Evaluation of the degree of internal short circuit in lithium-ion batteries based on a single-particle electrochemical model under various abuse scenarios

JIAO Jianfang; LIU Lianqi; YAO Yu; WANG Guang; XIE Jiale

doi:10.19799/j.cnki.2095-4239.2025.0910

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Evaluation of the degree of internal short circuit in lithium-ion batteries based on a single-particle electrochemical model under various abuse scenarios

Energy Storage Test\: Methods and Evaluation | 更新时间：2026-05-28

- Evaluation of the degree of internal short circuit in lithium-ion batteries based on a single-particle electrochemical model under various abuse scenarios
- Energy Storage Science and Technology Vol. 15, Issue 2, Pages: 637-646(2026)
- 作者机构：
  
  1.华北电力大学自动化系
  2.华北电力大学燕赵电力实验室
  3.华北电力大学电力工程系，河北保定 071003
- 作者简介：
- 基金信息：
- DOI：10.19799/j.cnki.2095-4239.2025.0910
  CLC： TK 02
- Received：13 October 2025，
  
  Revised：2025-10-31，
  
  Published：28 February 2026
- 稿件说明：
移动端阅览
焦建芳, 刘连起, 姚预, 等. 基于单颗粒电化学模型的多种滥用情况下锂电内短路程度评估[J]. 储能科学与技术, 2026, 15(2): 637-646.

JIAO Jianfang, LIU Lianqi, YAO Yu, et al. Evaluation of the degree of internal short circuit in lithium-ion batteries based on a single-particle electrochemical model under various abuse scenarios[J]. Energy Storage Science and Technology, 2026, 15(2): 637-646.
焦建芳, 刘连起, 姚预, 等. 基于单颗粒电化学模型的多种滥用情况下锂电内短路程度评估[J]. 储能科学与技术, 2026, 15(2): 637-646. DOI： 10.19799/j.cnki.2095-4239.2025.0910.

JIAO Jianfang, LIU Lianqi, YAO Yu, et al. Evaluation of the degree of internal short circuit in lithium-ion batteries based on a single-particle electrochemical model under various abuse scenarios[J]. Energy Storage Science and Technology, 2026, 15(2): 637-646. DOI： 10.19799/j.cnki.2095-4239.2025.0910.

摘要

通过辨识锂电池电化学模型参数来间接估计其内部状态，从而为电池状态监控与安全管理提供依据。为系统揭示不同滥用条件对锂电池内部状态的影响机制，本研究提出一种基于参数辨识的损伤机理判断方法。以松下NCR 18650BD型三元锂离子电池为研究对象，首先基于标准测试数据辨识健康状态下的电化学参数，建立正常电池参数集。随后，对3组样本电池分别进行过充、过放与过热3类典型滥用实验，并基于实验数据对损伤电池进行参数再辨识，获取关键电化学参数的变化信息。通过深入分析，揭示了不同类型滥用损伤对应的不同参数变化及其关联的微观失效机制。虽然各种滥用是耦合存在而不是孤立的，但是可以判断出过放引发的损伤主要表现为正极可用位点丧失，过热引发的损伤以界面反应与锂离子传输动力学退化为主，而过充引发的损伤则同时涉及锂库存损失与活性物质劣化。本研究系统构建了滥用条件与参数变化之间的定量关联，为锂电池精准状态监测、安全预警及寿命管理策略的优化提供了理论与实验支撑。

Abstract

The internal state of lithium-ion batteries (LIBs) can be indirectly estimated by identifying their electrochemical model parameters

thereby providing a basis for battery status monitoring and safety management. To systematically reveal the influence of different abuse conditions on the internal state of LIBs

this study proposes a parameter identification–based method for determining degradation mechanisms. Using a Panasonic NCR 18650BD ternary LIB as the study object

a reference parameter set for a normal cell was first established by identifying the electrochemical parameters of healthy batteries based on standard test data. Subsequently

three groups of LIB samples were subjected to three typical abuse tests: over-charge

over-discharge

and thermal abuse. The key electrochemical parameters were re-identified using the experimental data to track changes after damage. This study reveals distinct parameter variations corresponding to different abuse-induced degradation modes and their associated microscopic failure mechanisms through in-depth analysis. Although various abuse conditions typically manifest in coupled rather than isolated forms

our findings demonstrate that over-discharge primarily leads to cathode active site loss

thermal abuse predominantly causes interfacial reactions and lithium-ion transport kinetics deterioration

whereas over-charge simultaneously involves lithium inventory loss and active material degradation. This study systematically establishes quantitative correlations between abuse conditions and parameter evolution

providing theoretical and experimental foundations for precise state monitoring

safety warning

and optimized lifetime management strategies of LIBs.

