Integration of AI and ML in regenerative braking for electric vehicles: a review

Abstract

Electric vehicle technology has grown rapidly in recent years due to battery advancements, environmental concerns and supportive policies. Regenerative braking systems play a critical role in improving energy efficiency by converting kinetic energy into electrical energy, thereby extending battery life and vehicle range. However, conventional regenerative braking faces challenges in energy recovery, comfort, and adaptability. Optimizing energy recovery ensures prolonged battery life by preventing overcharging or undercharging, making EVs more sustainable and cost-effective. This review paper explores the integration of Artificial Intelligence and machine learning techniques in regenerative braking systems to overcome these challenges. This study examines AI techniques such as regression models, neural networks, deep reinforcement learning, fuzzy logic, genetic algorithm and swarm intelligence based techniques for regenerative braking. The study also compares AI-based strategies with traditional braking methods. Unlike previous studies, which focus on individual AI techniques, this paper provides a comparative analysis of multiple AI approaches, assessing their impact on braking performance and energy recovery, and propose a hybrid AI framework. This paper covers challenges in real-time implementation, road adaptability, and vehicle control integration. This paper also discusses future research that optimize braking performance like V2X communication, edge computing, and explainable AI etc.