FUSION OF MHSA LITHIUM BATTERY SOC ESTIMATES

Gao Jundong; Ma Zhiqiang; Liu Guangchen; Bao Caijilahu; Li Hongxun; Liu Yulong

doi:10.19912/j.0254-0096.tynxb.2023-1759

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (3) : 16-24. DOI: 10.19912/j.0254-0096.tynxb.2023-1759

FUSION OF MHSA LITHIUM BATTERY SOC ESTIMATES

Gao Jundong¹, Ma Zhiqiang^1,2, Liu Guangchen^2,3, Bao Caijilahu¹, Li Hongxun¹, Liu Yulong¹

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Abstract

A method based on long short-term memory network (LSTM) and multi-head Self-attention mechanism (LSTM-MHSA) was proposed to estimate state of charge (SOC) of power battery. The current, voltage and temperature of the power battery are used as inputs to the LSTM model, and the historical data features are selected and forgotten through a gating unit with memory function. LSTM processes all data features equally in terms of long sequence data and cannot effectively represent data features with greater impacts on SOC. MHSA unit is used to determine the feature focus according to the data characteristics, aiming at enhancing the global and local feature extraction ability of the model in the time series data and improving the LSTM’s insufficient focus on historical data features, therefore improve the model's estimation accuracy. Finally, the comparison and generalization experiments on public datasets indicate that the method reduces the average absolute error by 0.007, which proves that the method can effectively demonstrate long series data features under the influence of complex nonlinear relationships, thus improve the accuracy of SOC estimation.

Key words

lithium batteries / electric vehicles / battery storage / long short-term memory / state of charge

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Gao Jundong, Ma Zhiqiang, Liu Guangchen, Bao Caijilahu, Li Hongxun, Liu Yulong. FUSION OF MHSA LITHIUM BATTERY SOC ESTIMATES[J]. Acta Energiae Solaris Sinica. 2025, 46(3): 16-24 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1759

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