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Blending gear shift strategy design and comparison study for a battery electric city bus with AMT

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  • Lin, Cheng
  • Zhao, Mingjie
  • Pan, Hong
  • Yi, Jiang

Abstract

To improve the performance of heuristic strategy used in most of the electric city buses equipped with automated manual transmission (AMT) currently, this paper proposes a systematic blending extraction method to optimize and accelerate the shift schedule design process. The crucial related factors, including the shift time, transmission efficiency and various driving cycle features, are considered to assure the online practicability. Dynamic programming (DP) algorithm is applied over featured velocity profiles to explore the global optimal operating points offline. Then k-means clustering algorithm is adopted to extract the explicit optimal shift schedule, where the number of centroids is determined by hierarchical analysis process and a new distance calculation method is performed considering proper weighting factors to blend the shift points from different driving conditions. The stochastical driving cycle is generated randomly from the previous data and is used to validate the comprehensive performance by chassis dynamometer tests. A comparison study is conducted among the proposed and conventional shift strategies. Experimental results demonstrate that the extracted blending strategy can improve the energy consumption significantly and is proved to be efficient, flexible, and online implementable compared to the other strategies.

Suggested Citation

  • Lin, Cheng & Zhao, Mingjie & Pan, Hong & Yi, Jiang, 2019. "Blending gear shift strategy design and comparison study for a battery electric city bus with AMT," Energy, Elsevier, vol. 185(C), pages 1-14.
    • Handle: RePEc:eee:energy:v:185:y:2019:i:c:p:1-14
    DOI: 10.1016/j.energy.2019.07.004
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    Citations

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    Cited by:

    1. Kwon, Kihan & Lee, Jung-Hwan & Lim, Sang-Kil, 2023. "Optimization of multi-speed transmission for electric vehicles based on electrical and mechanical efficiency analysis," Applied Energy, Elsevier, vol. 342(C).
    2. Md Ragib Ahssan & Mehran Ektesabi & Saman Gorji, 2023. "Evaluation of a Three-Parameter Gearshift Strategy for a Two-Speed Transmission System in Electric Vehicles," Energies, MDPI, vol. 16(5), pages 1-28, March.
    3. Yu, Xiao & Lin, Cheng & Xie, Peng & Liang, Sheng, 2022. "A novel real-time energy management strategy based on Monte Carlo Tree Search for coupled powertrain platform via vehicle-to-cloud connectivity," Energy, Elsevier, vol. 256(C).
    4. Md Ragib Ahssan & Mehran Ektesabi & Saman Gorji, 2020. "Gear Ratio Optimization along with a Novel Gearshift Scheduling Strategy for a Two-Speed Transmission System in Electric Vehicle," Energies, MDPI, vol. 13(19), pages 1-24, September.
    5. Gao, Bingzhao & Meng, Dele & Shi, Wentong & Cai, Wenqi & Dong, Shiying & Zhang, Yuanjian & Chen, Hong, 2022. "Topology optimization and the evolution trends of two-speed transmission of EVs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    6. Shilei Zhou & Paul Walker & Yang Tian & Cong Thanh Nguyen & Nong Zhang, 2021. "Comparison on Energy Economy and Vibration Characteristics of Electric and Hydraulic in-Wheel Drive Vehicles," Energies, MDPI, vol. 14(8), pages 1-15, April.
    7. Bolin He & Yong Chen & Qiang Wei & Cong Wang & Changyin Wei & Xiaoyu Li, 2023. "Performance Comparison of Pure Electric Vehicles with Two-Speed Transmission and Adaptive Gear Shifting Strategy Design," Energies, MDPI, vol. 16(7), pages 1-21, March.
    8. Yu, Xiao & Lin, Cheng & Zhao, Mingjie & Yi, Jiang & Su, Yue & Liu, Huimin, 2022. "Optimal energy management strategy of a novel hybrid dual-motor transmission system for electric vehicles," Applied Energy, Elsevier, vol. 321(C).
    9. Kwon, Kihan & Jo, Junhyeong & Min, Seungjae, 2021. "Multi-objective gear ratio and shifting pattern optimization of multi-speed transmissions for electric vehicles considering variable transmission efficiency," Energy, Elsevier, vol. 236(C).

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