Browse Topic: Batteries

Items (4,898)
The electric vehicle thermal management system is a critical sub-systems of electric vehicles, and has a substantial impact on the driving range. The objective of this paper is to optimize the performance of the heat pump air conditioning system, battery, and motor thermal management system by adopting an integrated design. This approach is expected to effectively improve the COP (Coefficient of Performance) of cabin heating. An integrated thermal management system model of the heat pump air conditioning system, battery, and motor thermal management system is established using AMEsim. Key parameters, such as refrigerant temperature, pressure, and flow rate at the outlet of each component of the system are compared with the measured data to verify the correctness of the model established in this paper. Using the established model, the impact of compressor speed on the heating comfort of the cabin under high-temperature conditions in summer was studied, and a control strategy for rapid
Zhang, MinLi, LipingZhou, JianhuaHuang, YuZhen, RanShangguan, Wen-Bin
Over recent years, BorgWarner has intensified its efforts to explore and leverage trending technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to enhance products and processes. This includes digital twin technology, which has potential use cases for system behavior analysis, product optimization and predictive maintenance. This paper outlines the development process of a digital twin for a commercial vehicle battery, which serves as a demonstrator and learning platform for this technology. In order to assess the feasibility as well as hard- and software requirements, a cloud-based digital twin demonstrator was developed, integrating vehicle telemetry data with physics-based battery electric and thermal models, and an aging prediction algorithm. The key components are an Internet of Things (IoT) gateway, simulation models, data processing and ingestion pipelines, a machine learning algorithm for anomaly detection, and visualizations of telemetry and simulation
Bongards, AnitaLiu, XiaobingBeemer, MariaGajowski, DanielRama, NeerajShah, KeyaFallahdizcheh, Amirhossein
A reemergence of manufacturer interest in range-extended electric vehicles is being driven by increasing diversification of consumer interest in low carbon-intensity technologies in the passenger vehicle and other markets. A major advantage of range-extended electric vehicles is that they curtail consumer vehicle range anxiety while maintaining a lower vehicle cost when compared with battery electric vehicles (BEV). By incorporating a small liquid-fueled internal combustion engine (ICE), the range and “refueling” time of electrified vehicles can be significantly improved while overcoming issues with cost and weight faced by long-range battery packs. Compared to ICEs designed for non-hybrid and mild hybrid vehicles, the ICE in a range-extended electric vehicle has a unique set of requirements focused on compact size, low cost, and efficient operation within a limited engine map. A Range Extender (REx) 0.9L 2-cylinder engine was selected which prioritizes these attributes in a
Peters, NathanMarion, JoshuaPothuraju Subramanyam, SaiHoth, AlexanderBunce, Mike
The rapid expansion of the global electric vehicle (EV) market has significantly increased the demand for advanced thermal management solutions. Among these, the battery cold plate is a critical component, essential for maintaining optimal battery temperatures and ensuring efficient operation. As EV batteries increase in size, the thermal management requirements become more complex, necessitating the development of new alloys with enhanced strength and thermal conductivity. These advancements are crucial for the effective dissipation of heat and the ability to withstand the mechanical stresses associated with larger and more powerful batteries. The evolving performance demands of EVs are driving material innovation within the thermal management sector. This study aims to explore the global heat exchanger market trends from a material perspective, focusing on the evolution of the mechanical and thermal properties. Specifically, we investigated the transition from the traditional AA3003
Jalili, MehdiWang, XuRazm-poosh, Hadi
Fuel economy and the ability to maintain the state of charge (SOC) of the battery are two key metrics for the energy management of a full-power fuel cell hybrid vehicle fitted with a small-capacity battery pack. To achieve stable maintenance of SOC and near-optimal fuel consumption, this paper proposes an adaptive equivalent consumption minimization strategy (PA-ECMS) based on power prediction. The strategy realizes demand power prediction through a hybrid deep learning model, and periodically updates the optimal equivalent factor (EF) based on the predicted power to achieve SOC convergence and ensure fuel economy. Simulation results show that the hybrid deep learning network model has high prediction accuracy with a root mean square error (RMSE) of only 0.733 m/s. Compared with the traditional ECMS based on SOC feedback, the PA-ECMS effectively maintains the battery SOC in a more reasonable range, reduces the situation of the fuel cell directly charging the power cell in the high
Gao, XinyuJu, FeiChen, GangZong, YuhuaWang, Liangmo
