Battery state of health estimation: a structured review of models, methods and commercial devices

Ungurean, Lucian; Cârstoiu, Gabriel; Micea, Mihai V.; Groza, Voicu

doi:10.1002/er.3598

Cited by 199 publications

(111 citation statements)

References 88 publications

(173 reference statements)

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“…These models are used for various purposes such as the estimation of state of charge, [15][16][17][18][19] state of health, 18,20,21 and capacity. These mathematical models are broadly classified into 3 categories (1) empirical/data-based, (2) equivalent circuit, and (3) physics-based.…”

Section: Discussionmentioning

confidence: 99%

Fast computational framework for optimal life management of lithium ion batteries

et al. 2018

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Summary The determination of optimal charging profiles over cycle life of a lithium ion battery is a challenging problem that is extremely important for commercial applications. It is a difficult problem to solve owing to the complex degradation processes occurring inside the battery. Further, modeling of a realistic battery operation, let alone the degradation mechanisms, results in computationally expensive mathematical models. In the present study, a framework is developed towards addressing this problem by (1) developing a method to formulate extremely fast and accurate algebraic models that capture essential features such as charging time and aging characteristics described by battery models and (2) utilizing these algebraic models in an optimization framework involving genetic algorithms for determining the optimal charging profiles over the cycle life of the battery. The utility of the present framework in determining the optimal charging solutions is illustrated with various real‐life usage scenarios such as fast charging and extension of cycle life. The proposed solution can be utilized onboard for generating the optimal charging profiles over cycle life of the battery.

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Section: Discussionmentioning

confidence: 99%

Fast computational framework for optimal life management of lithium ion batteries

et al. 2018

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“…Although many kinds of EVs are selling around the world, some technical barriers remain on the way towards high safety, high efficiency, and extended lifetime. For example, the development of a reliable battery management system (BMS) is challenging considering the difficulties in accurate monitoring of key battery states like the state of charge (SOC) and state of health (SOH) …”

Section: Introductionmentioning

confidence: 99%

Two‐layer online state‐of‐charge estimation of lithium‐ion battery with current sensor bias correction

Feng

et al. 2019

Int J Energy Res

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Summary Because of the harsh working condition in electrified vehicles, the measured current and voltage signals typically contain non‐ignorable noises and bias, which potentially decline the accuracy of state‐of‐charge estimation. In this regard, the noise and bias corruption should be well addressed to maintain sufficient accuracy and robustness. This paper improves the existing methods in the literature from two aspects: (a) A novel offset‐free equivalent circuit model is developed to remove the current bias; and (b) based on the offset‐free equivalent circuit model, a two‐layer estimator is proposed to estimate the state of charge using real‐time identified model parameters. The robustness of the two‐layer estimator against model uncertainties and the aging effect is further evaluated. Simulation and experimental results show that the proposed two‐layer estimator can effectively attenuate the current bias and estimate the state of charge accurately with the error confined to ±4% under different levels of current bias and model uncertainties.

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“…As an electrochemical device, lithium‐ion pouch battery (LIPB) is widely applied to various energy systems because of its high‐energy density, long cycle life, no memory effect, low self‐discharge, and environment friendliness . Its working principle is converting chemical energy of the reaction between lithium ion and electrodes into electrical energy . A typical LIPB is composed of positive and negative electrodes, and the two electrodes are divided by a micro‐porous polymer (PE, PP, or PP/PE/PP) separator to avoid internal short circuit (ISC) of battery.…”

Section: Introductionmentioning

confidence: 99%

Resistance exterior force property of lithium‐ion pouch batteries with different positive materials

Hao

Xie

et al. 2019

Int J Energy Res

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Summary Lithium‐ion pouch battery (LIPB) is widely applied in different engineering fields, such as electric vehicles, aeronautics, astronautics, and among others. However, few complete mechanical models with deformation field are presented to predict internal short circuit (ISC) and resistance exterior force property of LIPB under exterior loading. In the present research, the indentation theoretical models with sinusoidal shape function are established to investigate resistance exterior force property of batteries with different positive materials under different punch shapes loading. The effects of indentation displacement and deformation region on indentation force are carried out. By the comparison of indentation force, present theoretical and numerical results are consistent with previous experimental results. The indentation force increases with the punch radius increase, but decreases with the indentation displacement increase. On the basis of energy conservation law, it is concluded that LIPB is more likely to produce mechanical failure and ISC when normalized deformation region is greater than or equal to 0.4 and plastic strain energy ratio is greater than or equal to 1.5. In addition, from resistance exterior force property point of view, LIPB has a better property to resist external force when LiMnNiCoO2, rather than LiCoO2 and Nanophosphate, is used as positive material. The research provides a reference for assessing the safety of LIPB and selecting suitable positive material, which possesses high energy capacity and resistance exterior force property.

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Battery state of health estimation: a structured review of models, methods and commercial devices

Cited by 199 publications

References 88 publications

Fast computational framework for optimal life management of lithium ion batteries

Fast computational framework for optimal life management of lithium ion batteries

Two‐layer online state‐of‐charge estimation of lithium‐ion battery with current sensor bias correction

Resistance exterior force property of lithium‐ion pouch batteries with different positive materials

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