Browse Topic: Battery management systems (BMS)

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Thermal Management Control strategy for Primary & Secondary Drive Cooling Hybrid Electric Tractor2026-26-0069To be published on 01/16/2026
Global emission norms are getting very strict due to combat the harmful pollutants from internal combustion engine. Hence internal combustion engine (ICE)-based agricultural tractors need to introduce complex after-treatment systems and fuel optimization to provide same or higher value to farmers as cost of these systems drive the overall cost of the product. Engineers around the world are building Electric vehicles to combat the problem and has range issues due to design constraints & Hybrid tractors have emerged as a promising intermittent solution. It helps in combining the advantages of respective ICE and electrification solutions while reducing overall vehicle emissions and enhances operational flexibility. This paper presents a modular thermal modes system developed for a hybrid electric tractor platform where a downsized diesel engine operates at optimal efficiency DC generator used to charge the battery & DC converter is used to charge the auxiliary battery. Battery which is
K, Sunil, D, Mari, Natarajan, Saravanan, Kumawat, Deepak, rojamanikandan, Arumugham, K, MALA, v, sridharan, Muniappan, Balakrishnan, Makana, Mohan
Developments in 2-Wheeler electric vehicle architecture2026-26-0199To be published on 01/16/2026
Electric vehicles are becoming more popular due to the low-cost investment for individual daily usage, such as traveling to nearby places, offices, and schools. There are environmental benefits that make them green and produce less pollution compared to traditional vehicles. Two-wheeler electric vehicles (EVs) have more electronic components compared to two-wheeler internal combustion engine (ICE) vehicles. The major components in two-wheeler EVs are the motor and battery. The traction motor is driven by the battery, Battery is a primary energy source in 2Wheeler electric vehicle. An electric vehicle comprises different major electronic components such as the battery management system (BMS), motor control unit (MCU), human-machine interface (HMI), and, in some cases, a vehicle control unit (VCU) as well. Considering a 48V architecture or less than 60V provides advantages of low system cost as it requires less effort for safety measures. Furthermore, this paper explores diverse
Karunakar, Praveen, K R, Amogh
Lithium-ion Battery’s State of Health Prediction: A Deep Learning Framework2026-26-0162To be published on 01/16/2026
The growing adoption of electric vehicles (EVs) has increased the need for reliable and efficient battery health monitoring systems. Lithium-ion batteries, widely used in EVs, undergo performance degradation over time, making the prediction of their health status a crucial task for ensuring safety, longevity, and operational efficiency. Traditional model-based approaches often face challenges due to their reliance on complex physical models and sensitivity to varying operational conditions. To overcome these limitations, data-driven methods are increasingly preferred for predictive maintenance. In this study, a data-driven framework is proposed for estimating the State of Health (SoH) of lithium-ion batteries. The framework is developed using data collected from multiple battery cells under diverse operating scenarios. Key steps such as data preprocessing, feature engineering, and model development are carried out to build robust machine learning and deep learning models. A focus is
Suryawanshi, Chaitanya Balasaheb, Nangare, Kapilraj, Gaikwad, Pooja
Electric Vehicle Battery’s Testing Challenges in era of Software Defined Vehicle2026-26-0159To be published on 01/16/2026
In era of Software Defined Vehicle (SDV), the whole ecosystem of automobile will be impacted. So, it is going to through several challenges for testing activities. In electric vehicle, most critical component is traction battery, which is controlled and operated through battery management system (BMS). BMS is an electronic system, where is going to function as per software of BMS. And in SDV, software is a key element, which is continuously keep on updating on regular basis. So, it means some of BMS functionalities, features or performance may be also altered on each time on software update, which may impact battery’s operating condition, if some scenario is not evaluated during earlier testing, then there are it may bring battery out of safe operating area, which may significantly impact battery safety, performance or cycle-life. In this paper, we are exploring that different testing requirements for EV Batteries, which may be part of testing practices under era of SDV. Here we will
BHATESHVAR, YOGESH KRISHAN, Mulay, Abhijit B
Validation of 0D-1D Co-Simulation Strategies in Cold Climate for ‘Full EV Digital Vehicle’2026-26-0448To be published on 01/16/2026
Electric Vehicles is embedded with increased software algorithms coupled with several physical systems. It demands the efficacy of components which are linked together to build a system. The digital models reviewed in this paper are at system-level and full vehicle-level, comprising several components and control design, analysis, and optimization. Systems pertaining to each functionality such as, AC loop, E-Powertrain, HEVC, Cooling system, Battery Management System, Vehicle control system etc. together make an 'Integrated Digital Vehicle'. Fidelity of Intersystem co-simulation [AMESIM + SIMULINK] is key to validate the thermal and energy management strategies. This paper elucidates the correlation of Digital Vehicle compared to Test for Thermal Strategy in different driving scenarios and Energy management. Validation of Digital vehicle with 52kWh, 40kWh High Voltage Battery for Intercity Travel of Customer usage and WLTP for Cold condition of -7degC). Also, to evaluate range
Sarapalli Ramachandran, Raghuveeran, Srinivasan, Rangarajan, Saravanan, Vivek, Dutta, Souham, Pichon, Martin, Leclerc, Cedric, Guemene, Alexis-Scott
Optimization and Evaluation of Thermal Interface Material Thickness and Distribution Patterns for Improved Heat Transfer in Liquid-Cooled Battery Pack2026-26-0432To be published on 01/16/2026
