A Practical Data Driven Battery State of Health Estimation for Electric Vehicles
IEEE Transactions on Industrial Electronics(2022)
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.
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Key words
Battery management system (BMS),battery state of health (SOH),data-driven algorithms,Li-ion batteries,neural networks (NNs)
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