Abstract: Distributed drive electric vehicles(DDEV), benefiting from their independently actuated wheel-end architecture, demonstrate significant potential in terms of control degrees of freedom and dynamic response flexibility. However, the highly coupled mechanical-electrical-road system inherent in DDEVs poses greater challenges for modeling accuracy, state estimation, and control strategy design. This paper presents a comprehensive review of recent advances in the modeling, state estimation, and control of DDEVs. Regarding modeling, the review focuses on key aspects of mechanical-electrical-road coupled systems, including road surface modeling, unbalanced magnetic force modeling of in-wheel motors, and longitudinal-lateral-vertical coupled dynamics modeling and mechanisms. For the problem of dynamic response estimation in complex systems, the paper summarizes approaches based on model-driven, data-driven, and hybrid model-data-driven methods, with particular attention to the applications of Kalman filtering, Transformer architectures, and physics-informed neural networks. In terms of control, various adaptive control strategies, disturbance rejection methods, and multi-objective optimization techniques under different control frameworks are reviewed. Finally, this paper summarizes and discusses the main challenges and future development trends in current research. It points out that future studies on DDEV should further enhance system behavior prediction under multi-physics coupling effects, multi-source heterogeneous information fusion, and end-to-end control methods based on artificial intelligence and physical prior knowledge.

Key words: distributed drive, muti-physics coupling, data-driven, model-driven