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Assessment of an Adaptive Efficient Thermal/Electric Skipping Control Strategy for the Management of a Parallel Plug-in Hybrid Electric Vehicle

Author

Listed:
  • Vincenzo De Bellis

    (Dipartimento di Ingegneria Industriale, Università di Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy)

  • Marco Piras

    (Dipartimento di Ingegneria Industriale, Università di Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy
    STEMS/CNR, Italian National Research Council, Via Marconi 4, 80125 Naples, Italy)

  • Enrica Malfi

    (Dipartimento di Ingegneria Industriale, Università di Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy)

Abstract

In the current scenario, where environmental concern determines the evolution of passenger cars, hybrid electric vehicles (HEV) represent a hub in the automotive sector to reach net-zero CO 2 emissions. To fully exploit the energy conversion potential of advanced powertrains, proper energy management strategies are mandatory. In this work, a simulation study is presented, aiming at developing a new control strategy for a P3 parallel plug-in HEV (PHEV). The simulation model is built on MATLAB/Simulink. The proposed strategy is based on an alternative utilization of the thermal engine and electric motor to provide the vehicle power demand (efficient thermal/electric skipping strategy (ETESS)). An adaptive function is then introduced to develop a charge-blended control strategy. Fuel consumption along different driving cycles is evaluated by applying the novel adaptive-ETESS (A-ETESS). To have a proper comparison, the same adaptive function is built on the equivalent consumption minimization strategy (ECMS). Processor-in-the-loop (PIL) simulations are performed to benchmark the A-ETESS. Simulation results highlighted that the proposed strategy provides for a fuel economy similar to ECMS (worse of about 2.5% on average) and a computational effort reduced by 99% on average, opening the possibility of real-time on-vehicle applications.

Suggested Citation

  • Vincenzo De Bellis & Marco Piras & Enrica Malfi, 2022. "Assessment of an Adaptive Efficient Thermal/Electric Skipping Control Strategy for the Management of a Parallel Plug-in Hybrid Electric Vehicle," Energies, MDPI, vol. 15(19), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7122-:d:927876
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    References listed on IDEAS

    as
    1. Vincenzo De Bellis & Enrica Malfi & Jean-Marc Zaccardi, 2021. "Development of an Efficient Thermal Electric Skipping Strategy for the Management of a Series/Parallel Hybrid Powertrain," Energies, MDPI, vol. 14(4), pages 1-24, February.
    2. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Lang, Kun, 2018. "An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 163(C), pages 837-848.
    3. 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.
    4. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    5. Yaqian Wang & Xiaohong Jiao, 2022. "Dual Heuristic Dynamic Programming Based Energy Management Control for Hybrid Electric Vehicles," Energies, MDPI, vol. 15(9), pages 1-19, April.
    6. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    7. Pierpaolo Polverino & Ivan Arsie & Cesare Pianese, 2021. "Optimal Energy Management for Hybrid Electric Vehicles Based on Dynamic Programming and Receding Horizon," Energies, MDPI, vol. 14(12), pages 1-11, June.
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