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A hierarchical energy management strategy for PHEVs: Optimizing SOC trajectory tracking performance using adaptive initial equivalent factor strategy (AIEFS)

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  • Jin, Nini
  • Jia, Feifei
  • Dai, Lihong
  • Liu, Haoye
  • Wang, Tianyou
  • Hu, Peng

Abstract

This paper proposes a Hierarchical Energy Management Strategy for PHEVs (HEMS-AIEFS), in which an Adaptive Initial Equivalent Factor Strategy was incorporated to improve real-time State of Charge (SOC) trajectory tracking performance. The HEMS-AIEFS operates on a two-layer structure. The upper layer uses a nodal SOC planning method, where dynamic programming (DP) is first applied offline to generate optimal SOC trajectories based on standard and real-world driving cycles. The cycles and SOC trajectories are then segmented, and relevant data are extracted to train neural network models that predict SOC node trajectories for future journeys. The lower layer implements the Predictive Equivalent Consumption Minimization Strategy (P-ECMS) to track the predicted SOC trajectory, incorporating AIEFS to set the initial equivalent factor (EF0). The results demonstrate that the incorporation of AIEFS significantly improves SOC trajectory tracking accuracy in P-ECMS. Compared to using a driving-distance and initial-SOC map method or a fixed EF0 method to set EF0, AIEFS reduces the mean squared error (MSE) between the actual and optimal SOC trajectories by 36.26 %–91.68 %, and decreases fuel consumption by 0.35 %–7.69 % under WLTC × 2 driving cycle, meanwhile, it demonstrates stronger adaptability across different SOC variation scenarios and driving cycles. Compared to Charge Depleting and Charge Sustaining (CD-CS) strategies, HEMS-AIEFS reduces fuel consumption by 1.84 %–8.47 % under various conditions.

Suggested Citation

  • Jin, Nini & Jia, Feifei & Dai, Lihong & Liu, Haoye & Wang, Tianyou & Hu, Peng, 2025. "A hierarchical energy management strategy for PHEVs: Optimizing SOC trajectory tracking performance using adaptive initial equivalent factor strategy (AIEFS)," Energy, Elsevier, vol. 318(C).
  • Handle: RePEc:eee:energy:v:318:y:2025:i:c:s036054422500458x
    DOI: 10.1016/j.energy.2025.134816
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    References listed on IDEAS

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    1. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    2. Yang, Ye & Zhang, Youtong & Tian, Jingyi & Li, Tao, 2020. "Adaptive real-time optimal energy management strategy for extender range electric vehicle," Energy, Elsevier, vol. 197(C).
    3. Lei, Zhenzhen & Qin, Datong & Hou, Liliang & Peng, Jingyu & Liu, Yonggang & Chen, Zheng, 2020. "An adaptive equivalent consumption minimization strategy for plug-in hybrid electric vehicles based on traffic information," Energy, Elsevier, vol. 190(C).
    4. Lu, Ziwang & Tian, He & sun, Yiwen & Li, Runfeng & Tian, Guangyu, 2023. "Neural network energy management strategy with optimal input features for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
    5. Zhang, Pei & Yan, Fuwu & Du, Changqing, 2015. "A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 88-104.
    6. Kong, Yan & Xu, Nan & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2021. "Acquisition of full-factor trip information for global optimization energy management in multi-energy source vehicles and the measure of the amount of information to be transmitted," Energy, Elsevier, vol. 236(C).
    7. Wang, Feng & Zhang, Jian & Xu, Xing & Cai, Yingfeng & Zhou, Zhiguang & Sun, Xiaoqiang, 2019. "A comprehensive dynamic efficiency-enhanced energy management strategy for plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 247(C), pages 657-669.
    8. Cui, Wei & Cui, Naxin & Li, Tao & Cui, Zhongrui & Du, Yi & Zhang, Chenghui, 2022. "An efficient multi-objective hierarchical energy management strategy for plug-in hybrid electric vehicle in connected scenario," Energy, Elsevier, vol. 257(C).
    9. Chen, Z. & Liu, Y. & Ye, M. & Zhang, Y. & Chen, Z. & Li, G., 2021. "A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    10. Sun, Chao & Sun, Fengchun & He, Hongwen, 2017. "Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1644-1653.
    11. Yonggang Liu & Jie Li & Ming Ye & Datong Qin & Yi Zhang & Zhenzhen Lei, 2017. "Optimal Energy Management Strategy for a Plug-in Hybrid Electric Vehicle Based on Road Grade Information," Energies, MDPI, vol. 10(4), pages 1-20, March.
    12. Zhou, Wei & Chen, Yaoqi & Zhai, Haoran & Zhang, Weigang, 2021. "Predictive energy management for a plug-in hybrid electric vehicle using driving profile segmentation and energy-based analytical SoC planning," Energy, Elsevier, vol. 220(C).
    13. Zhang, Hao & Fan, Qinhao & Liu, Shang & Li, Shengbo Eben & Huang, Jin & Wang, Zhi, 2021. "Hierarchical energy management strategy for plug-in hybrid electric powertrain integrated with dual-mode combustion engine," Applied Energy, Elsevier, vol. 304(C).
    14. Yu, Xiao & Lin, Cheng & Xie, Peng & Liang, Sheng, 2022. "A novel real-time energy management strategy based on Monte Carlo Tree Search for coupled powertrain platform via vehicle-to-cloud connectivity," Energy, Elsevier, vol. 256(C).
    15. Zhang, Hao & Lei, Nuo & Liu, Shang & Fan, Qinhao & Wang, Zhi, 2023. "Data-driven predictive energy consumption minimization strategy for connected plug-in hybrid electric vehicles," Energy, Elsevier, vol. 283(C).
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