E3S Web Conf.
Volume 182, 20202020 10th International Conference on Power, Energy and Electrical Engineering (CPEEE 2020)
|Number of page(s)||7|
|Section||Energy and Energy Storage Technology|
|Published online||31 July 2020|
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: email@example.com
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 (-10oC, 25oC, 60oC) 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.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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