This study investigates the impact of thermal imbalances on energy delivery and Battery State of Power (SoP) in immersion-cooled battery cells. It explores how these imbalances, which arise when cells within a module operate at different temperatures, lead to variations in internal resistance and inefficiencies in energy storage and discharge. Such imbalances critically affect the battery's SoP, representing the maximum charge or discharge power the system can support over specific time intervals. By analyzing SoP over 10-second durations and continuous, we assess how thermal imbalances influence both short-term and medium-term power capabilities. Temperature significantly impacts cell aging, and imbalances can accelerate degradation in some cells, ultimately affecting serviceability. To address these issues, we employ a high-level simulation framework that integrates advanced tools. GT-SUITE software optimizes thermal performance by adjusting coolant temperature and flow rate to
Meshginqalam, AtaNegro, SergioAtluri, PrasadTyagi, RamavtarSuzuki, JorgeK B, AnjushaCao, Yuyuan
To address the challenges of complex operational simulation for Electric Vehicles (EVs) caused by spatial-temporal variations and driver behavior heterogeneity, this study introduces a dynamic operation simulation model that integrates both data-driven and physics-based principles, referred to as the Electric Vehicle-Dynamic Operation Simulation (EV-DOS) model. The physics-based component encompasses critical aspects such as the powertrain energy transfer module, heat transfer module, charge/discharge module, and battery state estimation module. The data-driven component derives key features and labels from second-by-second real-world vehicle driving status data and incorporates a Long Short-Term Memory (LSTM) network to develop a State-of-Health (SOH) prediction model for the EV power pack. This model framework combines the interpretability of physical modeling with the rapid simulation capabilities of data-driven techniques under dynamic operating conditions. Finally, this study
Jing, HaoHU, JianyaoOuyang, JianhengOu, Shiqi(Shawn)
Fuel cell electric vehicles (FCEVs) are gaining increasing interest due to contributions to zero emissions and carbon neutrality. Thermal management of FCEVs is essential for fuel cell lifespan and vehicle driving performance, but there is a lack of specialized thermal balance test standards for FCEVs. Considering differences in heat generating mechanism between FCEVs and internal combustion engine vehicles (ICEVs), current thermal balance method for ICEVs should be amended to suit for FCHVs. This study discussed thermal balance performance of ICEV and FCHVs under various regulated test conditions based on thermal balance tests in wind tunnel of two FCEVs and an ICEV. FCEVs reported overheat risk during low-speed climbing test due to continuous large power output from fuel cell (FC). Frequent power source switches between FC and battery were observed under dual constrains of fuel cell temperature and battery state of charge (SOC). Significant temperature exceedance of ICEV occurred
Min, YihangFang, YanhuaHe, ChongMing, ChenMao, Zhifei
Lithium-ion batteries (LIBs) are critical components in electric vehicles (EVs) and renewable energy systems. However, conventional cooling techniques for LIBs often struggle to efficiently dissipate heat during fast charging and discharging, potentially compromising performance and safety. This study investigates the thermal performance of immersion cooling applied to an Electric Vehicle (EV) battery module comprised of NCA-chemistry-based cylindrical 21700 format Lithium-ion cells. The effectiveness of immersion cooling in reducing maximum cell temperature, temperature gradient, cell-to-cell temperature differential, and pressure drop within the battery module is evaluated on a detailed 3D model of a 360-cell immersion-cooled battery module that was developed, incorporating a well-established heat generation model based on theoretical analysis and experimental data to simulate the thermal characteristics of the battery system. The effects of the different fluid properties are first
Garcia, AntonioMicó, CarlosMarco-Gimeno, JavierElkourchi, Imad
Battery electric vehicles (BEVs) are well-suited for many passenger vehicle applications, but high cost, short range, and long recharging times have limited their growth in commercial vehicle markets. These constraints can be eliminated with plug-in hybrid electric vehicles (PHEVs) which combine many benefits of BEVs with those of conventional vehicles. In this study, research was conducted to determine the optimal hybrid electric powertrain system for a Class 3, light duty commercial vehicle. The key technologies used in this hybrid powertrain include engine downsizing, P3 architecture hybridization, and active thermal management of aftertreatment. A vehicle cost of ownership analysis was conducted to determine the economic viability, a very important consideration for commercial vehicles. Several combinations of E-motor and battery pack sizes were evaluated during the cost analysis and the best possible configuration was determined. The resulting vehicle powertrain demonstrated ~60
Meruva, PrathikMichlberger, AlexanderBachu, PruthviBitsis, Daniel Christopher
In the electrification of automotive and commercial vehicles, batteries are replacing internal combustion engines (ICE) with a battery only power system. The current process uses Linear Circuit Analysis (LCA) and assumes a passive load. The electronics are also assumed to have a constant input voltage from the source. A battery is not capable of providing constant input voltage under automotive use cases resulting in LCA not be applicable for all cases. The non-constant battery voltage will also influence the way electronics are modelled. One specific instance is an EV especially with the traction drive motors where the power demands are considered non-passive. The research will show the discharge behavior of batteries and the results of each of these discharge modes. The research will classify loads as either passive loads or non-passive loads and use the conservation of energy to model non-passive loads with a battery.
