Aging abnormality detection of lithium-ion batteries combining feature engineering and deep learning

The safety of battery packs is greatly affected by individual abnormal cells. However, it is challenging to diagnose abnormal aging batteries in the early stages due to the low abnormality rate and imperceptible initial performance deviations. This paper proposes a feature engineering and deep learning (DL)-based method for abnormal aging prognosis and end-of-life (EOL) prediction. The mathematical model of dimensionless indicators (DIs) is applied to partial voltage-capacity (V-Q) of one cycle to construct the optimal DIs with high sensitivity to abnormal aging. Taking the optimal DIs and partial V-Q as inputs, an abnormal aging prognosis model is established, and an ablation study is designed to verify the necessity of the selected DIs. Finally, the gated recurrent unit (GRU) is utilized to establish EOL prediction models for normal and abnormal degradation batteries, respectively. The proposed method is verified by a Lithium Cobalt Oxide (LiCoO2) battery dataset. The results indicate that the 100% prognosis of abnormal degradation batteries is achieved and the EOL prediction accuracy is less than 3.7%. This work highlights the rapid abnormal battery detection using data of one cycle without excessive battery testing, which contributes to the rational deployment of batteries and reduces the probability of failures during operation.