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AMT downshifting strategy design of HEV during regenerative braking process for energy conservation

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  • Li, Liang
  • Wang, Xiangyu
  • Xiong, Rui
  • He, Kai
  • Li, Xujian

Abstract

For hybrid electric vehicles (HEVs), regenerative braking might be the most effective way of energy conservation. However, the braking energy usually cannot be regenerated completely due to the limit of the motor maximum torque. When the braking torque provided by electric motor (EM) cannot meet the driver’s braking demand, the hydraulic brake system should provide extra braking torque, which results in braking energy loss. Therefore, it is meaningful adjusting EM work point to maximize the regenerative energy by transmission downshifting. In this paper, an HEV equipped with an automated manual transmission (AMT) is chosen as the study platform. First, simplified dynamic models of HEV system are built. Then, the process and advantages of AMT downshifting are analyzed and the characteristics of regenerative braking are obtained with different gear positions and different, of which two kinds of downshifting strategy are proposed on basis. At last, hardware-in-loop tests are carried out, and results show that the energy conservation of regenerative braking process with downshifting can be increased by 10.5–32.4% compared to that without downshifting.

Suggested Citation

  • Li, Liang & Wang, Xiangyu & Xiong, Rui & He, Kai & Li, Xujian, 2016. "AMT downshifting strategy design of HEV during regenerative braking process for energy conservation," Applied Energy, Elsevier, vol. 183(C), pages 914-925.
    • Handle: RePEc:eee:appene:v:183:y:2016:i:c:p:914-925
    DOI: 10.1016/j.apenergy.2016.09.031
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    1. Finesso, Roberto & Spessa, Ezio & Venditti, Mattia, 2016. "Cost-optimized design of a dual-mode diesel parallel hybrid electric vehicle for several driving missions and market scenarios," Applied Energy, Elsevier, vol. 177(C), pages 366-383.
    2. Li, Liang & You, Sixiong & Yang, Chao & Yan, Bingjie & Song, Jian & Chen, Zheng, 2016. "Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 162(C), pages 868-879.
    3. Yi, Chenyu & Epureanu, Bogdan I. & Hong, Sung-Kwon & Ge, Tony & Yang, Xiao Guang, 2016. "Modeling, control, and performance of a novel architecture of hybrid electric powertrain system," Applied Energy, Elsevier, vol. 178(C), pages 454-467.
    4. Li, Liang & Li, Xujian & Wang, Xiangyu & Song, Jian & He, Kai & Li, Chenfeng, 2016. "Analysis of downshift’s improvement to energy efficiency of an electric vehicle during regenerative braking," Applied Energy, Elsevier, vol. 176(C), pages 125-137.
    5. Tian, He & Lu, Ziwang & Wang, Xu & Zhang, Xinlong & Huang, Yong & Tian, Guangyu, 2016. "A length ratio based neural network energy management strategy for online control of plug-in hybrid electric city bus," Applied Energy, Elsevier, vol. 177(C), pages 71-80.
    6. Zhang, Shuo & Xiong, Rui & Cao, Jiayi, 2016. "Battery durability and longevity based power management for plug-in hybrid electric vehicle with hybrid energy storage system," Applied Energy, Elsevier, vol. 179(C), pages 316-328.
    7. Yang, Yalian & Hu, Xiaosong & Pei, Huanxin & Peng, Zhiyuan, 2016. "Comparison of power-split and parallel hybrid powertrain architectures with a single electric machine: Dynamic programming approach," Applied Energy, Elsevier, vol. 168(C), pages 683-690.
    8. Zhang, Shuo & Xiong, Rui & Sun, Fengchun, 2017. "Model predictive control for power management in a plug-in hybrid electric vehicle with a hybrid energy storage system," Applied Energy, Elsevier, vol. 185(P2), pages 1654-1662.
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    Cited by:

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    3. Qi, Lingfei & Wu, Xiaoping & Zeng, Xiaohui & Feng, Yan & Pan, Hongye & Zhang, Zutao & Yuan, Yanping, 2020. "An electro-mechanical braking energy recovery system based on coil springs for energy saving applications in electric vehicles," Energy, Elsevier, vol. 200(C).
    4. Wu, Jian & Wang, Xiangyu & Li, Liang & Qin, Cun'an & Du, Yongchang, 2018. "Hierarchical control strategy with battery aging consideration for hybrid electric vehicle regenerative braking control," Energy, Elsevier, vol. 145(C), pages 301-312.
    5. Yang Yang & Xiaolong He & Yi Zhang & Datong Qin, 2018. "Regenerative Braking Compensatory Control Strategy Considering CVT Power Loss for Hybrid Electric Vehicles," Energies, MDPI, vol. 11(3), pages 1-15, February.
    6. Pugi, L. & Pagliai, M. & Nocentini, A. & Lutzemberger, G. & Pretto, A., 2017. "Design of a hydraulic servo-actuation fed by a regenerative braking system," Applied Energy, Elsevier, vol. 187(C), pages 96-115.
    7. Zhao, Mingjie & Shi, Junhui & Lin, Cheng, 2019. "Optimization of integrated energy management for a dual-motor coaxial coupling propulsion electric city bus," Applied Energy, Elsevier, vol. 243(C), pages 21-34.
    8. 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).
    9. Ramesh Kumar Chidambaram & Dipankar Chatterjee & Barnali Barman & Partha Pratim Das & Dawid Taler & Jan Taler & Tomasz Sobota, 2023. "Effect of Regenerative Braking on Battery Life," Energies, MDPI, vol. 16(14), pages 1-24, July.
    10. Qiwei Lu & Bangbang He & Zhixuan Gao & Cheng Che & Xuteng Wei & Jihui Ma & Zhichun Zhang & Jiantao Luo, 2019. "An Optimized Regulation Scheme of Improving the Effective Utilization of the Regenerative Braking Energy of the Whole Railway Line," Energies, MDPI, vol. 12(21), pages 1-19, October.

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