Browse Topic: Battery management systems (BMS)

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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, Makana, Mohan, Natarajan, Saravanan, K, MALA
Development of a 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 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
System Level Temperature Distribution and Balancing in battery swapping stations2026-26-03611/16/2026
Battery charging systems generate heat during operation, which affects battery efficiency, discharge time, and overall lifespan. Effective thermal management is essential to prevent overheating, optimize heat dissipation, and ensure consistent performance. This study investigates testing methodologies to analyse thermal temperature distribution, detect imbalances, and implement effective balancings techniques. Testing was conducted in a controlled environmental chamber to evaluate heat dissipation behavior under varying conditions. Different dock-level temperatures were measured and optimized using multiple design configurations to assess their impact on thermal performance. The analysis identified thermal imbalances that contribute to uneven heat distribution and increased thermal stress, which can degrade battery performance over time. By refining system designs and incorporating optimized balancing strategies, the research demonstrated significant improvements in heat management
Kulkarni, Mukund, P, Hari Prasad Reddy, H, Manjunatha, Dash, Soumya
State of Health correction based on dynamic cell behaviour in EVs.2026-26-01831/16/2026
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
Validation of 0D-1D Co-Simulation Strategies in Cold Climate for ‘Full EV Digital Vehicle’2026-26-04481/16/2026
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 Pack2026-26-04321/16/2026
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 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
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 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
Lithium-ion Battery’s State of Health Prediction: A Deep Learning Framework2026-26-01621/16/2026
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
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
Simplified Simulation of Electric Vehicle Battery packs Using deep learning based Reduced Order Models2026-26-04031/16/2026
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
Dynamic Voltage EV System (DVEVS): Achieving Enhanced Electric Vehicle Efficiency and Performance through switchable Series-Parallel Battery Configuration2026-26-02031/16/2026
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
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 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
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 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
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 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
A Review on Fire Prevention and Suppression Solutions for EV Battery Packs2024-26-00121/16/2024
The lithium-ion batteries are susceptible to fires or explosions due to their extremely volatile nature. The energy-dense batteries, such as LiNi_0.8 Mn_0.1 Co_0.1 O_2/Graphite (NMC811) battery that meets the consumer range demands, are most vulnerable under thermal events. A wide number of solutions are being explored to suppress or prevent battery fires. The solutions range from integrating active cooling techniques, passive heat dissipation using heat carrier pads, thermal insulating materials to prevent thermal propagation, safety vents to remove ejecta, and protection circuitries with advanced battery management system. In this paper, we review various safety solutions employed in battery packs for preventing or suppressing potential fire during any thermal runaway event. The identified safety solutions also feature distinctive methods such as usage of hydrogel agents, aerosol fire suppressants and design features. Among the reviewed countermeasures, we provide a detailed analysis
H, Manjunatha, Nambisan T M, Praveen Kumar, R, Pavan, P, Hari Prasad Reddy, G M, Bharath, Kulkarni, Mukund Aravind, Sundaram, Saravanan
Electric Powertrain - Standardization of Adaptable Software in Loop System2024-26-01171/16/2024
Motivation With an increase in the complexity involved in modern-day ECUs (Electronic Control Unit), it is very important to verify and validate robustness, functionality, and reliability of ECUs. Till date, Hardware in Loop (HiL) based validation or vehicle level validation is generalized approach used for testing. However, this method requires physical setup, which can incur more cost and time during the development phase. Solution We believe in minimizing the software testing time using Software in Loop (SiL) validation. Creating virtual-ECUs and the plant models is an important step in SiL. We are presenting the modularized and scalable plant models in this paper. Adaptable SiL Solution for EV This paper focuses on the standardization of electric vehicle plant models, which can help users to reduce the software development and software testing time. We created the standardized plant models for following virtual ECUs, which can be used as modular units. • Battery Management System
