Izvorni znanstveni članak
https://doi.org/10.7305/automatika.2015.07.714
Fuzzy Energy Management Optimization for a Parallel Hybrid Electric Vehicle using Chaotic Non-dominated sorting Genetic Algorithm
Junyi Liang
; National Engineering Laboratory for Automotive Electronic Control Technology, Shanghai Jiao Tong University, Shanghai, China
Jianlong Zhang
; National Engineering Laboratory for Automotive Electronic Control Technology, Shanghai Jiao Tong University, Shanghai, China
Hu Zhang
; National Engineering Laboratory for Automotive Electronic Control Technology, Shanghai Jiao Tong University, Shanghai, China
Chengliang Yin
; National Engineering Laboratory for Automotive Electronic Control Technology, Shanghai Jiao Tong University, Shanghai, China
Sažetak
This paper presented a parallel hybrid electric vehicle (HEV) equipped with a hybrid energy storage system. To handle complex energy flow in the powertrain system of this HEV, a fuzzy-based energy management strategy was established. A chaotic multi-objective genetic algorithm, which optimizes the parameters of fuzzy membership functions, was also proposed to improve fuel economy and HC, CO, and NOx emissions. The main target of this algorithm is to escape from local optima and obtain high quality trade-off solutions. Chaotic initialization operator, chaotic crossover and mutation operators, chaotic disturbance operator, and chaotic local search operator were integrated into non-dominated sorting genetic algorithm II (NSGA-II) to form this new algorithm named chaotic NSGA-II (C-NSGA-II). Simulation results and comparisons demonstrated that chaotic operators can enhance searching ability for optimal solutions. In conclusion, C-NSGA-II is suitable for solving HEV energy management optimization problem.
Ključne riječi
Chaotic operator; Fuzzy logic; Hybrid electric vehicle; Multi-objective optimization; NSGA-II
Hrčak ID:
152850
URI
Datum izdavanja:
20.10.2015.
Posjeta: 1.747 *