State‐of‐Charge Prediction of Lithium‐Ion Batteries Based on Sparse Autoencoder and Gated Recurrent Unit Neural Network

Abstract

Lithium‐ion batteries are widely used in daily life because of their fast charging and high energy density. The accurate prediction of state‐of‐charge (SOC), critical for the quality evaluation and long‐term planning of lithium‐ion batteries, has still been challenging owing to sophisticated battery dynamics and ever‐changing ambient conditions. Herein, a joint sparse autoencoder (SAE) and gated recurrent unit (GRU) model are developed to improve SOC prediction performance. The sliding time window method is first utilized for data reconstruction to explore the continuous time trend and discrete state characteristics in multi‐cycle data of the lithium‐ion batteries. The SAE is then used to extract features from the reconstruction data. The GRU is finally applied to realize an accurate SOC prediction. The performance of proposed method is tested on a benchmark lithium‐ion battery degradation dataset and actual battery degradation experiments. Experimental tests show that the present model has the lowest root mean square error of 0.176 and 0.271, the lowest mean absolute error of 0.1396 and 0.2412, and the lowest mean square error of 0.03098 and 0.07355 on two datasets, respectively, demonstrating that the proposed method outperforms the state‐of‐the‐art methods in terms of accuracy and robustness.