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A Novel Control Algorithm Design for Hybrid Electric Vehicles Considering Energy Consumption and Emission Performance

Author

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  • Yuan Qiao

    (State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China)

  • Yizhou Song

    (State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
    The Joint Laboratory for Internet of Vehicles, Ministry of Education - China Mobile Communications Corporation, Beijing 100084, China)

  • Kaisheng Huang

    (State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
    The Joint Laboratory for Internet of Vehicles, Ministry of Education - China Mobile Communications Corporation, Beijing 100084, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100081, China)

Abstract

Under the severe challenge of increasingly stringent emission regulations and constantly improving fuel economy requirements, hybrid electric vehicles (HEVs) have attracted widespread attention in the auto industry as a practicable technical route of green vehicles. To address the considerations on energy consumption and emission performance simultaneously, a novel control algorithm design is proposed for the energy management system (EMS) of HEVs. First, energy consumption of the investigated P3 HEV powertrain is determined based on bench test data. Second, crucial performance indicators of NOx and particle emissions, prior to a catalytic converter, are also measured and processed as a prerequisite. A comprehensive objective function is established on the grounds of the Equivalent Consumption Minimization Strategy (ECMS) and corresponding simulation models are constructed in MATLAB/SIMULINK. Subsequently, the control algorithm is validated against the simulation results predicated on the Worldwide-Harmonized Light-Vehicle Test Procedure (WLTP).Integrated research contents include: (1) The searching process aimed at the optimal solution of the pre-established multi-parameter objective function is thoroughly investigated; (2) the impacts of weighting coefficients pertaining to two exhaust pollutants upon the specific configurations of the proposed control algorithm are discussed in detail; and (3) the comparison analysis of the simulation results obtained from ECMS and classical Dynamic Programming (DP), respectively, is performed.

Suggested Citation

  • Yuan Qiao & Yizhou Song & Kaisheng Huang, 2019. "A Novel Control Algorithm Design for Hybrid Electric Vehicles Considering Energy Consumption and Emission Performance," Energies, MDPI, vol. 12(14), pages 1-28, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2698-:d:248447
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    References listed on IDEAS

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    Cited by:

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    2. Pier Giuseppe Anselma, 2021. "Optimization-Driven Powertrain-Oriented Adaptive Cruise Control to Improve Energy Saving and Passenger Comfort," Energies, MDPI, vol. 14(10), pages 1-28, May.
    3. Qicheng Xue & Xin Zhang & Teng Teng & Jibao Zhang & Zhiyuan Feng & Qinyang Lv, 2020. "A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles," Energies, MDPI, vol. 13(20), pages 1-30, October.

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