Sajnani, Abhishek, Vernekar, Kiran, Gosavi, Rupesh, Naik, Venkatesh
Sensitivity Analysis of Advanced Non-Linear Observer for States Estimation of Lithium ion Batteries2023-01-700010/30/2023
Control observer-based estimation methods are getting a very rapid appreciation due to their better reliability, stability and ease of implementation in already controller-packed electric vehicles and energy storage systems. As a careful sensitivity analysis is the one vital tool to enhance the accuracy and robustness of lithium-ion battery’s states estimation, an experimental sensitivity analysis is proposed to enhance the accuracy and efficiency of battery states and parameter estimation of non-linear control observer. This paper categorically uses INR21700-M50T cells for experimental characteristic analysis of lithium-ion batteries. The results of this practical work are then used in the successful design, simulation and validation of an advanced proportional integral observer. The validated proportional-integral (PI) observer is then used to carry out the proposed sensitivity analysis, and deviations resulted in estimation accuracy due to the sensitivity of each parameter are
Saeed, Muhammad, Khalatbarisoltani, Arash, Zhongwei, Deng, Lu, Shuai, Xiaosong, Hu
Distributed Co-Simulation for Effective Development of Battery Management Functions2023-01-12006/26/2023
Electrification calls for a range of system components, that need to be developed and tested. On one hand, these system components include hardware components, like battery cells, modules, and packs, and battery management systems (BMS). On the other hand, software components are used for testing, e.g. algorithms for BMS or simulation models for batteries. Execution of tests on real batteries is typically time- and cost-intense, and includes considerable risks, leading to safety hazards. In this paper, we introduce a novel development and test approach for battery systems, that is driven by a unified, standardized interface between hardware- and software components and physical devices alike. Whereas established Hardware-in-the-Loop (HiL) systems are built on proprietary systems and environments, our approach is based on both open-source and industrial simulation software solutions. The Distributed Co-Simulation Protocol (DCP) is used to encapsulate and virtualize these components, as
Hrvanovic, Dino, Haberl, Hannes, Krammer, Martin, Scharrer, Matthias K.
Test:15/2SAE-PP-078102/15/2023
Test:15/2
Mutagaana, Festo
Next-Gen Maintenance Framework for Urban Air Mobility Vehicles2022-26-11655/26/2022
Urban Air Mobility (UAM) vehicles are set to revolutionize the mobility of man and material in near future. One of the key areas to explore is how operational efficiency can be maximized through the next generation Maintenance framework. Compared to the traditional aircrafts, the UAM vehicles will introduce many new and unique maintenance challenges such as: 1. The volume of UAM vehicles is going to be very high and involves frequent, short trips resulting in increased need for maintenance. 2. UAMs may be of hybrid nature (rotary and fixed wing) with a complex interplay of actuation and propeller-based controls. In addition, UAMs will have Electric Motors which involve complex maintenance of parts. 3. High Power Battery is another key component which involves handling the removal, replacement and charging, which requires an effective Battery Management System. 4. Efficient Inventory/Parts Management including NFF reduction is going to play a vital role because Parts, not labor, are the
Kulkarni, Sandeep, Elahi, Imtiaz, Kadeppagari, Murali, Panicker, Renju
Methods for State of Health Estimation and Remaining Useful Life Prognostics for EV Lithium-ion Batteries: A Review2022-01-08503/29/2022
Lithium-ion batteries (LIBs) power majority of battery electric vehicles due to their relatively better performance and reliability. LIBs offer high energy density, cycle life, power capability when used in their operating zones. However, LIBs experience ageing and performance degradation due to operating conditions and internal factors which if not characterised lead to unexpected battery failures combined with safety hazards. State of health (SOH) metric provides an effective measure on the battery health and be used to calculate the Remaining useful life (RuL) of the battery. Accurate SOH prediction in Electric vehicles is key to improve pack performance, enable preventive maintenance to reduce unexpected failures, enhance battery life and safety. EV battery packs are equipped with Battery Management Systems (BMS). State of art BMSs are equipped with advanced algorithms for state estimation and are IOT enabled to save field performance data over cloud. Currently, algorithms which
