Browse Topic: Battery management systems (BMS)

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Low-Voltage Electric Vehicle with a Reconfigurable Powertrain Architecture for Urban and Extra-Urban Mobility2026-37-0025To be published on 06/09/2026
The growing penetration of electric mobility in urban environments calls for vehicle solutions that balance accessibility, safety, and operational flexibility. However, high upfront costs and the widespread oversizing of battery systems remain significant barriers to large-scale adoption, particularly for short-range use cases. To address these limitations, this paper presents the Low Voltage Reconfigurable Electric Vehicle (LVREV), a modular vehicle concept designed to bridge the gap between L7e and M1 categories. The proposed architecture integrates a low-voltage electrical system (<60 V), a reconfigurable powertrain, and a modular, swappable battery, enabling mission-adaptive performance while reducing cost, weight, and energy consumption. The vehicle architecture features a dual 52 V inverter system coupled with a six-phase permanent magnet synchronous motor, optimized through model-based design and genetic algorithms. Energy storage consists of two independent swappable battery
Tramacere, Eugenio, Favelli, Stefano, Galluzzi, Renato, Tonoli, Andrea
Dual Battery Pack System for Multi-Voltage Electric Vehicle Operation2026-01-0392To be published on 04/07/2026
Electric Vehicles (EV) have become a major focus in the automotive industry. This paper introduces a propulsion system design, which supports the Wide Torque Band (WTB) concept to boost the power density of PM (permanent magnet) motors in EV Trucks resulting in performance, efficiency, and cost benefits. A selectable 400V/800V battery system has been developed to support the WTB concept and enhance the power density of permanent-magnet motors in electric vehicles. The RESS comprises two 400V battery packs that can be charged at 400V in parallel or at 800V in series via a DC fast-charging (DCFC) connection. In this study, an 800V driving mode was additionally implemented. A prototype battery management system (BMS) along with existing production voltage, current and temperature measurement block hardware are applied to perform mode switching, safety, and cell balancing. The success of this dual pack hardware enables high voltage dynamometer testing of a new 800V DU (Drive Unit) and
Zhu, Yongjie, Lee, Chunhao, Gopalakrishnan, Suresh, Namuduri, Chandra
Effects of Temperature and Mechanical Load on Li-Ion Battery Aging: Insights from Multi-Rate Reference Performance Test2026-01-0383To be published on 04/07/2026
As the utilization of lithium-ion batteries in electric vehicles expands, monitoring the usable cell capacity (UCC) is essential for ensuring accurate state-of-health (SOH) estimation. Battery performance degradation is influenced by temperature and constraints. Capacity tests in laboratory settings are typically conducted at low C-rates to approximate equilibrium conditions, whereas in real vehicle applications, charging currents are often much higher. This discrepancy in rates frequently results in deviations between laboratory characterization and on-board Battery Management Systems (BMS) capacity estimation. To investigate how C-rate of diagnostic Reference Performance Test (RPT) modulates aging effects under temperature and mechanical loading, we conducted long-term cycling tests on lithium iron phosphate/graphite pouch cells at 25°C and 45°C under different constrained conditions. The cycling protocol is a tiered multi-rate protocol. Cells were aged at Block1 under 1C, and UCC
Zhang, Shan, Niu, Zhice, Xia, Yong
Physical Test Validated Multi-Level Power Electronics for Next-Generation Electrified Vehicles Charging & Efficiency Improvement2026-01-0435To be published on 04/07/2026
Improving the energy efficiency of electrified vehicles remains a central objective in modern electric powertrains. Multi-level converters (MLCs) are widely recognised for lowering conversion losses relative to two-level inverters and improving total harmonic distortion (THD) in the sinusoidal supply to motors with a consequent reduction in motor losses. Despite this, sustained production-oriented validation at the integrated system level remains limited. This work introduces a multi-level converter architecture of the Battery Integrated Modular Multi-Level Converter (BIMMC) topology using Cascaded H-Bridge (CHB) architecture. It offers improvements in all key metrics of performance, cost, package size, mass and robustness compared to the current state-of-the-art two-level inverter system with distributed functions for charging available in the market today. The overall solution is highly functionally integrated. It supports four major functions required in electric vehicles without
Bao, Ran, Kalaiselvan, Prashanth, Rener, Kristof, Hallam, Philip, Shi PhD, Kai, Yue, William, Ma, He, Grimshaw, Andrew, Patel, Simon
Signal Integrity in Distributed BMS: Reliable SPI and Ethernet Communication for Next-Gen Evs2026-01-0171To be published on 04/07/2026
