Advancing Electric Vehicle Battery Management: Innovative Strategies for Enhanced Performance and Longevity Practices

Abstract

Electric vehicles (EVs) are increasingly seen as a viable option for addressing environmental issues and lessening our reliance on fossil fuels. Central to the functionality of EVs is the battery system, which significantly influences the vehicle's performance, driving range, and overall satisfaction for the user. To improve the efficiency and dependability of these batteries, sophisticated Battery Management Systems (BMS) have been developed. With the rise in popularity of EVs, there is a growing need for batteries that perform better and last longer. This paper focuses on the crucial aspect of managing EV batteries and conducts a thorough study of advanced fault tolerance techniques designed to enhance both the performance and longevity of EV batteries. The study covers various strategies such as Multi-Sensor Fusion Techniques, which combine data from multiple sensors to improve fault detection accuracy; Predictive Maintenance Models that develop algorithms to foresee and prevent battery failures; Machine Learning-Based Anomaly Detection that utilizes advanced algorithms for immediate identification and categorization of irregularities; Integrated Thermal Management systems within BMS to combat thermal degradation of batteries; and measures for Communication and Data Security to ensure the safety and integrity of data within the BMS network, protecting it from cyber threats and unauthorized access.