The electrical energy of a plug-in hybrid electric vehicle (PHEV) is provided by the internal combustion engine and grid power. The fuel consumption of a PHEV can be minimised through optimising the operation at all-electric range (AER). The AER may vary with the state of charge (SOC), the expected route characteristic, traffic and the electrical energy available dominated by the forthcoming charge opportunity. This research proposes an AER adaptive energy management strategy based on the equivalent consumption minimisation strategy (ECMS) and the forthcoming energy consumption prediction. The model of the equivalent factor (EF) is developed based on the required energy per unit distance (REPD). The corresponding correction factor of EF is optimised with the particle swarm optimisation and developed as a function of REPD, SOC and the AER. The artificial neural network is used to predict REPD which is applied to update the EF estimated model online. The proposed strategy is validated by the numerical simulation and hardware-in-loop experiment (HIL). The simulation and HIL experiment results demonstrate that the proposed strategy can further improve the fuel economy of PHEVs when compared with the traditional ECMS under different driving cycles.


    Access

    Access via TIB


    Export, share and cite



    AER adaptive control strategy via energy prediction for PHEV

    Lin, Xinyou / Zhou, Kuncheng / Li, Hailin | Wiley | 2019

    Free access

    Whole-Day Driving Prediction control strategy To minimize PHEV cost

    Palcu, Petru / Bauman, Jennifer | IEEE | 2017


    PHEV PHEV charging system and its control method

    YOO SANG HOON / RYU CHANG RYEOL / LEE JANG HYO | European Patent Office | 2016

    Free access

    PHEV PHEV charging system and its control method

    European Patent Office | 2021

    Free access

    PHEV Control appratus for drive garbage car PHEV

    European Patent Office | 2022

    Free access