关键词

Keywords

references

GU X, SHANG Y L, LI J L, et al. Early warning of thermal runaway based on state of safety for lithium-ion batteries[J]. Communications Engineering, 2025, 4: 106. DOI: 10.1038/s44172-025-00442-1.

郭煜, 王亦伟, 彭鹏, 等. 基于孤立森林算法的锂离子电池微内短路故障诊断方法[J]. 储能科学与技术, 2024, 13(11): 4102-4112. DOI: 10.19799/j.cnki.2095-4239.2024.0509.

GUO Y, WANG Y W, PENG P, et al. Fault diagnosis of micro-internal short circuits in lithium-ion battery using the isolated forest algorithm[J]. Energy Storage Science and Technology, 2024, 13(11): 4102-4112. DOI: 10.19799/j.cnki.2095-4239.2024. 0509.

LAGNONI M, SCARPELLI C, LUTZEMBERGER G, et al. Critical comparison of equivalent circuit and physics-based models for lithium-ion batteries: A graphite/lithium-iron-phosphate case study[J]. Journal of Energy Storage, 2024, 94: 112326. DOI: 10.1016/j.est.2024.112326.

CUI B H, WANG H, LI R L, et al. Long-sequence voltage series forecasting for internal short circuit early detection of lithium-ion batteries[J]. Patterns, 2023, 4(6): 100732. DOI: 10.1016/j.patter. 2023.100732.

段双明, 张胜利. 基于自适应多层RLS的锂离子电池参数辨识[J]. 储能科学与技术, 2024, 13(2): 712-720. DOI: 10.19799/j.cnki.2095-4239.2023.0605.

DUAN Shuangming, ZHANG Shengli. Lithium-ion battery parameter identification based on adaptive multilayer RLS[J]. Energy Storage Science and Technology, 2024, 13(2): 712-720. DOI: 10.19799/j.cnki.2095-4239.2023.0605.

孙丙香, 杨鑫, 周兴振, 等. 基于简化阻抗模型和比较元启发式算法的锂离子电池参数辨识方法[J]. 储能科学与技术, 2024, 13(9): 2952-2962. DOI: 10.19799/j.cnki.2095-4239.2024.0658.

SUN B X, YANG X, ZHOU X Z, et al. Comparative parametric study of metaheuristics based on impedance modeling for lithium-ion batteries[J]. Energy Storage Science and Technology, 2024, 13(9): 2952-2962. DOI: 10.19799/j.cnki.2095-4239.2024.0658.

林鹏, 刘涛, 金鹏, 等. 基于多新息辨识算法的锂离子电池等效电路模型参数辨识[J]. 储能科学与技术, 2023, 12(10): 3155-3169. DOI: 10.19799/j.cnki.2095-4239.2023.0358.

LIN P, LIU T, JIN P, et al. Identification of lithium-ion battery equivalent circuit model parameters based on the multi-innovation identification algorithm[J]. Energy Storage Science and Technology, 2023, 12(10): 3155-3169. DOI: 10.19799/j.cnki. 2095-4239.2023.0358.

DAI Y X, PANAHI A. Thermal runaway process in lithium-ion batteries: A review[J]. Next Energy, 2025, 6: 100186. DOI: 10. 1016/j.nxener.2024.100186.

WANG B C, HE Y B, LIU J, et al. Fast parameter identification of lithium-ion batteries via classification model-assisted Bayesian optimization[J ] . Energy, 2024, 288: 129667. DOI: 10.1016/j.energy. 2023 .129667.

LUO W, ZHANG S S, GAO Y F, et al. Review of mechanisms and detection methods of internal short circuits in lithium-ion batteries[J]. Ionics, 2025, 31(5): 3945-3964. DOI: 10.1007/s11581-025-06211-6.

AN Z J, SHI T L, DU X Z, et al. Experimental study on the internal short circuit and failure mechanism of lithium-ion batteries under mechanical abuse conditions[J]. Journal of Energy Storage, 2024, 89: 111819. DOI: 10.1016/j.est.2024.111819.

