GA-Based Fuzzy Energy Management System for FC/SC-Powered HEV Considering H2 Consumption and Load Variation

• Published in: IEEE transactions on fuzzy systems Vol. 26; no. 4; pp. 1833 - 1843
• Main Authors: Zhang, Ridong, Tao, Jili
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computer simulation; Constrained biobjective optimization; Consumption; Driving; Dynamic response; Energy management; fuel cell (FC)/supercapacitor (SC) power hybrid electric vehicle (HEV); Fuel cells; Fuel economy; Fuels; Fuzzy control; fuzzy plus filter energy management controller; Fuzzy systems; Genetic algorithms; Hybrid electric vehicles; Hydrogen; improved genetic algorithm (GA); Low pass filters; Optimization; Pruning
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2017.2779424

The combination of the fuel cell (FC) and supercapacitor for a hybrid electric vehicle (HEV) has the benefit of compensating for the slow dynamic response and avoiding reactant starvation of FC. Energy management system (EMS) is critical to HEV and a fuzzy controller plus low-pass filter is proposed to prolong the FC lifetime and decrease the hydrogen consumption. The constrained biobjective optimization problem for fuzzy EMS is then solved by an improved genetic algorithm (GA), where the decimal and rule base encoding, constraint handling, the pruning and maintain operator are designed to optimize both the fuzzy rule base and the parameters of the membership functions. Simulation results of highway fuel economy certification test, urban dynamometer driving schedule, and new European drive cycle illustrate that the proposed approach can smooth the output of FC with robustness and be implemented in real time, which decreases 19% current variation with about 10% increase of H 2 consumption.