An improved A-ECMS energy management for plug-in hybrid electric vehicles considering transient characteristics of engine
| Autor: | Hongwen He, Yiwen Shou, Jian Song |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Energy Reports, Vol 10, Iss , Pp 2006-2016 (2023) |
| Druh dokumentu: | article |
| ISSN: | 2352-4847 71677216 |
| DOI: | 10.1016/j.egyr.2023.08.085 |
| Popis: | The plug-in hybrid electric vehicles (PHEVs) are playing an increasingly important role in urban public transportation systems for their unique potential for energy saving and emission reduction. However, as an enabling technology for the cost-efficient operation of PHEVs, the current energy management strategies (EMSs) rarely consider the transient characteristics of the engine, especially the limit of engine transient performance and the extra fuel consumption due to engine state changes. To improve the energy-saving effect of EMSs, an optimization study on energy management considering engine transient characteristics for PHEV is carried out in this paper. Firstly, a high-precision transient fuel consumption model is established based on artificial neural network (ANN) to accurately evaluate the real fuel consumption of engine under steady and unsteady states. Secondly, an adaptive equivalent consumption minimization strategy (A-ECMS) is constructed for PHEV, and the engine transient performance boundary is defined in the strategy to avoid unreasonable engine power surge decision. Thirdly, the transient fuel consumption model is incorporated into the equivalent fuel consumption cost function of A-ECMS to fully consider the impact of engine transient fuel consumption on the real-time power allocation of PHEV. The results show that the improved strategy weakens the state fluctuation of the engine, and makes the engine run more smoothly, resulting in a 99.16% reduction in the extra fuel consumption due to engine state changes. Finally, the fuel economy of the PHEV under the combined driving cycle based on the C-WTVC improved by 3.37%. |
| Databáze: | Directory of Open Access Journals |
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