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A novel approach for electric powertrain optimization considering vehicle power performance, energy consumption and ride comfort

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  • Lei, Fei
  • Bai, Yingchun
  • Zhu, Wenhao
  • Liu, Jinhong

Abstract

This paper discussed the approach for designing an electric powertrain in saving energy while maintaining vehicle power performance and ride comfort. Usually, energy consumption can be improved by motor efficiency optimization. As an innovative layout, electric vehicle with in-wheel motor may suffer additional vibration due to the increase of unsprung weight. Thus, vehicle ride comfort, together with power performance and energy consumption, are considered in the powertrain design. The work is done on two levels. First, requirements for power performance, energy consumption and ride comfort are generated on the vehicle level. Second, the generated requirements are applied onto the in-wheel motor system, which is described by the motor torque, efficiency and weight models. Torque outputs, motor efficiency and lightweight are the corresponding requirements on the subsystem level. Then multi-objective global optimization is carried out on the subsystem level. The Pareto front indicates that lightweight and high efficiency are two conflicting objectives that cannot be compromised on the subsystem level. A constrained energy approach is proposed to determining the final optimal on the vehicle level with the goal of improving vehicle performance and energy consumption. The final solution has a lightweight ratio of 93.5% and motor efficiency of 92%.

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  • Lei, Fei & Bai, Yingchun & Zhu, Wenhao & Liu, Jinhong, 2019. "A novel approach for electric powertrain optimization considering vehicle power performance, energy consumption and ride comfort," Energy, Elsevier, vol. 167(C), pages 1040-1050.
    • Handle: RePEc:eee:energy:v:167:y:2019:i:c:p:1040-1050
    DOI: 10.1016/j.energy.2018.11.052
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    8. Bhattacharjee, Debraj & Ghosh, Tamal & Bhola, Prabha & Martinsen, Kristian & Dan, Pranab K., 2019. "Data-driven surrogate assisted evolutionary optimization of hybrid powertrain for improved fuel economy and performance," Energy, Elsevier, vol. 183(C), pages 235-248.
    9. Li, Shiying & Xu, Jun & Gao, Haonan & Tao, Tao & Mei, Xuesong, 2020. "Safety probability based multi-objective optimization of energy-harvesting suspension system," Energy, Elsevier, vol. 209(C).
    10. Ma, Fangwu & Yang, Yu & Wang, Jiawei & Liu, Zhenze & Li, Jinhang & Nie, Jiahong & Shen, Yucheng & Wu, Liang, 2019. "Predictive energy-saving optimization based on nonlinear model predictive control for cooperative connected vehicles platoon with V2V communication," Energy, Elsevier, vol. 189(C).
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    12. Jari Vepsäläinen, 2022. "Energy Demand Analysis and Powertrain Design of a High-Speed Delivery Robot Using Synthetic Driving Cycles," Energies, MDPI, vol. 15(6), pages 1-21, March.

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