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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FUSION OF MHSA LITHIUM BATTERY SOC ESTIMATES

更新时间：2025-10-18

- FUSION OF MHSA LITHIUM BATTERY SOC ESTIMATES
- Vol. 46, Issue 3, Pages: 16-24(2025)
- 作者机构：
  
  1. 内蒙古工业大学数据科学与应用学院,呼和浩特 010080
  2. 大规模储能技术教育部工程研究中心,呼和浩特 010080
  3. 内蒙古工业大学电力学院,呼和浩特 010080
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2023-1759
  CLC： TM912U469.72
- Published Online：07 April 2025，
  
  Published：2025
- 稿件说明：
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高俊东, 马志强, 刘广忱, 宝财吉拉呼, 李宏勋, 刘宇龙. 融合MHSA的锂电池SOC估计[J]. 太阳能学报, 2025, 46(3): 16-24.

Gao Jundong, Ma Zhiqiang, Liu Guangchen, et al. FUSION OF MHSA LITHIUM BATTERY SOC ESTIMATES[J]. 2025, 46(3): 16-24.
高俊东, 马志强, 刘广忱, 宝财吉拉呼, 李宏勋, 刘宇龙. 融合MHSA的锂电池SOC估计[J]. 太阳能学报, 2025, 46(3): 16-24. DOI： 10.19912/j.0254-0096.tynxb.2023-1759.

Gao Jundong, Ma Zhiqiang, Liu Guangchen, et al. FUSION OF MHSA LITHIUM BATTERY SOC ESTIMATES[J]. 2025, 46(3): 16-24. DOI： 10.19912/j.0254-0096.tynxb.2023-1759.

摘要

提出在长短期记忆网络（LSTM）基础上融合多头自注意力机制（LSTM-MHSA）的方法估计动力电池荷电状态（SOC）

将动力电池的电流、电压和温度作为LSTM模型输入

通过带有记忆功能的门控单元选择和遗忘历史数据特征;LSTM处理长序列数据会将所有数据特征同等对待

不能对SOC影响较大的数据特征有效表示

MHSA单元根据数据特性确定特征关注的焦点

提升模型在时序数据中全局和局部的特征提取能力

改进LSTM在对历史数据特征关注度不足和提高模型估计精度;最后在公开数据集上通过对比和泛化实验。结果显示该方法在平均绝对误差上减少0.007

验证在复杂非线性关系的影响下该方法能有效表示长序列数据特征

进而提高SOC估计的精度。

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.

关键词

Keywords

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