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Advanced ECMS for Hybrid Electric Heavy-Duty Trucks with Predictive Battery Discharge and Adaptive Operating Strategy under Real Driving Conditions

Author

Listed:
  • Sven Schulze

    (Institute for Alternative Propulsion Systems, FH Aachen University of Applied Sciences, Hohenstaufenallee 10, 52066 Aachen, Germany)

  • Günter Feyerl

    (Institute for Alternative Propulsion Systems, FH Aachen University of Applied Sciences, Hohenstaufenallee 10, 52066 Aachen, Germany)

  • Stefan Pischinger

    (Chair of Thermodynamics of Mobile Energy Conversion Systems, RWTH Aachen University, Forckenbeckstrasse 4, 52074 Aachen, Germany)

Abstract

To fulfil the CO 2 emission reduction targets of the European Union (EU), heavy-duty (HD) trucks need to operate 15% more efficiently by 2025 and 30% by 2030. Their electrification is necessary as conventional HD trucks are already optimized for the long-haul application. The resulting hybrid electric vehicle (HEV) truck gains most of the fuel saving potential by the recuperation of potential energy and its consecutive utilization. The key to utilizing the full potential of HEV-HD trucks is to maximize the amount of recuperated energy and ensure its intelligent usage while keeping the operating point of the internal combustion engine as efficient as possible. To achieve this goal, an intelligent energy management strategy (EMS) based on ECMS is developed for a parallel HEV-HD truck which uses predictive discharge of the battery and adaptive operating strategy regarding the height profile and the vehicle mass. The presented EMS can reproduce the global optimal operating strategy over long phases and lead to a fuel saving potential of up to 2% compared with a heuristic strategy. Furthermore, the fuel saving potential is correlated with the investigated boundary conditions to deepen the understanding of the impact of intelligent EMS for HEV-HD trucks.

Suggested Citation

  • Sven Schulze & Günter Feyerl & Stefan Pischinger, 2023. "Advanced ECMS for Hybrid Electric Heavy-Duty Trucks with Predictive Battery Discharge and Adaptive Operating Strategy under Real Driving Conditions," Energies, MDPI, vol. 16(13), pages 1-29, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5171-:d:1187360
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    References listed on IDEAS

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    1. García, Antonio & Monsalve-Serrano, Javier & Martinez-Boggio, Santiago & Gaillard, Patrick, 2021. "Impact of the hybrid electric architecture on the performance and emissions of a delivery truck with a dual-fuel RCCI engine," Applied Energy, Elsevier, vol. 301(C).
    2. Yang, Chao & Du, Siyu & Li, Liang & You, Sixong & Yang, Yiyong & Zhao, Yue, 2017. "Adaptive real-time optimal energy management strategy based on equivalent factors optimization for plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 203(C), pages 883-896.
    3. Pei Zhang & Xianpan Wu & Changqing Du & Hongming Xu & Huawu Wang, 2020. "Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization," Energies, MDPI, vol. 13(20), pages 1-20, October.
    4. García, Antonio & Monsalve-Serrano, Javier & Martinez-Boggio, Santiago & Gaillard, Patrick, 2021. "Emissions reduction by using e-components in 48 V mild hybrid trucks under dual-mode dual-fuel combustion," Applied Energy, Elsevier, vol. 299(C).
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