AI-Enabled Battery Management Systems for Electric Vehicles: Recent Trends in Control, Safety, and Energy Efficiency

Abstract

Increasing electrification and the swift growth of electric vehicles (EVs) have escalated the pressure on the development of sophisticated Battery Management Systems (BMS) that could bring superior performance and safety of the lithium batteries in highly dynamic operation conditions. This paper is a detailed survey of the latest development of Artificial Intelligence (AI)-enabled BMS architectures with focus on state estimation enhancement, fault detection, thermal control, as well as energy efficiency. In this particular context, it examines how artificial neural networks (ANNs), support vector machines (SVMs), deep learning (DL), and reinforcement learning (RL) has been applied to predict state-of-charge (SoC), state-of-health (SoH), and battery degradation behavior. The review is synthesis of the results of more than 40 peer-reviewed publications, which compare the AI-based solutions with the traditional model-driven estimating approaches. The findings indicate that the prediction chances are drastically enhanced (±1.5% SoC), faults are detected almost in real-time (>95% prediction accuracy) and the energy consumption is kept at an optimized level (up to 12% energy savings). Another challenge of implementation discussed in the paper is computational complexity, real-time constraints as well as availability of data. Summing up, AI-based BMS models can serve as a disruptive course and empower smart, forecasting, and energy-conscious battery management in EVs. Hybridization of data-centric learning models and embedded control platforms promises to release the next era of secure, efficient, and driverless electric mobility.