矿用场景下采用深度学习-安时积分云边协同的锂电池荷电状态估计

申勇; 杨雨康; 邹岢艺; 王成龙; 熊良潜; 刘东; 曹凤金; 章翔峰

doi:null

您当前的位置：

首页 >

文章列表页 >

矿用场景下采用深度学习-安时积分云边协同的锂电池荷电状态估计

更新时间：2026-06-11

- 矿用场景下采用深度学习-安时积分云边协同的锂电池荷电状态估计
- A Cloud-Edge Collaborative State of Charge Estimation Method for Lithium-Ion Batteries in Mining Environments Integrating Deep Learning and Ampere-Hour Integration
- 西安交通大学学报 2026年
- 作者机构：
  
  1.新疆大学智能制造现代产业学院，830047，乌鲁木齐
  2.长沙矿山研究院有限责任公司金属矿山安全技术国家重点实验室，410012，长沙
  3.厦门理工学院机械与汽车工程学院，361021，福建厦门
- 作者简介：
- 基金信息：
- DOI：
  中图分类号： TM912.9;TD63
- 收稿：2026-03-04，
  
  修回：2026-06-11，
  
  录用：2026-06-11，
- 稿件说明：
移动端阅览
申勇, 杨雨康, 邹岢艺, 等. 矿用场景下采用深度学习-安时积分云边协同的锂电池荷电状态估计[J/OL]. 西安交通大学学报, 2026.

SHEN Yong, YANG Yukang, ZOU Keyi, et al. A Cloud-Edge Collaborative State of Charge Estimation Method for Lithium-Ion Batteries in Mining Environments Integrating Deep Learning and Ampere-Hour Integration[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2026.
申勇, 杨雨康, 邹岢艺, 等. 矿用场景下采用深度学习-安时积分云边协同的锂电池荷电状态估计[J/OL]. 西安交通大学学报, 2026. DOI：

SHEN Yong, YANG Yukang, ZOU Keyi, et al. A Cloud-Edge Collaborative State of Charge Estimation Method for Lithium-Ion Batteries in Mining Environments Integrating Deep Learning and Ampere-Hour Integration[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2026. DOI：

摘要

为提升矿用新能源车辆的安全性能，进一步提高锂电池荷电状态（SOC）估计精度，面向矿用场景提出了一种融合深度学习的云边协同SOC估计框架及算法。首先，在边端通过电池管理系统进行参数采集，通过安时积分法实现本地SOC估算；其次，在云端构建双向长短期记忆网络与多头注意力机制的混合神经网络模型，并引入蜣螂优化算法优化模型超参数组合；最后，通过自适应无迹卡尔曼滤波融合云边数据校正SOC估计。模型通过US06、FUDS、DST工况对其有效性及性能进行测评。研究结果表明：云端模型性能较为良好，可有效实现SOC估算，FUDS工况下平均绝对误差为0.36%，均方根误差为0.50%，DST工况下平均绝对误差为0.40%，均方根误差为0.57%；云边融合模型进一步实现了SOC校正，FUDS工况下，较云端算法平均绝对误差和均方根误差分别降低了28.5%和37.9%，在DST工况下分别降低了26.9%和39.4%，模型具备一定的泛化性及鲁棒性。云边框架可为矿用场景下，锂电池SOC估计的工程应用提供理论参考，云边融合模型为提升矿用锂电池SOC预测精度提供实践路径。

Abstract

To enhance the safety performance of mining new energy vehicles and further improve the estimation accuracy of lithium battery state of charge (SOC)

this study proposes a cloud–edge collaborative SOC estimation framework and algorithm integrating deep learning for mining scenarios. At the edge end

parameters are collected through the battery management system

and local SOC estimation is performed using the ampere-hour integration method. In the cloud

a hybrid neural network model combining a bidirectional long short-term memory network and a multi-head attention mechanism is constructed

and the dung beetle optimization algorithm is introduced to optimize the hyperparameter combination of the model. Finally

adaptive unscented Kalman filtering is used to fuse cloud and edge data for SOC estimation correction. The effectiveness and performance of the model are evaluated under US06

FUDS

and DST operating conditions. The results show that the cloud-based model performs well and can effectively estimate SOC. Under FUDS conditions

the mean absolute error is 0.36% and the root mean square error is 0.50%; under DST conditions

the mean absolute error is 0.40% and the root mean square error is 0.57%. The cloud–edge fusion model further achieves SOC correction. Compared with the cloud-based algorithm

the mean absolute error and root mean square error are reduced by 28.5% and 37.9%

respectively

under FUDS conditions

and by 26.9% and 39.4%

respectively

under DST conditions. The model demonstrates a certain degree of generalization ability and robustness.The proposed cloud-edge framework can provide theoretical reference for the engineering application of lithium battery SOC estimation in mining scenarios

while the cloud–edge fusion model offers a practical approach for improving SOC prediction accuracy of mining lithium batteries.