Aphale, Siddharth
REVIEW ON ADVANCEMENTS IN BATTERY TECHNOLOGIES AND THEIR FUTURE SCOPE 2022-01-08533/29/2022
Over the past several decades, the number of electric vehicles (EVs) has continued to increase which estimates that millions of EVs will be on the road in the upcoming years. The heart of these EVs is the Li-ion battery which provides the required energy storage. Batteries have been widely applied in many high-power applications like electric vehicles (EVs) and hybrid electric vehicles (HEVs), where a suitable battery management system (BMS) plays an important role in ensuring safe and reliable operation of batteries. This paper aims to give a brief explanation on several key technologies which include Structural Battery, Battery Testing Equipment, Silicone-Based Thermal Interface Materials for Electric Vehicles, New Solid Electrolytes in EV Batteries, Cobalt Free Batteries. It also gives some information about a Battery Recycling Facility, which has come with a simple solution – Recycle the batteries. With the great knowledge of these battery technologies, the different automotive
Elumalai, Sangeethkumar, Mayakrishnan, Jaikumar, Velmurugan, Ramanathan, Anbu , Shravan
Development of lithium-ion battery test bench2022-01-08683/29/2022
In order to accurately, conveniently and safely test the relevant mechanical response of lithium-ion battery under various charge and discharge conditions, it is proposed to develop the corresponding test bench. The test bench mainly includes mechanical system and measurement and control system. For the measurement and control system, a monitoring and test system software is developed, which cooperates with the relevant hardware to realize the reliability and convenience of data acquisition. For the mechanical system, in order to ensure the local heat dissipation performance of the test system in the test bench, the heat dissipation simulation comparison of the designed local test systems with different structures is carried out to initially select the better test system structure; in order to ensure the safety of the lithium-ion battery during the charging and discharging test in the test bench and the loss ratio of the expansion force of the lithium-ion battery transmitted by the
qu, jie, huang, meihua
Framework for Co-estimation of State of Charge and State of Available Power for Battery Management Systems2022-01-08593/29/2022
The battery management system (BMS) ensures safe operation of cells defining a safe operation zone for charge/discharge currents, temperatures, provides cell balancing and fault management. The BMS is also responsible for defining limiting currents during acceleration, regenerative braking and gradient climb. Accurate state of charge (SOC) and state of available power (SoAP) estimation is required for these tasks. This enables energy management system to affect regulation over power flow in the vehicle thus improving battery performance and lifetime. The goal is to develop a computationally inexpensive algorithm for Soc and SoAP estimation which can be implemented in BMS for onboard state estimation tasks. In this work, a framework for co-estimation of SOC and SoAP is proposed. The algorithm is developed considering a series – parallel connected battery pack for light electric vehicle (electric scooter or bicycle) application. An equivalent circuit model (ECM) is formulated for the
Aphale, Siddharth
Battery Heat Load Estimation and modeling its Thermal management using Air Conditioning System of an Electric Vehicle2021-28-023110/1/2021
The performance of lithium-ion batteries and its service life depends on its operating temperature. Operating the battery above 45 °C degrades the performance of the battery and reduces its service life. The high-temperature operation also leads to thermal runaway. So there is a need to monitor the operating temperature and voltage output of the battery using a battery thermal management system to ensure its safety. Battery Thermal Management System (BTMS) is a part of the battery management system. The effectiveness of the battery thermal management system depends on the battery pack design, battery chemistry, vehicle operating characteristics and ambient conditions. In this work, a refrigerant-based BTMS is modeled using MATLAB Simulink. Refrigerant R134a used in the air conditioning system of an Electric Vehicle is used as an evaporative cooling medium to cool the batteries. The battery pack is submerged in the pool of refrigerant for cooling and to control the battery temperature
