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A comparative analysis of adaptive energy management for a hybrid electric vehicle via five driving condition recognition methods

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  • Xu, Bin
  • Wang, Hanchen

Abstract

– Adaptive powertrain control is of great importance to a high energy efficient vehicle and varying parameter recognition is the first step of adaptive control. However, a comparative study on different driving condition recognition methods is lacking in literature. In this study, five recognition methods are introduced and compared, namely fuzzy logic, clustering, Markov Decision Process, and supervised learning and navigation-based method. Three driving conditions are considered as recognition target, which are urban, suburban and highway conditions. 29 driving cycles and 4 driving cycles are used in training and validation, respectively. The training results show that all five methods have training accuracy above 86% with supervised learning leading the accuracy at 91.37%. The validation results from a hybrid electric vehicle show that the five prediction methods improve the fuel economy by 2.47%–4.58% in the four validation driving cycles when compared with constant prediction method. Based on the analysis of complexity and fuel economy performance, the navigation-based and clustering methods are recommended to apply in vehicle concept and production phase, respectively. This study can be used as a guidance to select driving condition recognition method for adaptive vehicle energy management.

Suggested Citation

  • Xu, Bin & Wang, Hanchen, 2023. "A comparative analysis of adaptive energy management for a hybrid electric vehicle via five driving condition recognition methods," Energy, Elsevier, vol. 269(C).
    • Handle: RePEc:eee:energy:v:269:y:2023:i:c:s0360544223001263
    DOI: 10.1016/j.energy.2023.126732
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    References listed on IDEAS

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    1. Song, Ziyou & Hou, Jun & Xu, Shaobing & Ouyang, Minggao & Li, Jianqiu, 2017. "The influence of driving cycle characteristics on the integrated optimization of hybrid energy storage system for electric city buses," Energy, Elsevier, vol. 135(C), pages 91-100.
    2. Hu, Jie & Liu, Di & Du, Changqing & Yan, Fuwu & Lv, Chen, 2020. "Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition," Energy, Elsevier, vol. 198(C).
    3. Zhenzhen Lei & Dong Cheng & Yonggang Liu & Datong Qin & Yi Zhang & Qingbo Xie, 2017. "A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition," Energies, MDPI, vol. 10(1), pages 1-20, January.
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