Multiobjective Optimization of Safety, Comfort, Fuel Economy, and Power Sources Durability for FCHEV in Car-Following Scenarios

• Published in: IEEE transactions on transportation electrification Vol. 9; no. 1; pp. 1797 - 1808
• Main Authors: Zhu, Longlong, Tao, Fazhan, Fu, Zhumu, Sun, Haochen, Ji, Baofeng, Chen, Qihong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-03-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive cruise control (ACC); Batteries; Car following; Cruise control; Degradation; degradation of power sources; Energy consumption; Energy efficiency; Energy management; energy management strategy (EMS); Equivalence; fuel cell/battery hybrid electric vehicle (FCHEV); Fuel cells; Fuel consumption; Fuel economy; Hybrid electric vehicles; multiobjective optimization; Multiple objective analysis; Optimization; Pareto optimization; Power management; Power sources; Powertrain; Tracking errors; Tradeoffs; Vehicle dynamics
• ISSN: 2332-7782; 2577-4212; 2332-7782
• DOI: 10.1109/TTE.2022.3193806

Safety, comfort, and energy-saving oriented car-following issue for fuel cell/battery hybrid electric vehicle (FCHEV) is a comprehensive problem of vehicle dynamic and fuel economy. Combining adaptive cruise control (ACC) and energy management strategy (EMS) is proven to be one promising method to realize multiobjective co-optimization. However, in majority of existing researches, degradation of power sources is always ignored and trade-off among different objectives remains a burning issue, in this article, a Pareto-based EMS under car-following scenarios is proposed. Specifically, degradation models of fuel cell/battery are established and incorporated into multiobjective optimization functions. Then, based on back-stepping technique and equivalent consumption minimization strategy (ECMS), an integrated framework of ACC and EMS is developed, which realizes the coordination between vehicle external longitudinal dynamics control and internal powertrain energy management. For getting the trade-off among the abovementioned objectives, a Pareto-involved multiobjective optimization method is proposed to optimize control parameters of the integrated framework of ACC and EMS. The simulation results of Urban Dynamometer Driving Schedule (UDDS) test highlight that compared with the weighted-sum methods, the proposed method can utmost reduce average tracking error by 15.14%, variation of speed by 8.61%, equivalent fuel consumption cost by 7.56%, and comprehensive power sources degradation cost by 17.78%.