State of Health Estimation and Remaining Useful Life Prediction of Electric Vehicles Based on Real W

This paper presents a method for remaining useful life (RUL) estimation of lithium-ion batteries based on its power fading model. Firstly, an empirical model of power fading is developed based on battery test data. Then, the obtained model has been used in a particle filtering (PF) framework for making end of life (EOL) predictions at various stages of its cycle life. Finally, the predictions were validated with battery power fade data. From the results it can be observed that as more volume of data becomes available, the accuracy of prediction gradually improves. The prognostics framework proposed in this work provides a systematic way for monitoring the state of health (SoH) of a battery.