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Analysis of cyclic aging performance of commercial Li4Ti5O12-based batteries at room temperature

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  • Liu, Sijia
  • Winter, Michaela
  • Lewerenz, Meinert
  • Becker, Jan
  • Sauer, Dirk Uwe
  • Ma, Zeyu
  • Jiang, Jiuchun

Abstract

Lithium-ion batteries with Li4Ti5O12 (LTO) negative electrodes have been recognized as a promising candidate over graphite-based batteries for the future energy storage systems (ESS), due to its excellent performance in rate capability, cycle life and inherent safety. Accurate identification of battery degradation mechanisms is of great significance for reliable management and sustainable capability of ESS. Unfortunately, only a few quantitative analysis regarding LTO batteries are drawn at extreme conditions including designed overcharge and cycling at high temperatures. In this paper, cyclic aging behaviors of commercial LTO batteries are investigated at room temperature (RT) by applying incremental capacity and differential voltage techniques. A typical sketch of regional divisions among the voltage curve of a LTO battery is presented to explain the effects induced by different degradation modes on battery performance. The results imply that the loss of active material in the positive electrode occupies at least 83% and 81% of the total capacity loss under cycling at 10C and 5C respectively, while pure loss of lithium inventory is detectable when cycling at 5C rather than at 10C. Depth of discharge is further considered as the dominant stress of LTO batteries during the high-rate operation at RT.

Suggested Citation

  • Liu, Sijia & Winter, Michaela & Lewerenz, Meinert & Becker, Jan & Sauer, Dirk Uwe & Ma, Zeyu & Jiang, Jiuchun, 2019. "Analysis of cyclic aging performance of commercial Li4Ti5O12-based batteries at room temperature," Energy, Elsevier, vol. 173(C), pages 1041-1053.
    • Handle: RePEc:eee:energy:v:173:y:2019:i:c:p:1041-1053
    DOI: 10.1016/j.energy.2019.02.150
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    References listed on IDEAS

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    5. Li, Weihan & Cui, Han & Nemeth, Thomas & Jansen, Jonathan & Ünlübayir, Cem & Wei, Zhongbao & Feng, Xuning & Han, Xuebing & Ouyang, Minggao & Dai, Haifeng & Wei, Xuezhe & Sauer, Dirk Uwe, 2021. "Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning," Applied Energy, Elsevier, vol. 293(C).
    6. Fei, Zicheng & Yang, Fangfang & Tsui, Kwok-Leung & Li, Lishuai & Zhang, Zijun, 2021. "Early prediction of battery lifetime via a machine learning based framework," Energy, Elsevier, vol. 225(C).
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    8. Xiong, Rui & Pan, Yue & Shen, Weixiang & Li, Hailong & Sun, Fengchun, 2020. "Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
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