Ingarra, Nicholas
Most of the plug-in electric vehicles (EVs) available today are retrofitted versions of the corresponding co-existing higher-volume internal combustion (IC) engine-based models. In order to make the former category of vehicles more attractive in terms of driving range, a Li-ion battery pack of substantive energy capacity (in kWh) is needed. The latter requirement is likely to add to the weight of an EV in relation to its conventional counterpart. This potential weight increase can to an extent be checked by aggressively scouring for opportunities for weight reduction of the BIW (Body-In-White) of the original platform. The current work suggests a practical and efficient CAE (Computer-Aided Engineering)-driven approach for weight optimization of the BIW of a vehicle without affecting its styling, modal frequencies and front crashworthiness performance. It is assumed that there would be no major changes to manufacturing resources associated with the current design although limited
Deb, AnindyaZhu, Feng
This paper presents the development of a new vehicle simulation software, the Power- and Usage-Based Simulator Tool (referred to as the Power-Based Model), designed to predict fuel consumption and evaluate advanced powertrain technologies for off-road mobile machinery. The Power-Based Model integrates current research on fuel consumption simulation in the off-road vehicle sector and serves as a platform for development of advanced powertrain technologies such as battery-electric and fuel cell powertrains. The tool predicts the battery capacity and hydrogen storage required for the transition to these advanced powertrains, allowing users to accurately calculate component sizes and reductions in fuel consumption. The Power-Based Model was developed with a strong focus on the unique operational characteristics of off-road machinery, ensuring that it realistically reflects real-world energy consumption and the competitive advantages of various fuel-saving technologies. This paper describes
Kim, NamdooSeo, JiguVijayagopal, RamBurnham, Andrewmakarczyk, DavidFreyermuth, Vincent
Charging a battery electric vehicle at extreme temperatures can lead to battery deterioration without proper thermal management. To avoid battery degradation, charging current is generally limited at extreme hot and cold battery temperatures. Splitting the wall power between charging and the thermal management system with the aim of minimizing charging time is a challenging problem especially with the strong thermal coupling with the charging current. Existing research focus on formulating the battery thermal management control problem as a minimum charging time optimal control problem. Such control strategy force the driver to charge with minimum time and higher charging cost irrespective of their driving schedule. This paper presents a driver-centric DCFC control framework by formulating the power split between thermal management and charging as an optimal control problem with the goal of improving the wall-to-vehicle energy efficiency. Proposed energy-efficient charging strategy
Gupta, ShobhitKang, Jun-MoZhu, YongjieLee, ChunhaoZanardelli, Wesley
On electric vehicle the low voltage (nominal 12 volt) battery serves mostly as an energy storage buffer for supporting features and actuators on the 12 volt power supply network. Within an EV, unlike an internal combustion engine car, as there is no cranking requirement needed to be supported by this battery, it presents a significant opportunity for downsizing. In a premium car there are a significant number of features which inhibits the car to go into “deep sleep” and hence remains on a “stand-by” mode of operation. During this period of stand-by the low voltage energy storage system needs to cater for up to 0.4 W when in sleep/standby mode of operation. To sustain longer periods of stand-by mode the low voltage battery needs to have enough stored energy to maintain the appropriate level of state of charge (SOC) so that enough critical threshold of SOC is maintained for 12 volts essential system startup at vehicle restart. This can potentially inhibit downsizing of the low voltage
Dutta, NilabzaOvers, Sheldon
Electrifying truck fleets has the potential to improve energy efficiency and reduce carbon emissions from the freight transportation sector. However, the range limitations and substantial capital costs with current battery technologies imposes constraints that challenge the overall cost feasibility of electrifying fleets for logistics companies. In this paper, we investigate the coupled routing and charge scheduling optimization of a delivery fleet serving a large urban area as one approach to discovering feasible pathways. To this end, we first build an improved energy consumption model for a Class 7-8 electric and diesel truck using a data-driven approach of generating energy consumption data from detailed powertrain simulations on numerous drive cycles. We then conduct several analyses on the impact of battery pack capacity, cost, and electricity prices on the amortized daily total cost of fleet electrification at different penetration levels, considering availability of fast
Wendimagegnehu, Yared TadesseAyalew, BeshahIvanco, AndrejHailemichael, Habtamu
In modern vehicles, effective thermal management is crucial for regulating temperatures across various components and sub-systems, ensuring optimal performance, efficiency, safety, and passenger comfort. As the industry shifts towards reducing carbon emissions, powertrain electrification - encompassing electric and hybrid vehicles - has emerged as a prominent trend. This transition introduces greater complexities, as the powertrain system must now precisely control the temperatures of not only traditional components but also batteries, power electronics, and motors. Typically, the performance of vehicle-level thermal management systems is fully evaluated only after physical prototypes are developed and tested, particularly during summer and winter road trials. Conducting development and validation at such a late stage in the development process significantly increases both development risks and costs. To address these challenges, a comprehensive vehicle-level thermal management
Xu, ZhengQiu, JieLu, YuanWang, Yingzhen
