Electric Vehicle Battery Management System Research Articles

Enhanced electric vehicle battery management system employing bat algorithm with chaotic diversification strategies

AbstractAs the demand for electric vehicles (EV) continues to increase, the need for effective charging and switching of battery systems becomes more important. This article presents a method using the Bat Algorithm (BA) improved by chaotic diversification as well as social education to optimize the power source replacement and the electric vehicle charging procedure. The plan is intended to solve the issues of payment delay and battery management failure. The algorithm searches for better positions by combining chaotic diversity, while social learning supports the coordination of battery stations. Thanks to extensive simulation and real‐world testing, our approach shows significant improvements in optimization and a reduced payback period. The results show that the suggested approach outperforms the current algorithms in terms of rotation speed and good solution. This research supports the development of efficient transportation by providing practical solutions to increase the efficiency of electric vehicle transfer and payment and ultimately encourage greater effort.

Robust Online Estimation of State of Health for Lithium-Ion Batteries Based on Capacities under Dynamical Operation Conditions

Lithium-ion batteries, as the main energy storage component of electric vehicles (EVs), play a crucial role in ensuring the safe and reliable operation of the battery systems through monitoring their state of health (SOH). However, temperature variations and battery aging have significant impacts on the internal parameters of lithium-ion batteries, and these changes directly correlate with the accuracy of battery SOH estimation. To address these issues, this paper proposes an estimation method for lithium-ion battery SOH that considers the impact of temperature. The method begins with reconstructing a second-order hybrid equivalent circuit model for lithium-ion batteries, through which an adaptive update rate for battery model parameters is designed. On this basis, a nonlinear observer for battery states is introduced by integrating filters to estimate SOH. The proposed method considers the impact of capacity in the design of the parameter adaptive update rate, enabling the capacity to be dynamically adjusted based on the actual state of the batteries. This reduces the cumulative error in the SOC observer and improves the modeling accuracy of battery models. Experimental results demonstrate that the method proposed in this paper exhibits exceptional performance in SOH estimation under different temperature conditions. The mean absolute error for SOH estimation does not exceed 0.5%, and the root mean square error does not exceed 0.2%. This method can significantly improve the estimation accuracy of SOH, providing a more efficient and accurate solution for battery management systems in EVs.

Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging

This paper investigates the application of hybrid reinforcement learning (RL) models to optimize lithium-ion batteries’ charging and discharging processes in electric vehicles (EVs). By integrating two advanced RL algorithms—deep Q-learning (DQL) and active-critic learning—within the framework of battery management systems (BMSs), this study aims to harness the combined strengths of these techniques to improve battery efficiency, performance, and lifespan. The hybrid models are put through their paces via simulation and experimental validation, demonstrating their capability to devise optimal battery management strategies. These strategies effectively adapt to variations in battery state of health (SOH) and state of charge (SOC) relative error, combat battery voltage aging, and adhere to complex operational constraints, including charging/discharging schedules. The results underscore the potential of RL-based hybrid models to enhance BMSs in EVs, offering tangible contributions towards more sustainable and reliable electric transportation systems.

Electric Vehicle Battery Management System with Charge Monitor and Fire Protection

This paper outlines the development and execution of an Electric Vehicle Battery Management System (BMS) outfitted with charge surveillance and fire prevention functionalities. As the utilization of electric vehicles (EVs) continues to grow, ensuring the safety and optimal functioning of battery packs becomes increasingly crucial. The suggested BMS merges sophisticated monitoring algorithms with fire suppression systems to alleviate risks linked with overcharging and thermal runaway incidents. Through thorough examination and validation procedures, our findings underscore the efficacy of the implemented BMS in ameliorating battery performance and guarding against potential dangers, thereby fostering the widespread embrace of electric mobility

EV BMS With Charge Monitor and Fire Detection

Electric vehicles (EVs) are undoubtedly the way of the future. However, as of 2023, EV technology has not reached its full potential in terms of efficiency and safety. The cause of majority of the electric vehicle fire events is a battery explosion or fire. This paper presents an integrated approach to manage EV battery systems, which combines a Battery Management System (BMS) with charge monitoring and fire detection. The system is built to continuously monitor the battery's voltage, current, and temperature and to immediately turn off the battery's input or output if any unexpected behaviour is noticed. Keywords: EVs, BMS, Temperature Sensor

Employment of Artificial Intelligence (AI) Techniques in Battery Management System (BMS) for Electric Vehicles (EV): Issues and Challenges

Rechargeable Lithium-ion batteries have been widely utilized in diverse mobility applications, including electric vehicles (EVs), due to their high energy density and prolonged lifespan. However, the performance characteristics of those batteries, in terms of stability, efficiency, and life cycle, greatly affect the overall performance of the EV. Therefore, a battery management system (BMS) is required to manage, monitor and enhance the performance of the EV battery pack. For that purpose, a variety of Artificial Intelligence (AI) techniques have been proposed in the literature to enhance BMS capabilities, such as monitoring, battery state estimation, fault detection and cell balancing. This paper explores the state-of-the-art research in AI techniques applied to EV BMS. Despite the growing interest in AI-driven BMS, there are notable gaps in the existing literature. Our primary output is a comprehensive classification and analysis of these AI techniques based on their objectives, applications, and performance metrics. This analysis addresses these gaps and provides valuable insights for selecting the most suitable AI technique to develop a reliable BMS for EVs with efficient energy management.