Kaliaperumal, MuthuKrishnan
Estimation of End of Life of Lithium-Ion Battery Based on Artificial Neural Network and Machine Learning Techniques2021-26-02189/22/2021
Various vehicle manufacturers are launching electric vehicles, which are more sustainable and environmentally friendly. The major component in electric vehicles is the battery, and its performance plays a vital role. Usually the end of life of a battery in the automobile sector is when the battery capacity reaches 80% of its maximum rated capacity. The capacity of a lithium-ion cell decreases with the number of cycles. So, a semi-empirical model is developed for estimating the maximum stored capacity at the end of each cycle. The parameters considered in the model explain the changes in battery internal structure, like capacity losses at different conditions. The capacity estimated using the semi-empirical model is further taken as the inputs for estimating capacity using the Artificial Neural Network (ANN) and Machine Learning (ML) techniques i.e. Linear Regression (LR), Gaussian Process Regression (GPR), Support Vector Machine methods (SVM). Artificial Neural Network model has been
Lahari, Kothala, Sharma, Minakshi, Baidya, Kapil
Analysis of Semiconductor based Pre-charge & Cut-off Circuits for 2W/3W Electric Vehicle Battery Management Systems2021-26-01689/22/2021
As the climate change & CO2 emissions are becoming prime concerns over the globe, Electric Vehicles (EV) are proving to be promising eco-friendly mobility solution. In India, the drive for transition to EV is gaining momentum. Batteries constitute a major chunk of EV cost. Battery Management systems (BMS) are of paramount importance from the aspect of safety, performance, usability & lifetime of EV. Along with fundamental function of monitoring (Cell Voltage, Pack voltage, pack current, cell/pack temperature), BMS must perform function of controlling (Charger/Load Connect, disconnect, Pre-charge) the battery pack in case of failure. In most vehicles, loads & chargers have high capacitance, causing high inrush currents into and from the BMS. This can not only damage the contactors (connect/disconnect circuits) and other load components but also affect the lifetime of cells within battery pack. Conventionally contactor-based cut-off & pre-charge circuits are present in EV's. However
Magar, Pradip, Deshpande, Rohan, Shinde, Rushikesh, Deo, Mayank, Chaudhary, Pramod
Comparative Analysis of the Model Based State of Charge Estimation Methods for Li Ion Batteries used in Electric Vehicle Applications2021-26-01639/22/2021
Electric vehicles are getting increasing importance because of the absence of vehicular emissions, simpler design and efficient control. Li Ion based battery pack is most preferred in electric vehicles due to higher power and energy density, low discharge rates and longer life. In order to operate Li Ion Cells in a safe and efficient manner, robust battery management system (BMS) is essential. Accurate estimation of State of Charge (SOC) is important BMS function. It affects many functions for e.g. remaining vehicle range estimation, cell balancing and cell protections etc. Coulomb counting method is simplest method to estimate SOC. However, it requires highly accurate and precise current sensor and requires information of initial SOC. Model Based SOC estimation techniques use mathematical model of the Li Ion cell to estimate the cell SOC. They are robust to the variations in the Li ion cell parameters as compared to the coulomb counting method. This paper compares various Model Based
ROUTRAY, SHUVAM, Nidubrolu, Kranthi, Chauhan, Abhisha, Kirtane, Chinmay Ashok, Dhamija, Lohit, Chaudhary, Pramod
Simulink Model for SoC Estimation using Extended Kalman filter2021-26-03829/22/2021
State of Charge (SoC) estimation of battery plays a key role in strategizing the power distribution across the vehicle in Battery Management System. In this paper a model for SoC estimation using Extended Kalman Filter (EKF) is developed in Simulink using Simscape library. This model uses second order Resistance-Capacitance (2RC) Equivalent Circuit Model (ECM) of Lithium Iron Phosphate (LFP) cell to simulate the cell behaviour. The parameter identification experiments were performed on a new and a used LFP cell respectively to identify two sets of parameters of ECM. The cell parameters are identified for the range of 0% to 100% SoC and it was found that the cell parameters vary as a function of SoC. Hence, variable resistance and capacitance blocks were used in the cell model so that the cell parameters can vary as a function of SoC. This facilitates the simulation of voltage drop due to internal resistances of the cell at the whole range of SoC, as it is impractical to measure the
Kachate, Niranjan, Sharma, Minakshi, Baidya, Kapil
