LSTM-Based Real-Time SOC Estimation of Lithium-Ion Batteries Using a Vehicle Driving Simulator

Currently, lithium-ion batteries (a type of secondary battery) are used as the primary sources of power in many applications due to their low energy loss as a result of their high energy density and low self-discharge rate, and their ability to store energy for a long time. However, due to the frequent charging and discharging of such batteries, overcharging is inevitable. This can cause system shutdowns, accidents, or property damage due to explosions. Therefore, it is necessary to accurately predict the state of charge (SOC) of batteries for stable and efficient usage. Hence, in this paper, we propose a SOC estimation method using a vehicle driving simulator. After manufacturing the simulator to perform the battery discharge experiment, voltage, current, and discharge-time data were collected. Using the collected data as input parameters for an RNN-based LSTM, we estimated the SOC of the battery and compared the errors to. We then used the developed LSTM surrogate model to conduct discharge experiments and simultaneously estimate the SOC in real-time.