Battery Thermal Management Systems (BTMS) play a critical role in ensuring the longevity, safety, and efficient operation of lithium-ion battery packs. These systems are designed to effectively dissipate the heat generated by the cells during vehicle operation, thereby maintaining a uniform temperature distribution across the battery modules, preventing overheating and mitigating the risk of thermal runaway. However, one of the primary challenges in BTMS design lies in achieving effective thermal contact between the battery cells and the cooling plate. Non-uniform or excessive application of Thermal Interface Materials (TIMs) without ensuring robustness and uniformity can increase interfacial thermal resistance, leading to significant temperature variations across the battery modules, which may trigger power limitations via the Battery Management System (BMS) and these thermal gradients can cause inefficient cooling, ultimately affecting battery performance and lifespan. In this study
K, Mathankumar, Jahagirdar, Manasi, Kumbhar, Makarand Shivaji
Development of a HIL Based Methodology for Over-The-Air Updates Validation2026-26-0480To be published on 01/16/2026
Over-the-Air (OTA) update technology has come forth as a transformative aider in the domain of automotive technology, allowing Original Equipment Manufacturers (OEMs) and Tier-1 suppliers of Electric vehicles (EVs) to frequently make software modifications, enhancements, and bug fixes that are essential to optimize the performance of powertrain components such as the motor controller unit (MCU), Battery Management System (BMS), and Vehicle Control Unit (VCU). This facilitates them to remotely supply updates to the vehicle firmware and software by giving inputs of calibration data without requiring physical access to the vehicle. However, as OTA updates have a direct impact on vehicle’s performance, safety and cybersecurity, a stringent e integration of Hardware-in-the-Loop (HIL) simulation into the OTA validation pipeline as a means to ensure reliability, safety, and functional correctness of updates before they are applied in the field. We present a structured approach of combining
Khare, Shivani, Karle, Ujjwala, Subramaniam, Anand
Simplified Simulation of Electric Vehicle Battery packs Using deep learning based Reduced Order Models2026-26-0403To be published on 01/16/2026
Accurate yet computationally efficient simulation of electric vehicle (EV) batteries is essential for system design, performance evaluation, and real-time control applications. This paper presents a simplified simulation framework for lithium-ion EV battery systems using reduced order models (ROMs) that balance fidelity and computational speed. The EV system simulation environment has been developed in MATLAB/Simulink environment to analyze Battery Management System (BMS) control system design and EV system performance. This simulation platform consists of BMS and other important EV controller models and high-fidelity plant models for battery and powertrain systems, which are modelled using Simulink and Simscape library blocks. Performing virtual testing, control design and system-level analysis with such high-fidelity plant models poses significant computational challenges and poor simulation performance. The focus of this paper is to enhance system level simulation performance by
Vernekar, Kiran
State of Health correction based on dynamic cell behaviour in EVs.2026-26-0183To be published on 01/16/2026
In electric vehicle (EV) applications, accurate estimation of State of Health (SOH) of lithium-ion battery packs is critical for ensuring performance reliability, operational safety and user confidence. SOH is a key parameter monitored by Battery Management System (BMS) to assess the remaining usable life of the battery and to make informed decisions regarding charging, discharging, power delivery, and maintenance scheduling. In traditional SOH estimation techniques often rely on simplistic full-cycle charge-discharge data or single-parameter tracking (such as voltage or internal resistance and other method like coulomb counting and kalman filter ), which do not account for variable field conditions such as partial and full state-of-charge usage condition, dynamic load profiles, and non-uniform aging. As a result, these methods can produce significant deviations in SOH prediction, potentially causing premature system-level shutdowns, inaccurate range estimation, and delayed detection
Nikam, Ashish, Tiwari, Awanish Shankar, Sodha, Nirav, Hariyani, Ganesh, Ambhore, Yogesh Gajanan
Dynamic Voltage EV System (DVEVS): Achieving Enhanced Electric Vehicle Efficiency and Performance through switchable Series-Parallel Battery Configuration2026-26-0203To be published on 01/16/2026
The study here investigates on the Electric vehicles (EVs), which are often segmented by range or performance, typically offered as distinct entities. Powertrain design also tends to be either settled or performance focused. A significant challenge in the automotive world is the charging time of EVs, a key performance indicator impacting customer convenience. Battery technology is evolving, allowing for potential architectural changes in battery modules over time. Issues such as cell balancing lead to energy wastage in batteries, and thermal management is critical as increased current causes higher I2R power losses and heat generation. Battery weight negatively affects EV efficiency and range. Adapting to new battery cell technologies and architectures can be costly. Addressing these challenges, this paper presents on Dynamic Voltage EV System (DVEVS) as a solution to enhance EV efficiency by dynamically adjusting battery voltage based on driving conditions. Such as Utilizing a smart
Amberkar S, Sunil, Raool, Anuj Rajesh, M G, Shivanag, RAJAPURAM, BHEEMA REDDY
Conceptual Framework for Real-Time Battery Health Estimation in Automotive Digital Twins Using Reinforcement Learning2026-26-0658To be published on 01/16/2026
The explosive growth of electric vehicles (EVs) calls forth the need for smart battery management systems that can perform health monitoring and predictive diagnostics in real-time. The conventional battery modeling methods mostly do not cover the complicated, dynamic behaviors coming from different usage patterns. The study outlines a structure that would use Reinforcement Learning (RL)-based AI agent as a part of the Battery Electrical Analogy (BEA) simulation platform. With the help of the AI agent, different health parameters such as State of Health (SOH), State of Charge (SOC), and the signs of early thermal runaway can be predicted in real-time. The suggested design takes advantage of the simulation-based approach to have the agent learn and utilizes a decentralized cloud architecture suitable for scaling and reducing the response time. The RL agent performs an essential role in the process by tagging along with the continuous learning and the adjustment of the battery conditions
Pardeshi, Rutuja Rahul, KONDHARE, MANISH, Sasi Kiran, Talabhaktula