Advanced BEV Battery Pack Thermal Simulation Model Development & Co-relation with Physical Testing2021-28-01389/15/2021
Battery Thermal management is a major challenge for occupant safety in an electric vehicle. Predicting the battery electrical losses and thermal behaviour is another challenge for the battery management system. Different virtual models are developed for cell level and pack level thermal evaluation. All these models have a varying degree of accuracy and limitation. The latest developed model is more accurate and can predict the battery cell & pack level temperatures. The cell temperature in the battery pack not only depends upon the cell inside physics but also depends upon cell outside cooling physics. Cell outside physics is simulated by 3D CFD software during the design process. The pack level simulations are performed considering extreme conditions and fixed boundary steady-state conditions. Heat transfer coefficient data from thermal simulations are extracted and used in the BMS model, and 1D thermal model is developed. There are two challenges that exist in BMS model. The first is
dol, Yogesh Pradeep, Virmalwar, Ajay, Soni, Gaurav
Battery TerminologyJ1715/2_202108 (Current)8/9/2021
This document contains definitions currently used in the automotive industry as they relate to energy storage and batteries for starting, lighting, and ignition applications, as well as for hybrid electric vehicles (HEV) and electric vehicles (EVs). It is intended that this document be a resource for those writing other battery, HEV, and EV documents, specifications, standards, or recommended practices. The use of the term “battery” in this document can be assumed to be a rechargeable battery (secondary battery). The terminology may be applied to other industries if desired.
Battery Terminology Committee
Electric-Drive Battery Pack System: Functional GuidelinesJ2289_202108 (Current)8/3/2021
This SAE Information Report describes common practices for design of battery systems for vehicles that utilize a rechargeable battery to provide or recover all or some traction energy for an electric drive system. It includes product description, physical requirements, electrical requirements, environmental requirements, safety requirements, storage and shipment characteristics, and labeling requirements. It also covers termination, retention, venting system, thermal management, and other features. This document does describe guidelines in proper packaging of the battery to meet the crash performance criteria detailed in SAE J1766. Also described are the normal and abnormal conditions that may be encountered in operation of a battery pack system
Battery Standards Testing Committee
Hybrid Electric Vehicle (HEV) and Electric Vehicle (EV) TerminologyJ1715_202105 (Historical)5/28/2021
This SAE Information Report contains definitions for HEV, PHEV, and EV terminology. It is intended that this document be a resource for those writing other HEV, PHEV, and EV documents, specifications, standards, or recommended practices.
Hybrid - EV Committee
Hybrid Electric Vehicle (HEV) and Electric Vehicle (EV) TerminologyJ1715_202105-TEST-REC (Historical)5/28/2021
This SAE Information Report contains definitions for HEV, PHEV, and EV terminology. It is intended that this document be a resource for those writing other HEV, PHEV, and EV documents, specifications, standards, or recommended practices.
Hybrid - EV Committee
Comparative Study between Equivalent Circuit and Recurrent Neural Network Battery Voltage Models2021-01-07594/6/2021
Lithium-ion battery (LIB) terminal voltage models are investigated using two modelling approaches. The first model is a third-order Thevenin equivalent circuit model (ECM), which consists of an open-circuit voltage in series with a nonlinear resistance and three parallel RC pairs. The parameters of the ECM are obtained by fitting the model to hybrid pulse power characterization (HPPC) test data. The parametrization of the ECM is performed through quadratic-based programming. The second is a novel modelling approach based on long short-term memory (LSTM) recurrent neural networks to estimate the battery terminal voltage. The LSTM is trained on multiple vehicle drive cycles at six different temperatures, including −20°C, without the necessity of battery characterization tests. The performance of both models is evaluated with four automotive drive cycles at each temperature. The results show that both models achieve acceptable performance at all temperatures. However, the LSTM performs
Naguib, Mina, Vidal, Carlos, Kollmeyer, Phillip, Malysz, Pawel, Gross, Oliver, Emadi, Ali
Research on Effect of Dynamic Working Condition on Electrochemical Impedance2021-01-07474/6/2021
