Cyber-Physical Optimization-Based Fuzzy Control Strategy for Plug-in Hybrid Electric Buses Using Iterative Modified Particle Swarm Optimization

Abstract

The fuel economy of plug-in hybrid electric bus (PHEB) is highly dependent on its energy management strategy (EMS). In practice, the fuzzy control (FC) is widely used in EMS due to its real-time performance and robustness. However, the FC with fixed parameters is difficult to obtain the optimal fuel economy under changing traffic conditions. Regarding this, the control parameters of FC need to be optimized, but this scheme needs to overcome the subsequent calculation burden and time consumption. Therefore, the design of a real-time EMS with parameter optimization is a challenging problem. Inspired by this issue, a cyber-physical optimization-based fuzzy EMS is proposed in this paper. Firstly, a cyber-physical system framework is formulated for PHEB to eliminate the conflict between parameter optimization and real-time operation of EMS. Secondly, considering the uncertainty of the vehicle environment, an IT2 FC with optimization parameters is designed for real-time torque allocation. Thirdly, an iterative modified particle swarm optimization (IMPSO) algorithm is proposed to optimize parameters to accurately and quickly converge to the optimal solution. Additionally, the optimization problem with multi-objective that takes battery life into account is introduced. Finally, simulation and hardware in loop test are used to discuss the performance of the proposed EMS. The results reveal that the IMPSO algorithm can improve the optimization effect. Compared to conventional rule-based and fuzzy-based strategies, the proposed EMS can reduce fuel consumption at least 10% and 4.5%, respectively. Meanwhile, it shows the proposed EMS could reduce the battery capacity loss by 6.42%∼9.72% with a slight increase in fuel consumption.