Electric vehicles rely on accurate estimation of battery states to operate safely and efficiently. Traditionally, the state estimation is pack level and based on empirical models developed to capture the dynamics of a representative battery pack and hence falls short in accounting for cell-to-cell variations. These variations become more pronounced as the cells age within a battery pack under non-homogeneous mechanical, thermal, manufacturing, and electrical conditions. It is challenging to adapt the traditional physics-based model to changing battery dynamics in real-time. To improve the state estimation at the cell level, a data-driven approach utilizing streamed data from vehicles enabled by connectivity has been shown in this paper. While traditional data-driven approaches result in large models and require large quantities of data for training, the proposed method relies on combining the underlying physics of the electrochemical model with novel data-driven modeling techniques
Gupta, ShobhitHegde, BharatkumarHaskara, IbrahimShieh, Su-YangChang, Insu
Toyota Motor Corporation pursuing an omnidirectional strategy that includes battery electric vehicle (BEV), plug-in hybrid electric vehicle (PHEV), and fuel cell electric vehicle (FCEV) to accelerate electrification. One of the technical challenges with our xEV batteries which feature good degradation resistance and long battery life, is that regenerative braking cannot be fully effective due to the decrease in regenerative power in some situations, such as low battery temperature. For the electrified vehicles with an internal combustion engine such as PHEVs, the solution has been running the engine to increase deceleration through engine braking during coasting. PHEVs are expected to extend their cruising range and enhance EV driving experience as "Practical BEVs". While increasing battery capacity and enhancing convenience, the restrictions on EV driving opportunity due to low battery temperature may negatively affect PHEV’s appealing. As an alternative, introducing a battery heater
Hoshino, Yu
This study presents a control co-design method that utilizes a bi-level optimization framework for parallel electric-hydraulic hybrid powertrains, specifically targeting heavy-duty vehicles like class 8 semi-trailer trucks. The primary objective is to minimize battery energy consumption, particularly under high torque demand at low speed, thereby extending both battery lifespan and vehicle driving range. The proposed method formulates a bi-level optimization problem to ensure global optimality in hydraulic energy storage sizing and the development of a high-level energy management strategy. Two nested loops are used: the outer loop applies a Genetic Algorithm (GA) to optimize key design parameters such as accumulator volume and pre-charged pressure, while the inner loop leverages Dynamic Programming (DP) to optimize the energy control strategy in an open-loop format without predefined structural constraints. Both loops use a single objective function, i.e. battery energy consumption
Taaghi, AmirhosseinYoon, Yongsoon
Battery health status and driving rangeof electric vehicles (EVs) are critical factors in determining their market penetration. Choosing an optimal charging strategy—specifying how, when, and for how long to charge based on the driver’s travel behavior—can significantly mitigate battery degradation and extend battery life. This study introduces an EV powertrain system energy model designed to enhance the prediction accuracy of battery status under real-world driving conditions. By integrating with the Q-learning approach, this studyprovides tailored recommendations on charging behaviors, including charger type, start time, and charging duration. This study innovatively considers the rental costs caused by the battery capacity not being able to meet the daily driving range. Simulating a typical three-year usage scenario for an average driver in New England, the results indicate that thecharging strategy proposed by this study reduces battery degradation rates by 1.53‰, 3.57‰, and 7.68
Wang, JiayiJing, HaoOu, Shiqi (Shawn)Lin, Zhenhong
This paper introduces a novel approach to optimize battery power usage and optimal engine torque for Axle disconnect device engagement under power constrained scenarios for range extended hybrid vehicles. Range extended hybrid architecture provides benefits of BEV architecture and relief the range anxiety that BEV drivers often have. The Axle disconnect device helps improve the efficiency of the battery power usage when it is disconnected and provides better drivability and performance to fulfill driver demand when it is connected [1]. Under power constraint scenario, the disconnect device engagement could take too long or eventually fail to engage and result in degradation for drivability and vehicle level performance. This novel approach is utilizing the engine to either generate more power to spin up the disconnect motor faster under discharge limited case or generate less power to allow the disconnect motor to spin down under charge limited case. The effectiveness of this approach
Sha, HangxingMadireddy, Krishna ChaitanyaBanuso, AbdulquadriKhanal, ShishirRock, JoePatel, Nadirsh
The operating temperature of lithium-ion battery (LIB) cells significantly influences their degradation behavior. In indirect liquid cooling systems, temperature variations within a Battery Electric Vehicle (BEV) LIB module are inevitable due to the increasing downstream temperature of the cooling medium as it absorbs heat. This leads to reduced temperature differentials between the cooling medium and the LIB cells. As a result, LIB cells located further along the flow path experience higher average temperatures than those at the front. Typically, a maximum average cell temperature difference of 5 K within LIB modules is considered acceptable. However, results from a conventional cooling system indicate that, when fast charging is exclusively used, this can lead to a 15.5 % difference in the total ampere-hours passed before the End-of-Life (EOL) is reached for the front and back LIB cells. To address this issue, a switchable thermal management system for the traction battery is
Auch, MarcusWeyershäuser, KonstantinKuthada, TimoWagner, Andreas