Impedance is an important parameter of power lithium-ion batteries, which can represent battery characteristics and can also be used as an indicator for battery fault diagnosis. Since Electrochemical Impedance Spectroscopy (EIS) includes various electrode processes and information, it is more significant and worthwhile for lithium-ion batteries research. However, it is quite difficult to attain EIS online because of the nonlinear characteristics of batteries. Therefore, this paper focus on studying the nonlinear impedance characteristics of lithium-ion batteries and proposing a new method to calculate the EIS online based on Fast Fourier Transform (FFT). Data similarity analysis is used to study the influence of resting time, excitation current amplitude, bias current amplitude and the state of charge (SOC) on the impedance quantitatively. Further research finds that the amplitude of impedance, voltage and current in frequency domain all conform to the law of power function well within
Wang, Xiaoman
Traction Control System of Electric Vehicle with 4 In-Wheel Motors using Lyapunov Stability Analysis Algorithm2021-01-01224/6/2021
A TCS strategy of electric vehicle with 4 in-wheel motors is proposed in this paper. The control method consists of three parts: target slip rate calculation, target torque calculation and coordination control. By using Lyapunov stability analysis algorithm, the target slip rate boundary which makes the system stable is obtained. The target torque of each wheel is calculated by PI controller. According to the engineering experience, the TCS coordinated control strategy under split friction coefficient (split-μ) road, and friction coefficient jump(μ jump) road is proposed. The test results show that this strategy can improve the acceleration comfort and yaw stability of vehicles on uniform low friction coefficient (low μ) , split-μ and μ jump road.
Zhao, Yongqiang, Hui, ZhouZehui, Cui, Jinlong
Communication for Plug-in Vehicles as a Distributed Energy SourceJ2847/3_202103 (Historical)3/23/2021
This document applies to a plug-in electric vehicle (PEV) which is equipped with an onboard inverter and communicates using IEEE 2030.5-2018. It is a supplement to the SEP2 standard, which supports the use cases defined by SAE J2836/3. It provides guidance for the use of the SEP2 distributed energy resource function set with a PEV. It also provides guidance for the use of the SEP2 flow reservation function set, when used for discharging. It is not intended to be a comprehensive guide to the use of SEP2 in a PEV. Note that in this document, SEP2 is used interchangeably with IEEE 2030.5-2018.
Hybrid - EV Committee
018 - Simulation of Advanced Regenerative Braking Strategies in a Series Plug-in Hybrid Electric VehicleSAE-PP-001411/23/2021
Regenerative braking is an important factor in improving hybrid electric vehicle efficiency. This paper proposes a new regenerative braking strategy that activates preemptively during a distracted driving scenario, before service brakes are utilized. The strategy uses onboard advanced driver assistance systems, such as forward facing radar, to detect when an object is approaching fast enough to enable regenerative braking in response. The proposed strategy is simulated on a full-vehicle model of a series plug-in hybrid electric vehicle. A driver model is developed to mimic the behavior of a distracted driver through delayed response time to the changing speed of a lead vehicle. Multiple trials are simulated using different combinations of existing regenerative braking strategies along with the proposed strategy. Results show that a preventative regenerative braking control strategy can recuperate significant amounts of energy while also improving vehicle safety. Coupling the proposed
Mutagaana, Festo
016 - State-of-Health Online Estimation for Li-Ion BatterySAE-PP-001391/22/2021
To realize a fast and high-precision online state-of-health (SOH) estimation of lithium-ion (Li-Ion) battery, this article proposes a novel SOH estimation method. This method consists of a new SOH model and parameters identification method based on an improved genetic algorithm (Improved-GA). The new SOH model combines the equivalent circuit model (ECM) and the data-driven model. The advantages lie in keeping the physical meaning of the ECM while improving its dynamic characteristics and accuracy. The improved-GA can effectively avoid falling into a local optimal problem and improve the convergence speed and search accuracy. So the advantages of the SOH estimation method proposed in this article are that it only relies on battery management systems (BMS) monitoring data and removes many assumptions in some other traditional ECM-based SOH estimation methods, so it is closer to the actual needs for electric vehicle (EV). By comparing with the traditional ECM-based SOH estimation method
011 - State-of-Health Online Estimation for Li-Ion BatterySAE-PP-001231/21/2021
