Energy management strategy based on convex optimization for fuel cell/battery/ultracapacitor hybrid vehicle

• Published in: Engineering optimization Vol. 56; no. 3; pp. 447 - 467
• Main Authors: Liu, Jinpeng, Fu, Zhumu, Tao, Fazhan, Jiao, Longyin, Chen, Qihong
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.03.2024; Taylor & Francis Ltd
• Subjects: adaptive fuzzy filter; Algorithms; Convex analysis; convex optimization; Convexity; Decoupling; Energy efficiency; Energy management; energy management strategy; equivalent consumption minimization strategy; Fuel cell hybrid electric vehicle; Fuel cells; Fuel consumption; Fuel economy; Hybrid electric vehicles; Optimization; Slack variables; Supercapacitors
• ISSN: 0305-215X; 1029-0273
• DOI: 10.1080/0305215X.2022.2155345

For a fuel cell hybrid electric vehicle (FCHEV) powered by a fuel cell (FC), a battery (BAT) and an ultracapacitor (UC), an improved convex-optimization-based energy management strategy (EMS) is proposed in this article. To protect the fuel cell and the battery from peak power, an adaptive fuzzy filter is used to complete the frequency decoupling of the power demand. Subsequently, an adaptive equivalent consumption minimization strategy (A-ECMS) is introduced to improve fuel economy. To improve the optimality of the EMS obtained, an improved convex optimization method is utilized by introducing a slack variable and a barrier function. Finally, based on experimental data and an improved convex optimization algorithm, the optimal EMS is calculated and verified by a series of simulations under different driving cycles. Simulation results confirm that, compared with the traditional ECMS-based EMS, the proposed EMS can improve fuel economy and achieve lower power fluctuation and optimal FC efficiency for the FCHEV.