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Volume 117
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Dual-driven battery life prediction based on data augmentation and capacity recovery identification
Zhongyang Liu a, Yukun Wang a, Jinxiu Chen a, Jinhao Meng b *, Long Kong a *
a Institute of Flexible Electronics, Northwestern Polytechnical University, Xi'an, 710129, China
b School of Electrical Engineering, Xi'an Jiao Tong University, Xi'an, 710049, China
10.1016/j.partic.2026.08.006
Volume 117, October 2026, Pages 440-452
Received 18 July 2026, Revised 29 July 2026, Accepted 7 August 2026, Available online 8 August 2026, Version of Record 20 August 2026.
E-mail: jinhao@xjtu.edu.cn; iamlkong@nwpu.edu.cn
Highlights

• Proposing RUL prediction framework for lithium-ion batteries.

• Incorporating CR dynamic identification and data augmentation.

• Addressing capacity fluctuation and few-shot scenario.

• Enhancing RUL prediction accuracy and robustness.


Abstract

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is a critical prerequisite for predictive maintenance, optimizing charge-discharge strategies, and ensuring their long-term safe operation. However, during the cell resting, side reactions among cell components, especially on electrolyte/electrode interface, lead to an unwanted temporary capacity recovery (CR). This phenomenon poses significant challenges to the accurate prediction of RUL. This study proposes a RUL prediction framework that incorporates dynamic identification of CR and data augmentation to concurrently address capacity fluctuation and few-shot scenario. In this study, input data purification is achieved through an innovative quasi-online identification and CR quantitative correction strategy, and training sample expansion is accomplished by conducting strategic data augmentation based on early-cycle data. The proposed framework can significantly improve the prediction accuracy of RUL for application scenarios with CR interference and limited early-cycle data.

Graphical abstract
Keywords
Lithium-ion battery; Remaining useful life; Gated recurrent unit; Data augmentation; Capacity recovery
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