To realize a fast and high-precision online state-of-health (SOH) estimation of lithium-ion (Li-Ion) battery, this article proposes a novel SOH estimation method. This method consists of a new SOH model and parameters identification method based on an improved genetic algorithm (Improved-GA). The new SOH model combines the equivalent circuit model (ECM) and the data-driven model. The advantages lie in keeping the physical meaning of the ECM while improving its dynamic characteristics and accuracy. The improved-GA can effectively avoid falling into a local optimal problem and improve the convergence speed and search accuracy. So the advantages of the SOH estimation method proposed in this article are that it only relies on battery management systems (BMS) monitoring data and removes many assumptions in some other traditional ECM-based SOH estimation methods, so it is closer to the actual needs for electric vehicle (EV). By comparing with the traditional ECM-based SOH estimation method
MobrxivNonAdmin, Lindsay
New SAE Wireless Charging standard is EV game-changer20TOFHP12_0512/1/2020
SAE International on October 22 announced publication of the first global standard that specifies, in a single document, both the electric vehicle and supply equipment (EVSE) ground-system requirements for wireless charging of electric vehicles (EV). The new standard, SAE J2954 (www.sae.org/standards/content/j2954_202010/), helps pave the way for charging without the need for plugging in - widely considered to be a key enabler for accelerating the adoption of EVs and autonomous vehicles. The new standard was more than a decade in the making. SAE kicked off its pioneering pre-competitive research at a time when few contemporary electric cars existed and wireless power transfer (WPT) systems for EVs were an unproven concept. The SAE J2954 Wireless Power Transfer and Alignment Taskforce worked since 2007 to thoroughly vet and test the technology, in partnership with government agencies, regulatory bodies and private-industry groups including the American Association of Medical
Shuttleworth, Jennifer
New SAE wireless charging standard is EV game-changer20AUTP11_0611/1/2020
SAE International on October 22 announced publication of the first global standard that specifies, in a single document, both the electric vehicle- and supply equipment (EVSE) ground-system requirements for wireless charging of electric vehicles (EV). The new standard, SAE J2954 [https://www.sae.org/standards/content/j2954_202010/], helps pave the way for charging without the need for plugging in - widely considered to be a key enabler for accelerating the adoption of EVs and autonomous vehicles. The new standard was more than a decade in the making. SAE kicked off its pioneering pre-competitive research at a time when few contemporary electric cars existed and wireless power transfer (WPT) systems for EVs were an unproven concept. The SAE J2954 Wireless Power Transfer and Alignment Taskforce worked since 2007 to thoroughly vet and test the technology, in partnership with government agencies, regulatory bodies and private-industry groups including the American Association of Medical
Shuttleworth, Jennifer
Recommended Practices for Shipping Transport and Handling of Automotive-Type Battery System - Lithium IonJ2950_202006 (Current)6/9/2020
This SAE Recommended Practice (RP) aids in the identification, handling, and shipping of lithium batteries to and from specified locations. It is the specific intent of this RP to identify, utilize, and reference existing U.S. and international hazardous materials (dangerous goods) transportation regulations, which are the only methodologies to be used to establish transportability. It is also the intent of this RP to provide recommendations to be used by service and shipping personnel for the purpose of determining a possibly damaged/defective battery’s transportability. In support of the service and shipping personnel, these recommendations seek to use standard tools of the trade and avoid laboratory type equipment.
Battery Transportation Committee
Recommended Practices for Shipping Transport and Handling of Automotive-Type Battery System - Lithium IonJ2950_202006-TEST-REC (Historical)6/9/2020
This SAE Recommended Practice (RP) aids in the identification, handling, and shipping of lithium batteries to and from specified locations. It is the specific intent of this RP to identify, utilize, and reference existing U.S. and international hazardous materials (dangerous goods) transportation regulations, which are the only methodologies to be used to establish transportability. It is also the intent of this RP to provide recommendations to be used by service and shipping personnel for the purpose of determining a possibly damaged/defective battery’s transportability. In support of the service and shipping personnel, these recommendations seek to use standard tools of the trade and avoid laboratory type equipment.
