State-of-Charge Estimation for Lead-Acid Battery Using Isolation Forest Algorithm and Long Short Term Memory Network With Attention Mechanism
Xinwei Zhang, Zhanying Li, Danhua Zhou, Ming Chen
Abstract
State of charge (SOC) is the most direct embodiment of the state of a lead-acid battery, and accurate estimation of SOC is helpful to ensure the safe use of the battery. However, the traditional estimation model has low precision and weak anti-interference. In this study, a new SOC estimation structure is proposed. This structure is based on the effective combination of the Isolation Forest (IF) anomaly detection algorithm and Long Short-Term Memory (LSTM) Network combined with Attention Mechanism (IF-LSTM-Attention). The Isolation Forest algorithm is used to effectively detect the missing values and outliers contained in the original data. Based on the actual charging and discharging data, a sliding window is constructed as the data of the model to give full play to the LSTM network length dependence. And LSTM network combined with Attention Mechanism achieves high-precision SOC estimation. In addition, the conventional dropout technique and Bayesian optimizer are used to improve the model training convergence rate. The results show that the IF-LSTM-Attention model proposed in this study has higher accuracy and better generalization ability than the conventional LSTM model and Back Propagation (BP) neural network model.