关键词

Keywords

references

胡循泉 , 耿莉敏 , 舒俊豪 , 等 . 采用全局健康因子和残差模型的锂离子电池健康状态估计 [J ] . 西安交通大学学报 , 2025 , 59 ( 04 ): 105 - 117 .

HU Xunquan 1 , GENG Limin1,SHU Junhao, et al. Esti- mation of Lithium-Ion Battery State of Health Using Global Health Factorand Residual Model [J ] . Journal of Xi’an Jiaotong University , 2025, 59 ( 04 ): 105 - 117 .

姚勇征 , 王奕渊 , 周福宝 . 煤矿井下锂离子蓄电池热失控燃爆风险防控技术研究现状与挑战 [J ] . 煤炭学报 , 2025 , 50 ( 04 ): 2158 - 2174 . DOI: 10.13225/j.cnki.jccs.2024.0825 http://dx.doi.org/10.13225/j.cnki.jccs.2024.0825 .

YAO Yongzheng ， WANG Yiyuan ， ZHOU Fubao . Research status & challenges on prevention and control technology for thermal runaway combustion-explosion of lithium-ion battery in coal mines [J ] . Journal of China Coal Society ， 2025 ， 50 ( 4 )： 2158 − 2174 .

梁叶 , 蔡孟冶 , 姜岩峰 , 等 . 基于修正安时积分法的锂电池荷电状态估算 [J/OL ] . 电源学报 , 1 - 13 [ 2026-01-16 ] . https://link.cnki.net/urlid/12.1420.TM.20250214.1637.004 https://link.cnki.net/urlid/12.1420.TM.20250214.1637.004 .

LIANG Ye , CAI Mengye , JIANG Yanfeng , et al . State of charge estimation of lithium battery based on modified ampere-hour integral method [J/OL ] . Journal of power supply , 1 - 13 [ 2026-01-16 ] . https://link.cnki.net/urlid/12.1420.TM.20250214.1637.004 https://link.cnki.net/urlid/12.1420.TM.20250214.1637.004 .

SUN D M . Yx WANG CM. et al State of charge estimation for Lithum-on batery basd on an Intelligent adaptive extended kalman filter with improved noise estimator [J ] . Energy , 2021 , 214 : 119025 .

LI X Y ， WANG Z P ， ZHANG L . Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles [J ] . Energy ， 2019 ， 174 ： 33 - 44 .

吴文进 , 吴晶 , 詹文法 , 等 . 基于云端数据的AEKF-LSTM融合算法预测电池SOC [J/OL ] . 电子测量与仪器学报 , 1 - 9 [ 2026-01-16 ] . https://link. cnki.net/ urlid/ 11.2488. tn.20251224.1801.098 https://link.cnki.net/urlid/11.2488.tn.20251224.1801.098 .

WU Wenjin , WU Jing , ZHAN Wenfa , et al . AEKF- LSTM fusion algorithm based on cloud data predicts battery SOC [J/OL ] . Journal of Electronic Measurement and Instrumentation , 1 - 9 [ 2026-01-16 ] . https: //link. cnki.net/ urlid/ 11.2488. tn.20251224. 1801. 098 https://link.cnki.net/urlid/11.2488.tn.20251224.1801.098 .

陈继永 , 吴兆宏 , 李金喜 . 矿用机车防爆锂电池SOC估算算法 [J ] . 煤矿安全 , 2019 , 50 ( 12 ): 102 - 105 . DOI: 10.13347/ j.cnki.mkaq.2019.12.023 http://dx.doi.org/10.13347/j.cnki.mkaq.2019.12.023 .

