A Comparative Study of Equivalent Factor Optimization Based on
Heuristic Algorithms for Hybrid Electric Vehicles 13-03-02-0015
This also appears in
SAE International Journal of Sustainable Transportation, Energy, Environment, & Policy-V131-13EJ
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.
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. Download Citation
Affiliated:
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, 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
Pages: 16
ISSN:
2640-642X
e-ISSN:
2640-6438
Related Topics:
Hybrid electric vehicles
Fuel consumption
Fuel economy
Optimization
Mathematical models
Simulation and modeling
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