Machine Learning Aided Predictions for Capacity Fade of Li-Ion Batteries. Issue 5 (1st May 2022)

• Record Type: Journal Article
• Authors: Penjuru, N. M. Hitesh; Reddy, G. Vineeth; R. Nair, Manikantan; Sahoo, Soumili; Mayank, ; Jiang, Jason; Ahmed, Joinal; Wang, Huizhi; Roy, Tribeni
• Is Part Of: Journal of the Electrochemical Society. Volume 169:Issue 5(2022)
• Publication Date: 2022-05-01
• Subjects: Electrochemistry -- Periodicals; 541.3705
• DOI: 10.1149/1945-7111/ac7102
• Languages: English
• ISSNs: 0013-4651

Abstract

Abstract : Future demands high power and high energy density devices that can be sustainably built and easily maintained. It is seen that among various energy storage devices, the demanding role lithium-ion batteries play in powering electronic gadgets to electric vehicles, is highly significant. Hence, the researchers around the world are trying to solve the riddles of the lithium-ion batteries and make it more efficient. One such problem that researchers are trying to solve is battery degradation and capacity fade. In this work, we made a battery forecasting model that can predict the capacity fade using electrochemical impedance spectroscopy (EIS) data. Two machine learning techniques like, support vector regression (SVR) and multi-linear regression (MLR) were utilized to analyse the data and predict the capacity fade for lithium-ion battery. Principal component analysis was also carried out to determine the most relevant feature from the data. From the analysis it was found that that SVR has a better prediction accuracy than MLR or pre-existing Gaussian process regression (GPR) results and among the two kernels of support vector regression, radial basis function (rbf) kernel has better prediction accuracy with R 2 score of 0.9194 than the linear kernel with R 2 score of 0.6559.