Battery Transportation Committee
Open approach to propulsion20TOFHP06_026/1/2020
Deutz CEO sees combination of sustainable-fuel combustion engines and electric drive systems powering the off-highway market. The economic climate has fast soured in recent months due to the COVID-19 pandemic, but Deutz has reason to be optimistic about the coming months and years. Prior to halting large parts of its production in Europe in April, the German drive-systems manufacturer showcased at ConExpo/Con-Agg 2020 a range of advanced technologies that it believes are necessary for future competitiveness. “It is vital that we make the necessary investment in order to secure the long-term success of the company, and that includes steadfastly pursuing the growth projects that we have already initiated,” said Dr. Frank Hiller, CEO and chairman of the Deutz AG board of management, in a recent statement about the negative impact of the coronavirus crisis on its engine business.
Gehm, Ryan
Towed Array Sonar SystemTBMG-368095/1/2020
Givat Shmuel, Israel +972-3-531-3333
Trade-Off Analysis and Systematic Optimization of a Heavy-Duty Diesel Hybrid Powertrain2020-01-08474/14/2020
While significant progress has been made in recent years to develop hybrid and battery electric vehicles for passenger car and light-duty applications to meet future fuel economy targets, the application of hybrid powertrains to heavy-duty truck applications has been very limited. The relatively lower energy and power density of batteries in comparison to diesel fuel and the operating profiles of most heavy-duty trucks, combine to make the application of hybrid powertrain for these applications more challenging. The high torque and power requirements of heavy-duty trucks over a long operating range, the majority of which is at constant cruise point, along with a high payback period, complexity, cost, weight and range anxiety, make the hybrid and battery electric solution less attractive than a conventional powertrain. However, certain heavy-duty applications, such as Class 6-7 urban vocational trucks, can benefit from hybridization due to their transient operating profiles and
Joshi, Satyum, Dahodwala, Mufaddel, Koehler, Erik W., Franke, Michael, Tomazic, Dean, Naber, Jeffrey
Energy Management of Dual Energy Source of Hydrogen Fuel Cell Hybrid Electric Vehicles2020-01-05954/14/2020
With the growing shortage of oil resources and the increasingly strict environmental regulations, countries are vigorously developing new energy vehicles, and as a truly zero-emission vehicle in the application, fuel cell electric vehicles can not only completely replace gasoline cars in term of fuel, but also have the advantages of high energy conversion efficiency, short hydrogenation time and long driving range. For Fuel Cell Hybrid Electric Vehicle (FCEV), and the Energy Management Control Strategy is the "core" of the whole vehicle control system, which has a direct and significant effect on the power and economy of the vehicle. In this paper, the "dual energy source system" composed of fuel cell and power battery is taken as the research object. Based on the proposed power system structure, a fuel cell hybrid power management control strategy is designed, and the simulation model based on Matlab/Simulink and real vehicle are adopted to perform performance verification on standard
Zhao, Yongqiang, Song, Haoyuan, Liu, Yuanzhi, Yu, Zhao
Sensorless Individual Cell Temperature Measurement by Means of Impedance Spectroscopy Using Standard Battery Management Systems of Electric Vehicles2020-01-08634/14/2020
Lithium ion technology is state of the art for actual hybrid and electrical vehicles. It is well known that lithium ion performance and safety characteristics strongly depend on temperature. Thus, reliable temperature measurement and control concepts for lithium ion cells are mandatory for applications in electrical cars. Temperature sensors for all individual cells increase the battery complexity and cost of a battery management system. Normally, temperature is measured on module level in current battery packs, without observation of the individual cell temperature. Sensorless cell impedance-based temperature measurement concepts have been published and are validated in laboratory studies. Dedicated test equipment is usually applied, which is not useful for automotive series application. This work describes a practical approach to enable impedance-based sensorless internal temperature measurement for all individual cells using state-of-the art battery management system components
Haussmann, Peter, Melbert, Joachim
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