Neural Network-Based State of Charge Estimation Method for Lithium-ion Batteries Based on Temperature

Lithium-ion batteries are commonly used in electric vehicles, mobile phones, and laptops. These batteries demonstrate several advantages, such as environmental friendliness, high energy density, and long life. However, battery overcharging and overdischarging may occur if the batteries are not monitored continuously. Overcharging causes fire and explosion casualties, and overdischarging causes a reduction in the battery capacity and life. In addition, the internal resistance of such batteries varies depending on their external temperature, electrolyte, cathode material, and other factors; the capacity of the batteries decreases with temperature. In this study, we develop a method for estimating the state of charge (SOC) using a neural network model that is best suited to the external temperature of such batteries based on their characteristics. During our simulation, we acquired data at temperatures of 25°C, 30°C, 35°C, and 40°C. Based on the temperature parameters, the voltage, current, and time parameters were obtained, and six cycles of the parameters based on the temperature were used for the experiment. Experimental data to verify the proposed method were obtained through a discharge experiment conducted using a vehicle driving simulator. The experimental data were provided as inputs to three types of neural network models: multilayer neural network (MNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The neural network models were trained and optimized for the specific temperatures measured during the experiment, and the SOC was estimated by selecting the most suitable model for each temperature. The experimental results revealed that the mean absolute errors of the MNN, LSTM, and GRU using the proposed method were 2.17%, 2.19%, and 2.15%, respectively, which are better than those of the conventional method (4.47%, 4.60%, and 4.40%). Finally, SOC estimation based on GRU using the proposed method was found to be 2.15%, which was the most accurate.