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Considering the wide application of lithium-ion battery in life, the prediction of the remaining life of lithium-ion battery has become a research hotspot. Studies show, due to the improvement of the technology level of lithium-ion battery, its life is getting longer and longer. Even under the condition of accelerated life test, it is difficult to obtain enough available data for research in a short term. In order to solve the problem of how to accurately predict the residual life with the data-driven method under the condition of small sample size, an overall trend virtual sample generation method based on differential evolution (OT-DEVSG) is proposed. This method uses a differential evolution algorithm with better optimization performance, and improves the original mega-trend-diffusion (MTD) method, the range of virtual samples is effectively constrained and the trend of samples can be estimated more accurately. The method can effectively generate a virtual sample data sequence with time parameters, and adapt the virtual sample to the real-life sample trend, which solves the problem of insufficient degradation data of the lithium-ion batteries. Finally, we validate the effectiveness of the OT-DEVSG method with three existing data sets. The experimental results show that the proposed OT-DEVSG method is effective for solving the problem of long-term life prediction of lithium-ion batteries.

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A Virtual Sample Generation Method Based on Differential Evolution Algorithm for Overall Trend of Small Sample Data: Used for Lithium-ion Battery Capacity Degradation Data