Energy Management Optimization of Master–Slave Hybrid Electric Vehicle under Rule‐Based Control Strategy. Issue 10 (29th July 2022) - Zhang, Zhen; Zhang, Tiezhu; Hong, Jichao; Zhang, Hongxin; Yang, Jian
Abstract : In response to the imperfections and issues with conventional fuel vehicles and electric vehicles (EVs), this article proposes a master–slave hybrid electric vehicle (MSHEV) with multiple energy sources. The research team establishes the rule‐based control strategy for MSHEV with the aid of reviewing existing theories. The control strategy enables the MSHEV to transition between several working modes. The simulation consequence verifies that the MSHEV has lower power consumption and energy loss than the EV under actual vehicle driving cycle. One of most critical initial steps in ameliorating a vehicle's performance is parameter optimization. This article selects the battery state of charge of MSHEV as the optimization objective and optimizes applicable parameters. Thereafter, an approximate model is constructed based on the Response Surface Model, and an optimization model is built based on the Multi‐Island Genetic Algorithm. The energy management of the optimized MSHEV is more reasonable and the state of charge is further enhanced. The accomplishment of this article is of considerable significance and reference value in the optimization of energy management of hybrid electric vehicles. Abstract : This article proposes a master–slave hybrid electric vehicle (MSHEV) with a rational structural arrangement and multiple power drives. MSHEV can effectively reduce peak motor torque and increase braking recovery torque. Based on the excellent performance of MSHEV in energy management, this article adopts the approximate model method and the optimization model method to optimize the battery state of charge.