Pseudo-two-dimensional model and impedance diagnosis of micro internal short circuit in lithium-ion cells

Kong, Xiangdong; Plett, Gregory L.; Trimboli, M. Scott; Zhang, Zhendong; Qiao, Dongdong; Zhao, Tongzheng; Zheng, Yuejiu

doi:10.1016/j.est.2019.101085

Cited by 108 publications

(41 citation statements)

References 34 publications

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“…According to the industrial standard GB/T 31484-2015, the C-rate of leakage current in the batteries must not exceed C/4480 where C (C-rate) represents the charging and discharging current of the battery. In case of the soft ISCr, maximum value of the self-discharg current (leakage current) is C/3.7 for the experiments [10]. When the self-discharg current reaches 274C, the temperature of the faulty batteries increases rapidly, and thermal runaway causing fire and explosion then occurs [11].…”

Section: Abbreviationsmentioning

confidence: 96%

“…To detect the ISCr in a single cell, several studies were conducted [10], [25]- [29]. The self-discharge current is used to detect the ISCr although a specific experimental equipment is required to measure the current [25].…”

Section: Abbreviationsmentioning

confidence: 99%

“…However, it is difficult to obtain numerous parameter values of the electrochemical-thermal model when it is applied. The variation of variables in the P2D model of ISCr is revealed and then used to diagnose the soft ISCr [10]. The problem with obtaining numerous parameters of the P2D model remains.…”

Section: Abbreviationsmentioning

confidence: 99%

“…The model-update process is continuously repeated until the end of the measured data. The two important factors affecting accuracy of the estimated R ISCr are a position of the SOC(p) and the difference between SOCs in (10). For the first factor, if the SOC(p) is determined as the SOC estimated initially, the SOC(p) is different in every battery charging cases because the batteries can have different initial SOCs before they are charged.…”

Section: Iscr Resistance Estimationmentioning

confidence: 99%

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Online Detection of Soft Internal Short Circuit in Lithium-Ion Batteries at Various Standard Charging Ranges

et al. 2020

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Soft internal short circuit (ISCr) in lithium-ion batteries is a latent risk, and it is a primary reason for thermal runaway with blaze and explosion. Early detection of ISCr is necessary to ensure safe utilization of the batteries. Based on the applications of batteries, load currents are considerably diverse and occasionally do not satisfy the persistent excitation condition, resulting in inaccurate detection of soft ISCr with existing model-based methods. Using constant current for standard charging of the batteries, this study proposes a novel and accurate model-based algorithm to detect soft ISCr online irrespective of the specific type of load currents. An equivalent circuit model of the battery with ISCr is used to extract open circuit voltage of the battery. Enhanced relationship between open circuit voltage and state of charge is obtained to estimate ISCr resistance as a fault index. To improve estimation accuracy of the fault index, factors affecting the ISCr resistance are analyzed and considered. Experiments incorporating various charging ranges and soft ISCr conditions below 100 are configured, and the proposed method is verified with experimental data. The results of the study indicate that the relative error of the estimated fault index does not exceed 6.4%; thereby, the battery management system is enabled to accurately detect an ISCr early.

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Section: Abbreviationsmentioning

confidence: 96%

Section: Abbreviationsmentioning

confidence: 99%

Section: Abbreviationsmentioning

confidence: 99%

Section: Iscr Resistance Estimationmentioning

confidence: 99%

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Online Detection of Soft Internal Short Circuit in Lithium-Ion Batteries at Various Standard Charging Ranges

et al. 2020

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“…Thanks to their explicit physical basis, EMs have been widely employed for the simulation of time consuming or dangerous experiments, such as short circuits [7] and battery ageing [8]. However, there are plenty of parameters in EMs, some of which cannot be obtained without assistance from battery manufacturers [9].…”

Section: Literature Reviewmentioning

confidence: 99%

A comparative study of fractional order models on state of charge estimation for lithium ion batteries

Tian

Xiong

Shen

et al. 2020

Preprint

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State of charge (SOC) estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles. Battery fractional order models (FOMs) which come from frequency-domain modelling have provided a distinct insight into SOC estimation. In this article, for the first time, we compare five state-of-the-art FOMs in terms of SOC estimation. Firstly, characterisation tests on lithium ion batteries are conducted, and the experimental results are used to identify FOM parameters. Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy. The model R(RQ)W shows superior identification accuracy than the other four FOMs. Secondly, the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles, memory lengths, ambient temperatures, cells and voltage/current drifts. The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs. Although more complex models can have better robustness against temperature variation, R(RQ), the simplest FOM, can overall provide satisfactory accuracy and generalisation ability. It can also maintain high accuracy even under the occurrence of sensor drift.

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Porous Electrode Modeling and its Applications to Li‐Ion Batteries

Chen

Danilov

Eichel

et al. 2022

Advanced Energy Materials

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Figure 12. a-c) Schematic representation of the electrode potential at electrode/electrolyte interfaces. [145] Red areas indicate the electrode region and blue the electrolyte region. a) The Butler-Volmer approach does not consider charges at the interface. b) More detailed representation of the electrode/ electrolyte interface, considering specifically adsorbed species (green area) and screening counter charges at the surface of the electrode (dark red) and in the electrolyte (dark blue). c) Reduced electrode/electrolyte interface considering the surface charge in the electrode and screening charge in the electrolyte. d) Typical examples of impedance spectra for a porous electrode with insertion-type reactions. [151] Impedance simulations of porous graphite electrodes in contact with electrolyte with e) various particle sizes and f) electrode porosities. [152] a-c) Reproduced with permission. [145]

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Pseudo-two-dimensional model and impedance diagnosis of micro internal short circuit in lithium-ion cells

Cited by 108 publications

References 34 publications

Online Detection of Soft Internal Short Circuit in Lithium-Ion Batteries at Various Standard Charging Ranges

Online Detection of Soft Internal Short Circuit in Lithium-Ion Batteries at Various Standard Charging Ranges

A comparative study of fractional order models on state of charge estimation for lithium ion batteries

Porous Electrode Modeling and its Applications to Li‐Ion Batteries

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