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

Author

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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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    References listed on IDEAS

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