The shift towards hybrid and electric powertrains in off-road vehicles aims to enhance mobility, extend range, and improve energy efficiency. However, heat pump-based battery thermal management systems in these vehicles continue to consume significant energy, impacting overall range and efficiency. Effective thermal management is essential for maintaining battery performance and safety, particularly in extreme conditions. Although high-fidelity models can capture the complex dynamics of heat pumps, real-time control within model-based optimization frameworks often depends on simplified models, which can degrade system performance. To address this, we propose a novel data-driven grey box control-oriented model (COM) that accurately represents the thermal dynamics of a vapor-compression refrigeration-based heat pump system. This COM is integrated into a model-predictive control (MPC) framework, optimizing thermal management during transient and burst-power operations of the battery pack
Sundar, AnirudhGhate, AtharvaZhu, QilunPrucka, RobertRuan, YeefengFigueroa-Santos, MiriamBarron, Morgan
Designing for the durability of motor vehicles requires accounting for various stress factors, including tractive loads, electrical loads, thermal loads, and structural loads. For electric vehicle propulsion systems, it is crucial to consider not just the magnitude and repeats of these loads but also their temporal sequence throughout the vehicle’s lifespan. The order and timing of these loads influence factors such as, charge and discharge cycles or active motor heating, which ultimately impact the damage to the propulsion system components like the cell and the motor. Traditionally, lifetime loads for durability assessments are derived from a single-user load profile consisting of a set of ‘representative’ drive cycles accounting for the cumulative damage equivalent to the real-world damage covered under warranty. This profile is typically based on historical usage data, user scenarios, and industry experience, but may not capture the diverse failure modes of the different propulsion
Ramakrishnan, SankaranKhapane, Prashant
Any vehicle traveling on roads interacts with various profiles of surface roughness, which can be best characterized by randomness. The resulting random vibrations not only expose passengers to unpleasant physical shakes and noises, but also impart fatigue damage to nearly everything installed on the vehicle. In today’s robust design process, it is highly desirable to predict fatigue damage in the early design phase, in order to prevent any durability problems in the future, especially for electric vehicles. Historically, the conventional approach to tackling the problem of fatigue damage has involved cycle-counting stress or strain responses, obtained through step-by-step numeric solutions in the time-domain. However, the most effective method of predicting fatigue in random vibration lies in the frequency domain. Such a spectrum-based approach is greatly advantageous because it does not have to deal with expensive and tedious simulations involving millions of time instants of
Yang, ZaneFouret, Charles
The demand for eco-friendly electric powertrains has increased significantly in recent years. Cells are the most crucial component of a battery pack, directly influencing the dimensions, range, lifespan, performance, and cost of electric vehicles. Lithium-ion cells outperform other cell chemistries due to their higher energy density, allowing for more compact and lightweight designs while providing longer operational ranges. It is crucial that lithium-ion cell packaging complies with assembly requirements to maximize its lifespan and ensure operational safety. Assembly force requirements of lithium-ion cells are critical to ensure optimal cell performance throughout its lifetime & enhance the longevity of the battery pack. The compression pad between cells ensures appropriate cell assembly pressure. The service life is how long a lithium-ion cell can operate effectively, while the cyclic life refers to the number of charge-discharge cycles before cell functional degradation. The cell
Varambally, VishakhaSithick basha, AbubakkerChalumuru, MadhuSasikumar, K
As part of the global effort to combat climate change, electric vehicles (EVs) are gaining popularity, even for long-haul commercial transportation. A battery pack is a critical component of an EV, and it contains several modules with many series- and parallel-connected electrochemical cells. Strict safety and operational limits are enforced on the cell-level to ensure safe operation of the battery pack. However, variations in the electrochemical properties among the cells in the pack causes some cells to reach the safety and operational limits faster than others. This limits the total power, and over time, the energy delivered and the lifetime of the battery pack. Maximizing the energy delivered by the battery pack (potentially also improving the battery pack’s lifetime) can be achieved by increasing cell-level control, and battery-integrated modular multilevel converters (BI-MMC) is presented as a solution. A BI-MMC has several series-connected DC-to-AC converters, commonly called
Balachandran, ArvindJonsson, TomasEriksson, Lars
The adoption of hybrid electric vehicles (HEVs) is becoming more popular during the last few years due to government incentives and favourable legislation both for automotive companies and final users. This type of vehicle claims very low carbon dioxide emissions while eliminating the range anxiety associated with battery electric vehicles thanks to the on-board range extender being able to recharge the battery throughout the journey. Unfortunately, the low emissions values are more representative of the particular mathematical model implemented by the legislation than the measured real driving emissions. Specifically, the legislation does not take into account the CO2 embedded in production of the batteries or of the electrical energy stored in it. This work analyses these aspects by means of a numerical model of the BMW i3 94Ah vehicle. The results obtained are collected from simulations conducted over the Worldwide harmonized Light vehicles Test Cycle (WLTC) by using the commercial
Turner, JamesVorraro, Giovanni
Diverse solutions will likely be needed to decarbonize the commercial truck sector in the United States. Battery-powered vehicles play a predominant role but in some cases, fuel cell trucks are more advantageous for the consumer. This study examines several medium- and heavy-duty applications designed for different driving range requirements to identify the design space where battery and fuel cell trucks are attractive. Also considered are the impacts of purchase price, fuel cost, and vehicle usage. We examine the top 10 truck classes as well as bus applications based on vehicle population, fuel usage, and driving distances. We assume a 2030 scenario where both batteries and FC systems become less costly and more efficient, as targeted by the U.S. Department of Energy. Even for smaller-class vehicles, where battery electric vehicles are expected to be the most economical among clean vehicle solutions, the results are not straightforward. Based on vehicle design, usage, and external
