One-Time Prediction of Battery Capacity Fade Curve under Multiple Fast Charging Strategies

• Published in: Batteries (Basel) 2024-03, Vol.10 (3), p.74
• Main Authors: Han, Xiaoming, Dai, Zhentao, Ren, Mifeng, Cui, Jing, Shi, Yunfeng
• Format: Article
• Language: English
• Subjects: Aging; Analysis; capacity fade curve; Charging; Computational linguistics; Datasets; Degradation; Electrolytes; End of life; Language processing; Lithium-ion batteries; lithium-ion battery; Matching; Methods; multiple fast charging strategies; Natural language interfaces; Neural networks; one-time prediction; Partial differential equations; Rechargeable batteries; Time series; trend matching; Trends
• ISSN: 2313-0105; 2313-0105
• DOI: 10.3390/batteries10030074

Using different fast charging strategies for lithium-ion batteries can affect the degradation rate of the batteries. In this case, predicting the capacity fade curve can facilitate the application of new batteries. Considering the impact of fast charging strategies on battery aging, a battery capacity degradation trajectory prediction method based on the TM-Seq2Seq (Trend Matching—Sequence-to-Sequence) model is proposed. This method uses data from the first 100 cycles to predict the future capacity fade curve and EOL (end of life) in one-time. First, features are extracted from the discharge voltage-capacity curve. Secondly, a sequence-to-sequence model based on CNN, SE-net, and GRU is designed. Finally, a trend matching loss function is designed based on the common characteristics of capacity fade curves to constrain the encoding features of the sequence-to-sequence model, facilitating the learning of the underlying relationship between inputs and outputs. TM-Seq2Seq model is verified on a public dataset with 132 battery cells and multiple fast charging strategies. The experimental results indicate that, compared to other popular models, the TM-Seq2Seq model has lower prediction errors.