Improved Power Management Under Uncertain Driving Conditions for Plug-In Hybrid Electric Vehicles via Intelligent Controller

An improved power management strategy for plug-in hybrid electric vehicles (PHEVs) is proposed in this paper. This strategy combines long-term power management with a short-term intelligent controller. In long-term power management, the motor and diesel engine torque are optimized using a chaotic improved generalized particle swarm optimization technique (CIGPSO). In order to reduce the computation time, a five-mode rule-based control system is employed, where the CIGPSO estimates the optimal values for the motor and engine torque in a hybrid mode, which manages the power between the motor and engine following a multi-objective cost function. This cost function reduces fuel usage as well as the drawn current from the battery, 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 fuzzy Takagi-Sugeno-Kang (IT2TSK) algorithm, which depends on human experts to overcome the uncertainties of the driving conditions. Lyapunov stability theory for the online controller is achieved. Furthermore, the SOC of the battery is estimated using an adaptive extended Kalman filter (AEKF). The performance of CIGPSO has been compared to the performance of the current state of art and resulted in saving up to 19.03%, 12.54%%, and 7.14% in terms of engine torque, motor torque, and battery SOC, respectively. The simulation results for the engine, motor, and battery are performed using real data to demonstrate the effectiveness of the proposed approach with comparative results.