Vijayagopal, RamBirky, Alicia
This study presents a detailed techno-economic assessment of battery-electric trucks, incorporating battery aging effects within a total cost of ownership (TCO) model. With increasingly stringent emissions regulations, battery-electric trucks are becoming a viable solution in Europe. However, due to uncertainty regarding their long-term cost-effectiveness and fleet operators’ profit-oriented priorities, there is an urgent need for accurate TCO assessment. Existing studies often overlook or oversimplify the impact of battery aging on overall costs. This work addresses this gap by introducing battery aging-related costs through an empirical battery degradation model, evaluated over the vehicle’s lifetime. Key aging costs include a refined estimation of battery residual value, influenced by degradation and remaining battery life, and potential battery replacement expenses. A case study on a VECTO group 9 truck used for regional delivery missions examines different payloads and battery
Costantino, TrentalessandroAcquarone, MatteoMiretti, FedericoSpessa, Ezio
Efficient thermal management is essential for maintaining the performance and safety of large-capacity battery packs. To overcome the limitations of traditional standalone air or liquid cooling methods, which often result in inadequate cooling and uneven temperature distribution, a hybrid air-liquid cooling structure was designed. A three-dimensional model was developed, and heat transfer and fluid flow characteristics were analyzed using computational fluid dynamics (CFD) simulations. Experimental validation was carried out through discharge temperature rise tests on individual battery cells and flow resistance tests on the liquid cooling plate. The thermal performance of the hybrid system was compared to that of standalone cooling methods under various discharge rates. The results indicated that the hybrid system significantly enhanced cooling performance, reducing the maximum temperature difference by 5.54°C and 3.37°C, and the peak temperature by 11.66°C and 4.5°C, compared to air
Li, HaoGuo, YimingZhou, FupengLi, KunyuanShangguan, Wen-Bin
Evaluating the structural strength and thermal performance of electric vehicle battery packs is crucial for enhancing safety and performance. In two-wheelers, the battery pack must withstand significant vibrational forces, shocks from impacts, and accidental drops, all of which can compromise the battery's structural integrity. A failure in this regard could lead to dangerous outcomes such as short circuits, fire, or even explosions, making the robustness of the battery pack crucial for both safety and performance. Conducting physical vibration, shock, and drop tests on a battery pack is one way of proving the robustness of the design, however it is time and resource consuming leading to an iterative approach of design improvement which also demands stringent safety measures and specialized equipment’s. The present work focuses on computer-aided virtual simulations at the design stage to evaluate the structural integrity of the battery pack assembly, optimize battery design, and reduce
Shinde, PranavBalachandran, KarthikGandhi, ChaitanyaMishra, SonuDeshmukh, HarishKarve, MadhuraChittur, SrikrishnaDas, Alok
This study presents a comparative analysis of Samsung lithium-ion batteries, which are the INR21700 30T high-power (HP) cell and INR21700 50E high-energy (HE) cell, examining their design differences and performance characteristics. Based on teardown data reported in literature, the HP cell features higher porosity, thicker current collectors, and thinner electrode coatings compared to the HE cell, while the HE cell incorporates approximately 6% silicon oxide in its graphite anode for increased energy density. Cell-level characterization test results demonstrated superior rate capability of the HP cell, maintaining 93.8% of its capacity at 2C discharge, while the HE cell retained 93.4% at 1.6C. The HP cell also exhibited better cycle life stability due to its silicon-free design. Pseudo-two-dimensional (P2D) models were constructed using both teardown experimental parameters and adjusted parameters. Simulation results revealed significant discrepancies using teardown parameters
Yao, QiKollmeyer, PhillipChen, JunranPanchal, SatyamGross, OliverEmadi, Ali
Rechargeable lithium batteries are widely used in the electric vehicle industry due to their long lifespan and high energy density. However, after long-term repeated charging and discharging, various electrochemical reactions inside lithium batteries can lead to performance degradation and even cause battery fires. Estimating the health status and predicting the remaining life of lithium batteries can provide insights into their future operating conditions, which is crucial for achieving fault warnings and ensuring the safe operation of battery-related equipment. In terms of predicting the health status of lithium batteries, this paper proposes a method based on an improved Long Short-Term Memory (LSTM) for health status estimation. This method first employs nearest neighbor component analysis to eliminate feature redundancy among the multidimensional health factors of the battery. Then, a differential grey wolf optimization algorithm (DEGWO) is used to globally optimize the
K, Meng Zi
Light weighting has been one of the focus areas in automotive design, which has assumed greater importance for electric vehicles due to sensitivity of electric range to mass of the vehicle and increased cost of the battery packs to meet range target with increasing mass of vehicle. Mass of vehicle interior components have significant impact of overall vehicle mass due to cascading effect. Hence mass of such components must be minimized during design synthesis, where multiple design configurations may be explored with tradeoffs with regard to meeting functional requirements which are often conflicting. Assist handle bracket is one of such components in vehicle which needs to meet mandatory safety requirement of FMVSS 201U that requires the bracket to be soft. At the same time, the bracket needs to have adequate stiffness and strength to meet perceived quality and durability requirements. These are conflicting requirements which are often difficult to meet using manual design iterations
