With the rapid development of the electric vehicle (EV) industry, the regenerative braking system (RBS) has become a pivotal technology for enhancing overall vehicle energy efficiency and safety. This article systematically reviews recent research advances, spanning macro-architecture, drive and energy-storage hardware, control strategies, and evaluation frameworks. It focuses on comparing the mechanisms and performance of six categories of intelligent control algorithms—fuzzy logic, neural networks, model predictive control, sliding-mode control, adaptive control, and learning-based algorithms—and, leveraging the structural advantages of four-wheel independent drive (4WID) electric vehicles, quantitatively analyzes improvements in energy-recovery efficiency and coordinated vehicle-dynamics control. The review further discusses how high-power-density motors, hybrid energy storage, brake-by-wire systems, and vehicle-road cooperation are pushing the upper limits of RBS performance, while revealing current technical bottlenecks in high-power recovery at low speeds, battery thermal safety, high-dimensional real-time optimization, and unified evaluation standards. A closed-loop evolutionary roadmap is proposed, consisting of the following stages: system integration, intelligent control, scenario prediction, hardware upgrading, and standard evaluation. This roadmap emphasizes the central roles of deep reinforcement learning, hierarchical model predictive control (MPC), and predictive energy management in the development of next-generation RBS. This review provides a comprehensive and forward-looking reference framework, aiming to accelerate the deployment of efficient, safe, and intelligent regenerative braking technologies.
Huang et al. (Thu,) studied this question.