AI-Enhanced Battery Management Optimization for Electric Vehicles

Abstract

The rapid adoption of electric vehicles (EVs) has intensified the need for advanced battery management systems (BMS) that can enhance battery performance, safety, and longevity. Traditional BMS approaches rely heavily on rule-based control algorithms and physical models, which often fall short in adapting to dynamic operating conditions and complex battery behaviors. This study explores the integration of artificial intelligence (AI) techniques, including machine learning and deep learning, to optimize battery management in EVs. The AI-enhanced BMS aims to improve state-of-charge (SOC) and state-of-health (SOH) estimation accuracy, predictive maintenance, and energy efficiency. By leveraging large datasets collected from battery sensors under diverse operating scenarios, AI models can learn intricate patterns and nonlinear behaviors that conventional methods may overlook. This capability enables proactive decision-making for charging/discharging cycles, thermal management, and fault detection. The research methodology includes a systematic literature review of AI applications in battery management prior to 2019, followed by the development and simulation of AI models such as artificial neural networks (ANN), support vector machines (SVM), and reinforcement learning (RL) algorithms. The models were evaluated based on prediction accuracy, computational efficiency, and robustness against noise and variability. Key findings indicate that AI-based methods significantly outperform traditional approaches in SOC and SOH estimation, achieving up to 10-15% improvement in accuracy. Reinforcement learning techniques show promise in real-time optimization of charging strategies to maximize battery lifespan. Challenges remain in ensuring model interpretability, data quality, and generalizability across different battery chemistries. The workflow for implementing AI-enhanced BMS involves data acquisition, model training, validation, integration with vehicle control units, and continuous learning during vehicle operation. Advantages include improved energy utilization, enhanced safety through early fault detection, and reduced operational costs. However, disadvantages such as increased system complexity, need for large training datasets, and computational requirements are also discussed. The study concludes that AI integration is pivotal for next-generation battery management in EVs, with future work focusing on hybrid AI-physics models, edge computing deployment, and standardized datasets to accelerate development and adoption.