A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 8 Issue 12
Dec.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Yang Zhao, Yanguang Cai and Qiwen Song, "Energy Control of Plug-In Hybrid Electric Vehicles Using Model Predictive Control With Route Preview," IEEE/CAA J. Autom. Sinica, vol. 8, no. 12, pp. 1948-1955, Dec. 2021. doi: 10.1109/JAS.2017.7510889
Citation: Yang Zhao, Yanguang Cai and Qiwen Song, "Energy Control of Plug-In Hybrid Electric Vehicles Using Model Predictive Control With Route Preview," IEEE/CAA J. Autom. Sinica, vol. 8, no. 12, pp. 1948-1955, Dec. 2021. doi: 10.1109/JAS.2017.7510889

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

doi: 10.1109/JAS.2017.7510889
More Information
  • 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 real-time 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 real-time 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.

     

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  • Recommended by Associate Editor Xiangyang Zhao.
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    Highlights

    • 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 traffific data, in which information fusion model and traffific prediction model are used to improve the information accuracy.
    • This paper presents real-time control strategies for PHEV energy management that takes advantage of traffific information preview. The proposed strategies aim at fuel minimization for PHEV. The dynamic programming combined with equivalent consumption minimization strategy is used to compute an optimal solution for real-time energy management.
    • We focus on how MPC minimizes the cost and improves vehicle energy control. The route features are identifified with information fusion model and prediction model for historical and realtime traffific data. 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.

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