Transforming Battery Management: The Role of Artificial Intelligence and Machine Learning in Digital Twin Technologies2026-26-0382To be published on 01/16/2026
The traditional Battery Management System (BMS) faces certain limitations in fully utilizing battery capacity and performance during the long cycle life operation of Electric Vehicles (EVs). These constraints include limited real-time data collection, low processing speed, lack of predictive maintenance, and very limited accuracy in predicting health and degradation chemistry. A Battery Digital Twin (BDT) can effectively address these limitations of the BMS. Battery Digital Twins (BDT) can be viewed as a cyber-physical system comprising four key elements: virtual representation, bidirectional connection, simulation, and connection across the life cycle phases of an EV battery. The performance of a Li-ion battery largely depends on the cathode chemistry, component design, and operating conditions. The battery should be manufactured in a manner (such as cylindrical or prismatic cell) that prevents explosion, leakage, and gas generation inside the battery. To enhance the performance and
Chaturvedi, Vikash, M, Venkatesan, Lanke, Siddhi, Subramaniam, Anand, Karle, Manish, Pandit, Rugved, Gupta, Drishti, Karle, Ujjwala Shailesh
Analysis of Second-Life electric vehicle batteries value maximizing through repurposing and resale, ways to achieve optimal performance and standardization challenges for testing and certification2026-26-0193To be published on 01/16/2026
As electric vehicle (EV) adoption accelerates globally, a growing volume of lithium-ion batteries are reaching end-of-life in their primary automotive application—despite retaining 60 to 80% of their original capacity. This presents a significant opportunity to extend battery utility through second-life applications such as stationary energy storage, microgrid support, and commercial backup systems. This paper analyzes the strategies for maximizing the residual value of second-life EV batteries through repurposing and resale, while also addressing the challenges associated with performance optimization and standardization of testing and certification procedures. The study evaluates the techno-economic viability of second-life batteries compared to new systems, emphasizing cost savings, environmental impact, and emerging market demand. Techniques for enhancing second-life performance are examined, including advanced state-of-health (SoH) diagnostics, machine learning models for usage
Agarwal, Pranjal, Penta, Amar
Automotive Battery Thermal Management by Federated Learning Methodology - Real Time Thermal Runaway Detection2025-28-040810/30/2025
Modern battery management systems, as part of Battery Digital Twin, include cloud-based predictive analytics algorithms. These algorithms predicts critical parameters like Thermal runaway events, state of health (SOH), state of charge (SOC), remaining useful life (RUL), etc. However, relying only on cloud-based computations adds significant latency to time-sensitive procedures such as thermal runaway monitoring. This is a very critical and safety function and delay is not acceptable, but automobiles operate in various areas throughout the intended path of travel, internet connectivity varies, resulting in a delay in data delivery to the cloud and similarly delay in return of the detected warning to the driver back in the vehicle. As a result, the inherent lag in data transfer between the cloud and vehicles challenges the present deployment of cloud-based real-time monitoring solutions. This study proposes application of Federated Learning and applying to a thermal runaway model in low
Sarkar, Prasanta
Development and Parameterization of a Physics-Based Ageing Model for Li-Ion Battery Cells2025-24-014009/07/2025
Although significant progress has been made on developing electrochemical models of Li-ion batteries performance, there is a significant gap in predictive, physics-based modelling of the degradation mechanisms. In this work, we perform a systematic experimental and modelling study to explore the potential of predictive battery ageing models. A commercial NMC pouch cell is initially characterized in detail using tear-down analysis, electrical and electrothermal tests to obtain electrochemical model parameters and validate its fidelity in a large range of operating conditions in terms of temperature, state-of-charge and load. The cell is then exposed to accelerated ageing operating conditions and its performance is monitored regularly to obtain its degradation rate in terms of capacity and resistance. The aged cell is also characterized by tear-down and optical techniques. The experimentally obtained test database is used to develop and validate the mathematical models that describe the
Koltsakis, Grigorios, Spyridopoulos, Spyridon, Chatziioannou, Panteleimon, Tentzos, Michail
Battery Datasets for Neural Network-Based State-of-Health Estimators: A Review and Implementative Workflow2025-24-013909/07/2025
Management of battery systems for electric vehicles has great importance to ensure safe and efficient operation. State-of-Charge and State-of-Health (SoH) are fundamental parameters to be taken under control even though they cannot be directly measured during vehicle operation. Some control approaches have gained increasing interest thanks to advances in sensor availability, edge computing and the development of big data. In particular, SoH estimation through machine learning (ML) and neural networks (NNs) has been thoroughly investigated due to their great flexibility and potential in mapping non-linear relations within data. The numerous studies available in the literature either employ different extracted features from data to train NNs, or directly use measurement signals as input. Additionally, many studies available in the literature are based on a limited number of publicly available datasets, which mainly encompass cylindrical battery cells with small capacity. Starting from
Chianese, Giovanni, Capasso, Clemente, Veneri, Ottorino
An Investigation of Battery Module Thermal Behavior Based on CFD Simulations and Experimental Tests2025-24-014409/07/2025
Battery management systems are among the key components in electric vehicles (EVs), which are increasingly replacing internal combustion engine (ICE) vehicles in the automotive industry. Battery management systems mainly focus on battery thermal management, efficiency, battery life and the safety conditions. Generally, lithium-ion batteries have been chosen in EV cars. Therefore, the internal resistance of Li-ion batteries plays a crucial role in the thermal behavior of the energy storage system. Most of the published studies rely on 0D-1D models to analyses single cell thermal behavior depending on the internal resistance at different ambient temperatures and charging/ discharging rates, and on the cooling system. However, these models, though fast, cannot provide detailed information about the temperature distribution within a cell or a module. Full 3D Computational Fluid Dynamics (CFD)- Conjugate Heat Transfer (CHT) simulations on the other hand, are very time consuming and require