Distributed battery management systems (BMS) are critical for scaling electric vehicle packs to hundreds of cells, but reliable high-speed communication between modules remains a challenge. Daisy-chained SPI and CAN FD are widely deployed today, while Ethernet is being evaluated for next-generation systems that require higher bandwidth, synchronization, and diagnostics. This paper examines the signal integrity (SI) challenges facing distributed BMS communication, including skew, jitter, crosstalk, and electromagnetic interference (EMI) across PCB traces and wiring harnesses. HyperLynx and SPICE-based simulations are combined with experimental results on a 192-cell test platform to quantify the impact of layout constraints, impedance mismatches, and harness parasitic. Results show that poor SI design can reduce signal margins by more than 18 dB, leading to data corruption and diagnostic failures. Results show poor SI design can reduce signal margins by 18 dB, causing data errors
Abdul Karim, Abdul Salam
A CFD Based Multi-Physics Modeling Approach to Predict Vent Gas Flow Paths Due to Can Melting during Prismatic Cell Thermal Runaway2026-01-0407To be published on 04/07/2026
As the automotive industry increasingly adopts high-energy-density batteries, ensuring vehicle safety against catastrophic thermal runaway (TR) has become paramount. Predicting the complex failure sequence of prismatic cells, requires high-fidelity simulation tools that can capture tightly coupled physical phenomena. This paper presents a comprehensive, three-dimensional multi-physics Computational Fluid Dynamics (CFD) framework designed to simulate the entire TR event. The simulation originates with a multi-step Arrhenius chemical kinetics model to calculate the heat and gas generated by the primary exothermic reactions. This process drives a rapid increase in internal temperature and pressure, which is resolved by the model’s fluid dynamics solver. The initial vent opening is triggered when this internal pressure exceeds a predefined mechanical burst threshold, simulating a realistic seal rupture. Concurrently, a Conjugate Heat Transfer (CHT) analysis calculates the temperature
Mukherjee, Swarnava, Schlautman, Jeff, Srinivasan, Chiranth
Towards Plating-Free Charging: Lightweight Prediction of Lithium Plating Current Limits2026-01-0387To be published on 04/07/2026
Lithium plating is a critical barrier to fast charging in electric and hybrid-electric vehicles, occurring at high state of charge (SOC) or low temperatures when Li+ deposits as metallic lithium on the anode surface instead of intercalating into graphite. At low temperatures, plated lithium may form dendrites that pierce the separator and trigger thermal runaway, while at high SOC, irreversible plating accelerates capacity fade by depleting cyclable lithium. Despite extensive study, lithium plating remains difficult to incorporate into battery management systems (BMS) due to computational complexity and the challenge of real-time detection, leading to reliance on conservative lookup maps. This work presents a lightweight empirical model for predicting plating-free charging limits in lithium nickel manganese cobalt (NMC) cells. A high-fidelity pseudo-2D electrochemical model was exercised across a wide range of charge rates and temperatures to capture the coupled effects of SOC
Sundar, Anirudh, Ghate, Atharva, Zhu, Qilun, Prucka, Robert, Barron, Morgan, Figueroa-Santos, Miriam
Optimization of a Feedforward Neural Network Machine Learning Battery Model for an Automotive Pouch Cell2026-01-0386To be published on 04/07/2026
Modern battery management systems have a critical need for highly accurate battery terminal voltage models, which are a key component of algorithms which estimate or predict power capability, range, temperature, and other factors. While electro-chemical and equivalent circuit models are widely used for this purpose, they typically struggle to capture the complex, non-linear dynamics and degradation effects inherent in real-world battery operation, necessitating frequent recalibration. This study addresses these limitations by proposing a robust, data-driven approach for terminal voltage estimation using a feedforward neural network (FNN) machine learning model. An LG E66 cell, a high-performance lithium-ion battery with a Nickel Cobalt Manganese (NCM) cathode, was selected for this study due to its prevalent use in light duty electric vehicles. Data collected at temperatures ranging from 40 °C to -20 °C is used to train and test the models, with model error reported for HWFET, UDDS
Dehury, Biswanath, Nahidmobarakeh, Lucas, Panchal, Satyam, Gross, Oliver, Kollmeyer, Phillip
Fault Diagnosis Method of Lithium-Ion Batteries Based on OCSVM and RLMQD Algorithms2026-01-70202/27/2026
