Combining machine learning algorithms and an incremental capacity analysis on 18650 cell under different cycling temperature and SOC range

Underwriters Laboratories Taiwan Co. Ltd., No. 260, Daye Rd., Beitou Dist., Taipei City 112, Taiwan

^* Corresponding author: john.lai@ul.com

Abstract

A novel way to apply machine learning algorithms on the incremental capacity analysis (dQ/dV) is developed to identify battery cycling conditions under different temperatures and working SOC ranges. Batteries are cycled under each combination of temperatures (-10^oC, 25^oC, 60^oC) and SOC ranges (0-10%, 25-75%, 90-100%, 0-100%) up to 60 equivalent cycles. The discharge data is transformed into dQ/dV-V curve and its features of the peaks and valleys are further taken for machine learning. Both supervised and unsupervised machine learning algorithms (PCA and LDA) are applied to classify batteries in terms of temperature or SOC range. The results reveal that batteries cycled under different temperatures can be identified separately regardless of the working SOC range. When splitting 60 samples with a ratio of training set equals to 0.85, the remaining test set gives an identification accuracy of 89% in temperature and 67% in working SOC range.