Karaca, Cem, Olmeda, Pablo, Margot, Xandra, Postrioti, Lucio, Baldinelli, Giorgio
Application of a Chemical Kinetic Modeling Approach within a Fully Coupled Computational Fluid Dynamics Simulation of Battery Cells during Thermal Runaway14-14-02-001406/11/2025
The objective of the current study is to systematically evaluate the battery thermal runaway heat release rate through chemical kinetics and then study its effect on battery module and pack level. For this purpose, a chemistry solver has been developed, capable of simultaneously solving the thermal runaway kinetics in multiple battery cells with the cell-specific chemistry model and battery active material compositions. This developed solid body chemistry (SBC) solver assumes a homogeneous system in the specified geometrical selection. A 3D representation can be achieved by setting up multiple solver selections in one solid domain (battery cell) as the SBC solver is capable of handling multiple selections, chemistry models, and battery active material compositions. Further, the SBC solver is fully integrated in a commercial three-dimensional computational fluid dynamics (3D-CFD) code. Thus, enabling to simulate the real-life thermal runaway applications covering the battery module and
Chittipotula, Thirumalesha, Eder, Lucas, Uhl, Thomas
Plug-In Hybrid and Range Extender NVH – Challenges and Solutions2025-01-010105/05/2025
The automotive industry continues to develop new powertrain and vehicle technologies aimed at reducing overall vehicle-level fuel consumption. While the use of electrified propulsion systems is expected to play an increasingly important role in helping OEMs meet fleet CO2 reduction targets, hybridized propulsion solutions will continue to play a vital role in the electrification strategy of vehicle manufacturers. Plug-in hybrid electric vehicles (PHEV) and range extender vehicles (REx) come with unique NVH challenges due to their different possible operation modes. First, the paper outlines different driveline and vehicle architectures for PHEV and REx. Given the multiple general architectures, as well as operation modes which typically accompany these vehicles, NVH characterizations and noise source-path analysis can be more complicated than conventional vehicles. In the following steps, typical NVH related challenges are highlighted and potential solutions for NVH optimization are
Wellmann, Thomas, Ford, Alex, Pruetz, Jeffrey
Hyperparameter-Optimized Neural Network for Precise Battery State of Charge Estimation2025-01-821004/01/2025
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, Sandeep, Admane, Chinmay
Concept of On-Board Diagnostics System of Vehicle Electric Drive2025-01-809504/01/2025
The problem of monitoring the parametric failures of a traction electric drive unit consisting of an inverter, a traction machine and a gearbox when interacting with a battery management system has been solved. The strategy for solving the problem is considered for an electric drive with three-phase synchronous and induction machines. The drive power elements perform electromechanical energy conversion with additional losses. The losses are caused by deviations of the element parameters from the nominal values during operation. Monitoring gradual failures by additional losses is adopted as a key concept of on-board diagnostics. Deviation monitoring places increased demands on the information support and accuracy of mathematical models of power elements. We take into account that the first harmonics of currents and voltages of a three-phase circuit are the dominant energy source, higher harmonics of PWM appear as harmonic losses, and mechanical losses in the rotor and gearbox can be
Smolin, Victor, Gladyshev, Sergey, Topolskaya, Irina
Learning-Based Cell Level Battery Digital Twin for Electric Vehicles2025-01-838604/01/2025
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, Shobhit, Hegde, Bharatkumar, Haskara, Ibrahim, Shieh, Su-Yang, Chang, Insu
Investigation of Impedance Evolution for Lithium Plating Detection during Multi-Stage Constant Current Fast Charging of Lithium-Ion Batteries2025-01-812404/01/2025
aThe lengthy charging time of lithium-ion batteries for electric vehicles (EVs) significantly affect their acceptance. Reducing charging time requires high-power fast charging. However, such fast charging can trigger various side reactions, leading to safety and durability issues. Among these, lithium plating is a major concern as it can reduce battery capacity and potentially cause internal short circuits or even thermal runaway. Currently, multi-stage constant current charging (MCCC) protocols are widely adopted. However, the difficulty in effectively detecting lithium plating during the MCCC process significantly limits the charging power. Therefore, it is urgent to explore a method to detect lithium plating during the MCCC process. In this study, the impedance evolution during the MCCC procedure was first investigated. Then a method based on the impedance variation patterns was proposed to detect lithium plating. Besides, the reason for the behavior of impedance changes was further
Shen, Yudong, Wang, Xueyuan, Wu, Hang, Wei, Xuezhe, Dai, Haifeng
Multiphysics-Multiscale Reduced Order Model based Design Optimization of the Immersion Cooled Battery System2025-01-818504/01/2025
A vital aspect of Ultra-Fast Charging (UFC) Li-Ion battery pack is its thermal management system, which impacts safety, performance, and cell longevity. Immersion cooling technology is more effective compared to indirect cold plate as heat can dissipate much quicker and has a potential to mitigate the thermal runaway propagation, improve pack overall performance, and cell life significantly. For design optimization and getting better insight, high fidelity Multiphysics-Multiscale simulations are required. Equivalent Circuit Model (ECM) based electro-thermally coupled multi-physics CFD simulations are performed to optimize the innovative busbar design, of a recently developed immersion cooled battery pack, which enables the capability to remove individual cell. Further, high fidelity 3D transient flow-thermal simulations have helped in optimizing the coolant flow direction, inlet positions, cell spacing and separator design for efficient flow distribution in the module. While high
