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Exploration the pathways of connected electric vehicle design: A vehicle-environment cooperation energy management strategy

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  • Hou, Zhuoran
  • Guo, Jianhua
  • Li, Jihao
  • Hu, Jinchen
  • Sun, Wen
  • Zhang, Yuanjian

Abstract

The advance in Internet of Vehicles (IoVs) enables an information-aggregated environment, underpinning the connected electric vehicle (cEV) development. The sensed multi-range driving condition information can consummate energy management in cEVs. IoVs based cEV design is in initial stage. The solutions that demonstrate the critical role of IoVs in optimal energy management in real time have not reach to state-of-the-art. In this study, a vehicle-environment cooperation energy management strategy (VEC-EMS) is proposed for cEV based on the explicitly framed cooperation mechanism in IoVs. First, an IoVs framework and inner cooperation mechanism are elaborated. Then, the VEC-EMS, empowered robustness to varying driving conditions in real-time optimal implementation, is designed. The adaptability to driving conditions is attained by a future vehicle status observer (FVSO), which integrates the improved radial basis function neural network (iRBF-NN) based velocity prediction and extreme gradient boosting decision tree (XGBoost) based driving condition identification. The optimality in instant energy management is accomplished via dynamic assignment of the optimized control thresholds according to the results of FVSO. The control thresholds are optimized by the improved Beetle Antennae Search (iBAS). At last, evaluation manifests that the proposed EMS can manage power flow within the electric powertrain, highlighting its anticipated preferable performance which increases by nearly 8% compared with a normal rule-based energy management strategy.

Suggested Citation

  • Hou, Zhuoran & Guo, Jianhua & Li, Jihao & Hu, Jinchen & Sun, Wen & Zhang, Yuanjian, 2023. "Exploration the pathways of connected electric vehicle design: A vehicle-environment cooperation energy management strategy," Energy, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:energy:v:271:y:2023:i:c:s0360544223004759
    DOI: 10.1016/j.energy.2023.127081
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    References listed on IDEAS

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    Cited by:

    1. Qin, Yanyan & Xiao, Tengfei & Wang, Hua, 2024. "Optimization strategy for connected automated vehicles to reduce energy consumption on freeway in rainy weather," Energy, Elsevier, vol. 296(C).
    2. Heping Jia & Qianxin Ma & Yun Li & Mingguang Liu & Dunnan Liu, 2023. "Integrating Electric Vehicles to Power Grids: A Review on Modeling, Regulation, and Market Operation," Energies, MDPI, vol. 16(17), pages 1-18, August.

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