To address the challenges of recognizing abnormal states, detecting subtle early warning signs, and quantifying fault severity in scenarios involving simultaneous multiple faults in lithium-ion batteries, this study proposes a dual-layer fault diagnosis framework that integrates One-Class Support Vector Machine (OCSVM) and Robust Local Mahalanobis Distance Quantile (RLMQD) algorithm. First, a three-dimensional multi-scale feature space, incorporating voltage, kurtosis, and voltage change rate, is constructed to detect abnormal battery states via OCSVM and dynamically filter abnormal time periods with improved adaptability. Second, a computationally efficient RLMQD-based quantization algorithm is developed, which employs a small-scale sliding window and adaptively selects healthy cells to construct reference distributions. By incorporating low-quantile thresholds, the algorithm enhances early abnormality detection and significantly reduces false positives. Subsequently, fault severity
Wei, Fuxing, Yang, Libing, Wang, Zonglei, Xia, Xuelei, Shen, Jiangwei, Chen, Zheng
Research on State of Charge Estimation Method for Lithium Iron Phosphate Batteries Based on Expansion Force2026-01-70402/27/2026
To enhance the accuracy and robustness of State of Charge (SOC) estimation for lithium iron phosphate (LiFePO₄) batteries and to overcome the limitations of traditional electrical signal-based methods—such as cumulative errors in Coulomb counting and the need for rest periods in open-circuit voltage (OCV) methods—this study proposes a novel SOC fusion estimation algorithm based on mechanical expansion force signals. Addressing the challenge of feature extraction, a model framework integrating the Sparrow Search Algorithm (SSA), Least Squares Support Vector Machine (LSSVM), and Adaptive Extended Kalman Filter (AEKF) is developed. The state equation is constructed via Coulomb counting, while SSA optimizes the LSSVM to establish an observation model centered on expansion force as the input. The AEKF is employed to achieve real-time, precise SOC prediction. Experimental validation under varying temperatures (25°C, 35°C) and dynamic driving cycles (FUDS, UDDS) demonstrate that this fusion
Du, Jinqiao, Rao, Bo, Tian, Jie, Wu, Yizeng, Xu, Haoming, Jiang, Jiuchun
Rapid Battery RUL Prediction under Different Fast-Charging Protocols via Domain-Adaptive Deep Learning and Model Compression2026-01-70032/27/2026
Accurate and rapid remaining useful life (RUL) prediction of batteries under various extreme conditions is crucial for battery management systems. However, existing methods often face challenges such as limited datasets under extreme conditions, high model complexity, and weak interpretability. Therefore, this paper proposes a hybrid framework based on pruning domain-adaptive convolutional neural networks (CNN) and long short-term memory (LSTM) to study RUL prediction under different fast-charging conditions using the MIT dataset. First, four voltage-related feature matrices are extracted. Using maximum mean discrepancy (MMD) constraints, the CNN-LSTM is trained with source domain and limited target domain data to align distributions. Neuron pruning is then applied to the fully connected layer to compress the model. Results demonstrate that under sparse target domain data, the domain adaptation approach achieves significantly lower prediction errors than fine-tuning. The pruned model
Huang, Mingyue, Chen, Hongxu, Luan, Weiling
Failure Analysis of Li-Cu Reference Electrode in NCM811||Gr Pouch Battery2026-01-70122/27/2026
Reliable monitoring of the internal state of lithium-ion batteries (LIBs) is crucial for mitigating potential safety hazards. The incorporation of a reference electrode (RE) within the battery constitutes a vital approach for achieving single-electrode monitoring and understanding changes in electrode state during cycling. Among these, the lithium-copper reference electrode (Li-Cu RE) is particularly cost-effective and straightforward to prepare, being fabricated by depositing lithium onto a copper wire. However, Li-Cu RE exhibits a relatively short effective lifespan during long-term cycling, thereby limiting its practical application. In this work, based on a self-fabricated three-electrode single-layer pouch cell, the microstructural changes before and after failure of the Li-Cu RE were characterized and analyzed, revealing its failure evolution process. Post-failure microstructures observations exhibit marked porosity in the electrode, attributed to substantial depletion of surface
Hu, Jiaxing, Luan, Weiling, Chen, Haofeng, Chen, Ying
Joint Estimation of State of Charge and Capacity throughout the Full Service Life Cycle for Li-Ion Batteries2026-01-70212/27/2026
Accurate SOC and capacity estimation is essential for the safe operation of lithium-ion batteries. However, model parameters drift due to temperature variations and aging. This study proposes a migration-model-based method for joint estimation of SOC and capacity over a wide range of temperatures and degradation levels. The WSPF algorithm identifies migration factors in real time and applies them to estimate SOC and capacity under nonlinear, non-Gaussian conditions. Validation under various test conditions demonstrates clear advantages. Compared to EKF, the migration-model-based algorithm reduces the maximum RMSE of SOC estimation to 0.55%. For capacity estimation, it achieves a maximum RMSE of 1.15%. The estimation accuracy remains high throughout temperature changes and aging, highlighting the robustness and applicability of the proposed method for real-world battery management systems.
