Optimizing Electric Vehicle Performance: Advanced Health Monitoring and Adaptive Strategies in Battery Management Systems

Abstract

In the shift towards eco-friendly transportation, Electric Vehicles (EVs) have become a vital alternative. At the core of EVs lies the battery pack, whose efficient functioning and durability are crucial. This study delves into the integration of a health check feature in EV Battery Management Systems (BMS), aiming to enhance battery efficiency and vehicle performance significantly. The BMS is instrumental in supervising, managing, and optimizing battery function. Integrating a health check allows for the early identification of issues like cell wear, loss of capacity, and thermal anomalies, enabling the system to recognize and address problems early. This foresight aids in avoiding sudden failures, cutting down on repair expenses, and increasing user satisfaction. The BMS also tailors its approaches based on immediate State of Charge (SOC) and State of Health (SOH) data, refining charging and discharging methods. Adapting these factors to the battery's status helps prolong its life and offers drivers more precise driving range predictions. The study further highlights the need for adaptive control in equalizing charge among individual cells, which promotes better energy use and extends battery life. Temperature control strategies are also optimized according to health status, keeping the battery within its ideal operational temperature. The research also points out the significance of user involvement, predictive upkeep, and data analysis for ongoing enhancements. Educated drivers can enhance efficiency through mindful choices about driving patterns, charging intervals, and maintenance routines. This research is beneficial not just for individual EV users but also aids in minimizing environmental harm and encourages the broader adoption of electric vehicles in today's auto industry