A new power management strategy for plug-in hybrid electric vehicles based on an intelligent controller integrated with CIGPSO algorithm

Abstract

This paper proposes an improved power management strategy for plug-in hybrid electric vehicles (PHEVs). This strategy consists of a long-term power management approach and a short-term intelligent controller. In the long-term power management, a chaotic improved generalized particle swarm optimization technique (CIGPSO) is used to optimize the motor and diesel engine torques. In order to reduce the computation time, a five-mode rule-based control system is employed, where the CIGPSO estimates the optimal values of the motor and engine torques in a hybrid mode, which manages the power between the motor and engine in accordance with a multi objective cost function. This cost function reduces fuel usage as well as the drawn current from the battery with taking into account the process of the battery aging. Moreover, the CIGPSO is able to obtain the state of charge (SoC) curve of the battery during the charging and discharging of the battery throughout the trip. The short-term controller is designed using an interval type-2 Takagi-Sugeno-Kang fuzzy (IT2TSK) algorithm which depends on human experts to overcome the uncertainties of the diverse driving conditions. Lyapunov stability theory for the online controller is achieved. The proposed strategy reduces the energy consumption compared to other strategies such as generalized PSO and improved multi-objective PSO algorithms. The simulation results are performed using real data for the engine, motor, and battery to demonstrate the flexibility, viability and effectiveness of the proposed approach with comparative results. Index terms—Plug-in hybrid electric vehicle, power management strategy, chaotic improved generalized particle swarm optimization algorithm, rule-based control, interval type-2 Takagi-Sugeno-Kang fuzzy algorithm.