A novel combined multi-battery dataset based approach for enhanced prediction accuracy of data driven prognostic models in capacity estimation of lithium ion batteries

Abstract

To ensure smooth and reliable operations of battery systems, reliable prognosis with accurate prediction of State of Health of lithium ion batteries is of utmost importance. However, battery degradation is a complex challenge involving many electrochemical reactions at anode, separator, cathode and electrolyte/electrode interfaces. Also, there is significant effect of the operating conditions on the battery degradation. Various machine learning techniques have been applied to estimate the capacity and State of Health of lithium ion batteries to ensure reliable operation and timely maintenance. In this paper, we study the Gaussian Process Regression (GPR) and Support Vector Machine (SVM) model-based approaches in estimating the capacity and State of Health of batteries. Battery capacity and State of Health estimations are carried out using GPR and SVM models and the predictions comparatively studied for accuracy based on RMSE values. The prediction accuracy is further compared with respect to single sensor and multi sensor data. Further, a combined multi battery data set model is used to improve the prediction accuracy. Combining the data of multiple batteries with similar operating conditions for training a model resulted in higher prediction accuracy.

Keywords

• Capacity estimation
• State of health
• Gaussian process regression
• Support vector machine
• Lithium ion battery