Capacity fading knee-point recognition method and life prediction for lithium-ion batteries using segmented capacity degradation model

• Published in: Reliability engineering & system safety 2024-11, Vol.251, p.110395, Article 110395
• Main Authors: Zhang, Jianping, Zhang, Yinjie, Fu, Jian, Zhao, Dawen, Liu, Ping, Zhang, Zhiwei
• Format: Article
• Language: English
• Subjects: GA-SVR; GA-Weibull; Knee point identification; Life prediction; Lithium-ion battery; Segmented capacity degradation model
• ISSN: 0951-8320
• DOI: 10.1016/j.ress.2024.110395

•The segmented capacity degradation models including GA-Weibull and GA-SVR are built.•The segmented model accurately reveals the decaying rule of li-ion battery capacity.•Life prediction of segmented models has a lower error than that of unsegmented ones.•A knee point recognition method with high precision is proposed based on GA-Weibull.•A linear relationship between the knee point and the cycle life is reflected. Aiming at the problem that the cycle life of the battery is hard to be accurately estimated, a segmented capacity degradation model is established based on the trend of capacity degradation rate. By applying a genetic algorithm (GA) to optimize the model parameters, GA-Weibull and GA-SVR (support vector regression) degradation models and their corresponding segmented models are established, and the battery life is calculated. Furthermore, a method taking the RMSE as the target is put forward to calculate the knee-point of capacity degradation. Finally, the life is predicted by segmented models. The results show that compared with unsegmented models, describing the process of capacity degradation by segmented models reduces the error notably, makes the determination coefficient closer to 1, and improves the model accuracy effectively. Later, the proposed method makes the fitting error of the segmented model smaller than that of Bacon-Watts method, thus obtaining a more accurate knee-point. In addition, it is found that there exists linear relationship between the estimated knee-point and the battery life. Finally, the prediction results based on segmented model show that the prediction accuracy of segmented GA-SVR model is higher, and the MAPE is only 4.32 %. The relevant results can provide some guidance for the reliability evaluation and product quality management of lithium-ion batteries, which is conducive to establishing more accurate models for battery life prediction.