Prediction of State of Charge for Lead-acid Batteries Based on GRU Network and Isolated Forest

Abstract

Accurate prediction of the state of charge (SOC) of lead-acid batteries is the key to ensuring battery life. In this paper, a new combined SOC prediction model IF-GRU (Isolation Forest, Gated Recurrent Unit) is proposed. The model combines the Isolation Forest anomaly detection algorithm and the Gated Recurrent Network. The Isolation Forest algorithm is used to detect anomalous and missing values in the raw data. Length dependence of the GRU network can be further utilized to perform high-accuracy SOC estimation by implementing a sliding window that takes into account the data's charging and discharging details. In addition, the conventional Adam optimizer is utilized to improve the convergence speed of model training. The experimental data demonstrate that the IF-GRU model proposed in this paper has higher prediction accuracy and convergence speed with a RMSE of 1.59% compared with traditional LSTM network, GRU network, and BP network.