Achieving both high energy efficiency and extended battery lifespan remains a major challenge in distributed in-wheel motor electric vehicles. This study proposes a hybrid multi-objective optimization framework combining the Asynchronous Advantage Actor–Critic (A3C) algorithm with an Improved Sparrow Search Algorithm (ISSA). The A3C algorithm performs real-time torque allocation to maximize instantaneous energy efficiency, while the ISSA optimizes long-term battery management strategies by explicitly embedding capacity fading and thermal safety constraints. To coordinate these conflicting objectives across different time scales, a dynamic weight adjustment mechanism and a cross-scale shared memory pool are introduced. Simulation results under NEDC, WLTC, and CLTC driving cycles demonstrate that the framework significantly outperforms conventional algorithms in terms of convergence speed, energy consumption reduction, and battery durability. Furthermore, Hardware-in-the-Loop (HIL) experiments confirm the framework’s excellent real-time performance and control accuracy on a physical controller, validating its engineering feasibility.
Zhao et al. (Mon,) studied this question.