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Real-time optimal energy management strategy for a dual-mode power-split hybrid electric vehicle based on an explicit model predictive control algorithm

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Listed:
  • Li, Xunming
  • Han, Lijin
  • Liu, Hui
  • Wang, Weida
  • Xiang, Changle

Abstract

To improve fuel economy and reduce online computation time and microprocessor hardware resources, a real-time implementable energy management strategy for a dual-mode power-split hybrid electric vehicle (HEV) based on an explicit model predictive control (EMPC) method is proposed in this paper. The proposed strategy includes an accurate control-oriented model and a dynamic process coordination control algorithm. The energy management optimal control problem is formulated as a multiparameter quadratic programming optimization problem, and the EMPC control laws are obtained by solving the multiparameter quadratic programming problem offline. The laws are then used online to realize real-time control. A traditional model predictive control (MPC)-based control strategy, DP-based control strategy and rule-based control strategy are considered benchmark strategies for verification of the proposed EMPC-based energy management strategy. The simulation results indicate the EMPC controller has far lower microprocessor hardware costs than the MPC controller but equivalent control performance. As the prediction horizon increases, fuel consumption remains nearly the same between the MPC-based control strategy and EMPC-based control strategy. The consumption time of the MPC-based control strategy increases significantly, while the consumption time of the EMPC-based control strategy is nearly unchanged. Compared with the benchmark algorithms, the elapsed time of the EMPC controller maximum reduced by 97.46%, and the fuel economy improved by 23.37%.

Suggested Citation

  • Li, Xunming & Han, Lijin & Liu, Hui & Wang, Weida & Xiang, Changle, 2019. "Real-time optimal energy management strategy for a dual-mode power-split hybrid electric vehicle based on an explicit model predictive control algorithm," Energy, Elsevier, vol. 172(C), pages 1161-1178.
    • Handle: RePEc:eee:energy:v:172:y:2019:i:c:p:1161-1178
    DOI: 10.1016/j.energy.2019.01.052
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    References listed on IDEAS

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