Lithium-ion cell-to-cell variation during battery electric vehicle operation

Schuster, Simon F.; Brand, Martin J.; Berg, Philipp; Gleissenberger, Markus; Jossen, Andreas

doi:10.1016/j.jpowsour.2015.08.001

Cited by 221 publications

(81 citation statements)

References 27 publications

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“…The extent of initial cell-to-cell variations within a batch of cells, although not reported often enough, has been the topic of multiple studies in the literature [4][5][6][8][9][10][11][12][13]. Researchers have reported statistical studies on 12 [14], 50 [5,15,16], 100 [17][18][19][20], and 1000 [8][9][10]21] cells.…”

Section: Introductionmentioning

confidence: 99%

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Intrinsic Variability in the Degradation of a Batch of Commercial 18650 Lithium-Ion Cells

2018

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Abstract:The use of lithium batteries for power and energy-hungry applications has risen drastically in recent years. For such applications, it is necessary to connect the batteries in large assemblies of cells in series and parallel. With a large number of cells operating together, it is necessary to understand their intrinsic variabilities, not only at the initial stage but also upon aging. In this study, we studied a batch of commercial cells to address their initial cell-to-cell variations and also the variations induced by cycling. To do so, we not only tracked several metrics associated with cell performance, the maximum capacity, the resistance, and the rate capability but also the degradation mechanism via a non-invasive quantification of the loss of lithium inventory (LLI), the loss of active material (LAM) and the kinetic degradation on both electrodes. We found that, even with small initial cell-to-cell variations, significant variations will be observed upon aging because the cells degrade at a different pace. We also observed that these variations were not correlated with the initial variations.

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Section: Introductionmentioning

confidence: 99%

“…Researchers have reported statistical studies on 12 [14], 50 [5,15,16], 100 [17][18][19][20], and 1000 [8][9][10]21] cells. All of these studies determined that cells within a batch are different and that these differences need to be quantified and taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

Intrinsic Variability in the Degradation of a Batch of Commercial 18650 Lithium-Ion Cells

2018

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“…The arrangement of many smaller batteries into a battery pack results in high costs due to the additional auxiliary parts, the need for a battery management system to safely and efficiently utilize all those cells, and typically the need for a heat removal system which also adds significant additional weight. [53][54][55][56] One promising approach to improve current density and also simplify battery system design and upgrades by decoupling the power and energy units in the cell is a RFB, which will be discussed below.…”

Section: A Conventional Static Batteriesmentioning

confidence: 99%

Review Article: Flow battery systems with solid electroactive materials

Koenig

2017

Journal of Vacuum Science &Amp; Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phen

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Energy storage is increasingly important for a diversity of applications. Batteries can be used to store solar or wind energy providing power when the Sun is not shining or wind speed is insufficient to meet power demands. For large scale energy storage, solutions that are both economically and environmentally friendly are limited. Flow batteries are a type of battery technology which is not as well-known as the types of batteries used for consumer electronics, but they provide potential opportunities for large scale energy storage. These batteries have electrochemical recharging capabilities without emissions as is the case for other rechargeable battery technologies; however, with flow batteries, the power and energy are decoupled which is more similar to the operation of fuel cells. This decoupling provides the flexibility of independently designing the power output unit and energy storage unit, which can provide cost and time advantages and simplify future upgrades to the battery systems. One major challenge of the existing commercial flow battery technologies is their limited energy density due to the solubility limits of the electroactive species. Improvements to the energy density of flow batteries would reduce their installed footprint, transportation costs, and installation costs and may open up new applications. This review will discuss the background, current progress, and future directions of one unique class of flow batteries that attempt to improve on the energy density of flow batteries by switching to solid electroactive materials, rather than dissolved redox compounds, to provide the electrochemical energy storage.

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“…Also, the decay patterns of different Li-ion batteries, used for nonlinear mixed effect modeling, provide holistic information for better prognosis of similar batteries, since heterogeneous charge capacity decay information from different batteries with similar characteristics will help to develop better-fit generic prediction models for the RUL. This study will incorporate the uncertainties in the parametric values by using a multiphase decay sigmoidal model, to estimate the charge capacity decay trend of the batteries and use a two-parameter Weibull distribution function for fitting the RUL distribution, which has been shown to be robust for RUL estimation [29][30][31] to determine the characteristic life of the batteries at 70% end-of life (EOL) failure threshold. Since battery charge capacity decay is expected to be a non-reversible process (synonymous to wear-out failures), the use of the two-parameter Weibull function for estimating the expected characteristic life at a given EOL threshold will give an indication of the RUL and the SOH of the battery at a given cycle.…”

Section: Formulation Of Charge Capacity Decay Model Considering Uncermentioning

confidence: 99%

Statistical Characterization of the State-of-Health of Lithium-Ion Batteries with Weibull Distribution Function—A Consideration of Random Effect Model in Charge Capacity Decay Estimation

Ossai

Raghavan

2017

Batteries

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Effective prognosis of lithium-ion batteries involves the inclusion of the influences of uncertainties that can be incorporated through random effect parameters in a nonlinear mixed effect degradation model framework. This study is geared towards the estimation of the reliability of lithium-ion batteries, using parametric effects determination involving uncertainty, using a multiphase decay patterned sigmoidal model, experimental data and the Weibull distribution function. The random effect model, which uses Maximum Likelihood Estimation (MLE) and Stochastic Approximation Expectation Maximization (SAEM) algorithm to predict the parametric values, was found to estimate the remaining useful life (RUL) to an accuracy of more than 98%. The State-of-Health (SOH) of the batteries was estimated using the Weibull distribution function, which is found to be an appropriate formulation to use.

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Lithium-ion cell-to-cell variation during battery electric vehicle operation

Cited by 221 publications

References 27 publications

Intrinsic Variability in the Degradation of a Batch of Commercial 18650 Lithium-Ion Cells

Intrinsic Variability in the Degradation of a Batch of Commercial 18650 Lithium-Ion Cells

Review Article: Flow battery systems with solid electroactive materials

Statistical Characterization of the State-of-Health of Lithium-Ion Batteries with Weibull Distribution Function—A Consideration of Random Effect Model in Charge Capacity Decay Estimation

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