A Novel State-of-Charge Estimation Method for Lithium-Ion Battery Using GDAformer and Online Correction

Abstract

Lithium-ion batteries have been developed as the most widely used energy storage equipment and power batteries. State-of-charge (SOC) of the battery is a key index to evaluate the remaining range of electric vehicles. The existing SOC estimation methods perform unsatisfactorily on the multivariate long-time series data produced by battery operation. In this article, a graph deviation-based autoformer is proposed to realize accurate SOC estimation. The GD-based input module utilizes the graph structure with embedding vectors to extract spatial features and detect outliers. Encoder and decoder can acquire the temporal cycle dependencies in the data, using sequence decomposition block and auto-correlation mechanism instead of self-attention mechanism. Meanwhile, the online detection method can filter out noise and fluctuations to enhance the accuracy and robustness of the estimation results. The average values of normalized root mean square error, normalized mean absolute error, and R ² achieved in the experiments are 0.0057, 0.0042, and 0.9995 respectively, which indicates superior performance on SOC estimation compared to other state-of-the-art methods. The method also has excellent generalization capability for new driving modes and new temperatures, which shows promising potential in practical applications.