Tyagi, Ramavtar, Negro, Sergio, Baranowski, Alex, Atluri, Prasad
Impact of Temperature and Current Rate Protocols on the Aging of Lithium-Ion Batteries from Capacity Testing and Impedance Analysis2025-01-813104/01/2025
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, Antonio, Monsalve-Serrano, Javier, Egea, Juan Manuel H., Bekaert, Emilie, Herran, Alvaro, Marco-Gimeno, Javier
Research on the Thermal Balance Test Method of Fuel Cell Electric Vehicles2025-01-815304/01/2025
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
Fang, Yanhua, Min, Yihang, Ming, Chen, Li, Hongtao, Li, Dongsheng, He, Chong, Mao, Zhifei
Extreme-Fast Charging Performance Optimization of Immersion-Cooled Battery Systems with Cylindrical Cells2025-01-816904/01/2025
Efficient and robust optimization frameworks are essential to develop and parametrize battery management system (BMS) controls algorithms. In such multi-physics application, the tradeoff between fast-charging performance and aging degradation needs to be solved while simultaneously preventing the onset of thermal runaway. To this end, a multi-objective optimization framework was developed for immersion-cooled battery systems that provides optimal charging rates and dielectric flowrates while minimizing aging and charging time objectives. The developed production-oriented framework consists of a fully coupled, lumped electro-thermal-aging model for cylindrical cells with core-to-surface and immersion-cooling heat transfer, the latter controlled by the dielectric fluid flowrate. The modeled core temperatures are inputs to a semi-empirical aging degradation model, in which a fast-aging solver computes the updated capacity and internal resistance over multiple timescales, which in turn
Suzuki, Jorge, Tran, Manh-Kien, Tyagi, Ramavtar, Meshginqalam, Ata, Zhou, Zijie, Nakhla, David, Atluri, Prasad
A Data-Driven Framework for Battery Capacity Estimation in Real-World Electric Vehicles Using Virtual Impedance and Incremental Capacity Analysis2025-01-856104/01/2025
Accurate battery capacity estimation is critical for ensuring the safe and reliable operation of electric vehicles (EVs) and addressing user range anxiety. However, predicting battery health is challenging due to the non-linearity, non-measurability, and complex multi-stress operating conditions that characterize battery performance. Incremental capacity curves and electrochemical impedance spectroscopy (EIS) are effective tools for reflecting battery aging, but their practical application has limitations. This paper presents a novel method for battery capacity estimation using charging segment data derived from real-world operating conditions monitored by the vehicle's Battery Management System (BMS). The proposed approach begins with a detailed statistical analysis of voltage data to determine optimal charging capacity intervals and involves selecting appropriate voltage ranges to compute equivalent full-charge capacities. Experimental tests are performed to measure battery charging
Tao, Siyi, Zhu, Jiangong, Li, Yuan, Chang, Wei, Dai, Haifeng, Wei, Xuezhe
How to Detect Battery Failures QuickerTBMG-5281304/01/2025
Batteries in electric vehicles can fail quickly, sometimes catching fire without much warning. Sandia National Laboratories is working to detect these failures early and provide sufficient warning time to vehicle occupants.
FPT's 37-kWh battery pack dives into marine market25TOFHP04_0704/01/2025
FPT Industrial formed its ePowertrain department in 2018 and since has developed a range of electric drivelines, battery packs and battery management systems (BMS) targeting on-road commercial vehicle markets. Now the company is taking its ePowertrain portfolio to the water, announcing the entry of its eBS 37 EVO battery pack to the marine sector. The new 37-kWh battery pack that FPT Industrial initially developed for light commercial vehicles and minibuses incorporates NMC 811 lithium-ion technology in 96s2p cell configuration for effective energy density (>140 Wh/kg) and depth-of-discharge (95%). The company claims the design results in reduced battery weight, at 260 kg (573 lb).
Gehm, Ryan
Cloud-Based Monitoring of Lithium-Ion Battery Management Systems for Health Estimation in Manufacturing Industries2025-28-020002/07/2025
The increasing reliance on lithium-ion batteries in manufacturing necessitates advanced monitoring techniques to ensure their longevity and reliability. Cloud technology offers a solution by enabling real-time data collection, analysis, and accessibility, facilitating thorough monitoring and predictive maintenance. Digital twin technology, creating a virtual replica of the physical battery system, provides a platform for simulating real-world conditions and predicting potential issues before they arise. By integrating sensor data and historical usage patterns, the digital twin model can accurately predict battery degradation, aiding in timely maintenance strategies. This proactive approach enhances battery operational efficiency and extends lifespan, leading to cost savings and improved safety. The paper explores using cloud-based monitoring systems to enhance the health estimation and management of lithium-ion batteries. A comprehensive feasibility study on adopting battery digital
Zeeshan, Mohammad, Akre, Vineet
Powering Precision: Ensemble Learning for Superior Battery Health Estimation2025-28-020402/07/2025
Due to energy competition and scarcity of natural gas resources in recent years, fossil fuels have been significantly replaced by renewable energy sources. Because of this, battery electric vehicles (EVs) and hybrid electric vehicles (HEVs) are getting adopted instead of internal combustion engine (ICE) vehicles. The main component of electric vehicles and hybrid vehicles is the battery management system (BMS), which is necessary to ensure that the battery pack operates efficiently, reliably, and effectively. The battery should not degrade its performance by charging and discharging too much, which can lead to serious failures if the battery is left to its end of life. This paper aims to present a novel Machine learning-based battery health estimation algorithm by mitigating risks associated with real-time battery data. This study used proprietary data collected from nickel-cobalt-aluminum (NCA) chemistry battery cells in electric vehicles. Machine learning models are trained to
Joshi, Umita, Mandhana, Abhishek
Automotive Engineering: February 202525AUTP0202/06/2025
