| Autor: |
Yifei Zhang, Lijun Diao, Zheming Jin, Haoying Pei, Chunmei Xu |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Energy Reports, Vol 9, Iss , Pp 662-670 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2352-4847 |
| DOI: |
10.1016/j.egyr.2023.05.214 |
| Popis: |
The driving characteristics of a hybrid propulsion system (HPS) depend on operational management strategies. However, the differences in output and response of various types of power sources make real-time management of operating conditions complex. In this study, an operational management strategy, which combines adaptive equivalent consumption minimization strategy (AECMS) and general regression neural network (GRNN), is proposed for optimizing power controls within hybrid power sources. Under this strategy, the optimal function of the AECMS is adaptively calculated by the load profile changes based on GRNN’s prediction results, and it is more suitable for the HPS. Furthermore, the proposed GRNN-AECMS method can minimize fuel consumption and meet requirements for power tracking. From simulation results, fuel consumption under GRNN-AECMS method can be reduced by 2.715 kg, or 31.8%. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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