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

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Listed:
  • 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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    1. Guo, Lingxiong & Zhang, Xudong & Zou, Yuan & Guo, Ningyuan & Li, Jianwei & Du, Guodong, 2021. "Cost-optimal energy management strategy for plug-in hybrid electric vehicles with variable horizon speed prediction and adaptive state-of-charge reference," Energy, Elsevier, vol. 232(C).
    2. Guo, Ningyuan & Zhang, Xudong & Zou, Yuan & Guo, Lingxiong & Du, Guodong, 2021. "Real-time predictive energy management of plug-in hybrid electric vehicles for coordination of fuel economy and battery degradation," Energy, Elsevier, vol. 214(C).
    3. Wang, Weida & Guo, Xinghua & Yang, Chao & Zhang, Yuanbo & Zhao, Yulong & Huang, Denggao & Xiang, Changle, 2022. "A multi-objective optimization energy management strategy for power split HEV based on velocity prediction," Energy, Elsevier, vol. 238(PA).
    4. Xiang, Changle & Ding, Feng & Wang, Weida & He, Wei, 2017. "Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control," Applied Energy, Elsevier, vol. 189(C), pages 640-653.
    5. Cui, Wei & Cui, Naxin & Li, Tao & Cui, Zhongrui & Du, Yi & Zhang, Chenghui, 2022. "An efficient multi-objective hierarchical energy management strategy for plug-in hybrid electric vehicle in connected scenario," Energy, Elsevier, vol. 257(C).
    6. Niu, Dongxiao & Yu, Min & Sun, Lijie & Gao, Tian & Wang, Keke, 2022. "Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism," Applied Energy, Elsevier, vol. 313(C).
    7. Sun, Chao & Sun, Fengchun & He, Hongwen, 2017. "Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1644-1653.
    8. Zhang, Hailong & Peng, Jiankun & Tan, Huachun & Dong, Hanxuan & Ding, Fan & Ran, Bin, 2020. "Tackling SOC long-term dynamic for energy management of hybrid electric buses via adaptive policy optimization," Applied Energy, Elsevier, vol. 269(C).
    9. Hu, Jiayi & Li, Jianqiu & Hu, Zunyan & Xu, Liangfei & Ouyang, Minggao, 2021. "Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming," Energy, Elsevier, vol. 215(PA).
    10. Guo, Hongqiang & Lu, Silong & Hui, Hongzhong & Bao, Chunjiang & Shangguan, Jinyong, 2019. "Receding horizon control-based energy management for plug-in hybrid electric buses using a predictive model of terminal SOC constraint in consideration of stochastic vehicle mass," Energy, Elsevier, vol. 176(C), pages 292-308.
    11. Xie, Shanshan & He, Hongwen & Peng, Jiankun, 2017. "An energy management strategy based on stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 196(C), pages 279-288.
    12. Tian, Xiang & Cai, Yingfeng & Sun, Xiaodong & Zhu, Zhen & Xu, Yiqiang, 2019. "An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses," Energy, Elsevier, vol. 189(C).
    Full references (including those not matched with items on IDEAS)

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