Liu, Weiqiang, Chen, Zheng, Wei, Fuxing, Shen, Jiangwei
Artificial Intelligence and Cloud Synergy for Scalable Battery Management Systems2026-28-01192/12/2026
Due to the rapid transformation of EVs and the battery storage system, the battery management system (BMS) is essential to ensure optimal performance of the battery storage piles. A BMS monitors and controls parameters such as SOC, voltage, current, and temperature. A traditional BMS has a minimum support of analytics, and it’s limited to local processing. However, when the battery information is uploaded to the internet, it becomes easier to manage maintenance and track the battery’s performance from anywhere in the world. This Cloud-based system is easy and made earlier, thereby giving a system alarm before the issue becomes big. Managing many batteries at once saves a significant amount of money in places like EV charging stations and Energy Storage Systems (BESS). Software updates to the system can also be sent remotely. Also, a BMS connected to the cloud can be used to support weaker grids in an instant if it needs the reactive power support. Cloud integration of BMS with the grid
R, Rajarajeswari, N, Kalaiarasi, Francis, Elgin Calister
Analysis of Second-Life Electric Vehicle Batteries Value Maximizing through Repurposing and Resale, Ways to Achieve Optimal Performance and Standardization Challenges for Testing and Certification2026-26-01931/16/2026
As electric vehicle (EV) adoption accelerates globally, a growing volume of lithium-ion batteries are reaching an 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
Dynamic Voltage EV System (DVEVS): Achieving Enhanced Electric Vehicle Efficiency and Performance through a Switchable Series-Parallel Battery Configuration2026-26-02031/16/2026
The electric vehicle (EV) industry is relentlessly pursuing advancements to enhance efficiency, extend driving range and improve overall performance. A notable limitation of conventional EVs is their fixed-voltage battery architecture, which necessitates compromises in powertrain design and can result in suboptimal efficiency under varying driving conditions. The Dynamic Voltage EV System (DVEVS) presents a transformative solution, allowing the battery pack to dynamically reconfigure its cells between series and parallel connections. This review explores the core principles of DVEVS, including battery topology, power-electronics-based switching, and the integration of hybrid energy storage solutions such as electric double-layer capacitors (EDLCs). We explore the foundational concepts of battery reconfiguration, delve into specific implementation strategies such as power-electronics-based switching and hybrid energy storage systems and address the critical need for adaptive thermal
Amberkar S, Sunil, Raool, Anuj Rajesh, M G, Shivanag, Rajapuram, Bheema Reddy
Validation of 0D-1D Co-Simulation Strategies in Cold Conditions for ‘Full EV Digital Vehicle’2026-26-04481/16/2026
Electric Vehicles (EV) are 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 many components and control design, analysis, and optimization. Systems pertaining to each functionality such as, A/C (Air Conditioning) loop, E-Powertrain (Electric Powertrain), HEVC (Hybrid Electric Vehicle Controller), Cooling system, Battery Management System (BMS), Vehicle control system etc. together make an ‘Integrated Digital Vehicle.’ Fidelity of Intersystem co-simulation [AMESIM + SIMULINK] is key to validating thermal and energy 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
Sarapalli Ramachandran, Raghuveeran, Srinivasan, Rangarajan, Saravanan, Vivek, Dutta, Souham, Pichon, Martin, Leclerc, Cedric, Guemene, Alexis-Scott
Simplified Simulation of Electric Vehicle Battery Packs Using Deep Learning Based Reduced Order Models2026-26-04031/16/2026
The increasing adoption of electric vehicles (EVs), efficient and accurate battery modeling has become crucial for reliable performance evaluation and control system design. However, maintaining high accuracy in simulations generally requires complex computations, which can limit real-time applicability and scalability. High-fidelity battery models often require significant computational time, making them unsuitable for real-time simulations and large-scale system integration. This paper presents the application of Simulink Reduced Order Models (ROM) to simplify the simulation of EV batteries while maintaining acceptable levels of accuracy. The EV simulation environment has been developed in MATLAB/Simulink to analyze Battery Management System (BMS) control system design and assess EV system level performance. This simulation platform consists of BMS and other important EV controller models and high-fidelity plant models for battery and powertrain systems. While these high-fidelity
Vernekar, Kiran