R, RajapandianKoppaka, Vinaya
As the utilization of lithium-ion batteries in electric vehicles becomes increasingly prevalent, there has been a growing focus on the mechanical properties of lithium-ion battery cores. The current collector significantly impacts the tensile properties of the electrode and the internal fracture of the battery cell. The stripping process tends to cause additional damage to the current collector, so tensile testing is not able to obtain in-situ mechanical properties of the current collector. Therefore, nanoindentation tests are required to acquire the in situ mechanical properties of the current collector. Nanoindentation testing represents the primary methodology for the determination of the mechanical properties of thin films. The Oliver-Pharr method is the standard approach used by commercial indentation instruments for the evaluation of mechanical properties in materials. Nevertheless, this approach is constrained by the limitations imposed by the sample boundary conditions. To
Dai, RuiSun, ZhiweiPark, JeongjinXia, YongZhou, Qing
Thanks to greatly increased energy density of battery, the average driving range of an electric vehicle has been advanced quite a lot. However, drastic reduction of driving range in cold ambient conditions still greatly restricts the wide application of electric vehicles. This paper presents a methodology of establishing multi-discipline coupled full vehicle model in AMESim to investigate the energy consumption of a pure electric vehicle in cold ambient conditions. Different strategies of battery heating through Positive Temperature Coefficient (PTC) part and/or combination of Motor Waste Heat Recovery (MWHR) were also investigated to study whether there is an improvement of driving range. Firstly, basic framework of the full vehicle model established in AMESim was introduced. Next, modeling details of individual sub-systems were illustrated respectively. Then, full vehicle energy consumption test was carried out in -7°C ambient condition to check the simulation accuracy. Finally, a
Zhou, ShuaiLiu, HuaijuYU, HuiliYan, XuYan, Junjie
The proliferation of the electric vehicle (EVs) in the US market led to an increase in the average vehicle weight due to the assembly of the larger high-voltage (HV) batteries. To comply with this weight increase and to meet stringent US regulations and Consumer Ratings requirements, Vehicle front-end rigidity (stiffness) has increased substantially. This increased stiffness in the larger vehicles (Large EV pickups/SUVs) may have a significant impact during collision with smaller vehicles. To address this issue, it is necessary to consider adopting a vehicle compatibility test like Euro NCAP MPDB (European New Car Assessment Program Moving Progressive Deformable Barrier) for the North American market as well. This study examines the influence of mass across vehicle classes and compares the structural variations for each impact class. The Euro NCAP MPDB (European New Car Assessment Program Moving Progressive Deformable Barrier) protocol referenced for this analysis. Our evaluation
Kusnoorkar, HarshaKoraddi, BasavarajGuerrero, MichaelSripada, Venu VinodTangirala, Ravi
For electrical vehicle (EV) automotive body-in-white (BIW) structures, protection of passengers and battery in crash event becomes equally important. In addition to energy absorption, intrusion protection for battery and vehicle becomes extremely important and GPa advanced high strength steels (AHSS) including press hardened steels (PHS), DP/MP/CP/GEN3 steels have become material of choice for design for those components. Higher yield strength materials especially in 980/1180MPa MP and CP category are chosen for part design over conventional low yield strength DP. In this study, the forming characteristics including both global and local formability are evaluated and compared among 980 DP/MP grades. Formability test such as forming limit curve (FLC), true fracture strain, V bend, half dome, and hole expansion tests are conducted. Microstructure analysis to understand the effect of different grain structure and phases of DP/MP grades is also accomplished. A T-shape laboratory die trials
Shih, Hua-ChuPednekar, VasantShi, MingSingh, JatinderTedesco, SarahWu, Wei
Accurate estimation of the state of charge (SoC) of battery cells is crucial for the efficient management and longevity of battery systems, particularly in electric vehicles and renewable energy storage. This paper presents an approach utilizing a nonlinear autoregressive exogenous (NARX) model to estimate the SoC of battery cells. The proposed method leverages hyperparameter optimization to determine the optimal configuration of the neural network, including the number of neurons, the number of hidden layers, the number of feedback loops, the best activation function, and the most effective learning rate. The primary objective of this research is to minimize the estimation error of the SOC to within 2%, thereby enhancing the reliability and performance of battery management systems. The hyperparameter optimization process involves a systematic search and evaluation of various configurations to identify the most effective neural network architecture. This process is critical as it
Saini, SandeepAdmane, Chinmay
This paper aims to model and simulate a design specification for a fuel cell electric powertrain tailored for Extreme H motorsport applications. A comprehensive numerical model of the powertrain was constructed using GT-SUITE v2024, integrating the 2025 Extreme H regulations, which include specifications for the fuel cell stack, electric motors, hydrogen storage, and battery systems. A detailed drive cycle representing the real-world driving patterns of Extreme E vehicles was developed, utilizing kinematic parameters derived from literature and real-world data. The performance of the Extreme H powertrain was benchmarked against the Toyota Mirai fuel cell vehicle to validate the simulation accuracy under the same racing conditions. The proposed design delivers a maximum power output of 400 kW, with 75 kW supplied by the fuel cell and 325 kW by the battery, ensuring optimal performance within the constraints set by the Extreme H 2025 regulations. Additionally, the design maintains an
