State of Health Estimation of Lithium-Ion Batteries Using Capacity Fade and Internal Resistance Growth Models

Abstract

In this paper, a method for the estimation of remaining useful lifetime (RUL) of lithium-ion batteries has been presented based on a combination of its capacity degradation and internal resistance growth models. The capacity degradation model is developed recently based on battery capacity test data. An empirical model for internal resistance growth is also developed based on electrochemical-impedance spectroscopy (EIS) test data. The obtained models are used in a particle filtering (PF) framework for making end-of-lifetime (EOL) predictions at various phases of its lifecycle. Further, the above two models were fused together to obtain a new degradation model for RUL estimation. It has been observed that the fused degradation model has improved the standard deviation of prediction as compared to the individual degradation models by maintaining satisfactory prediction accuracy. The effect of parameter variations on the performance of the PF algorithm has also been studied. Finally, the predictions are validated with experimental data. From the results it can be observed that with the availability of longer volume of data, the prediction accuracy gradually improves. The prognostics framework proposed in this paper provides a structured way for monitoring the state of health (SoH) of a battery.