Research on the remaining useful life prediction method of lithium‐ion batteries based on aging feature extraction and multi‐kernel relevance vector machine optimization model

• Published in: International journal of energy research Vol. 46; no. 10; pp. 13931 - 13946
• Main Authors: Qiu, Jing‐Song, Fan, Yong‐Cun, Wang, Shun‐Li, Yang, Xiao, Qiao, Jia‐Lu, Liu, Dong‐Lei
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01.08.2022; Hindawi Limited
• Subjects: Accuracy; Ageing; Aging; aging feature; Algorithms; Batteries; Charging; Coefficients; Datasets; Feature extraction; improved gray wolf constrained optimization algorithm; Kernel functions; Life prediction; Lithium; Lithium-ion batteries; lithium‐ion battery; Machine learning; multi‐kernel relevance vector machine; Optimization models; Prediction models; remaining useful life; Temperature data; Useful life
• ISSN: 0363-907X; 1099-114X
• DOI: 10.1002/er.8110

Summary Lithium‐ion batteries are used in a wide range of applications due to their cleanliness and stability, and the health management of lithium‐ion batteries has become a necessity. The most important aspect of health management is the prediction of the remaining useful life (RUL) of the battery. Therefore, a RUL estimation model based on the aging factor of the charging process and an improved multi‐kernel relevance vector machine is proposed in order to achieve high accuracy estimation of the RUL of lithium‐ion batteries. First, eight aging features highly correlated with lithium‐ion batteries capacity degradation are extracted based on charging current, voltage, and temperature data, then, their correlation is proved using gray relation analysis. Secondly, the improved gray wolf constrained optimization algorithm is used to determine the kernel function combination coefficients of the multi‐kernel relevance vector machine, and the RUL prediction model of the improved multi‐kernel relevance vector machine is established. Finally, using the battery dataset from NASA, aging data of three datasets, 24°C, 43°C, and 4°C, with a total of 11 batteries, were selected for validation. The validation results show that the improved multi‐kernel relevance vector machine prediction model has higher prediction accuracy and more robust long‐term prediction capability, with RUL prediction error less than 10 cycles and MAE less than 0.05, both of which are better than that of the single‐kernel relevance vector machine model and other multi‐kernel relevance vector machine models. Using charging data, eight sets of charging features were analyzed and extracted as indirect aging features of Li‐ion batteries. The capacity degradation process of Li‐ion battery is simulated using a fused kernel function formed by linear combination of three kernel functions, and the improved GWO algorithm is used to optimize the combination coefficients to obtain an improved multi‐kernel RVM model to predict the RUL of Li‐ion battery.