Moreno Medina, JavierSamuel, Stephen
Accurate estimation of crucial quantities in automotive drivetrain systems is essential for optimizing performance, durability, and emissions. However, the presence of time delays, arising from tasks scheduling and communication latency between control units, can significantly hinder the effectiveness of advance control algorithms. Closed-loop performance is often limited by the equivalent time delay between the control action command, its effect on the system, and the measurement of the reaction. Frequently, commands and measurements originate from different sources, requiring precise coordination to accurately estimate the driveline response. This paper presents a novel model-based approach that integrates Kalman filtering with horizon prediction techniques to effectively address time-delay compensation. By leveraging the descriptive capabilities of physics-based models, the proposed method enables to overcome synchronization misalignment between commands, actuations and measurements
Rostiti, CristianPatel, NadirshCatkin, Bilal
Phase change energy storage devices are extensively utilized in latent heat thermal energy storage and hold significant potential for application in the thermal management of automotive batteries. By harnessing the high-density energy storage capabilities of phase change materials to absorb heat released by the batteries, followed by timely release and utilization, there is a substantial improvement in energy efficiency. However, the thermal conductivity of medium and low temperature phase change materials is poor, leading to its inefficient utilization. This paper focuses on optimizing the structure of a phase change heat exchanger in a phase change energy storage device to improve its performance. A basic design of the phase change heat exchanger is used as an example, and fin structure is added to enhance its heat exchange capabilities. A predictive surrogate model is built using numerical simulation, with the dimension and number of fins as design variables, and heat flow density
Zhang, HaonanSun, MingzheZheng, HaoyunZhang, Tianming
As electric vehicles (EVs) become increasingly prevalent, ensuring the safety of their battery systems is paramount. Lithium-ion batteries, present unique safety challenges due to their high energy density and the potential for failure under certain conditions. There is an extensive amount of research on pouch and cylindrical cells, however, prismatic cells have not received similar attention. This study presents an extensive series of experimental tests conducted on prismatic cells from two different manufacturers. These tests include flat punch, hemispherical punch, axial compression and three-point bending tests, all designed to assess the cells’ mechanical properties and failure behavior. A model was developed simulating the behavior of the cell under local loading scenarios. While this paper focuses primarily on testing methodologies, initial findings and an introductory FEA model, future work will incorporate these experimental results into detailed FEA models across all loading
Patanwala, HuzefaSong, YihanSahraei, Elham
This study looks into the impact of temperature on the aging of lithium-ion batteries, which are an important component of energy storage systems in electric vehicles. To evaluate battery capacity over time, experiments were carried out at two temperatures, 25°C and 50°C, imitating real-world vehicle circumstances. Pristine cells were initially assessed in terms of capacity and internal resistance. Aging results from cycling indicate that higher operating temperatures, particularly under aggressive conditions (fast charging), lead to accelerated battery degradation due to heat accumulation. Charging at 2C resulted in fast degradation at both temperatures, with the battery reaching its End Of Life (EOL), 80% capacity, in fewer than 200 cycles. Surprisingly, cycling at 50°C resulted in a longer lifespan than 25°C for 1C charge/discharge rates. The 1C charge and 2C discharge regimen at 50°C produced the best results, retaining more than 80% capacity even after 600 cycles. This shows that
Garcia, AntonioMonsalve-Serrano, JavierEgea, Juan Manuel H.Bekaert, EmilieHerran, AlvaroMarco-Gimeno, Javier
A method for performance calculation and experimental method of a high voltage heater system in electric vehicles is proposed. Firstly, heater outlet temperature and pressure drop of the heater are used as metrics to compare simulation results with experimental data, thereby validating the established model. Then, simulations are performed on two heater flow channel configurations: a cavity flow channel and a cooling fin flow channel. It is observed that the latter significantly reduces the heating plate temperature. This reduction enhances the protection of heating elements and extends their operational lifespan, demonstrating the advantages of incorporating cooling fins into the flow channel structure. The optimization variables for multi-objective optimization include the fin unit length, fin height, fin thickness, fin width, and spacing between two adjacent rows of fins. The optimization objectives include pressure drop, heat transfer efficiency, and heating plate temperature
Gong, MingWang, XihuiWang, DongdongShangguan, Wen-Bin
With Rapid growth of Electric Vehicles (EVs) in the market challenges such as driving range, charging infrastructure, and reducing charging time needs to be addressed. Unlike traditional Internal combustion vehicles, EVs have limited heating sources and primarily uses electricity from the running battery, which reduces driving range. Additionally, during winter operation, it is necessary to prevent window fogging to ensure better visibility, which requires introducing cold outside air into the cabin. This significantly increases the energy consumption for heating and the driving range can be reduced to half of the normal range. This study introduces the Ceramic Humidity Regulator (CHR), a compact and energy-efficient device developed to address driving range improvement. The CHR uses a desiccant system to dehumidify the cabin, which can prevent window fogging without introducing cold outside air, thereby reducing heating energy consumption. A desiccant system typically consists of two
Hamada, TakafumiShinoda, NarimasaKonno, YoshikiIhara, YukioIto, Masaki
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