A trip distance adaptive real-time optimal energy management strategy for a plug-in hybrid vehicle integrated driving condition prediction. (25th August 2022)

• Record Type: Journal Article
• Authors: Lin, Xinyou; Zhang, Jiajin; Su, Lian
• Is Part Of: Journal of energy storage. Volume 52:Part C(2022)
• Publication Date: 2022-08-25
• Subjects: Energy management -- Driving condition prediction -- Artificial neural network -- PHEV; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2022.105055
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: To achieve better fuel economy for plug-in hybrid electric vehicles (PHEVs), this paper proposes a novel improved adaptive equivalent consumption minimization strategy (A-ECMS) integrated driving condition prediction using artificial neural network (ANN) combined the least-squares with forgetting factors. Firstly, the ANN method and the least-square with forgetting factors are used to predict the velocity of the vehicle and the slope of the road. Then a trip adaptive ECMS is proposed which the equivalent factor (EF) is adjusted in real-time according to the remaining distance. Furthermore, the driving condition prediction technology is integrated into A-ECMS to decrease fuel consumption further. Besides, the impact of different preview horizon lengths on fuel consumption is analyzed. Finally, a simulation study is conducted for applying the proposed strategy to a practical trip path in the Fuzhou road network. Simulation results show that, compared with CD-CS, the A-ECMS combined with driving condition prediction can achieve better fuel economy with a fuel consumption reduction by 12.1%, which effectively improves the fuel economy of the PHEV. Highlights: Driving condition prediction using artificial neural network combined the least-square with forgetting factors. Trip distance adaptive equivalent consumption minimization strategy (A-ECMS) is applied for a PHEV. Driving condition prediction technology is integrated into A-ECMS. Real-world speed profile is used for assessing the proposed method.