基于数字孪生的高精度SOC和温度联合估计方法

doi:10.19799/j.cnki.2095-4239.2025.0192

储能科学与技术 ›› 2025, Vol. 14 ›› Issue (9): 3567-3580.doi: 10.19799/j.cnki.2095-4239.2025.0192

• 储能测试与评价 • 上一篇

基于数字孪生的高精度SOC和温度联合估计方法

封居强¹^,²(), 张成知¹, 陈雨杭¹

^1.淮南师范学院机械与电气工程学院，安徽淮南 232008
^2.安徽理工大学深部煤矿开采响应与灾害防控国家重点实验室，安徽淮南 232001

收稿日期:2025-02-26 修回日期:2025-04-19 出版日期:2025-09-28 发布日期:2025-09-05
通讯作者: 封居强 E-mail:fjq5060912@126.com
作者简介:封居强（1985—），男，副教授，研究方向为信号检测与故障，E-mail：fjq5060912@126.com。
基金资助:
安徽省高校中青年教师培养行动项目(YQYB2023030);淮南师范学院校级科研项目、重点教育教学改革研究项目(2024XJZD012);淮南师范学院校级科研项目、重点教育教学改革研究项目(2024hsjyxm14)

A high-precision SOC and temperature joint estimation method based on rapid prototype modeling

Juqiang FENG¹^,²(), Chengzhi ZHANG¹, Yuhang CHEN¹

^1.School of Mechanical and Electrical Engineering, Huainan Normal University, Huainan 232008, Anhui, China
^2.State Key Laboratory of Deep Coal Mining Response and Disaster Prevention, Anhui University of Science & Technology, Huainan 232001, Anhui, China

Received:2025-02-26 Revised:2025-04-19 Online:2025-09-28 Published:2025-09-05
Contact: Juqiang FENG E-mail:fjq5060912@126.com

摘要/Abstract

摘要：

矿用锂离子电池在煤矿极端工况下面临严峻的安全性与可靠性挑战。虽然高精度物理建模是潜在解决方案，但传统实验方法存在成本高、风险大的局限性，而机理模型又难以适应实际复杂工况。为此，本研究提出一种基于数字孪生协同的模型构建框架。以228 Ah矿用锂离子电池为研究对象，利用改进一阶RC等效电路模型，建立了考虑温度、倍率、SOC和老化等多因素耦合的电池特性表征体系。基于Simulink/Simscape多物理场协同仿真平台，构建了融合电化学、热力学和状态估计算法的数字孪生系统，并集成了对流热传递、UKF和EKF估计算法模块，实现SOC和温度联合估计的对比分析。UKF估计的实验结果表明：在BBDST工况下，25 ℃、45 ℃和60 ℃恒温条件下SOC估计的最大允许误差(MPE)分别为0.3937%、0.4347%和0.5067%，温度估计的MPE分别为0.74 ℃、1 ℃和0.9613 ℃。在DST工况下，三个恒温条件下SOC估计的MPE分别为0.1829%、0.0034%和0.0035%，温度估计的MPE分别为0.6 ℃、0.9992 ℃和0.9740 ℃。结果验证了该模型具有优异的温度适应性和泛化能力。为下一代智能BMS开发提供了可靠的数字孪生验证平台，具有重要的理论价值和广阔的工程应用前景。

关键词: 矿用锂离子电池, 数字孪生, SOC与温度联合估计, Simulink/Simscape

Abstract:

