Fuzzy energy management strategy for electric vehicle combining driving cycle construction and air-conditioning load identification. (February 2021)

• Record Type: Journal Article
• Authors: Pan, Chaofeng; Tao, Yuanxue; Wang, Limei; Li, Huanhuan; Yang, Jufeng
• Is Part Of: Advances in mechanical engineering. Volume 13:Number 2(2021)
• Publication Date: 2021-02
• Subjects: Driving mode prediction -- energy management strategy -- electric vehicles -- fuzzy clustering algorithm -- hardware in loop; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/1687814021994381
• Languages: English
• ISSNs: 1687-8132

Abstract

Energy management strategy is developed by considering the random and air conditioning load fluctuation, which greatly affected the torque control of the electric motor in electric vehicle. Firstly, the vehicle power consumption model is established, based on the influencing factors of electric vehicle energy consumption: random load and air conditioning load. Therefore, driving conditions with random characteristics representing the actual random load are constructed. According to the clustered characteristic parameters, the driving conditions were classified as different driving modes. Secondly, the mode of predicted condition was taken as a variable to evaluate the logic threshold strategy and fuzzy control strategy in which the influence of air conditioning was considered. Finally, under the condition of New European Driving Cycle (NEDC), the proposed management strategy was simulated in software environment, and the hardware in-loop (HIL) test was performed to verify the strategy. The simulation and HIL test results show that the proposed energy management strategy can increase the driving range by considering the load fluctuation of air conditioning. Furthermore, the strategy combining the driving mode prediction can alleviate the decline rate of SOC. And the fuzzy control strategy has better adaptability in complex conditions and lower battery energy consumption rate.