All the tech that's fit to print After CES revealed the next generation of ADAS and AV tech, as well as in-cabin communications, here's what the landscape looks like as we move into 2025. Suppliers prepare for a drop in EV market share Tier 1s that kept a foot in both the electric and ICE worlds say they're prepared for the changes ahead. A week with the Hyundai Way From 'Being a First Mover in Hydrogen' to cute city EVs, Hyundai's future technology plans run the gamut. Editorial Recuperating from AI-induced CES fatigue Supplier Eye Near-term focus can obscure reliable path forward Bosch goes right with wrong-way alerts, racing hand control Gentex improves dimmable glass visors with vanity mirror Ceres Holographics, Appotronics team for windshield-wide laser HUD Marelli says new BMS offers deeper insight into state of EV battery cells AVSC develops best practices for traceable AV safety inspection protocols 2025 BMW M5 first drive: Stuffed like schnitzel with PHEV tech Product Briefs
A Multi-Time Scale FFRLS-AEKF Joint Algorithm for Lithium-Ion Battery State of Charge Estimation2025-01-700001/31/2025
Accurate and reliable SOC estimation plays a vital role in the engineering application and development of LIBs. A multi-time scale joint algorithm combining FFRLS and AEKF is introduced in this paper. The FFRLS algorithm is employed for online parameter identification of a second-order resistance-capacitance ECM, while the AEKF algorithm estimates the SOC. To account for the time-varying nature of model parameters and SOC, different sampling periods are selected, enabling the parameter identification and SOC estimation processes to operate on distinct time scales. Experimental results demonstrate that, under constant current conditions at room temperature, the multi-time scale FFRLS-AEKF joint algorithm can maintain a high level of accuracy while reducing the computational burden, with MAE and RMSE values of 0.0111 and 0.0129, respectively. Simultaneously, a public data set is used to prove the application of the algorithm in complex operating conditions, and the computed results of
Liang, Dan, Yang, Bo, Liu, Bing, Liu, Shuai, Cao, Chang
Advanced SOH and SOC Prediction Models for Lithium-Ion Batteries: Integrating Machine Learning with Battery Management Systems2025-01-701501/31/2025
This paper focuses on the development and validation of predictive models for battery management systems, specifically targeting State of Health (SOH) and State of Charge (SOC) estimation, as well as the design of a comprehensive Battery Management System (BMS). The study begins by establishing and evaluating SOH prediction models, employing both linear regression and Long Short-Term Memory (LSTM) algorithms. Comparative analysis is conducted to assess the prediction accuracy between Recurrent Neural Networks (RNN) and LSTM, highlighting the superior performance of the LSTM algorithm in forecasting battery health. The second part of the paper addresses SOC estimation, outlining common methods and introducing an Extended Kalman Filter (EKF) algorithm for real-time SOC prediction. The EKF model is constructed through three primary stages: the establishment of the observed signal section, the ECU section, and the algorithmic structure itself. Rigorous validation confirms the EKF model’s
Yuan, Chuo, Bao, Zhiming, Li, Weizhuo, Liu, Zezheng, Zhao, Xu, Jiao, Kui
Fusion Control Strategy Based on Rule and Dynamic Objective of Heat Pump Type Thermal Management System for Electric Vehicles2025-01-702301/31/2025
Thermal management system of electric vehicles (EVs) is critical for the vehicle's safety and stability. While maintaining the components within their optimal temperature ranges, it is also essential to reduce the energy consumption of thermal management system. Firstly, a kind of architecture for the integrated thermal management system (ITMS) is proposed, which can operate in multiple modes to meet various demands. Two typical operating modes for vehicle cooling in summer and heating in winter, which utilizes the residual heat from the electric drive system, are respectively introduced. The ITMS based on heat pump enables efficient heat transfer between different components. Subsequently, an ITMS model is developed, including subsystems such as the battery system, powertrain system, heat pump system and cabin system. The description of modeling process for each subsystem is provided in detail. The model is tested under world light vehicle test cycle (WLTC) condition of six different
Zhao, Luhao, Tan, Piqiang, Yang, Xiaomei, Yao, Chaojie, Liu, Xiang
Correlation Analysis of Automotive Battery State-of-Health Indicators for Advanced Battery Management Systems2025-01-701201/31/2025
With increasing global attention on environmental issues and the greenhouse effect, electric vehicles (EVs) have become a focal point for sustainable transportation solutions. Lithium-ion batteries are integral to EVs due to their high energy density, elevated operating voltage, and long service life. However, their performance is highly influenced by factors such as ambient temperature, charge and discharge rates, and aging processes. To enhance the safety, reliability, and efficiency of lithium-ion battery systems, it is critical to develop a robust and advanced battery management system (BMS) that can monitor battery states accurately and in real-time. A key aspect of BMS design is the estimation and prediction of the battery's state of health (SOH). Accurately characterizing SOH during actual usage conditions is essential for optimal battery performance and longevity. This study investigates various SOH indicator extraction methods reported in the literature, including features
Long, Tianfeng, Shang, Huaqing, Liu, Xiaoqi, Zhang, Pengcheng, Yue, Meiling, Meng, Jianwen
Design of a Passive Balancing Battery Acquisition Device2025-01-701401/31/2025
A power battery parameter acquisition device was designed and developed with STM32 as the core, featuring the functions of a battery management system (BMS) to ensure the safety and stability of the battery pack during operation. The device includes functions such as battery charge and discharge management, battery safety protection, and battery status monitoring, enabling real-time monitoring of cell parameters. The hardware design covers the power circuit, charge and discharge cycle circuit, battery acquisition circuit, communication module circuit, and single-cell balancing circuit. The software part completes the design and development of each functional module. This paper addresses issues in battery management systems, such as low accuracy in battery parameter acquisition, inconsistencies between individual cells, and weak BMS balancing capabilities. The developed acquisition device can collect parameters for 15 series-connected power batteries, and conduct sampling tests of cell
