Capacity estimation of lithium-ion cells by combining model-based and data-driven methods based on a sequential extended Kalman filter

Lai, Xin; Wei, Yi; Cui, Yifan; Qin, Chao; Han, Xiao; Sun, Tao; Zhou, Long; Zheng, Yuejiu

doi:10.1016/j.energy.2020.119233

Cited by 102 publications

(28 citation statements)

References 44 publications

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“…The quantitative analysis methods are further divided into analytical model methods and data‐driven methods. The qualitative analysis method usually can be the fault tree method, 38 which generally can judge causality logically and perform early fault classification and risk analysis, as shown in Figure 2. When the separator fails to insulate the cathode and the anode, the ISC occurs.…”

Section: Inducements Of Iscmentioning

confidence: 99%

A review of the internal short circuit mechanism in lithium‐ion batteries: Inducement, detection and prevention

Huang

Liu

et al. 2021

Int J Energy Res

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Summary Internal short circuit (ISC) of lithium‐ion battery is one of the most common reasons for thermal runaway, commonly caused by mechanical abuse, electrical abuse and thermal abuse. This study comprehensively summarizes the inducement, detection and prevention of the ISC. Firstly, the fault tree is utilized to analyze the ISC inducement, including the abuse conditions, improper manufacture and design defects. Secondly, the ISC substitute triggering methods (utilizing mechanical deformation from the outside or utilizing controllable components introduced inside) are summarized considering the randomness of ISC, which have high repeatability. Moreover, these methods can be used to investigate the evolution and the hazard boundary of ISC. The ISC severity is determined by the ISC type, ISC location, ISC area, battery state of charge, capacity, material and structure. There are four types of ISC mode, the danger extends ranking is aluminum‐anode > aluminum‐copper > cathode‐copper > cathode‐anode. The ISC evolution is presented based on the upper summary. Then, the ISC detection methods are reviewed: (1) comparing the measured data with the predicted value from the model; (2) detecting whether the battery has self‐discharge; (3) comparing based on the battery inconsistency and (4) other signals. Finally, the prevention strategies are summarized, which can be used to reduce the ISC risk by blocking electron or lithium‐ion channels in the battery cell.

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Section: Inducements Of Iscmentioning

confidence: 99%

A review of the internal short circuit mechanism in lithium‐ion batteries: Inducement, detection and prevention

Huang

Liu

et al. 2021

Int J Energy Res

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“…A model‐based UKF is designed in Reference 17. The combination between the model and the data‐driven by mean of a sequential EKF has been used in Reference 18 to estimate the battery capacity. A fuzzy logic intelligent strategy was used in Reference 19 to model a Li‐ion battery.…”

Section: Introductionmentioning

confidence: 99%

Optimal parameter identification strategy applied to lithium‐ion battery model

et al. 2021

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Summary This paper presents an optimal parameter identification strategy of the lithium‐ion (Li‐ion) battery model applying a recent metaheuristic artificial ecosystem‐based optimization (AEO) algorithm, which proves its ability in terms of both convergence speed and complexity. The key idea is to update the battery model parameters using the optimizer outputs. In the current paper, the battery model is based on the Shepherd model. To demonstrate the superiority of the suggested method of identification, the test results are compared in terms of efficiency, convergence speed, and accuracy of identification with those obtained by the salp swarm algorithm, the political optimizer, the equilibrium optimizer, and particle swarm optimization. Through the optimization procedure, the undetermined parameters of the battery model are employed as decision variables, but the root‐mean‐square error between estimated data and battery data is assigned to be an objective function must be minimal. The results showed the superior identification ability of the AEO compared to the other optimizers. This optimizer achieved 99.9% identification efficiency, which makes it an ideal solution for battery identification. Besides its identification efficiency, the AEO is much faster than the other optimizers, as the results show.

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“…SOC estimation has garnered an abundance of attention in the last decade. [2][3][4][5][6] Most of the present SOC estimation algorithms need huge amounts of battery data for accurate estimation. 7,8 Researchers in the recent past have focused on developing data-driven techniques to predict battery aging too.…”

Section: Introductionmentioning

confidence: 99%

Generating high‐fidelity synthetic battery parameter data: Solving sparse dataset challenges

et al. 2021

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Global burgeoning pollution levels have led to massive efforts being made to electrify all modes of transportation in the coming decade. Most of the Electric Vehicles (EVs) in the automotive domain are powered by Lithium-ion batteries (LIBs). Steady increase in number of EVs has led to generation of enormous amounts of data. Most of this data is related to the usage of LIBs and its parameters such as voltage, current, and temperature values. Recent data-driven

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Capacity estimation of lithium-ion cells by combining model-based and data-driven methods based on a sequential extended Kalman filter

Cited by 102 publications

References 44 publications

A review of the internal short circuit mechanism in lithium‐ion batteries: Inducement, detection and prevention

A review of the internal short circuit mechanism in lithium‐ion batteries: Inducement, detection and prevention

Optimal parameter identification strategy applied to lithium‐ion battery model

Generating high‐fidelity synthetic battery parameter data: Solving sparse dataset challenges

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