EV BMS With Charge Monitor and Fire Detection

Electric vehicles (EVs) are undoubtedly the way of the future. However, as of 2023, EV technology has not reached its full potential in terms of efficiency and safety. The cause of majority of the electric vehicle fire events is a battery explosion or fire. This paper presents an integrated approach to manage EV battery systems, which combines a Battery Management System (BMS) with charge monitoring and fire detection. The system is built to continuously monitor the battery’s voltage, current, and temperature and to immediately turn off the battery’s input or output if any unexpected behaviour is noticed.

IoT Based Electric Vehicle Battery Management System for Enhance Battery Life

This abstract describes the concept of an IoT-based electric vehicle battery management system that can enhance the battery life of electric vehicles.By utilizing IoT technologies such as cloud computing, big data analytics, and machine learning algorithms, this system can monitor and analyze data in real-time, including charging patterns, driving habits, and battery performance. This enables the system to optimize the charging and discharging process, which in turn prolongs the battery life. In this project, we propose an Internet of Things (IoT) based BMS for EVs that integrates real-time data monitoring, analysis, and control to enhance battery life.The proposed system includes a range of sensors, such as temperature sensors, current sensors, and voltage sensors, that collect real-time data from the EV battery. This data is transmitted wirelessly to an IoT gateway, which then forwards it to a cloud-based platform for analysis and storage. The platform uses machine learning algorithms to identify patterns and anomalies in the data, and provides insights to improve battery performance and extend battery life.

Design of Electric Vehicle Battery Management System

The Battery Management System (BMS) is a fundamental component of electric vehicles, primarily utilized to ensure battery safety and enhance battery lifespan. This article presents a design for both the hardware and software components of the BMS, enabling battery monitoring and management. The hardware component encompasses the design of voltage acquisition circuitry, second-order filtering circuitry, sampling and holding circuitry, CAN bus communication circuitry, and other relevant features. The software section comprises subroutines for battery information collection, equalization circuitry, SOC estimation, and other relevant features. The BMS developed in this study successfully collects voltage, temperature, current, and other relevant information, and accurately estimates SOC and other crucial parameters. Testing confirmed that the battery management system precisely collects battery voltage, current, and temperature information, while the SOC estimation achieves a relatively high degree of accuracy.

A three-time-scale dual extended Kalman filtering for parameter and state estimation of Li-ion battery

Estimation of battery state and parameters play an important role in electric vehicle battery management system (BMS). Second-order RC model is applied, the initial parameters of battery model are determined by experiments. Data points of open circuit voltage and state of charge (OCV-SOC) are determined by experiment. Different function forms are used to fit the OCV-SOC discrete points, and the function form with great fitting effect is selected as the OCV-SOC fitting form. Dual extended Kalman filter which is divided into Parameter filter and state filter is applied. Battery state in state filter is a fast-time-varying parameter, The battery model parameters in parameter filter are divided into two parts. the battery model parameters are classified according to the influence of each parameter on the terminal voltage. A longer sampling time is applied to the parameters that have a strong impact on the terminal voltage, and a longest sampling time is applied to the parameters that have a weak impact on the terminal voltage. The time-scale classification method is validated both quantitatively and qualitatively. Compared with the previous methods, the three-time-scale classification method can reduce the number of parameter updates.

Design and Implementation of SAE J1939 and Modbus Communication Protocols for Electric Vehicle

The charging station has a vital role in the electric vehicle sector. The charging station provides supply (AC or DC) to vehicles as per requirements. The charging station infrastructure includes software and hardware that ensure energy transfer and safety. Communication is mandatory to transmit messages that contain information from the battery management system and charger. This research focuses on implementing the communication between the charger controller and the battery management system. This paper adopts the controller area network (CAN) bus charger communication protocol based on the SAE J1939 standard from the Society of Automotive Engineers. The data are transmitted over a network to facilitate the information that is to be conveyed by an electronic control unit. The vehicle communicates via the battery management system to the charger controller using CAN communication. The charger power modules with AC to DC and DC to DC converters uses Modbus communication protocol. Therefore, this paper integrates CAN bus and Modbus communication protocols and implements the communication between charger and electric vehicle battery management system using a cost-effective Arduino UNO micro-controller. Using the CAN bus module (MCP2515) and Modbus module (MAX485), the distance between the electric vehicle and the charger is increased. Finally, the communication is validated using PCAN View software.

