| Autor: |
Jianhua Guo, Zhiqi Guo, Liang Chu, Di Zhao, Jincheng Hu, Zhuoran Hou |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Sensors, Vol 22, Iss 16, p 6256 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1424-8220 |
| DOI: |
10.3390/s22166256 |
| Popis: |
Energy management strategies are vitally important to give full play to energy-saving four-wheel-drive plug-in hybrid electric vehicles (4WD PHEV). This paper proposes a novel dual-adaptive equivalent consumption minimization strategy (DA-ECMS) for the complex multi-energy system in the 4WD PHEV. In this strategy, management of the multi-energy system is optimized by introducing the categories of future driving conditions to adjust the equivalent factors and improving the adaptability and economy of driving conditions. Firstly, a self-organizing neural network (SOM) and grey wolf optimizer (GWO) are adopted to classify the driving condition categories and optimize the multi-dimensional equivalent factors offline. Secondly, SOM is adopted to identify driving condition categories and the multi-dimensional equivalent factors are matched. Finally, the DA-ECMS completes the multi-energy optimization management of the front axle multi-energy sources and the electric driving system and releases the energy-saving potential of the 4WD PHEV. Simulation results show that, compared with the rule-based strategy, the economy in the DA-ECMS is improved by 13.31%. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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