CHEN Jiyong , WU Zhaohong , LI Jinxi . SOC Estimation Algorithm for Mine Locomotive Explosion-proof Lithium Battery [J ] . Safety in coal mines , 2019 , 50 ( 12 ): 102 - 105 . DOI: 10.13347/ j.cnki.mkaq.2019.12.023 http://dx.doi.org/10.13347/j.cnki.mkaq.2019.12.023 .

罗勇 , 祁朋伟 , 黄欢 , 等 . 基于容量修正的安时积分SOC估算方法研究 [J ] . 汽车工程 , 2020 , 42 ( 05 ): 681 - 687 . DOI: 10.19562/j.chinasae.qcgc.2020.05.017 http://dx.doi.org/10.19562/j.chinasae.qcgc.2020.05.017 .

Luo Yong , Qi Pengwei , Huang Huan , et al . Study on battery SOC estimation by ampere-hour integral method with capacity correction [J ] . Automotive Engineering , 2020 , 42 ( 05 ): 681 - 687 . DOI: 10.19562/j.chinasae. qcgc.2020. 05.017 http://dx.doi.org/10.19562/j.chinasae.qcgc.2020.05.017 .

丁镇涛 , 邓涛 , 李志飞 , 等 . 基于安时积分和无迹卡尔曼滤波的锂离子电池SOC估算方法研究 [J ] . 中国机械工程 , 2020 , 31 ( 15 ): 1823 - 1830 .

DING Zhentao , DENG Tao , LI Zhifei , et al . SOC Estimation of Lithium-ion Battery Based on Ampere Hour Integral and Unscented Kalman Filter [J ] . China mechanical engineering , 2020 , 31 ( 15 ): 1823 - 1830 .

李昆 , 赵理 , 赵博阳 , 等 . 基于频繁项统计的流-安时积分SOC估计方法 [J ] . 重庆理工大学学报(自然科学) , 2022 , 36 ( 03 ): 19 - 27 .

LI Kun ， ZHAO Li ， ZHAO Boyang ， et al ． SOC estimation method of flow-amp-hour integral based on frequent item statistics [J ] ． Journal of Chongqing University of Technology ( Natural Science) , 2022 , 36 ( 3 ) : 19 － 27 ．

Yang F , Li W , Li C , et al . State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network [J ] . Energy , 2019 , 175 (MAY 15 ): 66 - 75 . DOI: 10.1016/ j.energy.2019.03.059 http://dx.doi.org/10.1016/j.energy.2019.03.059 .

CHEMALI E ， KOLLMEYER P J ， PREINDL M ， et al . Long short-term memory networks for accurate state ofcharge estimation of Li-ion batteries [J ] . IEEE Transactions on Industrial Electronics , 2017 , 65 ( 8 )： 6730 - 6739 ．

BIAN Chong ， HE Huoliang ， YANG Shunkun ． Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries [J ] ． Energy ， 2020 ， 191 ： 116538 ．

陈继斌 , 李雯雯 , 孙彦玺 , 等 . 基于卷积-双向长短期记忆网络的电池SOC预测 [J ] . 电源技术 , 2022 , 46 ( 05 ): 532 - 535 .

CHEN Jibin , LI Wenwen , SUN Yanxi ,, et al . Battery SOC prediction based on convolution bidirectional long term

and short term memory network [J ] ． Chinese journal of power sources , 2022 , 46 ( 05 ): 532 - 535 .

杜伟 , 王圣 , 李健 , 等 . 基于CNN-LSTM-AM模型的储能锂离子电池荷电状态预测 [J ] . 电工技术学报 , 2025 , 40 ( 09 ): 2982 - 2995 . DOI: 10.19595/j.cnki.1000-6753.tces.240706 http://dx.doi.org/10.19595/j.cnki.1000-6753.tces.240706 .

Du Wei , Wang Sheng , Li Jian , et al . Prediction of State of Charge for Energy Storage Lithium-Ion Batteries Based on CNN-LSTM-AM Model . [J ] . Transactions of China Electrotechnical Society , 2025 , 40 ( 09 ): 2982 - 2995 . DOI: 10.19595/j.cnki.1000-6753.tces.240706 http://dx.doi.org/10.19595/j.cnki.1000-6753.tces.240706 .