Transforming Battery Management: The Role of Artificial Intelligence and Machine Learning in Digital Twin Technologies2026-26-03821/16/2026
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 minimal 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 safety
Chaturvedi, Vikash, M, Venkatesan, Lanke, Siddhi, Subramaniam, Anand, Karle, Manish, Pandit, Rugved, Gupta, Drishti, Karle, Ujjwala Shailesh
Conceptual Framework for Real-Time Battery Health Estimation in Automotive Digital Twins Using Reinforcement Learning2026-26-06581/16/2026
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 modelling 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
Pardeshi, Rutuja Rahul, Kondhare, Manish, Sasi Kiran, Talabhaktula
Predictive Modelling of Battery Behaviour for Enhanced Energy Management in Electric Vehicles2026-26-03851/16/2026
The transition to electric vehicles is a significant change as the world moves toward sustainable objectives, and thus the effective usage of energy and batter functioning. However, accurate battery modelling and monitoring is still challenging due to its highly nonlinear behaviour because of its dependencies with temperature variations, aging effects, and variable load conditions. To address these complexities, there are smart battery management systems that monitor the key parameters like voltage, current, temperature, and State of Charge, ensuring safe and efficient battery operation. At the same time, this may not completely capture the battery's dynamic aging behaviour. Here, digital twin emerges as the powerful solution, which replicates the complete physical system into a virtual platform where we can monitor, predict and control. This research paper shows the digital twin solution framework developed for the real-time monitoring and prediction of key battery parameters and
G, Ayana, Gumma, Muralidhar
Developments in 2-Wheeler Electric Vehicle Architecture2026-26-01991/16/2026
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
Thermal Management Control Strategy for Primary & Secondary Drive Cooling Hybrid Electric Tractor2026-26-00691/16/2026
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
Electric Vehicle Battery’s Testing Challenges in Era of Software Defined Vehicle2026-26-01591/16/2026
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 significant 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
State of Health Estimation Based on Dynamic Cell Behaviour in EVs2026-26-01831/16/2026
In electric vehicle (EV) applications, accurate estimation of State of Health (SOH) of lithium ion battery pack is critical for ensuring its performance, reliability, operational safety and user confidence. SOH is a key parameter monitored by Battery Management System (BMS) to check 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 commonly rely on simplistic full-cycle charge-discharge data or single-parameter tracking (such as voltage or internal resistance) and other method like coulomb counting. Kalman filter, model based method such as equivalent circuit modelling, data driven models etc. This methods not consider 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 estimation, potentially causing
Nikam, Ashish, Tiwari, Awanish Shankar, Sodha, Nirav, Hariyani, Ganesh, Ambhore, Yogesh Gajanan
Development of HIL Based Methodology for Over-The-Air Updates Validation2026-26-04801/16/2026
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 validation methodology is of prime importance prior to deployment process. This paper explores the integration of Hardware-in-Loop (HIL) simulation into the OTA validation pipeline as a means to ensure reliability, safety, and functional correctness of
Khare, Shivani, Karle, Ujjwala, Subramaniam, Anand
Optimization and Evaluation of Thermal Interface Material Thickness and Distribution Patterns for Improved Heat Transfer in Liquid-Cooled Battery Pack2026-26-04321/16/2026
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 better 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 chances 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 changes can cause inefficient cooling, ultimately affecting battery performance and lifespan. In this paper, a
K, Mathankumar, Jahagirdar, Manasi, Kumbhar, Makarand Shivaji
Lithium-Ion Battery’s State of Health Prediction: A Deep Learning Framework2026-26-01621/16/2026
This paper presents a comprehensive study on predictive maintenance of lithium-ion batteries in electric vehicles (EVs) using data-driven approaches. The study involves collecting data from four individual battery cells, each subjected to various charging and discharging parameters. After preprocessing the data, we apply feature extraction techniques to extract relevant features. Subsequent data analysis guides the development of machine learning (ML) and deep learning (DL) models on the combined dataset of the four cells. A crucial aspect of this study involves addressing measurement noise inherent in cellwise data. Through innovative techniques, we mitigate the effects of measurement noise, improving the accuracy and robustness of our models. The proposed DL models demonstrate remarkable efficiency in handling noise, leading to superior predictive performance in estimating State of Health (SoH) as degraded capacity. The findings of this research offer valuable insights into
