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The application of hybrid energy storage system with electrified continuously variable transmission in battery electric vehicle

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  • Ruan, Jiageng
  • Song, Qiang
  • Yang, Weiwei

Abstract

Due to the fact that demand for battery power increasing dramatically with the fast development of battery electric vehicles (BEVs), and poor power density prevents batteries absorbing more braking energy, leads to the so-called range phobia and presents a significant barrier to BEV commercialization. As one of the promising solutions, a supercapacitor-based hybrid energy storage system (HESS) is fast becoming the automotive industry trend, and there are widespread attempts of integrating multi-speed transmission in BEVs to improve electric machine efficiency. As part of the response to the above barriers, an electrified continuously variable transmission (CVT) is proposed along with HESS for BEV in this study. The improvements of passenger BEV in terms of vehicle dynamic performance, energy saving, and battery life extending through applying electrified CVT and HESS will be investigated. The impact of CVT on battery health improvement via HESS will also be analyzed. Specifically, a designed gear ratio varying control strategy is developed for electrified CVT to improve energy efficiency and vehicle performance. A vehicle performance comparison of electrified CVT and traditional single speed BEV is provided. Additionally, the potential benefit of the HESS and CVT combination to driving range, battery life and manufacturing/user cost is reported. Results reveals that the proposed CVT not only provides considerable benefit to BEV dynamic and economic performance, it also shows potential to improve the performance of HESS in terms of extending battery lifetime.

Suggested Citation

  • Ruan, Jiageng & Song, Qiang & Yang, Weiwei, 2019. "The application of hybrid energy storage system with electrified continuously variable transmission in battery electric vehicle," Energy, Elsevier, vol. 183(C), pages 315-330.
    • Handle: RePEc:eee:energy:v:183:y:2019:i:c:p:315-330
    DOI: 10.1016/j.energy.2019.06.095
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    6. Nguyễn, Bảo-Huy & Vo-Duy, Thanh & Henggeler Antunes, Carlos & Trovão, João Pedro F., 2021. "Multi-objective benchmark for energy management of dual-source electric vehicles: An optimal control approach," Energy, Elsevier, vol. 223(C).
    7. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Kong, Xiaodan & Yan, Xingda, 2021. "Optimal sizing and sensitivity analysis of a battery-supercapacitor energy storage system for electric vehicles," Energy, Elsevier, vol. 221(C).
    8. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
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    12. Zhu, Tao & Lot, Roberto & Wills, Richard G.A. & Yan, Xingda, 2020. "Sizing a battery-supercapacitor energy storage system with battery degradation consideration for high-performance electric vehicles," Energy, Elsevier, vol. 208(C).
    13. Xu, Huanwei & Wu, Lingfeng & Xiong, Shizhe & Li, Wei & Garg, Akhil & Gao, Liang, 2023. "An improved CNN-LSTM model-based state-of-health estimation approach for lithium-ion batteries," Energy, Elsevier, vol. 276(C).
    14. Zhang, Ying & Li, Yan-Fu, 2022. "Prognostics and health management of Lithium-ion battery using deep learning methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    15. Ying Yang & Zhenpo Wang & Shuo Wang & Ni Lin, 2022. "An Investigation of Opportunity Charging with Hybrid Energy Storage System on Electric Bus with Two-Speed Transmission," Sustainability, MDPI, vol. 14(19), pages 1-13, September.
    16. Yang, Bo & Wang, Junting & Zhang, Xiaoshun & Yu, Lei & Shu, Hongchun & Yu, Tao & Sun, Liming, 2020. "Control of SMES systems in distribution networks with renewable energy integration: A perturbation estimation approach," Energy, Elsevier, vol. 202(C).

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