DUAN X D, WANG H C, JIA Y K, et al. A multiphysics understanding of internal short circuit mechanisms in lithium-ion batteries upon mechanical stress abuse[J]. Energy Storage Materials, 2022, 45: 667-679. DOI: 10.1016/j.ensm.2021.12.018.

乔亚军, 任怡茂, 谭子健, 张袆柔, 吴伟雄. 锂离子电池单层电芯内短路建模与热失控触发特性[J]. 储能科学与技术, 2024, 13(10): 3491-3503.

QIAO Yajun, REN Yimao, TAN Zijian, ZHANG Huirou, WU Weixiong. Modeling internal short circuit and thermal runaway triggers in single-layer lithium-ion battery cells[J]. Energy Storage Science and Technology, 2024, 13(10): 3491-3503.

严芷涵, 王学远, 魏学哲, 等. 基于电化学阻抗谱几何解析的锂离子电池健康状态评估[J/OL]. 储能科学与技术, 1-11[2025-12-19].https://doi.org/10.19799/j.cnki.2095-4239.2025.0635.

YAN Zhihan, WANG Xueyuan, WEI Xuezhe, DAI Haifeng. State of Health assessment for lithium-ion batteries based on geometric analysis of electrochemical impedance spectroscopy[J]. Energy Storage Science and Technology, 1-11[2025-12-19].https://doi.org/10.19799/j.cnki.2095-4239.2025.0635.

ZENG Z X, AN X, PENG C B, et al. Study on the thermal runaway behavior and mechanism of 18650 lithium-ion battery induced by external short circuit[J]. Applied Thermal Engineering, 2025, 258: 124569. DOI: 10.1016/j.applthermaleng.2024.124569.

LIU Z H, XU J, ZHAO Z X, et al. A novel battery active equalization-based internal short circuit fault diagnosis method for lithium-ion batteries[J]. Energy, 2025, 333: 137421. DOI: 10. 1016/j.energy.2025.137421.

ZHU G Y, SUN T, XU Y W, et al. Identification of internal short-circuit faults in lithium-ion batteries based on a multi-machine learning fusion[J]. Batteries, 2023, 9(3): DOI: 10.3390/batteries9030154.

ZHANG W F, LYU N W, JIN Y. Internal short circuit warning method of parallel lithium-ion module based on loop current detection[C]//The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles (ICEIV 2022). Singapore: Springer, 2023: 487-493. DOI: 10.1007/978-981-99-1027-4_50.

YUAN Z C, PAN Y, WANG H B, et al. Fault data generation of lithium ion batteries based on digital twin: A case for internal short circuit[J]. Journal of Energy Storage, 2023, 64: 107113. DOI: 10. 1016/j.est.2023.107113.

YUAN H T, CUI N X, LI C L, et al. Early stage internal short circuit fault diagnosis for lithium-ion batteries based on local-outlier detection[J]. Journal of Energy Storage, 2023, 57: 106196. DOI: 10.1016/j.est.2022.106196.

MA J, HUANG P F, BAI Z H. Numerical study on the critical characteristics of internal short-circuit hot spot in lithium-ion batteries[J]. Process Safety and Environmental Protection, 2024, 185: 1373-1384. DOI: 10.1016/j.psep.2024.04.005.

陈静, 孙杰, 李吉刚, 等. 基于电化学阻抗谱的锂离子电池热失控早期预警方法研究进展[J]. 储能科学与技术, 2025, 14(12): 4780-4794. DOI:10.19799/j.cnki.2095-4239.2025.0577.

CHEN Jing, SUN Jie, LI Jigang, et al. Research progress of early warning methods for thermal runaway of lithium-ion batteries based on electrochemical impedance spectroscopy[J]. Energy Storage Science and Technology, 2025, 14(12): 4780-4794.doi: 10.19799/j.cnki.2095-4239.2025.0577.

GAO R J, LIANG H, ZHANG Y F, et al. Characterization of lithium-ion batteries after suffering micro short circuit induced by mechanical stress abuse[J]. Applied Energy, 2024, 374: 123931. DOI: 10.1016/j.apenergy.2024.123931.

LIU J L, LIU J L, SUN L, et al. External short circuit of lithium-ion battery after high temperature aging[J]. Thermal Science and Engineering Progress, 2024, 54: 102870. DOI: 10.1016/j.tsep. 2024.102870.

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