Rapid prediction method for thermal runaway propagation in battery pack based on lumped thermal resistance network and electric circuit analogy

Jiang, Zhao; Qu, Zhiguo; Zhang, J.F.; Rao, Zhonghao

doi:10.1016/j.apenergy.2020.115007

Cited by 98 publications

(31 citation statements)

References 23 publications

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“…In their study, TR propagation features were discussed with different TR trigger locations, and the TR prevention effect of PCM was evaluated. The proposed prediction method shows a good ability to resolve the TR propagation problem 76 …”

Section: Heat Generation Mechanisms and Thermal Models Of Libsmentioning

confidence: 99%

A review on battery thermal management and its digital improvement‐based cyber hierarchy and interactional network

Liang

Shen

Zhang

et al. 2022

Intl J of Energy Research

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Section: Heat Generation Mechanisms and Thermal Models Of Libsmentioning

confidence: 99%

A review on battery thermal management and its digital improvement‐based cyber hierarchy and interactional network

Liang

Shen

Zhang

et al. 2022

Intl J of Energy Research

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“…16 Therefore, it has become an urgent problem to give early warning and improve battery safety to prevent battery thermal runaway in advance. [17][18][19] Effective early temperature prediction in the initial stage is the key to avoid thermal runaway accidents. 20,21 At present, the main method adopted by battery management system is to install temperature sensors on the battery surface and estimate the average temperature of the battery by using the temperature measured by multiple sensors.…”

Section: Introductionmentioning

confidence: 99%

Temperature prediction of lithium‐ion batteries based on electrochemical impedance spectrum: A review

Wang

Duan

et al. 2022

Intl J of Energy Research

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Summary With the rapid development of global electric vehicles, artificial intelligence, and aerospace, lithium‐ion batteries (LIBs) have become more and more widely used due to their high property. More and more disasters are caused by battery combustion. Among them, the temperature prediction of LIBs is the key to prevent the occurrence of fire. At present, using surface temperature sensor to measure the temperature of LIBs is the main method. High‐capacity LIB packs used in electric vehicles and grid‐tied stationary energy storage system essentially consist of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell, especially at the cell core, is not practically feasible from the solution cost, space, and weight point of view. So developing a new method for battery temperature prediction has become an urgent problem to be solved. Electrochemical impedance spectroscopy (EIS) is a widely applied non‐destructive method of characterization of LIBs. In recent years, methods of predicting LIBs temperature by EIS have been developed. The prediction of LIBs temperature based on EIS has the advantages of high real‐time performance and prediction accuracy, and the device is simple and practical. The proposed method has a good development prospect in electric vehicles and other fields and can effectively solve the current problems of LIBs temperature prediction. Therefore, it is urgent to summarize these works to promote the next development. This review summarizes the main methods of using EIS to predict the temperature of LIBs in recent years, including the methods based on the impedance, phase shift, and intercept frequency. The principle and application of various methods are reviewed. The advantages and disadvantages of different methods and the future development direction are discussed. Highlights Use EIS to quickly and effectively predict the internal temperature changes of LIBs. No hardware temperature sensors and thermal model are required. The methods to predict battery temperature based on impedance, phase shift, and intercept frequency are reviewed.

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“…28 The combustion of a single cell in the sealed battery pack affects other healthy cells, leading to systematic failure of the power system. 16,29 Therefore, the prevention is inseparable from the prediction of thermal runaway. There are three crucial checkpoints of this process: preventing the connection between internal and external spaces, preventing combustion, and controlling the spread of heat inside the battery compartment.…”

Section: Introductionmentioning

confidence: 99%

Safety challenges and safety measures of Li‐ion batteries

Chen

Gao

Sun

2021

Energy Science & Engineering

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Rapid prediction method for thermal runaway propagation in battery pack based on lumped thermal resistance network and electric circuit analogy

Cited by 98 publications

References 23 publications

A review on battery thermal management and its digital improvement‐based cyber hierarchy and interactional network

A review on battery thermal management and its digital improvement‐based cyber hierarchy and interactional network

Temperature prediction of lithium‐ion batteries based on electrochemical impedance spectrum: A review

Safety challenges and safety measures of Li‐ion batteries

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