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An eco-driving strategy for electric vehicle based on the powertrain

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  • Liao, Peng
  • Tang, Tie-Qiao
  • Liu, Ronghui
  • Huang, Hai-Jun

Abstract

Energy waste inside the powertrain of electric vehicles (EVs) due to non-optimized acceleration caused by the improper powertrain control variables is not generally considered when designing EVs' optimal driving strategies. To fill this gap, this study designs an optimal EV driving strategy taking a holistic approach by explicitly considering the powertrain's internal functionalities to minimize total energy consumption. Firstly, the battery thermal effect is introduced into the powertrain-based EV longitudinal dynamics model, aiming to improve the calculation accuracy of battery state of charge (SOC). Secondly, the eco-driving strategy for three basic driving modes is designed. Finally, the strategy feasibility is verified by its sensitivity to SOC and environment temperature, and its adaptability in realistic driving conditions is tested. Simulation results show that the extremely low SOC can drastically disturb the powertrain, causing acceleration and EV efficiency reduction up to 68.78% and 33.12% in the acceleration process, respectively. However, environment temperature has little effect on the powertrain. The required distance and time to complete the same speed-change task as NEDC are respectively reduced by 17.13% and 12.12%. The proportion of different driving modes in urban and suburban driving conditions is nearly consistent with the applicability preference of the corresponding strategies. The outcomes of this study suggest that the proposed strategy can sufficiently utilize the powertrain coupling effect, and the EV energy consumption is limited by the battery degradation and the electromotor limitation.

Suggested Citation

  • Liao, Peng & Tang, Tie-Qiao & Liu, Ronghui & Huang, Hai-Jun, 2021. "An eco-driving strategy for electric vehicle based on the powertrain," Applied Energy, Elsevier, vol. 302(C).
    • Handle: RePEc:eee:appene:v:302:y:2021:i:c:s0306261921009594
    DOI: 10.1016/j.apenergy.2021.117583
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    Cited by:

    1. Ouyang, Xu & Xu, Min, 2022. "Promoting green transportation under the belt and Road Initiative: Locating charging stations considering electric vehicle users’ travel behavior," Transport Policy, Elsevier, vol. 116(C), pages 58-80.
    2. Alexander Koch & Lorenzo Nicoletti & Thomas Herrmann & Markus Lienkamp, 2022. "Implementation and Analyses of an Eco-Driving Algorithm for Different Battery Electric Powertrain Topologies Based on a Split Loss Integration Approach," Energies, MDPI, vol. 15(15), pages 1-29, July.
    3. Shi, Xiaoyu & Zhang, Jian & Jiang, Xia & Chen, Juan & Hao, Wei & Wang, Bo, 2024. "Learning eco-driving strategies from human driving trajectories," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    4. Dimitrios Rimpas & Stavrοs D. Kaminaris & Dimitrios D. Piromalis & George Vokas & Konstantinos G. Arvanitis & Christos-Spyridon Karavas, 2023. "Comparative Review of Motor Technologies for Electric Vehicles Powered by a Hybrid Energy Storage System Based on Multi-Criteria Analysis," Energies, MDPI, vol. 16(6), pages 1-24, March.
    5. Edward Kozłowski & Piotr Wiśniowski & Maciej Gis & Magdalena Zimakowska-Laskowska & Anna Borucka, 2024. "Vehicle Acceleration and Speed as Factors Determining Energy Consumption in Electric Vehicles," Energies, MDPI, vol. 17(16), pages 1-16, August.
    6. Liu, Qingling & Xu, Xiaowen, 2024. "A platoon-based eco-driving control mechanism for low-density traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    7. Zhong, Hao & Lei, Fei & Zhu, Wenhao & Zhang, Zhe, 2022. "An operation efficacy-oriented predictive control management for power-redistributable lithium-ion battery pack," Energy, Elsevier, vol. 251(C).

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