A Practical Data-Driven Battery State-of-Health Estimation for Electric Vehicles
Saeed Khaleghi Rahimian, Yifan Tang
Abstract
In this article, to estimate the battery state of health (SOH) under realistic electric vehicle (EV) conditions, a robust and efficient data-driven algorithm is developed and validated through comprehensive battery life testing. More than 50 state-of-the-art EV battery cells have been tested under a variety of cycling conditions with different charging protocols, dynamic driving cycles, voltage ranges, pulse rates, and temperatures. Some of the cells have also been tested under a combination of cycling and storage conditions, constant current and multistep charging, and a periodic temperature variation that mimics real life conditions. Only partial data (voltage, current, and temperature) within a narrow state-of-charge range under a dynamic driving condition are required to extract the health indicators. A neural network is trained to find the mapping between the health features and the battery SOH. The life test data are divided into three groups. The first dataset (≈55% of data) is used for training and initial validation and testing, whereas the second and third datasets (≈45% of data) are entirely used for the final validation and testing to minimize the network overfitting. The results show that the SOH estimation root-mean-squared error for all datasets is less than 0.9%, signifying the fidelity and reliability of the proposed method.