Ge, Renzhou, Duan, Chendong
Toward the Progression of Sustainable Structural Batteries: State-of-the-Art Review13-05-03-002001/02/2025
In order to deploy renewable energy sources for balanced power generation and consumption, batteries are crucial. The large weight and significant drain on the energy efficiency of conventional batteries urge the development of structural batteries storing electrical energy in load-bearing structural components. With the current shift to a green economy and growing demand for batteries, it is increasingly important to find sustainable solutions for structural batteries as well. Sustainable structural batteries (SSBs) have strong attraction due to their lightweight, design flexibility, high energy efficiency, and reduced impact on the environment. Along with sustainability, these structural batteries increase volumetric energy density, resulting in a 20% increase in efficiency and incorporate energy storage capabilities with structural components, realizing the concept of massless energy storage. However, the significant problems in commercializing SSBs are associated with their
Kusekar, Sambhaji Kashinath, Pirani, Mahdi, Birajdar, Vyankatesh Dhanraj, Borkar, Tushar, Farahani, Saeed
Evaluation of Fast Charging Efficiency under Extreme Environmental Conditions in Brazil2024-36-008712/20/2024
Fast chargers are necessary for the success of vehicle electrification. These devices can achieve a battery charge rate greater than 4C, significantly increasing the amount of heat generated by the battery. Additionally, the operating temperature of the storage device directly influences the device’s efficiency and lifespan. Given the importance of operation temperature, the Battery Management System (BMS) plays a key role in mitigating heat generation and degradation effects. Despite BMS optimizing battery operation under all possible conditions, the use of fast chargers in extremely hot and cold environments still lowers overall efficiency. In these two worst-case scenarios, the thermal system must manage the ideal charging temperature by consuming part of the energy supplied by the charger. The present work aims to evaluate the charging energy efficiency and time with fast charger utilization, considering the Brazil’s minimum and maximum temperatures registered in 2020. In order to
Pires, Rodrigo Alonso, Pontes, Diego Augusto, Souza, Rafael Barbosa, Oliveira, Matheus Leonardo Araújo, Rodrigues, Luiz Fernando Alves, Fernandes, Heder, Maia, Thales Alexandre Carvalho
UWB Based Communication for Electric Vehicle Battery Management System2024-28-014912/05/2024
The Battery Management System (BMS) plays a vital role in managing the energy present in the high voltage battery pack of electric vehicles. The wired battery management system is commonly used in automotive applications. The known difficulties with the wired battery management system includes the intricate wiring harness, wiring failures, system scalability and high implementation costs. To mitigate the above challenges, the wireless battery management system is proposed. Several wireless protocols, including BLE, Zigbee, and 2.4GHz proprietary protocol, are being examined for wireless BMS. However, there are technical difficulties with these protocols to be applied in the battery pack environment. This research paper looks at the Ultra-Wide Band (UWB) communication protocol for wireless BMS, considering UWB’s efficiency low latency and robust Radio Frequency (RF) performance. The UWB protocol is used to communicate between the Cell Supervisory Circuit (CSC) and the Battery Management
Dannana, Arun Kumar, Subbiah Subbulakshmi, Nallaperumal, Chandirasekaran, Ramachandran, Beemarajan, Mutharasu
A Multivariable Function Based Optimised Charging Algorithm for Electric Vehicle Battery Management Systems2024-28-022812/05/2024
Conventional Constant Current- Constant voltage (CC-CV) based charging techniques initially consist of Constant Current (CC) phase for quick charging of the battery till it reaches the safety voltage limit wherein the Constant Voltage (CV) phase starts. Then the CV phase of the charging ensures safe charging of the battery till it is fully charged but it takes comparatively a long duration of time to the amount of charge pumped into the battery. Adoption of efficient charging algorithms are crucial for optimising the charging time, reducing the range anxiety and improving the long-term health of electric vehicle (EV) batteries. This paper proposes an innovative charging algorithm that optimises the transition from Constant Current (CC) to Constant Voltage (CV) charging stages utilising a multivariable function based on the real-time data of State-of-Charge (SoC), temperature, State-of-Health (SoH) and battery impedance parameters. By dynamically adjusting the charging parameters based
Rajawat, Shiv Pratap, Moorthi, Sathiya, Soni, Lokesh, Jain, Swati
SoC Estimation for LFP Battery Using Extended Kalman Filter and Particle Filter with Adaptive Battery Parameters2024-28-022312/05/2024
In recent years, Lithium Iron Phosphate (LFP) has become a popular choice for Li-ion battery (LIB) chemistry in Electric Vehicles (EVs) and energy storage systems (ESS) due to its safety, long lifecycle, absence of cobalt and nickel, and reliance on common raw materials, which mitigates supply chain challenges. State-of-charge (SoC) is a crucial parameter for optimal and safe battery operation. With advancements in battery technology, there is an increasing need to develop and refine existing estimation techniques for accurately determining critical battery parameters like SoC. LFP batteries' flat voltage characteristics over a wide SoC range challenge traditional SoC estimation algorithms, leading to less accurate estimations. To address these challenges, this study proposes EKF and PF-based SoC estimation algorithms for LFP batteries. A second-order RC Equivalent Circuit Model (ECM) was used as the dynamic battery model, with model parameters varying as a function of SoC and
Ns, Farhan Ahamed Hameed, Jha, Kaushal, Shankar Ram, C S
Battery Digital Twin with Edge Deployment2024-28-012012/05/2024
