Energy Control of Plug-In Hybrid Electric Vehicles Using Model Predictive Control With Route Preview

• Published in: IEEE/CAA journal of automatica sinica Vol. 8; no. 12; pp. 1948 - 4854
• Main Authors: Zhao, Yang, Cai, Yanguang, Song, Qiwen
• Format: Journal Article
• Language: English
• Published: Piscataway Chinese Association of Automation (CAA) 01.12.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Department of Mechanical and Electrical Engineering,Guangdong University of Science and Technology,Dongguan 523083,China%School of Automation,Guangdong University of Technology,Guangzhou 510006,China%Transport Infrastructure Investment Company,Guangzhou 510620,China
• Subjects: Computational modeling; Data integration; Dynamic programming; Electric vehicles; Energy management; Engines; Hybrid electric vehicles; Mathematical model; model predictive control (MPC); optimal control; Optimization; plug-in hybrid electric vehicle (PHEV); Prediction models; Predictive control; Predictive models; Real time; Real-time systems; Torque; Traffic information; Traffic models; plug-in hybrid electric vehicle (PHEV); optimal control; Energy management; model predictive control (MPC)
• ISSN: 2329-9266; 2329-9274
• DOI: 10.1109/JAS.2017.7510889

The paper proposes an adoption of slope, elevation, speed and route distance preview to achieve optimal energy management of plug-in hybrid electric vehicles (PHEVs). The approach is to identify route features from historical and realtime traffic data, in which information fusion model and traffic prediction model are used to improve the information accuracy. Then, dynamic programming combined with equivalent consumption minimization strategy is used to compute an optimal solution for real-time energy management. The solution is the reference for PHEV energy management control along the route. To improve the system's ability of handling changing situation, the study further explores predictive control model in the realtime control of the energy. A simulation is performed to model PHEV under above energy control strategy with route preview. The results show that the average fuel consumption of PHEV along the previewed route with model predictive control (MPC) strategy can be reduced compared with optimal strategy and base control strategy.