融合MHSA的锂电池SOC估计

高俊东; 马志强; 刘广忱; 宝财吉拉呼; 李宏勋; 刘宇龙

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

欢迎来到能源电力期刊网！

English Version 读者登录期刊登录

您当前的位置：

首页 >

文章列表页 >

融合MHSA的锂电池SOC估计

更新时间：2025-10-18

- 融合MHSA的锂电池SOC估计
- FUSION OF MHSA LITHIUM BATTERY SOC ESTIMATES
- 太阳能学报 2025年46卷第3期页码：16-24
- 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：
  
  国家自然科学基金（51867020）;内蒙古自治区高等学校碳达峰碳中和研究项目（STZX202307）;内蒙古自治区研究生科研创新项目（KC2024044S）
- DOI：10.19912/j.0254-0096.tynxb.2023-1759
  中图分类号： TM912U469.72
- 网络出版：2025-04-07，
  
  纸质出版：2025
- 稿件说明：
移动端阅览
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

references

何冰琛, 杨薛明, 王劲松, 等. 基于PCA-GPR的锂离子电池剩余使用寿命预测[J]. 太阳能学报, 2022, 43(5): 484-491.

陈海生, 李泓, 马文涛, 等. 2021年中国储能技术研究进展[J]. 储能科学与技术, 2022, 11(3): 1052-1076.

许青, 滕婕. 退役动力电池多场景梯次利用优化研究[J]. 太阳能学报, 2023, 44(10): 541-549.

郑良天, 康丽霞, 黄贤坤, 等. 适应多负载电池储能系统的拓扑结构优化重构方法[J]. 化工进展, 2022, 41(10): 5630-5636.

管鸿盛, 钱诚, 徐炳辉, 等. 融合自注意力机制与门控循环单元网络的宽工况锂离子电池SOC估计[J]. 储能科学与技术, 2023, 12(7): 2229-2237.

XU W, XU J L, YAN X F.Lithium-ion battery state of charge and parameters joint estimation using cubature Kalman filter and particle filter[J]. Journal of power electronics, 2020, 20(1): 292-307.

ZHENG L F, ZHU J G, LU D D C, et al. Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries[J]. Energy, 2018, 150: 759-769.

赵轩, 李美莹, 余强, 等. 电动汽车动力锂电池状态估计综述[J]. 中国公路学报, 2023, 36(6): 254-283.

ZHANG M Y, FAN X B.Design of battery management system based on improved ampere-hour integration method[J]. International journal of electric and hybrid vehicles, 2022, 14(1/2): 1.

HE Z, LI Y, SUN Y, et al.State-of-charge estimation of lithium ion batteries based on adaptive iterative extended Kalman filter[J]. Journal of energy storage, 2021, 39: 102593.

SHAH A, SHAH K, SHAH C, et al.State of charge, remaining useful life and knee point estimation based on artificial intelligence and machine learning in lithium-ion EV batteries: a comprehensive review[J]. Renewable energy focus, 2022, 42: 146-164.

LIU Z, DANG X J, JING B Q, et al.A novel model-based state of charge estimation for lithium-ion battery using adaptive robust iterative cubature Kalman filter[J]. Electric power systems research, 2019, 177: 105951.

HOW D N T, HANNAN M A, HOSSAIN LIPU M S, et al. State of charge estimation for lithium-ion batteries using model-based and data-driven methods: a review[J]. IEEE access, 2019, 7: 136116-136136.

MOO C S, NG K S, CHEN Y P, et al.State-of-charge estimation with open-circuit-voltage for lead-acid batteries[C]//2007 Power Conversion Conference-Nagoya. Nagoya, Japan, 2007: 758-762.

PANG Z Y, YANG K, SONG Z X, et al.Research on SOC estimation of lithium battery based on electrochemical impedance spectroscopy[C]//2023 6th International Conference on Energy, Electrical and Power Engineering (CEEPE). Guangzhou, China, 2023: 1638-1643.

高进, 胡红顶, 姚胜华. 基于改进安时积分法的锂离子电池组SOC估算[J]. 湖北汽车工业学院学报, 2022, 36(4): 56-60.

WANG Y Q, LI L, DING Q S, et al.Lithium-ion battery SOC estimation based on an improved adaptive extended Kalman filter[C]//2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA). Chengdu, China, 2021: 417-421.

SUN B X, WANG L F.The SOC estimation of NIMH battery pack for HEV based on BP neural network[C]//2009 International Workshop on Intelligent Systems and Applications. Wuhan, China, 2009: 1-4.

RUMELHART D E, HINTON G E, WILLIAMS R J.Learning representations by back-propagating errors[J]. Nature, 1986, 323: 533-536.

DONG C, WANG G L.Estimation of power battery SOC based on improved BP neural network[C]//2014 IEEE International Conference on Mechatronics and Automation. Tianjin, China, 2014: 2022-2027.

ASHWINI K P, PRADEEP K S, MANDHAR K A, et al.Recurrent neural network based data-driven SOC estimation in lithium-ion battery[C]//2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). Ballar, India, 2023: 1-6.

HOCHREITERr S, SCHMIDHUBER J.Long short-term memory[J]. Neural computation, 1997, 9(8): 1735-1780.

CHENG A Y, WANG Y, CHENG Z L, et al.State of charge estimation for batteries using recurrent neural networks[C]//2018 Chinese Automation Congress (CAC). Xi’an, China, 2018: 390-395.

朱元富, 贺文武, 李建兴, 等. 基于Bi-LSTM/Bi-GRU循环神经网络的锂电池SOC估计[J]. 储能科学与技术, 2021, 10(3): 1163-1176.

SONG X B, YANG F F, WANG D, et al.Combined CNN-LSTM network for state-of-charge estimation of lithium-ion batteries[J]. IEEE access, 2019, 7: 88894-88902.

LECUN Y, BOTTOU L, BENGIO Y, et al.Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.

VASWANI A, SHAZEER N, PARMAR N, et al.Attention is all you need[J]. Advances in neural information processing systems, 2017, 30.

DENG Z W, XU L, LIU H A, et al.Prognostics of battery capacity based on charging data and data-driven methods for on-road vehicles[J]. Applied energy, 2023, 339: 120954.

裴晟, 陈全世, 林成涛. 基于支持向量回归的电池SOC估计方法研究[J]. 电源技术, 2007, 31(3): 242-243, 252.

李超然, 肖飞, 樊亚翔. 基于循环神经网络的锂电池SOC估算方法[J]. 海军工程大学学报, 2019, 31(6): 107-112.

张艺迪, 孙晖. 基于Bi-LSTM的电动直臂车磷酸铁锂电池SOC估计[J]. 能源工程, 2023, 43(1): 37-42.

VAPNIK V N.An overview of statistical learning theory[J]. IEEE transactions on neural networks, 1999, 10(5): 988-999.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

储能MPC平抑分散式风电并网功率波动策略研究

融合动态卷积Transformer与CMA-ES的锂电池寿命预测方法

电动汽车回馈-防抱死制动中储氢系统博弈协同控制

考虑多重USI和新能源消纳的EV集群优化调度