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Transferable representation modelling for real-time energy management of the plug-in hybrid vehicle based on k-fold fuzzy learning and Gaussian process regression

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  • Zhou, Quan
  • Li, Yanfei
  • Zhao, Dezong
  • Li, Ji
  • Williams, Huw
  • Xu, Hongming
  • Yan, Fuwu

Abstract

Electric vehicles, including plug-in hybrids, are important for achieving net-zero emission and will dominate road transportation in the future. Energy management, which optimizes the onboard energy usage, is a critical functionality of electric vehicles. It is usually developed following the model-based routine, which is conventionally costly and time-consuming and is hard to meet the increasing market competition in the digital era. To reduce the development workload for the energy management controller, this paper studies an innovative transfer learning routine. A new transferable representation control model is proposed by incorporating two promising artificial intelligence technologies, adaptive neural fuzzy inference system and Gaussian process regression, where the former applies k-fold cross valudation to build a neural fuzzy system for real-time implementation of offline optimization result, and the later connects the neural fuzzy system with a ‘deeper’ architecture to transfer the offline optimization knowledge learnt at source domain to new target domains. By introducing a concept of control utility that evaluates vehicle energy efficiency with a penalty on usage of battery energy, experimental evaluations based on the hardware-in-the-loop testing platform are conducted. Competitive real-time control ultility values (as much as 90% of offline benchmarking results) can be achieved by the proposed control method. They are over 27% higher than that achieved by the neural-network-based model.

Suggested Citation

  • Zhou, Quan & Li, Yanfei & Zhao, Dezong & Li, Ji & Williams, Huw & Xu, Hongming & Yan, Fuwu, 2022. "Transferable representation modelling for real-time energy management of the plug-in hybrid vehicle based on k-fold fuzzy learning and Gaussian process regression," Applied Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921011776
    DOI: 10.1016/j.apenergy.2021.117853
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    References listed on IDEAS

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    6. Chen, Ruihu & Yang, Chao & Ma, Yue & Wang, Weida & Wang, Muyao & Du, Xuelong, 2022. "Online learning predictive power coordinated control strategy for off-road hybrid electric vehicles considering the dynamic response of engine generator set," Applied Energy, Elsevier, vol. 323(C).
    7. Qiu, Dawei & Wang, Yi & Zhang, Tingqi & Sun, Mingyang & Strbac, Goran, 2023. "Hierarchical multi-agent reinforcement learning for repair crews dispatch control towards multi-energy microgrid resilience," Applied Energy, Elsevier, vol. 336(C).
    8. Qian Zhang & Shaopeng Tian, 2023. "Energy Consumption Prediction and Control Algorithm for Hybrid Electric Vehicles Based on an Equivalent Minimum Fuel Consumption Model," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    9. Wang, Bingzheng & Yu, Xiaoli & Xu, Hongming & Wu, Qian & Wang, Lei & Huang, Rui & Li, Zhi & Zhou, Quan, 2022. "Scenario analysis, management, and optimization of a new Vehicle-to-Micro-Grid (V2μG) network based on off-grid renewable building energy systems," Applied Energy, Elsevier, vol. 325(C).
    10. Abd-Elhaleem, Sameh & Shoeib, Walaa & Sobaih, Abdel Azim, 2023. "A new power management strategy for plug-in hybrid electric vehicles based on an intelligent controller integrated with CIGPSO algorithm," Energy, Elsevier, vol. 265(C).

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