Machine Learning Aided Predictions for Capacity Fade of Li-Ion Batteries

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.

Automated summary

Future demands devices with high power and high energy density. Lithium-ion batteries play a significant role in powering electronic gadgets and electric vehicles. Researchers are trying to solve the problem of battery degradation and capacity fade. This study developed a battery forecasting model using electrochemical impedance spectroscopy data, and employed support vector regression and multi-linear regression to predict capacity fade. The results showed that support vector regression with radial basis function kernel had better prediction accuracy.