Today's battery management systems include cloud-based predictive analytics technologies. When the first data is sent to the cloud, battery digital twin models begin to run. This allows for the prediction of critical parameters such as state of charge (SOC), state of health (SOH), remaining useful life (RUL), and the possibility of thermal runaway events. The battery and the automobile are dynamic systems that must be monitored in real time. However, relying only on cloud-based computations adds significant latency to time-sensitive procedures such as thermal runaway monitoring. Because automobiles operate in various areas throughout the intended path of travel, internet connectivity varies, resulting in a delay in data delivery to the cloud. As a result, the inherent lag in data transfer between the cloud and cars challenges the present deployment of cloud-based real-time monitoring solutions. This study proposes applying a thermal runaway model on edge devices as a strategy to reduce
Sarkar, Prasanta, Pardeshi, Rutuja, Kharwandikar, Anand, Kondhare, Manish
Precise and Robust SoC Estimation Using Adaptive Filters-Based Techniques for Electric Trucks2024-28-015212/05/2024
The traction for zero emission vehicles in the transportation industry is creating a focus on Battery Electric vehicles (BEV) as one of the potential alternate powertrain sources. To operate BEV safely and efficiently battery operating conditions and health is of utmost importance. Battery management system (BMS) controller is needed for optimized and safe operation of high voltage (HV) battery. For correct behavior of BMS, accuracy of state of charge (SoC) estimation is important. SoC is an important and decisive factor for deciding operating limits such as current limits, voltage limits and battery operational range (charge-discharge interval). Inaccurate SoC estimation can accelerate battery aging and cause damage to it. The current state of art deploys coulomb counting technique for SoC calculation, this approach encounters challenges like sensor noises and initial SoC error (carried from the previous charge-discharge cycle). This paper mainly focuses on exploring various
Kumar, Raman, AHMAD, MD SAIF, Challa, Krishna, Ranjan, Ashish, Bayya, Madhuri
Equivalent Circuit Modeling of Sodium-Ion Batteries Incorporating Temperature Effects: A Comprehensive Investigation2024-28-022512/05/2024
The transition from Internal Combustion Engine (ICE) Vehicles to Electric Vehicles (EVs) has catalyzed significant advancements in battery technology, prioritizing safer and more reliable energy storage solutions. Although Lithium Iron Phosphate (LFP) batteries are recognized for their safety, they rely on critical and market-volatile elements such as copper, lithium, and graphite. To address these challenges, sodium-ion batteries (SIBs) have emerged as sustainable alternatives that are particularly suited for low-speed EVs. Ensuring the seamless integration of SIBs into EV battery packs necessitates preparedness for the rapid evolution of SIB technology. Model-based approaches, including Equivalent Circuit Models (ECMs), are crucial for developing advanced Battery Management Systems (BMSs) and State of Charge (SoC) estimation algorithms that enable precise battery control. This study comprehensively evaluates various order Resistance-Capacitance (RC) ECM configurations to accurately
Ns, Farhan Ahamed Hameed, Gupta, Shubham, Jha, Kaushal
Correlation between Cell Thickness Profile and Lithium Plating: Applications in Lithium Plating Detection and Capacity Rollover Diagnostics2024-01-700911/15/2024
In the realm of low-altitude flight power systems, such as electric vertical take-off and landing (eVTOL), ensuring the safety and optimal performance of batteries is of utmost importance. Lithium (Li) plating, a phenomenon that affects battery performance and safety, has garnered significant attention in recent years. This study investigates the intricate relationship between Li plating and the growth profile of cell thickness in Li-ion batteries. Previous research often overlooked this critical aspect, but our investigation reveals compelling insights. Notably, even during early stage of capacity fade (~ 5%), Li plating persists, leading to a remarkable final cell thickness growth exceeding 20% at an alarming 80% capacity fade. These findings suggest the potential of utilizing cell thickness growth as a novel criterion for qualifying and selecting cells, in addition to the conventional measure of capacity degradation. Monitoring the growth profile of cell thickness can enhance the
Zhang, Jian, Zheng, Yiting
Predicting the Impact of Temperature on eVTOL Battery Systems Using Experimental Data from Airworthiness-Certified Fixed-Wing Electric Aircraft2024-01-702111/15/2024
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
Ma, Xin, Ding, Shuiting, Pan, Yilun
Equalization System Engineering Design for reliable Battery Management on eVTOL2024-01-702711/15/2024
Electrical vertical take-off and landing vehicle (eVTOL) are more and more popular in future’s urban mobility. How to improve the reliability of the battery, is the key problem. Battery Management System (BMS) through the battery status monitoring, charging and discharging control, temperature management, fault diagnosis, battery equalisation and other core measures to improve the battery reliability and performance, of which battery equalisation technology plays a vital role. BMS manages batteries through battery status monitoring, charging and discharging control, temperature management, fault diagnosis, battery equalisation and other core measures to ensure the safety, reliability and performance of batteries. This paper analyses the inconsistency mechanism of batteries, introduces the classification of mainstream balancing circuits, describes the advantages and disadvantages of different types of balancing technologies, introduces the practical application scheme of passive
Feng, Guo, Zhang, Xinfeng, Li, Hong Dun, Yue, Han
Ionization Based Sensor for Early Detection of Thermal Runaway Events in Lithium-Ion Batteries2024-01-432611/05/2024
Lithium-ion and lithium-metal battery cells are susceptible to a phenomenon known as thermal runaway under failure conditions. Given their widespread use in applications such as electric vehicles, portable electronics, and energy storage systems, early detection of thermal runaway is crucial for ensuring the safety of these battery systems. Thermal runaway entails a rapid escalation in battery cell temperature accompanied by the emission of flammable lithium ions, particulates, electrons, hydrocarbons, and hydrogen gases. These gases pose a significant ignition risk, potentially leading to fires and endangering occupants and bystanders. Therefore, the timely detection of thermal runaway is paramount for ensuring safety in proximity to such battery systems. Traditionally, thermal runaway sensors comprise intricate assemblies of pressure, temperature, and gas sensors, strategically positioned at the pressure relief valve of battery modules. Calibration of all sensors is essential to
Mansour, Youssef
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