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An Online Adaptive Internal Short Circuit Detection Method of Lithium-Ion Battery

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Abstract

Internal short circuit (ISC) is a critical cause for the dangerous thermal runaway of lithium-ion battery (LIB); thus, the accurate early-stage detection of the ISC failure is critical to improving the safety of electric vehicles. In this paper, a model-based and self-diagnostic method for online ISC detection of LIB is proposed using the measured load current and terminal voltage. An equivalent circuit model is built to describe the characteristics of ISC cell. A discrete-time regression model is formulated for the faulty cell model through the system transfer function, based on which the electrical model parameters are adapted online to keep the model accurate. Furthermore, an online ISC detection method is exploited by incorporating an extended Kalman filter-based state of charge estimator, an abnormal charge depletion-based ISC current estimator, and an ISC resistance estimator based on the recursive least squares method with variant forgetting factor. The proposed method shows a self-diagnostic merit relying on the single-cell measurements, which makes it free from the extra uncertainty caused by other cells in the system. Experimental results suggest that the online parameterized model can accurately predict the voltage dynamics of LIB. The proposed diagnostic method can accurately identify the ISC resistance online, thereby contributing to the early-stage detection of ISC fault in the LIB.

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Abbreviations

CC-CV:

Constant Current-Constant Voltage

ECM:

Equivalent Circuit Model

EKF:

Extended Kalman Filter

ER:

Equivalent Resistance

EV:

Electric Vehicle

FUDS:

Federal Urban Driving Schedule

HEV:

Hybrid Electric Vehicle

HPE:

Hybrid Pulse Experiment

ISC:

Internal Short Circuit

LIB:

Lithium-Ion Battery

MDM:

Mean-Difference Model

MAE:

Mean Absolute Error

OCV:

Open Circuit Voltage

RC:

Resistor–Capacitor

RLS:

Recursive Least Square

RLSVF:

Recursive Least Squares with Variant Forgetting Factor

RMSE:

Root-Mean-Squared Error

SOC:

State of Charge

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Acknowledgment

This work is supported by the National Key R&D Program of China (No. 2017YFB0103802).

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Authors and Affiliations

  1. National Engineering Laboratory of Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, 5 ZhongGuanCun South Street, Haidian District, Beijing, 100181, China

    Jian Hu, Zhongbao Wei & Hongwen He

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  1. Jian Hu
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  2. Zhongbao Wei
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  3. Hongwen He
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Correspondence to Zhongbao Wei.

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Hu, J., Wei, Z. & He, H. An Online Adaptive Internal Short Circuit Detection Method of Lithium-Ion Battery. Automot. Innov. 4, 93–102 (2021). https://doi.org/10.1007/s42154-020-00127-9

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  • DOI: https://doi.org/10.1007/s42154-020-00127-9

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