Predicting battery life with early cyclic data by machine learning. Issue 6 (6th November 2019)

• Record Type: Journal Article
• Authors: Zhu, Shan; Zhao, Naiqin; Sha, Junwei
• Is Part Of: Energy storage. Volume 1:Issue 6(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-11-06
• Subjects: lifetime prediction -- lithium‐ion battery -- machine learning; Energy storage -- Periodicals; Energy storage; Periodicals; 621.04205
• DOI: 10.1002/est2.98
• Languages: English
• ISSNs: 2578-4862

Abstract

Abstract: This work applies machine learning tools to achieve the early prediction of commercial battery life. We compared the prediction accuracy of different machine learning algorithms to the battery database. Among various algorithms, the decision tree (DT) method exhibits the highest accuracy of 95.2% to predict whether the battery can maintain above 80% initial capacity after 550 cycles. Using the initial two cycles of data, DT proposes that the change of discharge capacity is the main feature to estimate the lifetime type of batteries. Given the first 100 cycles, the factor with the maximum weight turns to the internal resistance for estimating the battery lifetime. Abstract :