Mnn And Lstm-Based Real-Time State Of Charge Estimation Of Lithium-Ion Batteries Using A Vehicle Driving Simulator

Abstract—Lithium-ion batteries (a type of secondary battery) are now used as a power source in many applications due to their high energy density, low self-discharge rates, and ability to store long-term energy. However, overcharging is inevitable due to frequent charging and discharging of these batteries. This may result in property damage caused by system shutdown, accident, or explosion. Therefore, reliable and efficient use requires accurate prediction of the battery state of charge (SOC). In this paper, a method of estimating SOC using vehicle simulator operation is proposed. After manufacturing the simulator for the battery discharge experiment, voltage, current, and discharge-time data were collected. The collected data was used as input parameters for multilayer neural network (MNN) and recurrent neural network–based long short-term memory (LSTM) to predict SOC of batteries and compare errors. In addition, discharge experiments and SOC estimates were performed in real time using the developed MNN and LSTM surrogate models.