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AGRU and convex optimization based energy management for plug-in hybrid electric bus considering battery aging

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  • Du, Yi
  • Cui, Naxin
  • Cui, Wei
  • Li, Tao
  • Ren, Fei
  • Zhang, Chenghui

Abstract

For improving energy saving performance and extending battery life of plug-in hybrid electric bus (PHEB), a real-time predictive energy management strategy (EMS) is proposed in this study, which combines the velocity prediction model with the energy optimization strategy based on the convex optimization algorithm. First, the attention mechanism is embedded to the gate recurrent unit (GRU) deep learning algorithm to achieve sequence to sequence prediction for PHEB velocity in an efficient way. Second, a multi-objective receding horizon control (RHC) framework for coordinating fuel economy and battery aging cost is established by incorporating the predicted vehicle velocity and the spatial domain based state of charge (SOC) reference trajectory that conforms to the PHEB driving routine. The model convexity of power components such as engine and battery of PHEB is processed, and the alternating direction method of multipliers (ADMM) approach is adopted to solve the optimal control problem of fuel, electricity and battery aging cost in real time to fulfill the goal of real-time energy management. Finally, both the simulation and hardware-in-the-loop (HIL) experiments have verified the superiority of the strategy with respect to velocity forecasting accuracy, computational burden, energy saving and battery aging mitigation.

Suggested Citation

  • Du, Yi & Cui, Naxin & Cui, Wei & Li, Tao & Ren, Fei & Zhang, Chenghui, 2023. "AGRU and convex optimization based energy management for plug-in hybrid electric bus considering battery aging," Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:energy:v:277:y:2023:i:c:s0360544223009829
    DOI: 10.1016/j.energy.2023.127588
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    References listed on IDEAS

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