Lithium-Ion Battery Prognostics based on Support Vector Regression and Time-Series Analysis

Battery prognostics is a promising area that aids in managing potential risks in times of failure of an event. One common way of estimating battery health is to monitor its capacity continuously. Traditionally, several methods like Kalman filter, Extended Kalman filter and data-driven models have been studied extensively. However, support vector machines offer a better generalization ability around every training sample and obtain a sparse solution, we leverage this to our capacity estimation problem. In this manuscript, the class of Support vector regressors (SVR) is utilized to predict battery capacity using multi-channel profiles of voltage, current and temperature. Publicly available battery dataset from NASA is utilized to model a capacity estimation problem and models like classical
$\varepsilon$
-SVR, Least square support vector regression (LSSVR), Twin support vector regression (TSVR), Huber-SVR and
$\mathcal{L}_{1}$
-norm support vector regression are tested on three different battery sets. Results indicate superior performance of TSVR in terms of root mean squared error (RMSE) which is found as low as 0.0235 when compared to existing studies. Further, to qualitatively analyze the regression methods, a residual analysis in the form of the Durbin-Watson test is carried out.