LAPSE:2023.36280
Published Article
LAPSE:2023.36280
Energy Consumption Optimization Strategy of Hybrid Vehicle Based on NSGA-II Genetic Algorithm
July 7, 2023
Hybrid electric vehicles (HEVs) have certain advantages over internal combustion engines in terms of energy consumption and emission performance. However, the transmission system parameters are uncertain. The low matching between the engine and the power transmission system makes it a big problem to improve the efficiency of hybrid vehicles. Therefore, the multi-objective optimization design of hybrid vehicles is studied. The transmission system parameters of hybrid vehicles are analyzed from the objective function, decision variables, and constraints. The NSGA-II algorithm with elite strategy is introduced to realize the optimal selection of parameters and formulation of energy consumption optimization strategy. The results showed that the multi-objective optimization algorithm could adjust the position of the working point of the engine and improve the efficiency by more than 10%. There was an average difference of 2.15% after the improvement in the fuel consumption of four-gear vehicles. The fuel consumption per 100 km decreases by more than 3%. The maximum climbing gradient of the whole vehicle was 33.9%. The power factor of the direct gear of the maximum power factor increases by 15% after the improvement. The multi-objective energy consumption optimization design of hybrid vehicles proposed in the study can effectively improve the economic and dynamic performance of the whole vehicle and reduce fuel consumption. It provides a reference for the optimization of the hybrid vehicle transmission system.
Record ID
Keywords
drive system, economy, hybrid vehicle, NSGA-II genetic algorithm, power
Subject
Suggested Citation
Wang X, Ji W, Gao Y. Energy Consumption Optimization Strategy of Hybrid Vehicle Based on NSGA-II Genetic Algorithm. (2023). LAPSE:2023.36280
Author Affiliations
Wang X: Henan Polytechnic, Zhengzhou 450046, China
Ji W: Henan Polytechnic, Zhengzhou 450046, China
Gao Y: Henan Polytechnic, Zhengzhou 450046, China
Ji W: Henan Polytechnic, Zhengzhou 450046, China
Gao Y: Henan Polytechnic, Zhengzhou 450046, China
Journal Name
Processes
Volume
11
Issue
6
First Page
1735
Year
2023
Publication Date
2023-06-06
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr11061735, Publication Type: Journal Article
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Published Article
LAPSE:2023.36280
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External Link
doi:10.3390/pr11061735
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Version History
[v1] (Original Submission)
Jul 7, 2023
Verified by curator on
Jul 7, 2023
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v1
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https://psecommunity.org/LAPSE:2023.36280
Original Submitter
Calvin Tsay
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