Modeling of rechargeable batteries

Hierarchically structured active materials in lithium‐ion battery (LIB) electrodes with porous secondary particles are promising candidates to increase the gravimetric energy density and rate performance of the cell. However, there are still aspects to this technology which are not fully understood. Herein, a mathematical model for half cells with hierarchically structured electrodes aiming at delivering a better insight and obtaining a deeper knowledge to this matter is presented. First, the classical half‐cell model originating back to Newman and coworkers is revisited and the basic assumptions for the electrochemically based equations are presented. As a next step, the mathematical framework of the volume averaging method is used to consistently extend the classical to the hierarchically structured half‐cell model. For both models, the full set of boundary conditions for the half‐cell setup is presented. Finally, the hierarchically structured half‐cell model is validated by experiments taken from literature and parametric studies are conducted. The results suggest that, while the rate‐limiting factor for the classical half‐cells is the diffusion coefficient of the active material, in case of the hierarchically structured half‐cells, it is the combination of electronic conductivity and inner morphology of the secondary particles.

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“…However, only little effort was taken to model hierarchically structured electrodes. [ 12 ]…”

Section: Introductionmentioning

confidence: 99%

Electrochemical Modeling of Hierarchically Structured Lithium‐Ion Battery Electrodes

Birkholz

Kamlah

2021

Energy Tech

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“…The classification of the models normally depends on the modelling strategy behind the individual model; hence, there are many classifications. A general one is to classify models as being electrochemical, equivalent circuit and hybrid [24]. Alternatively, models can also be classified as physical, empirical, semi-empirical, mathematical etc.…”

Section: Symbolmentioning

confidence: 99%

Mechanistic modelling of cyclic voltage-capacity response for lithium-ion batteries

Nagode

Gosar

Sweeney

et al. 2019

Energy

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“…The simplified electriccircuit models [15] [16] [17] are computationally fast and can be easily incorporated into more complex systems and simulations tools. [33] Unfortunately, they do not integrate non-linear capacity behaviour which leads to inaccurate predictions of the remaining battery capacity and operating time [15] -which is crucial for battery management systems. Imprecise prediction of remaining battery capacity can lead to battery over-discharging and over-charging limiting its lifespan.…”

Section: Introductionmentioning

confidence: 99%

Model-Free Non-Invasive Health Assessment for Battery Energy Storage Assets

Sobon

Stephen

2021

IEEE Access

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With the increasing application of battery energy storage in buildings, networks and transportation, an emerging challenge to overall system resilience is in understanding the constituent asset health. Current battery energy storage considerations focus on adhering to the technical specification of the service in the short term, rather than the long-term consequences to battery health. However, accurately determining battery health generally requires invasive measurements or computationally expensive physicsbased models which do not scale up to a fleet of assets cost-effectively. This paper alternatively proposes capturing cumulative maloperation through a physics model-free proxy for cell health, articulated via the strong influence misuse has on the internal chemical state. A Hidden Markov Chain approach is used to automatically recognize violations of chemistry specific usage preferences from sequences of observed charging actions. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios.

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Modeling of rechargeable batteries

Cited by 7 publications

References 60 publications

Electrochemical Modeling of Hierarchically Structured Lithium‐Ion Battery Electrodes

Electrochemical Modeling of Hierarchically Structured Lithium‐Ion Battery Electrodes

Mechanistic modelling of cyclic voltage-capacity response for lithium-ion batteries

Model-Free Non-Invasive Health Assessment for Battery Energy Storage Assets

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