Lithium-ion Battery State of Charge Estimation Based on Gated Recurrent Unit Encoder-decoder

The state of charge (SOC) estimation technology matters in battery management systems (BMS), and its accuracy requirements have been increasing as the range of uses for lithium-ion batteries expands. In order to achieve more accurate SOC estimation, this paper proposes an SOC estimation technique based on the gated recurrent unit (GRU) encoder-decoder (ED). With the ED framework, the dependencies of the input sequence are bi-directionally captured by the encoder using a bi-directional GUR network, and the encoder condenses the related information of the input sequence into a context vector, which is subsequently unlocked by the decoder using a unidirectional GRU network. Compared to the previously proposed recurrent neural networks, such end-to-end models can better learn the sequence information from the input sequences to build a more accurate nonlinear SOC estimation model. The simulation experiments demonstrate that the proposed GRU-ED model achieves the best SOC estimation under a fixed temperature compared to 3 kinds of bidirectional recurrent neural networks. Moreover, it accurately estimates the SOC with a low mean absolute error (MAE) and maximum error (MAX) of 0.92% and 4.96% under the changing ambient temperatures. © 2024 Power System Technology Press. All rights reserved.