Remaining Useful Life Early Prediction of Batteries Based on the Differential Voltage and Differential Capacity Curves
Sajad Saraygord Afshari, Shihao Cui, Xiangyang Xu, Xihui Liang
Abstract
Accurate prediction of the remaining useful life (RUL) of batteries is of great importance for the health management of different equipment and machines, such as electric vehicles and smartphones. It gives operators information about when the battery should be replaced. Predicting the batteries’ RUL using the data only from early cycles can also be beneficial for manufacturers. For example, it can reduce the batteries’ testing costs during the research and development phase. This article focuses on batteries’ RUL early prediction using data-driven methods. The differential capacity ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$dQ/dV)$ </tex-math></inline-formula> and differential voltage ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$dV/dQ)$ </tex-math></inline-formula> curves can reveal the potential capacity and voltage of a battery, respectively, and they are known to be indicators of the batteries’ degradation. We will present a practical method for batteries’ RUL early prediction using features extracted from those two curves. Accordingly, 19 features generated from the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$dQ/dV$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$dV/dQ$ </tex-math></inline-formula> curves are analyzed and extracted. The Sparse Bayesian Learning (SBL) method is a popular machine learning method in the field of RUL prediction, and it is used to achieve an RUL early prediction for batteries. In the end, the training and test errors are investigated to evaluate the presented method’s efficiency. Moreover, we compared our results with two other methods (lasso and elastic net), which have been recognized as best performing methods in this field so far, and the comparisons showed our proposed method outperforms those two methods in the term of accuracy. The presented method is generic and can be used for RUL early prediction of different batteries.