Agnostic Battery Management System Capacity Estimation for Electric Vehicles

Battery degradation is a main concern for electric vehicle (EV) users, and a reliable capacity estimation is of major importance. Every EV battery management system (BMS) provides a variety of information, including measured current and voltage, and estimated capacity of the battery. However, these estimations are not transparent and are manufacturer-specific, although measurement accuracy is unknown. This article uses extensive measurements from six diverse EVs to compare and assess capacity estimation with three different methods: (1) reading capacity estimation from the BMS through the central area network (CAN)-bus, (2) using an empirical capacity estimation (ECE) method with external current measurements, and (3) using the same method with measurements coming from the BMS. We show that the use of BMS current measurements provides consistent capacity estimation (a difference of approximately 1%) and can circumvent the need for costly experimental equipment and DC chargers. This data can simplify the ECE method only by using an on-board diagnostics port (OBDII) reader and an AC charger, as the car measures the current directly at the battery terminals.

A Review of Lithium-Ion Battery Capacity Estimation Methods for Onboard Battery Management Systems: Recent Progress and Perspectives

With the widespread use of Lithium-ion (Li-ion) batteries in Electric Vehicles (EVs), Hybrid EVs and Renewable Energy Systems (RESs), much attention has been given to Battery Management System (BMSs). By monitoring the terminal voltage, current and temperature, BMS can evaluate the status of the Li-ion batteries and manage the operation of cells in a battery pack, which is fundamental for the high efficiency operation of EVs and smart grids. Battery capacity estimation is one of the key functions in the BMS, and battery capacity indicates the maximum storage capability of a battery which is essential for the battery State-of-Charge (SOC) estimation and lifespan management. This paper mainly focusses on a review of capacity estimation methods for BMS in EVs and RES and provides practical and feasible advice for capacity estimation with onboard BMSs. In this work, the mechanisms of Li-ion batteries capacity degradation are analyzed first, and then the recent processes for capacity estimation in BMSs are reviewed, including the direct measurement method, analysis-based method, SOC-based method and data-driven method. After a comprehensive review and comparison, the future prospective of onboard capacity estimation is also discussed. This paper aims to help design and choose a suitable capacity estimation method for BMS application, which can benefit the lifespan management of Li-ion batteries in EVs and RESs.

Toward safe carbon–neutral transportation: Battery internal short circuit diagnosis based on cloud data for electric vehicles

To promote electrified transportation and achieve carbon neutrality, Li-ion batteries with excellent energy storage performance are widely adopted in electric vehicles (EVs). However, internal short circuit (ISC) of batteries is a serious safety hazard of EVs. ISC diagnosis using the powerful storage and computing capabilities of the cloud platform is a promising method to enhance the safety performance of battery packs in EVs, but the recording period of cloud data is larger than that of battery management systems in EVs, which brings great challenges to the existing ISC diagnosis methods. In this paper, the new mean-normalization is employed to amplify the voltage characteristics of the ISC cell in the battery pack. The adaptive Kalman filter algorithm is deployed to filter the mean-normalization values to obtain the vivid ISC features. Furthermore, the density-based spatial clustering of applications with noise algorithm is exploited to automatically detect and locate the ISC cell. Even with the 40 s sparse data in the real vehicle, the ISC cell also can be timely diagnosed within 2.51 h.

IoT Network Management within the Electric Vehicle Battery Management System

IoT Network Management within the Electric Vehicle Battery Management System

Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

Cell balancing is a vital function of battery management system (BMS), which is implemented to extend the battery run time and service life. Various cell balancing techniques are being focused due to the growing requirements of larger and superior performance battery packs. The passive balancing approach is the most popular because of its low cost and easy implementation. As the balancing energy is dissipated as heat by the balancing resistors, an appropriate thermal scheme of the balancing system is necessary, to keep the BMS board temperature under a tolerable limit. In this paper, optimum selection of balancing resistor with respect to degree of cell imbalance, balancing time, C- rate, and temperature rise using machine learning (ML) based balancing control algorithm is proposed to improve the balancing time and optimal power loss management. Variable resistors are utilised in the passive balancing system, in order to optimize the power loss and to obtain optimal thermal characterization. The performance of the proposed system is evaluated using back propagation neural network (BPNN), radial basis neural network (RBNN), and long short term memory (LSTM). Error analysis of the balancing system is done to optimize balancing parameters and the proposed algorithms are compared using performance indices such as mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) to validate the balancing model performance. The possible optimization scope for implementing passive balancing using machine learning algorithms are experimented in the Matlab-Simscape environment.