Mining lithium-ion batteries face severe safety and reliability challenges under extreme working conditions in coal mines. Although high-precision physical modeling is a potential solution, traditional experimental methods are limited by high cost and risk, while mechanism-based models struggle to adapt to actual complex working conditions. To address this, a collaborative estimation framework based on digital twins is proposed. Taking a 228 Ah mining lithium-ion battery as the object, a battery characteristic characterization system considering multi-factor coupling of temperature, multiplicity, state of charge (SOC), and aging is established by improving the first-order RC equivalent circuit model. Based on the Simulink/Simscape multiphysics field co-simulation platform, a digital twin system integrating electrochemical, thermodynamic, and state estimation algorithms is constructed. The convective heat transfer, unscented Kalman filter (UKF), and extended Kalman filter (EKF) modules are integrated to perform comparative analyses of SOC and temperature joint estimation. The experimental results of UKF show that the maximum permissible errors (MPE) of SOC estimation under BBDST conditions are 0.3937%, 0.4347%, and 0.5067% at 25 ℃, 45 ℃, and 60 ℃, respectively, while the MPE of temperature estimation are 0.74 ℃, 1 ℃, and 0.9613 ℃. Under DST conditions, the MPE of SOC estimation are 0.1829%, 0.0034%, and 0.0035% at 25 ℃, 45 ℃, and 60 ℃, respectively, and the MPE of temperature estimation are 0.6 ℃, 0.9992 ℃, and 0.9740 ℃. The results confirm that the model possesses excellent temperature adaptability and generalization capability, serving as a reliable digital twin verification platform for next-generation intelligent battery management system (BMS) development. This provides significant theoretical value and broad engineering application prospects.

Key words: mining lithium-ion battery, digital twin, SOC and temperature joint estimation, Simulink/Simscape

中图分类号:

TP 391.9

封居强, 张成知, 陈雨杭. 基于数字孪生的高精度SOC和温度联合估计方法[J]. 储能科学与技术, 2025, 14(9): 3567-3580.

Juqiang FENG, Chengzhi ZHANG, Yuhang CHEN. A high-precision SOC and temperature joint estimation method based on rapid prototype modeling[J]. Energy Storage Science and Technology, 2025, 14(9): 3567-3580.

图/表 23

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表1

BBDST工况SOC估计误差分析"

估计方法	SOC范围	25 ℃		45 ℃		60 ℃
估计方法	SOC范围	RMSE/%	$δ m i n$ ~ $δ m a x$ /%	RMSE/%	$δ m i n$ ~ $δ m a x$ /%	RMSE/%	$δ m i n$ ~ $δ m a x$ /%
UKF	1~0.95	0.0090	-0.0162~-0.0090	0.0090	-0.0091~0.0090	0.0090	-0.0091~-0.009
	0.95~0.05	0.0078	-0.0246~0.0088	0.0078	-0.0090~0.0058	0.0078	-0.0090~0.0045
	0.05~0.01	0.1048	-0.3937~0.0775	0.1823	-0.4347~0.0316	0.2264	-0.5067~0.0290

EKF	1~0.95	0.0128	-0.0162~-0.0090	0.0098	-0.0106~0.0090	0.0091	-0.0091~-0.009
	0.95~0.05	0.0101	-0.0246~0.0127	0.0081	-0.0241~0.0072	0.0880	-0.2030~0.0042
	0.05~0.01	0.2145	-0.3937~0.4091	0.3102	-0.6726~0.4473	0.3519	-0.7899~0.4664

表1

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表2

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表3

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表5

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估计方法	SOC 范围	误差/%
估计方法	SOC 范围	RMSE	Min~Max
UKF	1~0.95	0.0034	-0.0035~-0.0034
	0.95~0.05	0.0021	-0.0035~0.0183
	0.05~0.01	0.0544	-0.1829~-0.0004

EKF	1~0.95	0.0180	-0.0283~-0.0034
	0.95~0.05	0.0150	-0.0454~0.0194
	0.05~0.01	0.1776	-0.5616~-0.0001

工况温度	估计方法	SOC 范围	误差/%
工况温度	估计方法	SOC 范围	RMSE	Min~Max
45 ℃	UKF	1~0.67	0.0033	-0.0034~-0.0033
45 ℃	EKF	1~0.67	0.0069	-0.0268~0.0140

60 ℃	UKF	1~0.67	0.0034	-0.0035~-0.0034
60 ℃	EKF	1~0.67	0.0069	-0.0531~-0.0030

基于数字孪生的高精度SOC和温度联合估计方法

A high-precision SOC and temperature joint estimation method based on rapid prototype modeling

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温度	误差/℃
温度	Min	Max	RMSE
25 ℃	-0.6	0.4750	0.1834
45 ℃	-0.9992	-0.2662	0.5962
60 ℃	0.2392	0.9740	0.6274

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