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

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 fil...

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Published inEngineering optimization Vol. 56; no. 3; pp. 447 - 467
Main Authors Liu, Jinpeng, Fu, Zhumu, Tao, Fazhan, Jiao, Longyin, Chen, Qihong
Format Journal Article
LanguageEnglish
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
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Abstract 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.
AbstractList 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.
Author Liu, Jinpeng
Tao, Fazhan
Jiao, Longyin
Fu, Zhumu
Chen, Qihong
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CitedBy_id crossref_primary_10_1080_15567036_2024_2336173
crossref_primary_10_1016_j_energy_2024_132771
crossref_primary_10_1016_j_ijhydene_2024_07_237
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Snippet 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...
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SubjectTerms 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
Title Energy management strategy based on convex optimization for fuel cell/battery/ultracapacitor hybrid vehicle
URI https://www.tandfonline.com/doi/abs/10.1080/0305215X.2022.2155345
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