Machine Learning-Enhanced Aging-Aware Energy Management Strategy for Electric Vehicles

This paper presents a novel method to enhance electric vehicle (EV) performance and lifespan via a Machine Learning-Enhanced Aging-Aware Energy Management Strategy (EMS). Despite the growing popularity of EVs, they struggle with limited range and battery degradation. Existing EMS research primarily focuses on dual-source systems. This study addresses the gap for single-source battery EVs. The proposed EMS, developed from 20 hours of battery data across various driving cycles, integrates machine learning (ML) models and Genetic Algorithm (GA) optimization. Validated over 600 hours of testing, it reduces power consumption by up to 22% and mitigates battery aging by up to 20%.