高温区锂离子电池超高脉冲工况SOC估计方法研究

鲁诗默; 孙丙香; 明海; 付智城; 范鑫源

doi:10.19783/j.cnki.pspc.256008

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高温区锂离子电池超高脉冲工况SOC估计方法研究

电动汽车充换电与驱动系统专题 | 更新时间：2026-07-19

- 高温区锂离子电池超高脉冲工况SOC估计方法研究
- Study of SOC estimation method for lithium-ion battery under ultra-high pulse conditions in high-temperature regions
- 电力系统保护与控制 2026年54卷第14期
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- DOI：10.19783/j.cnki.pspc.256008
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- 纸质出版：2026
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鲁诗默, 孙丙香, 明海, 等. 高温区锂离子电池超高脉冲工况SOC估计方法研究[J]. 电力系统保护与控制, 2026,54(14).

LU Shimo, SUN Bingxiang, MING Hai, et al. Study of SOC estimation method for lithium-ion battery under ultra-high pulse conditions in high-temperature regions[J]. Power System Protection and Control, 2026, 54(14).
鲁诗默, 孙丙香, 明海, 等. 高温区锂离子电池超高脉冲工况SOC估计方法研究[J]. 电力系统保护与控制, 2026,54(14). DOI： 10.19783/j.cnki.pspc.256008.

LU Shimo, SUN Bingxiang, MING Hai, et al. Study of SOC estimation method for lithium-ion battery under ultra-high pulse conditions in high-temperature regions[J]. Power System Protection and Control, 2026, 54(14). DOI： 10.19783/j.cnki.pspc.256008.

摘要

在超高脉冲工况下，锂离子电池具有单次放电时间短、电流高达数十倍以及温度升高快等特点。针对超高脉冲工况下电池荷电(state of charge

SOC)估计精度不足的问题，提出基于拓展长短期记忆(extended long short-term memory

xLSTM)神经网络模型的电池SOC估计方法。首先，将长短期记忆(long short-term memory

LSTM)神经网络中激活函数改为指数型激活函数，向量形式扩展为矩阵形式，从而提高模型估计能力。然后，在包含高温区的环境温度(25 ℃、35 ℃和45 ℃)下，高功率磷酸铁锂电池以10 s放电周期、50%占空比进行20~50 C脉冲放电实验，所提模型与多种神经网络模型的估计结果进行对比，并对所提模型在高温区的SOC估计性能进行验证。最后，将含有测量误差的低采样频率数据输入至xLSTM神经网络模型，xLSTM神经网络模型的估计结果与安时积分法估计结果进行对比，验证了所提模型的可行性和鲁棒性。

Abstract

Under ultra-high pulse conditions

lithium-ion battery exhibits characteristics such as extremely short discharge duration

discharge currents reaching tens of times the nominal value

and rapid temperature rise. To address inaccurate state of charge (SOC) estimation under such conditions

a SOC estimation method based on an extended long short-term memory (xLSTM) neural network model is proposed. First

the activation function in the long short-term memory (LSTM) neural network is replaced with an exponential function

and the vector form is extended to a matrix formulation

thereby improving the model's estimation performance. Then

pulse discharge experiments are conducted on high-power LiFePO4 batteries under high-temperature conditions (25 ℃

35 ℃

and 45 ℃)

with a 10 s discharge cycle and 50% duty cycle in the 20–50 C range. The proposed method is compared with several neural networks to verify its SOC estimation performance in high temperature regions. Finally

low sampling frequency data with measurement noise are input into the xLSTM model

and its results are compared with those obtained from the ampere-hour integral method

demonstrating the feasibility and robustness of the proposed model.

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Keywords

references

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