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

Author

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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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    1. Lin, Xinyou & Wu, Jiayun & Wei, Yimin, 2021. "An ensemble learning velocity prediction-based energy management strategy for a plug-in hybrid electric vehicle considering driving pattern adaptive reference SOC," Energy, Elsevier, vol. 234(C).
    2. García, Antonio & Carlucci, Paolo & Monsalve-Serrano, Javier & Valletta, Andrea & Martínez-Boggio, Santiago, 2021. "Energy management optimization for a power-split hybrid in a dual-mode RCCI-CDC engine," Applied Energy, Elsevier, vol. 302(C).
    3. Jianzhong Chen & Yang Zhou & Jing Li & Huan Liang & Zekai Lv & Yanmei Hu, 2022. "Modeling of cooperative adaptive cruise control vehicle and its effect on traffic flow," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 33(02), pages 1-18, February.
    4. Xing, Yang & Lv, Chen & Cao, Dongpu & Lu, Chao, 2020. "Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling," Applied Energy, Elsevier, vol. 261(C).
    5. Wang, Siyang & Lin, Xianke, 2020. "Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios," Applied Energy, Elsevier, vol. 271(C).
    6. Sánchez, Marcelino & Delprat, Sébastien & Hofman, Theo, 2020. "Energy management of hybrid vehicles with state constraints: A penalty and implicit Hamiltonian minimization approach," Applied Energy, Elsevier, vol. 260(C).
    7. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Real-time energy management for fuel cell electric vehicle using speed prediction-based model predictive control considering performance degradation," Applied Energy, Elsevier, vol. 304(C).
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    Cited by:

    1. Jiang, Zewei & Hou, Zhuoran & Chu, Liang & Zhao, Di & Jiang, Jingjing & Yang, Jun & Zhang, Yuanjian, 2025. "An explicit predictive controller for fuel-cell electric vehicles incorporating the hierarchical architecture," Applied Energy, Elsevier, vol. 383(C).
    2. 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).
    3. Haishan Wu & Liang Li & Xiangyu Wang, 2025. "A Combined Energy Management Strategy for Heavy-Duty Trucks Based on Global Traffic Information Optimization," Sustainability, MDPI, vol. 17(14), pages 1-24, July.
    4. 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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