丁伟 , 邹复民 , 刘吉顺 , 等 . 基于CNN-BiLSTM- Attention的电动装载机电池荷电状态预测 [J/OL ] . 电源学报 , 1 - 12 [ 2026-03-04 ] . https://link.cnki.net/urlid/12.1420. TM.20240426.1024.024 https://link.cnki.net/urlid/12.1420.TM.20240426.1024.024

DING Wei , ZOU Fumin , LIU Jishun , et al . State-of-charge prediction of electric loader battery based on CNN- BiLSTM-Attention [J/OL ] . Journal of power supply , 1 - 12 [ 2026-03-04 ] . https://link.cnki.net /urlid/ 12. 1420. TM.20240426.1024.024 https://link.cnki.net/urlid/12.1420.TM.20240426.1024.024 .

张林 , 巫春玲 , 黄鑫蓉 , 等 . 采用深度学习的全寿命周期锂电池荷电状态估计 [J ] . 西安交通大学学报 , 2024 , 58 ( 10 ): 36 - 43 .

ZHANG Lin , WU Chunling , HUANG Xinrong , et al . Deep learning-based State-of-Charge estimation for Lithium-lon batteries across the entire life cyclel [J ] . Journal of Xi’an Jiaotong University , 2024 , 58 ( 10 ): 36 - 43 .

高俊东 , 马志强 , 刘广忱 , 等 . 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 http://dx.doi.org/10.19912/j.0254-0096.tynxb.2023-1759 .

Gao Jundong , Ma Zhiqiang , Liu Guangchen , et al . Fusion of mhsa lithium battery soc estimates [J ] . Acta Energiae Solaris Sinica , 2025 , 46 ( 3 ): 16 - 24 . DOI: 10.19912/j.0254-0096. tynxb. 2023-1759 http://dx.doi.org/10.19912/j.0254-0096.tynxb.2023-1759 .

Lu Wenjie , Li Jiazheng , Wang Jingyang , et al . A CNN-BiLSTM-AM method for stock price prediction [J ] . Neural Computing and Applications , 2021 , 33 ( 10 ): 4741 - 4753

刘自兵 , 崔双喜 , 刘庆庆 . 基于融合多尺度卷积与多头注意力机制的WGAN-GP新能源场景生成方法 [J ] . 电网技术 , 2026 , 50 ( 01 ): 272 - 283 . DOI: 10.13335/j.1000- 3673. pst. 2025.1065 http://dx.doi.org/10.13335/j.1000-3673.pst.2025.1065 .

LIU Zibing , CUI Shuangxi , LIU Qingqing . New energy scenario generation method based on an improved WGAN-GP integrating multi-scale convolution and Multi-head Attention mechanisms [J ] . Power system technology , 2026 , 50 ( 01 ): 272 - 283 . DOI: 10.13335/j.1000- 3673. pst. 2025.1065 http://dx.doi.org/10.13335/j.1000-3673.pst.2025.1065 .

汪强 , 李清阳 , 席磊 , 等 . 融合多头注意力机制的LSTM冬小麦需水量预测 [J ] . 中国农业大学学报 , 2025 , 30 ( 04 ): 38 - 50 .

Wang Qiang , Li Qingyang , Xi Lei , et al . LSTM winter wheat water demand prediction based on multihead attention mechanism [J ] . Journal of China Agricultural University , 2025 , 30 ( 04 ): 38 - 50 .

XUE J K ， SHEN B . Dung beetle optimizer： a new metaheuristic algorithm for global optimization [J ] . The journal of supercomputing ， 2023 ， 79 （ 7 ）： 7305 - 7336 .

Yang K , Wang B , Gong Z , et al . Temperature collaborative control strategy using dung beetle optimizer in proton exchange membrane fuel cell system [J ] . Renewable Energy , 2026 , 256(pa): 123946 . DOI: 10.1016/j.renene.2025.123946 http://dx.doi.org/10.1016/j.renene.2025.123946 .

Mirjalili S , Mirjalili S M , Lewis A . Grey wolf optimizer [J ] . Advances in engineering software , 2014 , 69 : 46 - 61 .

Aaha B , Sm C , Hf D , et al . Harris hawks optimization: Algorithm and applications [J ] . Future Generation Computer Systems , 2019 , 97 : 849 - 872 .

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

锂电池双向均衡及模型预测控制优化

锂离子电池全生命周期的动静态荷电状态估算方法

锂电池分数阶建模与荷电状态研究

双自适应衰减卡尔曼滤波锂电池荷电状态估计

采用Morlet小波的锂电池相对健康状态估计