LSTM for State of Charge Estimation of Lithium Polymer Battery on Jetson Nano

Battery Management System (BMS) is an important element for batteries. It can protect the batteries from operating in dangerous conditions. BMS consists of many items. One of them is State of Charge (SoC), which indicates the batteries' charge level. Currently, we are unable to measure the internal states of the battery directly. Therefore, in order to obtain the information of SoC, we need to estimate it. This paper proposes Long Short-Term Memory (LSTM) to estimate SoC of lithium polymer battery, where NVIDIA Jetson Nano handles the computation. Based on the experiments, we have RMSE scores of 1.797 and 1.976, respectively, for training and testing. Those results show that it is possible to employ LSTM on NVIDIA Jetson Nano for estimating the SoC of lithium polymer battery. © 2021 IEEE.