical batteries and the liquid-cooled shell and multiple flow channels inside the shell for the coolant flow. The maximum temperature, maximum temperature difference, and pressure drop of the battery module were taken as the performance evaluation indexes, and the expectation function was introduced to obtain the optimal flow channel arrangement of the shell. The temperature rise of the battery in the discharge phase is significantly greater than that in the charging phase. After the battery is charged at a constant current and voltage to the charging cutoff voltage, the battery temperature starts to drop rapidly in spite of the ongoing charging at small current. As the coolant flow rate increases, the maximum temperature of the battery module decreases, but the pressure drop increases significantly, while the temperature difference remains unchanged. Experimental measurement was also conducted to verify the simulation results.
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 . 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.
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)
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