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Adaptive energy management in automated hybrid electric vehicles with flexible torque request

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  • Zhang, Fengqi
  • Hu, Xiaosong
  • Langari, Reza
  • Wang, Lihua
  • Cui, Yahui
  • Pang, Hui

Abstract

Rapidly-evolving technologies for vehicle electrification and automation offer increased opportunities to enhance the performance and efficiency of energy management strategies for automated hybrid electric vehicles (A-HEVs). In this context, an adaptive energy management approach based on an equivalent consumption minimization strategy (ECMS) framework is developed to optimize gearshift commands and torque distribution for an automated parallel HEV. This methodology utilizes the emerging idea of flexible torque request by considering drivability and fuel economy simultaneously. The gearshift map is extracted from optimal results with ECMS, as treated in the dynamic programming (DP)-based strategy, to avoid frequent gearshift events thereby considering both drivability and fuel economy. An adaptive energy management strategy with flexible torque request is then reformulated by a modified ECMS, seeking better performance for the powertrain optimization. As a result, utilizing the flexible torque request, the torque distribution and gearshift commands are jointly optimized in the same framework. A sensitivity study for different parameters is explored, and adaptation laws of the main parameters are also devised for the proposed approach. Finally, simulations are performed in two driving cases to demonstrate the effectiveness of the proposed method. Results confirm that the proposed methodology produces a promising fuel efficiency, relative to the one with fixed torque request, while ensuring good drivability and traffic efficiency.

Suggested Citation

  • Zhang, Fengqi & Hu, Xiaosong & Langari, Reza & Wang, Lihua & Cui, Yahui & Pang, Hui, 2021. "Adaptive energy management in automated hybrid electric vehicles with flexible torque request," Energy, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:energy:v:214:y:2021:i:c:s0360544220319800
    DOI: 10.1016/j.energy.2020.118873
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    References listed on IDEAS

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    2. V. Mounica & Y. P. Obulesu, 2022. "Hybrid Power Management Strategy with Fuel Cell, Battery, and Supercapacitor for Fuel Economy in Hybrid Electric Vehicle Application," Energies, MDPI, vol. 15(12), pages 1-25, June.
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    7. Alcázar-García, Désirée & Romeral Martínez, José Luis, 2022. "Model-based design validation and optimization of drive systems in electric, hybrid, plug-in hybrid and fuel cell vehicles," Energy, Elsevier, vol. 254(PA).
    8. Vamsi Krishna Reddy, Aala Kalananda & Venkata Lakshmi Narayana, Komanapalli, 2022. "Meta-heuristics optimization in electric vehicles -an extensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    9. Lin Li & Serdar Coskun & Jiaze Wang & Youming Fan & Fengqi Zhang & Reza Langari, 2021. "Velocity Prediction Based on Vehicle Lateral Risk Assessment and Traffic Flow: A Brief Review and Application Examples," Energies, MDPI, vol. 14(12), pages 1-30, June.
    10. Pan, Shuai & Fulton, Lewis M. & Roy, Anirban & Jung, Jia & Choi, Yunsoo & Gao, H. Oliver, 2021. "Shared use of electric autonomous vehicles: Air quality and health impacts of future mobility in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    11. Piotr Powroźnik & Paweł Szcześniak & Krzysztof Turchan & Miłosz Krysik & Igor Koropiecki & Krzysztof Piotrowski, 2022. "An Elastic Energy Management Algorithm in a Hierarchical Control System with Distributed Control Devices," Energies, MDPI, vol. 15(13), pages 1-24, June.
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