A new power management strategy for plug-in hybrid electric vehicles based on an intelligent controller integrated with CIGPSO algorithm. (15th February 2023)

• Record Type: Journal Article
• Authors: Abd-Elhaleem, Sameh; Shoeib, Walaa; Sobaih, Abdel Azim
• Is Part Of: Energy. Volume 265(2023)
• Publication Date: 2023-02-15
• Subjects: Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.126153
• Languages: English
• ISSNs: 0360-5442

Abstract

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. Highlights: This paper presents a new intelligent method for power management in plug-in hybrid electric vehicles (PHEVs)depending on a long-term power management and a short-term controller. The long-term power management depends on a chaotic improved generalized particle swarm optimization algorithm (CIGPSO) to obtain the optimal value of motor and engine torques. 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 proved. The proposed technique improves the energy consumption compared to other techniques such as generalized particle swarm optimization GEPSO and improved multi-objective PSO (IMOPSO).