SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output

Junxiong Chen, Yu Zhang, Ji Wu, Weisong Cheng and Qiao Zhu

Energy, 2023, vol. 262, issue PA

Abstract: The state of charge (SOC) estimation of lithium-ion battery (LIB) based on recurrent neural network (RNN) has been a popular research due to its suitability for time series data prediction. However, there are significant output fluctuations in solo network, which lead to unstable SOC estimation performance. To solve this problem, this paper proposes a novel long short-term memory recurrent neural network (LSTM-RNN) with extended input (EI) and constrained output (CO) for battery SOC estimation, named EI-LSTM-CO. For the network input, an additional slow time-varying information sliding window average voltage is introduced to enhance the ability of network to map the nonlinear characteristics of the battery and reduce the output SOC fluctuations. In terms of the network output, a state flow strategy based on the Ampere-hour integration (AhI) is designed to constrain the variation between adjacent output SOCs of the network to smooth the network output and further improve the SOC estimation performance. In the experiments, the LiFePO4 battery datasets at various temperatures are used to validate the SOC estimation performance and generalization ability. In particular, the root mean square error (RMSE) and the maximum error (MAXE) of the proposed method on unknown data are less than 1.3% and 3.2% respectively.

Keywords: State of charge estimation; LiFePO4 battery; Long short-term memory; Recurrent neural network; Extended input; Constrained output (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222022575

DOI: 10.1016/j.energy.2022.125375

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