A Cloud-Edge Collaborative State of Charge Estimation Method for Lithium-Ion Batteries in Mining Environments Integrating Deep Learning and Ampere-Hour Integration

SHEN Yong; YANG Yukang; ZOU Keyi; WANG Chenglong; XIONG Liangqian; LIU Dong; CAO Fengjin; ZHANG Xiangfeng

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A Cloud-Edge Collaborative State of Charge Estimation Method for Lithium-Ion Batteries in Mining Environments Integrating Deep Learning and Ampere-Hour Integration

更新时间：2026-06-11

- A Cloud-Edge Collaborative State of Charge Estimation Method for Lithium-Ion Batteries in Mining Environments Integrating Deep Learning and Ampere-Hour Integration
- JOURNAL OF XI’AN JIAOTONG UNIVERSITY (2026)
- 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：
  CLC： TM912.9;TD63
- Received：04 March 2026，
  
  Revised：2026-06-11，
  
  Accepted：11 June 2026，
- 稿件说明：
移动端阅览
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：

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.

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references

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