Suryawanshi, Chaitanya Balasaheb, Nangare, Kapilraj, Gaikwad, Pooja
A Comparative Lifecycle Assessment Framework for Automotive Battery Storage Systems13-07-01-000112/19/2025
The transportation sector faces heightened scrutiny to implement sustainable technologies due to market trends, escalating climate change and dwindling fossil fuel reserves. Given the decarbonization efforts underway in the sector, there are now rising concerns over the sustainability challenges in electric vehicle (EV) adoption. This study leverages ISO 14040 Lifecycle Assessment methodology to evaluate EVs, internal combustion engine vehicles (ICEVs), and hybrid electric vehicles (HEVs) spanning cradle-to-grave lifecycle phases. To accomplish this an enhanced triadic sustainability metric (TSM) is introduced that integrates greenhouse gas emissions (GHG), energy consumption, and resource depletion. Results indicate EVs emit approximately 29% fewer GHG emissions than ICEVs but about 4% more than HEVs on the current the US grid, with breakeven sustainability achieved within a moderate mileage range compared to ICEVs. Renewable energy integration on the grid significantly enhances EV
Koech, Mercy Chelangat, Fahimi, Babak, Balsara, Poras T., Miller, John
Constraint Enforcement of a Lithium-Ion Battery Pack Safety-Critical System for Electric Hybrid Vehicles2025-36-016012/18/2025
System robustness and performance are essential considerations in controller design to ensure reference tracking, disturbance rejection, and resilience to modeling uncertainties. However, guaranteeing that the system operates within safe bounds becomes a priority in safety-critical applications, even if performance must be compromised temporarily. One prominent example is the thermal management of lithium-ion battery packs, where temperature must be strictly controlled to prevent degradation and avoid hazardous thermal runaway events. In these systems, temperature constraints must consistently be enforced, regardless of external disturbances or control errors. Traditional strategies, such as Model Predictive Control (MPC), can explicitly handle such constraints but often require solving high-dimensional optimization problems, making real-time implementation computationally demanding. To overcome these limitations, this study investigates the use of a Constraint Enforcement strategy to
Ebner, Eric Rossini, Fernandes, Lucas Pasqual, Leal, Gustavo Nobre, Neto, Cyro Albuquerque, Leonardi, Fabrizio
Control Strategies for a Hybrid Electric-Combustion Powertrain Applied to a Test Bench2025-36-024612/18/2025
This paper presents the design and implementation of a test bench intended for the development and validation of control strategies applied to a hybrid-electric powertrain. The setup combines a 48 V SEG BRM electric machine with a small-displacement internal combustion engine (ICE), the HONDA GX160, operating in a parallel hybrid configuration. The platform was developed to improve energy efficiency in comparison to a conventional ICE-only system. Modifications were carried out on an existing test bench at Instituto Mauá de Tecnologia, including the fabrication of a new enclosure for the battery pack and its battery management system (BMS), as well as the integration of a Vector VN8911 real-time controller. A custom control strategy was implemented and experimentally evaluated using a predefined drive cycle under two conditions: (I) ICE-only operation and (II) hybrid-electric operation with the proposed strategy. Results showed a fuel consumption reduction of approximately 13% with the
Polizio, Yuri, Zabeu, Clayton, Pasquale, Gian, Pinheiro, Giovana, Vieira, Renato
Automotive Engineering: December 202525AUTP1212/4/2025
IAA 2025 Coverage from Munich of IAA 2025 Qualcomm's Superbrains are here to help with automated driving Arbe chipset improves automated driving decisions DeepDrive's MG 250 generator brings dual-rotor power to PHEVs Horse Powertrain's Future Hybrid System can hybridize EVs Reducing SoC and SoH estimation errors: challenges and solutions in modern BMS New approaches to make SoC and SoH parameters more accurate will be required as battery demand keeps growing in the coming years. Driving safety forward: How simulation and MODSIM accelerate ADAS innovation Simulation has become mission-critical for ADAS development. Model-based systems engineering can integrate modeling and simulation from the start of the design process. Revolutionizing vehicle technology through intelligent actuators A look at E/E complexity at the endpoints, where microcontrollers play a crucial role. Automated fiber placement with Ramy Harik AFP can build complex, lightweight structures, but cost concerns keep its
