Advancements in Battery Technology for Electric Vehicles: Integrating Machine Learning for Enhanced Performance

The development of batteries will have a major impact on how electric vehicles (EVs) evolve. The State of Charge (SoC) and State of Health (SoH) evaluations made by current Battery Management Systems (BMS) are frequently erroneous due to the use of antiquated algorithms. It results in inefficient use of energy and lower battery lifespans. To improve BMS performance, the research suggests a unique method that integrates machine learning (ML) techniques. ML models are trained to predict battery behavior more accurately and adaptively by utilizing extensive information from battery sensors, past usage logs, and environmental variables. Certain machine learning algorithms, such as LSTM for estimating SoC, SVM for evaluating SoH, and RL for optimizing charging, exhibit better performance metrics in comparison to conventional techniques. The results show significant improvements over the existing systems in terms of energy efficiency (+12 units), battery lifespan (+25 years), and charging time (-8 hours). The ML-integrated BMS offers dependable and sustainable electric transportation solutions in addition to improving EV performance.