State of Charge and State of Energy Estimation for Lithium-Ion Batteries Based on a Long Short-Term Memory Neural Network

Abstract

State of charge (SOC) and state of energy (SOE) are two crucial battery states which correspond to available capacity in Ah and available energy in Wh, respectively. Both of them play a pivotal role in battery management, however, the joint estimation of the two states was rarely studied. This study investigates a novel data-driven method that can estimate SOC and SOE simultaneously based on a long short-term memory (LSTM) deep neural network. The proposed algorithm is validated with two dynamic driven cycles under various working conditions, such as different temperatures, different battery material and noise interference. The mean absolute error (MAE) of SOC and SOE estimation achieve 0.91% and 1.09% under a fixed temperature condition, 0.63% and 0.64% for a different battery, and 1.32% and 1.19% with noise interference, respectively. The computational burden and network setting are also studied. In addition, the performance of the proposed method is compared with other popular algorithms, including support vector regression (SVR), random forest (RF) and simple recurrent neural network (Simple RNN). The results show that the proposed method obtains higher accuracy and robustness. This study provides a new way of conducting multiple state estimation of batteries using a deep-learning approach.