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Optimization framework for multi-objective energy management strategy in hybrid electric vehicles integrating explainable artificial intelligence

Author

Listed:
  • Lu, Zhiyuan
  • Wang, Hu
  • He, Guanzhang
  • Chen, Yong
  • Li, Zihou
  • Zheng, Zunqing
  • Yao, Mingfa
  • Zhang, Song
  • Wang, Hao

Abstract

This study presents a multi-objective optimization (MOO) and explainable artificial intelligence (XAI) integrated framework for establishing MOO strategies in hybrid electric vehicle (HEV) and analyzing the decision-making patterns of the system. Specifically, the study first establishes a power-split HEV model and incorporates fuel consumption, electricity consumption, battery degradation, and the number of engine start-stops into the system evaluation metrics. Subsequently, the dynamic programming (DP) algorithm is employed to develop an offline globally optimal multi-objective strategy based on the multi-indicator vehicle model, and the non-dominated sorting genetic algorithm-II (NSGA-II) is used to perform MOO of the strategy's cost function. By combining the cognition-driven analytical hierarchy process (AHP) decision-making method with the data-driven technique for order preference by similarity to ideal solution (TOPSIS) method, the AHP-TOPSIS decision-making method is used to select the optimal solution from the Pareto frontier. Tree-based XAI methods are introduced, employing mean decrease impurity (MDI) and partial dependence plots (PDP) to analyze the interaction mechanisms among the four objectives during the decision-making process. A double-layer random forest (RF) energy management strategy is constructed, combining a five-fold cross-validated RF pattern recognition model with an engine power prediction model based on the optimal solution dataset. The results demonstrate that the multi-objective strategy exhibits better overall performance compared to single-objective strategies. The proposed double-layer RF strategy reduces fuel consumption by 1.8 %, maintains similar electricity consumption, decreases battery degradation by 7.1 %, and reduces the number of engine start-stops by 82.1 % compared to a rule-based (RB) strategy, with minimal deviation from the DP strategy. This validates the superior performance of the strategy in multi-objective control processes.

Suggested Citation

  • Lu, Zhiyuan & Wang, Hu & He, Guanzhang & Chen, Yong & Li, Zihou & Zheng, Zunqing & Yao, Mingfa & Zhang, Song & Wang, Hao, 2025. "Optimization framework for multi-objective energy management strategy in hybrid electric vehicles integrating explainable artificial intelligence," Applied Energy, Elsevier, vol. 399(C).
    • Handle: RePEc:eee:appene:v:399:y:2025:i:c:s0306261925012140
    DOI: 10.1016/j.apenergy.2025.126484
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    References listed on IDEAS

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    1. Chen, Yong & Lu, Zhiyuan & Liu, Heng & Wang, Hu & Zheng, Zunqing & Wang, Changhui & Sun, Xingyu & Xu, Linxun & Yao, Mingfa, 2024. "Machine learning-based design of target property-oriented fuels using explainable artificial intelligence," Energy, Elsevier, vol. 300(C).
    2. Manoranjan Pal & Premananda Bharati, 2019. "Introduction to Correlation and Linear Regression Analysis," Springer Books, in: Applications of Regression Techniques, chapter 0, pages 1-18, Springer.
    3. Egbue, Ona & Long, Suzanna, 2012. "Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions," Energy Policy, Elsevier, vol. 48(C), pages 717-729.
    4. Yongliang Zheng & Feng He & Xinze Shen & Xuesheng Jiang, 2020. "Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine," Energies, MDPI, vol. 13(2), pages 1-18, January.
    5. 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.
    6. Tran, Dai-Duong & Vafaeipour, Majid & El Baghdadi, Mohamed & Barrero, Ricardo & Van Mierlo, Joeri & Hegazy, Omar, 2020. "Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    7. Chen, Jiayu & Kuboyama, Tatsuya & Shen, Tielong, 2025. "Collective behavior information-based design approach to energy management strategy for large-scale population of HEVs," Applied Energy, Elsevier, vol. 377(PC).
    8. Akshit Kurani & Pavan Doshi & Aarya Vakharia & Manan Shah, 2023. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting," Annals of Data Science, Springer, vol. 10(1), pages 183-208, February.
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