期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 18, 期 8, 页码 2181-2191出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2016.2633542
关键词
Plug-in hybrid electric vehicle; intelligent transportation system; energy management; evolutionary algorithm
资金
- National Center for Sustainable Transportation
Plug-in hybrid electric vehicles (PHEVs) have been regarded as one of several promising countermeasures to transportation-related energy use and air quality issues. Compared with conventional hybrid electric vehicles, developing an energy management system (EMS) for PHEVs is more challenging due to their more complex powertrain. In this paper, we propose a generic framework of online EMS for PHEVs that is based on an evolutionary algorithm. It includes several control strategies for managing battery state-of-charge (SOC). Extensive simulation testing and evaluation using real-world traffic data indicates that the different SOC control strategies of the proposed online EMS all outperform the conventional control strategy. Out of all the SOC control strategies, the self-adaptive one is the most adaptive to real-time traffic conditions and the most robust to the uncertainties in recharging opportunity. A comparison to the existing models also employing short-term prediction shows that the proposed model can achieve the best fuel economy improvement but requiring less trip information.
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