Exploring the Hysteresis Effect of Li-ion Batteries: A Machine Learning based Approach

With the rapid development of electric vehicle industry, the battery management system of electric vehicle is the focus of research. Battery management is not only related to the safe driving of electric vehicles, but also the basis of intelligent driving of electric vehicles. The state-of-charge (SoC) estimation of battery is very important in battery management system. The battery is in a state of power consumption when the electric vehicle is running, but when the electric vehicle is braked, the kinetic energy will also be converted into electric energy to charge the battery. The acceleration and braking of electric vehicles are frequently switched. Therefore, the working conditions of electric vehicle batteries are complex, and the influence of battery hysteresis on the accuracy of SoC estimation cannot be ignored. In this paper, a lithium ion battery model considering hysteresis effect based on machine learning is proposed. The experiment was designed to collect the data of small cycle charge and discharge of the battery. The data were used to train the long short-term memory (LSTM) neural network model, and a battery model with hysteresis effect was obtained. It is verified that the model performs well in the test set, and the error of hysteresis voltage can be reduced to 0.002V. This model can be used for SoC estimation considering hysteresis effect.