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Potential of electric vehicle batteries second use in energy storage systems: The case of China

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  • Geng, Jingxuan
  • Gao, Suofen
  • Sun, Xin
  • Liu, Zongwei
  • Zhao, Fuquan
  • Hao, Han

Abstract

Battery second use, which extracts additional values from retired electric vehicle batteries through repurposing them in energy storage systems, is promising in reducing the demand for new batteries. However, the potential scale of battery second use and the consequent battery conservation benefits are largely unexplored. This study bridges such a research gap by simulating the dynamic interactions between vehicle batteries and batteries used in energy storage systems in China's context. Battery supply, use and disposal with and without implementing battery second use are compared. The results show that until 2050, more than 16 TWh of Li-ion batteries are expected to be retired from electric vehicles. If these retired batteries are put into second use, the accumulative new battery demand of battery energy storage systems can be reduced from 2.1 to 5.1 TWh to 0–1.4 TWh under different scenarios, implying a 73–100% decrease. This research justifies the necessity of developing battery second use and calls for joint efforts from the government, industry and academia.

Suggested Citation

  • Geng, Jingxuan & Gao, Suofen & Sun, Xin & Liu, Zongwei & Zhao, Fuquan & Hao, Han, 2022. "Potential of electric vehicle batteries second use in energy storage systems: The case of China," Energy, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:energy:v:253:y:2022:i:c:s0360544222010623
    DOI: 10.1016/j.energy.2022.124159
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    3. Ruifei Ma & Shengyu Tao & Xin Sun & Yifang Ren & Chongbo Sun & Guanjun Ji & Jiahe Xu & Xuecen Wang & Xuan Zhang & Qiuwei Wu & Guangmin Zhou, 2024. "Pathway decisions for reuse and recycling of retired lithium-ion batteries considering economic and environmental functions," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    4. Wang, Fengfei & Tang, Shengjin & Han, Xuebing & Yu, Chuanqiang & Sun, Xiaoyan & Lu, Languang & Ouyang, Minggao, 2024. "Capacity prediction of lithium-ion batteries with fusing aging information," Energy, Elsevier, vol. 293(C).
    5. Zhang, Yue & Wang, Yeqin & Zhang, Chu & Qiao, Xiujie & Ge, Yida & Li, Xi & Peng, Tian & Nazir, Muhammad Shahzad, 2024. "State-of-health estimation for lithium-ion battery via an evolutionary Stacking ensemble learning paradigm of random vector functional link and active-state-tracking long–short-term memory neural netw," Applied Energy, Elsevier, vol. 356(C).
    6. Xiong, Wei & Xie, Fang & Xu, Gang & Li, Yumei & Li, Ben & Mo, Yimin & Ma, Fei & Wei, Keke, 2023. "Co-estimation of the model parameter and state of charge for retired lithium-ion batteries over a wide temperature range and battery degradation scope," Renewable Energy, Elsevier, vol. 218(C).
    7. Mao, Ning & Gadkari, Siddharth & Wang, Zhirong & Zhang, Teng & Bai, Jinglong & Cai, Qiong, 2023. "A comparative analysis of lithium-ion batteries with different cathodes under overheating and nail penetration conditions," Energy, Elsevier, vol. 278(PB).
    8. Tostado-Véliz, Marcos & Jordehi, Ahmad Rezaee & Mansouri, Seyed Amir & Jurado, Francisco, 2023. "A two-stage IGDT-stochastic model for optimal scheduling of energy communities with intelligent parking lots," Energy, Elsevier, vol. 263(PD).

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