Energy management of plug-in hybrid electric vehicle based on trip characteristic prediction. (July 2020)

• Record Type: Journal Article
• Authors: Wang, Pengyu; Li, Jinke; Yu, Yuanbin; Xiong, Xiaoyong; Zhao, Shijie; Shen, Wangsheng
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 234:Number 8(2020)
• Page Start: 2239
• Page End: 2259
• Publication Date: 2020-07
• Subjects: Plug-in hybrid electric vehicle -- trajectory prediction -- dynamic programming -- energy management strategy -- SOC trajectory planning; Mechanical engineering -- Congresses; Transportation engineering -- Congresses; 629.2
• DOI: 10.1177/0954407020904464
• Languages: English
• ISSNs: 0954-4070

Abstract

In the research regarding plug-in hybrid electric vehicle energy management strategies, the use of global positioning system and intelligent transportation system information to optimize control strategy will be the future trend, and this is relatively scarce in the existing researches. Therefore, an adaptive energy management strategy of plug-in hybrid electric vehicle based on trip characteristic prediction was investigated in this paper, and the main achievement is to suggest a way to determine the reference state of charge for control strategy using global positioning system or intelligent transportation system information. First, given the historical driving data of a driver by global positioning system, the important location points of the commuting routes were discovered. Second, a Markov trajectory prediction model based on the key points was established to predict and identify the driving routes. As such, the trip characteristics, such as information of mileage and driving cycles, were collected. Then, five typical driving cycles were extracted. According to the trip characteristic information, the optimal battery state of charge consumption regulation of plug-in hybrid electric vehicle was realized using a dynamic programming algorithm. This algorithm was applied to the research of state of charge trajectory planning algorithm. Moreover, an adaptive equivalent consumption minimization strategy based on state of charge planning trajectory was developed. The comparison of different control strategies proved that the developed strategy uses battery power reasonably and reduces fuel consumption of the vehicle.