A Hybrid Machine Learning Framework for Intelligent Battery Management in Electric Vehicles: SOC, SOH, and RUL Prediction Using Ensemble Models

Abstract

Control The increasing environmental impact of Fossil Fuels coupled with Escalating Demand for Renewable Energy Solutions propel the market to Progress Efficient Battery Management Systems (BMS). Smart BMS technology may be crucial for developing innovative battery systems with higher energy density and capacity that are more efficient, lighter in weight, and have longer service lives. EV Industry Value The growing demand for smart transportation solutions from sectors such as electric vehicles (EVs) and portable electronic devices has ushered in the development of advanced BMS technologies. This paper presents an evaluation of ML algorithms using MATLAB to predict critical battery health indicators, including state-of-charge (SOC), state-of-health (SOH) and remaining useful life (RUL). This paper deals with comparison of performance prediction of models: Multiple Regression, K-Nearest Neighbors (KNN), Decision Trees and Random Forest (RF). To increase the prediction accuracy another RF–GB hybrid ensemble model (from Random Forest and Gradient Boosting) is proposed. The experimental results indicate that, under dynamic operating conditions, the prediction accuracy of SOC, SOH and RUL reaches 98%, 97% and 96 %respectively by applying RF–GB model compared with well-known approaches. The ML framework can additionally accelerate the inverse design of battery materials and support the development of next-generation batteries with 20–25% energy density increases and >30% elimination of toxic constituents. It allows for an additional range of real-time performance and charging optimization along with fast parking density, which is all accomplished through the data. Overall, the proposed intelligent hybrid ML-based BMS is a highly scalable and sustainable solution for next-generation energy storage systems leading the way towards safe, efficient, eco-friendly modern electric mobility development integrated into our society.