Battery modelling and state of charge estimation methods for Energy Management in Electric Vehicle-A review

To reduce global warming, the Electric Vehicles (EV) are more attracting Worldwide for replacement of conventional IC engine vehicle but the main problem is driving range and the cost of EV is very high compared to a conventional vehicle. The driving range is mainly depending on the type of battery and size of the battery pack used in EV, for long driving range more number of batteries are required which automatically increase the weight and cost of EV. An effective battery management system will increase battery life and driving range of the EV with less number of batteries. In battery management system of EV the battery is major component but battery is costly and managing power of the battery is very much essential in EV technology. Majority of the issues can be solved by developing advanced battery management system (BMS) in EV such as, Battery modelling, accurate battery state of charge and state of health estimation, which can provide an exact driving range of EV and charging/discharging strategies work more effectively. This review paper mainly focuses on different battery modelling techniques and existing battery SOC estimation methods, issues and challenges.

Real-Time Estimation of Model Parameters and State-of-Charge of Li-Ion Batteries in Electric Vehicles Using a New Mixed Estimation Model

A precise estimation of the state of charge (SoC) of the lithium-ion battery is crucial for reducing range-anxiety and improving the performance of the electric vehicle (EV) battery management system. An accurate estimation of the SoC, however, has remained elusive due to the complex and nonlinear behavior of the battery. In this article, a new mixed estimation model (MEM) for the battery parameters and the SoC estimation is proposed, where the route is specified before the travel. The new MEM uses a combination of a battery power-based method (BPBM), a combined model, and a partial adaptive forgetting factor recursive least-square (PAFF-RLS) SoC calibration algorithm to make use of the best characteristics of each model to determine a better and more accurate SoC estimation. The partial adaptive forgetting factors solves the issue of the different rate changes in the battery parameters and reduces the complexity of the algorithm compared to the fully adaptive recursive models. The BPBM allows various traveling factors to be included in the model, such as the environmental conditions, the effect of auxiliary loads, and the traffic congestion. To verify the validity of the PAFF-RLS algorithm, two laboratory tests using real-time driving cycles have been conducted on a 2012 Nissan Leaf 31.1 Ah Manganese-oxide Li-ion battery cell. The effectiveness of the MEM model has been demonstrated by driving the Nissan Leaf along two selected routes in Australia. The results demonstrate the great accuracy of the proposed method for the SoC estimation, when compared with those from the previous models.

Active cell balancing for electric vehicle battery management system

The shrink in accessibility of petroleum products and increment in asset request are eventual outcomes for Electrical Vehicles (EVs). The battery has an impact on the performance of electrical vehicles, the driving range. Lithium ion (Li-ion) chemistry is extremely sensitive to overcharge and deep discharge, which can harm the battery, shortening its period of time, and even inflicting risky things. The Battery Management System (BMS) comprises of the consequent parts: management, equalization and protection. Of the three components, equalization is that the most crucial with respect to the durability of the battery framework. The ability of the full pack diminishes rapidly amid the procedure which leads to degradation of the full battery framework. This condition is extreme once the battery incorporates a more number of cells in series and frequent charging is conveyed through the battery string. The cell imbalance during charging, discharging is a major issue in battery systems used in EVs. To circumvent the cell imbalance, cell balancing is used. Cell balancing enhances battery safety and extends battery life. This paper discusses about different active balancing method to increase the life span of the battery module. Based on the comparison, the inductor based balancing method for 60V battery system is implemented in the MATLAB/Simscape environment and the results are discussed.

Capacity estimation for lithium-ion battery using experimental feature interval approach

The estimation of lithium-ion battery capacity is of great importance in electric vehicle battery management system (BMS), its results will contribute to controlling the battery to have both excellent output performance and long life. However, there still lacks generalized approaches for different kinds of batteries with high estimating accuracy. Therefore, an experimental feature interval approach for LiFePO4 (LFP) and LiNixCoyMn1-x-yO2 (NCM) capacity estimating is proposed in this paper. Firstly, two concepts of feature interval and remaining charge electricity (RCE) are defined, then partial charging electricity based on incremental capacity analysis is used to estimate capacity. According to the results, there is a strong linear relationship between RCE and capacity. We can obtain capacity directly through this linear function by calculating RCE from the feature interval to the end of charge. A satisfying estimation performance is verified by the results of another experiment data, where the accuracy is more than 98.5%. Moreover, it is found that this approach can be used to NCM battery by modifying the linear fitting weights. This proposed approach is verified in NASA dataset, with the estimating deviations less than 2.4%. Further, the proposed estimating approach may serve as a reference for batteries from other manufactures.