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Equivalent Consumption Minimization Strategy Based on Belt Drive System Characteristic Maps for P0 Hybrid Electric Vehicles

Author

Listed:
  • Shailesh Hegde

    (Center for Automotive Research and Sustainable Mobility, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy)

  • Angelo Bonfitto

    (Center for Automotive Research and Sustainable Mobility, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy)

  • Renato Galluzzi

    (Center for Automotive Research and Sustainable Mobility, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy
    School of Engineering and Sciences, Tecnologico de Monterrey, Mexico City 14380, Mexico)

  • Luis M. Castellanos Molina

    (Center for Automotive Research and Sustainable Mobility, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy)

  • Nicola Amati

    (Center for Automotive Research and Sustainable Mobility, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy)

  • Andrea Tonoli

    (Center for Automotive Research and Sustainable Mobility, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy)

Abstract

A P0 system is used in hybrid automobiles to improve engine economy and performance. An essential element of the P0 system for effectively transmitting power to the drive train is the belt drive system (BDS). The features of electric machine (EM) and internal combustion engines (ICE) are taken into account by standard energy management systems, such as the equivalent consumption minimization strategy (ECMS). In order to maximize the effectiveness of the P0 system, this work provides a novel formulation of the ECMS, which considers the power loss map of the BDS in addition to the characteristic maps of EM and ICE. A test bench is built up to characterize the BDS, and it is verified using an open-loop Hardware in the Loop (HIL) in the WLTP driving cycle. To find the most appropriate equivalence factors for the ECMS, which would ordinarily be tuned through trial and error, a genetic algorithm (GA) is used. With regard to the standard ECMS, the proposed methodology intends to reduce fuel usage and CO 2 emissions. Two belts in BDS were tested in the WLTP to achieve CO 2 savings of 1.1 and 0.9 [g/km], indicating the enhancement of system performance by using the BDS power loss maps in ECMS.

Suggested Citation

  • Shailesh Hegde & Angelo Bonfitto & Renato Galluzzi & Luis M. Castellanos Molina & Nicola Amati & Andrea Tonoli, 2023. "Equivalent Consumption Minimization Strategy Based on Belt Drive System Characteristic Maps for P0 Hybrid Electric Vehicles," Energies, MDPI, vol. 16(1), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:1:p:487-:d:1022619
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    References listed on IDEAS

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    1. Angelo Bonfitto, 2020. "A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks," Energies, MDPI, vol. 13(10), pages 1-13, May.
    2. Aiyun Gao & Xiaozhong Deng & Mingzhu Zhang & Zhumu Fu, 2017. "Design and Validation of Real-Time Optimal Control with ECMS to Minimize Energy Consumption for Parallel Hybrid Electric Vehicles," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-13, January.
    3. Torres, J.L. & Gonzalez, R. & Gimenez, A. & Lopez, J., 2014. "Energy management strategy for plug-in hybrid electric vehicles. A comparative study," Applied Energy, Elsevier, vol. 113(C), pages 816-824.
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