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A Comparative Study of Equivalent Factor Optimization Based on Heuristic Algorithms for Hybrid Electric Vehicles
- Qingxing Zheng - Wuhan University of Technology, School of Automotive Engineering, China Wuhan University of Technology, Hubei Key Laboratory of Advanced Technology for Automotive Components, China Hubei Collaborative Innovation Center for Automotive Components Technology, China ,
- Shaopeng Tian - Wuhan University of Technology, School of Automotive Engineering, China Wuhan University of Technology, Hubei Key Laboratory of Advanced Technology for Automotive Components, China Hubei Collaborative Innovation Center for Automotive Components Technology, China Wuhan University of Technology, Hubei Research Center for New Energy & Intelligent Connected Vehicle, China ,
- Wenbin Wang - Wuhan University of Technology, School of Automotive Engineering, China Wuhan University of Technology, Hubei Key Laboratory of Advanced Technology for Automotive Components, China Hubei Collaborative Innovation Center for Automotive Components Technology, China ,
- Qian Zhang - Wuhan University of Technology, School of Automotive Engineering, China Wuhan University of Technology, Hubei Key Laboratory of Advanced Technology for Automotive Components, China Hubei Collaborative Innovation Center for Automotive Components Technology, China ,
- Wen Cai - Wuhan University of Technology, School of Automotive Engineering, China Wuhan University of Technology, Hubei Key Laboratory of Advanced Technology for Automotive Components, China Hubei Collaborative Innovation Center for Automotive Components Technology, China
Journal Article
13-03-02-0015
ISSN: 2640-642X, e-ISSN: 2640-6438
Sector:
Topic:
Citation:
Zheng, Q., Tian, S., Wang, W., Zhang, Q. et al., "A Comparative Study of Equivalent Factor Optimization Based on Heuristic Algorithms for Hybrid Electric Vehicles," SAE J. STEEP 3(2):187-201, 2022, https://doi.org/10.4271/13-03-02-0015.
Language:
English
Abstract:
The equivalent consumption minimization strategy (ECMS) is an instantaneous
optimization method implemented online for hybrid electric vehicles (HEVs) to
improve fuel economy. To fulfill the near-optimal performance of ECMS,
equivalent factors (EFs) must be well tuned for different powertrains and
driving cycles. This study proposes a hierarchical offline optimization
framework which tunes the penalty value of state of charge (SOC) balance in the
outer layer and optimizes EFs based on heuristic algorithms in the inner layer.
A comprehensive analysis is conducted to evaluate three heuristic algorithms,
including the genetic algorithm (GA), the nonlinear-inertia-decreasing particle
swarm optimization algorithm (NLPSO), and the novel firefly algorithm (FA). The
traversal optimization method (TOM) is chosen as the benchmark. Besides, a
sensitivity analysis is carried out to reveal the impact of the penalty value on
the battery SOC balance. The simulation results confirm that the battery SOC
balance positively relates to the penalty value. However, the biggest penalty
value is not always the optimal choice. The comparative results show that the GA
and NLPSO can lessen 99.86% and 99.87% of the computational burden but increase
the fuel consumption by 4.44% and 4.26%, respectively, compared to TOM. On the
other hand, FA can reduce computational time by 99.87% with a 1.85% loss of fuel
economy, which is superior to the other two heuristic algorithms in terms of
optimality and computational cost.