A robust non-linear method for the state-of-health estimation for lithium-ion batteries based on dissipativity theory for electric vehicle applications

State of health estimation is one of the most important functions for managing lithium batteries, especially for high-current applications. The health level of the battery cannot be measured directly with any sensor, so accurate methods should estimate it. This paper aims to present an accurate health level estimation algorithm for lithium batteries based on a non-linear robust estimator and based on dissipativity theory for electric vehicle applications. In this article, first, a non-linear dynamics of the Li battery is considered based on electrical topology. After that, based on the dissipativity theory, a robust estimation algorithm is designed for the SoC estimation with the model uncertainty consideration. Then the internal resistance of the battery is estimated to be used for the state-of-health estimation. Because of the accurate and robust estimation of the SoC, the SoH estimation is also accurate and robust. This article considers model uncertainties as a disturbance term in the battery dynamic model, and a robust estimator is designed to estimate the charge level. By estimating the charge level, the internal resistance and the health level of the battery are obtained with high accuracy. Laboratory data and the results that have investigated the suggested method’s performance show the proposed method’s good and effective performance in estimating the charge level, the internal resistance, and the battery’s health level. According to the experiments, the proposed method can improve the SoC and voltage terminal estimation accuracies as much as 3% and 0.4 V, respectively.