Reinforcement Learning for Torque Vectoring in Electric Vehicles: A Review of Stability and Energy Optimization Methods

Abstract

Torque vectoring can enhance dynamic stability and concurrently enable efficient energy management in electric vehicles (EVs) through optimized torque distribution. Nevertheless, conventional torque vectoring schemes often rely on fixed models and tuning, limiting their adaptability. Reinforcement learning (RL) and its model-free versions employing deep neural networks allow the development of control policies through direct interaction with the environment, making it suitable for complex and nonlinear dynamics. This paper presents a comprehensive survey of recent research on the application of RL for torque vectoring and energy optimization in EVs. An overview of conventional direct yaw control (DYC) approaches, their objectives, and common hierarchical strategies are initially studied to establish a foundation for discussing model-free RL-based torque vectoring. A description of RL in the context of stability-oriented control and energy optimization, key components, operational processes, and their classifications are studied. The primary emphasis is on RL-based torque vectoring and energy management in EVs to improve yaw stability, reduce energy consumption, and manage trade-offs under real-time constraints. Overall, RL-based controllers provide enhanced adaptability to modeling inaccuracies and facilitate more straightforward multi-objective design for simultaneous energy management and stability control, making them promising alternatives to conventional model-based methods.