Reducing SoC and SoH estimation errors: challenges and solutions in modern BMS25AUTP12_0612/1/2025
New approaches to make SoC and SoH parameters more accurate will be required as battery demand keeps growing in the coming years. As the demand for accurate, reliable, and intelligent battery management systems continues to grow, overcoming state of charge (SoC) and state of health (SoH) estimation errors becomes more relevant than ever. The battery performance topic is getting especially critical, as electric vehicles, renewable energy storage systems, and portable electronics are now commonplace. This growing demand puts additional pressure on battery performance while also reinforcing the need for accurate SoC and SoH parameters. However, precisely estimating SoC and SoH parameters remains challenging, as their accuracy depends on several factors. Among these are hardware malfunctions and data quality issues that stand in the way of accurate SoC and SoH estimation.
Andrushchak, Volodymyr
Pickering 5-amp battery simulator speeds up BMS testing25AUTP12_1512/1/2025
Engineers looking for a new way to simulate battery cells as they develop new battery management systems might be interested in the latest PXI battery simulator modules from Pickering Interfaces. The new single-slot simulators can be 2- or 4-channel and are capable of supplying up to 8 volts and 5 Amps per channel. and the ground (1000V isolation) and, as a result, series connections can simulate batteries in a stacked architecture. The company said the channels are fully isolated from each other (750V isolation channel to channel). The names of the new modules - 41-754 (PXI) and 43-754 (PXIe) - give away one of Pickering's attitudes when it comes to introducing new products: don't abandon the old stuff.
Blanco, Sebastian
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
Battery Datasets for Neural Network-Based State-of-Health Estimators: A Review and Implementative Workflow2025-24-01399/7/2025
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
Development and Parameterization of a Physics-Based Ageing Model for Li-Ion Battery Cells2025-24-01409/7/2025
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
An Investigation of Battery Module Thermal Behavior Based on CFD Simulations and Experimental Tests2025-24-01449/7/2025
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 Runaway14-14-02-00146/11/2025
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 Solutions2025-01-01015/5/2025
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
Design and Implementation of a Daisy-Chain Integrated BMS with SOC/SOP Co-Estimation for Formula Student Electric Car Accumulators2026-01-06474/7/2025
This paper presents a daisy-chain integrated Battery Management System (BMS) architecture, along with a combined State of Charge (SOC) and State of Power (SOP) co-estimation algorithm for a Formula Student Electric Car’s accumulator. The proposed design employs NXP’s S32K series microcontroller as the primary control unit and NXP’s MC33771C as the analog front-end (AFE) chip. Key features of the BMS include real-time monitoring of battery voltage and current, precise SOC and SOP estimation, cell voltage balancing, charge/discharge and pre-charge management, thermal regulation, and insulation detection. The BMS software development process follows a Model-Based Development (MBD) methodology, encompassing system modeling, analysis, validation, and the automated generation of code, test cases, and documentation. Accurate monitoring of SOC and SOP is essential for both optimal performance and safety. To address the challenges of estimating SOC and SOP under highly dynamic conditions, a
Zhu, Zhonghui
Hyperparameter-Optimized Neural Network for Precise Battery State of Charge Estimation2025-01-82104/1/2025
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 Drive2025-01-80954/1/2025
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 Vehicles2025-01-83864/1/2025
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 Batteries2025-01-81244/1/2025
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 System2025-01-81854/1/2025
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 Analysis2025-01-81314/1/2025
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 Vehicles2025-01-81534/1/2025
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 Cells2025-01-81694/1/2025
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 Analysis2025-01-85614/1/2025
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
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