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Data-driven predictive energy consumption minimization strategy for connected plug-in hybrid electric vehicles

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  • Zhang, Hao
  • Lei, Nuo
  • Liu, Shang
  • Fan, Qinhao
  • Wang, Zhi

Abstract

Powertrain electrification incorporating advanced combustion-based dedicated hybrid engines (DHEs) is an effective and affordable approach to automotive energy saving. To explore the concealed fuel-saving potential of connected plug-in hybrid electric vehicles (CPHEVs) and manage engine dynamics, a data-driven predictive energy consumption minimization strategy (D-PECMS) is proposed in a hierarchical framework. The strategy relies on multi-source trip information provided by advanced driving assistance systems (ADAS) combined with maps and realizes power demand prediction by designing a multivariable long-term and short-term memory (M-LSTM) network. The upper level adopts dynamic programming (DP) to realize SOC planning, while the bottom layer utilizes D-PECMS to achieve computationally-efficient energy management with the engine combustion process being regulated in transient. This strategy is featured with predictive SOC tracking ability with less computational burden and look-ahead engine start-stop control. To ensure the credibility of validation, bench test data are used from a high-efficiency spark-induced compression ignition (SICI) engine to model the CPHEV, and the real-world driving scenarios are reconstructed based on real-time traffic data collected in China. The proposed D-PECMS strategy is tested through comprehensive experiments and compared against both the adaptive ECMS and offline DP. The results demonstrate that the proposed strategy effectively reduces fuel consumption by 3.1% and 13.2% in contrast to the adaptive ECMS and rule-based control respectively. Moreover, the D-PECMS strategy successfully avoids frequent engine operation mode switching as well as engine startup and shutdown.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223019084
    DOI: 10.1016/j.energy.2023.128514
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    as
    1. 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).
    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. Ju, Fei & Murgovski, Nikolce & Zhuang, Weichao & Hu, Xiaosong & Song, Ziyou & Wang, Liangmo, 2023. "Predictive energy management with engine switching control for hybrid electric vehicle via ADMM," Energy, Elsevier, vol. 263(PE).
    4. Jin, Yue & Yang, Lin & Du, Mao & Qiang, Jiaxi & Li, Jingzhong & Chen, Yuxuan & Tu, Jiayu, 2023. "Two-scale based energy management for connected plug-in hybrid electric vehicles with global optimal energy consumption and state-of-charge trajectory prediction," Energy, Elsevier, vol. 267(C).
    5. 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).
    6. Tian, Xiang & Cai, Yingfeng & Sun, Xiaodong & Zhu, Zhen & Xu, Yiqiang, 2019. "An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses," Energy, Elsevier, vol. 189(C).
    7. Zhang, Yuanjian & Gao, Bingzhao & Jiang, Jingjing & Liu, Chengyuan & Zhao, Dezong & Zhou, Quan & Chen, Zheng & Lei, Zhenzhen, 2023. "Cooperative power management for range extended electric vehicle based on internet of vehicles," Energy, Elsevier, vol. 273(C).
    8. Barbosa, Társis Prado & Eckert, Jony Javorski & Roso, Vinícius Rückert & Pujatti, Fabrício José Pacheco & da Silva, Leonardo Adolpho Rodrigues & Horta Gutiérrez, Juan Carlos, 2021. "Fuel saving and lower pollutants emissions using an ethanol-fueled engine in a hydraulic hybrid passengers vehicle," Energy, Elsevier, vol. 235(C).
    9. Chen, Zhihang & Liu, Yonggang & Zhang, Yuanjian & Lei, Zhenzhen & Chen, Zheng & Li, Guang, 2022. "A neural network-based ECMS for optimized energy management of plug-in hybrid electric vehicles," Energy, Elsevier, vol. 243(C).
    10. Fan, Likang & Wang, Yufei & Wei, Hongqian & Zhang, Youtong & Zheng, Pengyu & Huang, Tianyi & Li, Wei, 2022. "A GA-based online real-time optimized energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 241(C).
    11. Tang, Wenbin & Wang, Yaqian & Jiao, Xiaohong & Ren, Lina, 2023. "Hierarchical energy management strategy based on adaptive dynamic programming for hybrid electric vehicles in car-following scenarios," Energy, Elsevier, vol. 265(C).
    12. Zhou, Quan & Du, Changqing & Wu, Dongmei & Huang, Cheng & Yan, Fuwu, 2023. "A tolerant sequential correction predictive energy management strategy of hybrid electric vehicles with adaptive mesh discretization," Energy, Elsevier, vol. 274(C).
    13. Li, Yapeng & Wang, Feng & Tang, Xiaolin & Hu, Xiaosong & Lin, Xianke, 2022. "Convex optimization-based predictive and bi-level energy management for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 257(C).
    14. Yang, Ningkang & Ruan, Shumin & Han, Lijin & Liu, Hui & Guo, Lingxiong & Xiang, Changle, 2023. "Reinforcement learning-based real-time intelligent energy management for hybrid electric vehicles in a model predictive control framework," Energy, Elsevier, vol. 270(C).
    15. Hunicz, Jacek & Mikulski, Maciej & Koszałka, Grzegorz & Ignaciuk, Piotr, 2020. "Detailed analysis of combustion stability in a spark-assisted compression ignition engine under nearly stoichiometric and heavy EGR conditions," Applied Energy, Elsevier, vol. 280(C).
    16. Wang, Weida & Guo, Xinghua & Yang, Chao & Zhang, Yuanbo & Zhao, Yulong & Huang, Denggao & Xiang, Changle, 2022. "A multi-objective optimization energy management strategy for power split HEV based on velocity prediction," Energy, Elsevier, vol. 238(PA).
    17. Zhang, Hao & Liu, Shang & Lei, Nuo & Fan, Qinhao & Wang, Zhi, 2022. "Leveraging the benefits of ethanol-fueled advanced combustion and supervisory control optimization in hybrid biofuel-electric vehicles," Applied Energy, Elsevier, vol. 326(C).
    18. Du, Yongchang & Zhao, Yue & Wang, Qinpu & Zhang, Yuanbo & Xia, Huaicheng, 2016. "Trip-oriented stochastic optimal energy management strategy for plug-in hybrid electric bus," Energy, Elsevier, vol. 115(P1), pages 1259-1271.
    19. 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).
    20. 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).
    21. Xu, Guangyue & Schwarz, Peter & Yang, Hualiu, 2020. "Adjusting energy consumption structure to achieve China's CO2 emissions peak," Renewable and Sustainable Energy Reviews, Elsevier, vol. 122(C).
    22. Ruan, Jiageng & Wu, Changcheng & Liang, Zhaowen & Liu, Kai & Li, Bin & Li, Weihan & Li, Tongyang, 2023. "The application of machine learning-based energy management strategy in a multi-mode plug-in hybrid electric vehicle, part II: Deep deterministic policy gradient algorithm design for electric mode," Energy, Elsevier, vol. 269(C).
    23. Liu, Yonggang & Huang, Bin & Yang, Yang & Lei, Zhenzhen & Zhang, Yuanjian & Chen, Zheng, 2022. "Hierarchical speed planning and energy management for autonomous plug-in hybrid electric vehicle in vehicle-following environment," Energy, Elsevier, vol. 260(C).
    24. Fan, Qinhao & Liu, Shang & Qi, Yunliang & Cai, Kaiyuan & Wang, Zhi, 2021. "Investigation into ethanol effects on combustion and particle number emissions in a spark-ignition to compression-ignition (SICI) engine," Energy, Elsevier, vol. 233(C).
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