DandeLiion v1: An Extremely Fast Solver for the Newman Model of Lithium-Ion Battery (Dis)charge

Korotkin, Ivan; Sahu, Smita; O’Kane, Simon; Richardson, Giles; Foster, Jamie M.

doi:10.1149/1945-7111/ac085f

Cited by 27 publications

(26 citation statements)

References 38 publications

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“…The resulting five-dimensional problem is extremely computationally complex and requires either powerful computational resources or highly advanced numerical methods (e.g. [64]) to solve to a satisfactory degree of accuracy.…”

Section: Doyle-fuller-newman Model To Single Particle Modelmentioning

confidence: 99%

“…This approach requires only the spatial derivatives to be discretised, leading to a large set of coupled time-dependent ordinary differential equations (ODEs) and algebraic equations that can then be solved using a standard package, such as MATLAB's ode15s. Different techniques can be employed to discretise the spatial operators in the governing PDEs, examples being: finite volume methods [72], control volume methods [144], and finite element methods [54,64]. Depending on the nature of the PDEs that comprise the model, different systems of temporal equations arise from the spatial discretisation.…”

Section: Accepted Manuscript 4 Selecting An Appropriate Modelmentioning

confidence: 99%

“…This means that the DFN model is significantly more computationally complex than SPMe, being two-dimensional in space as opposed to SPMe which is only one-dimensional. Despite its relative computational complexity there are software packages capable of solving the DFN model extremely rapidly and efficiently [64,124], and while speed and computer resources are not particularly pressing issues for a single simulation of a discharge curve, they become much more significant when multiple coupled versions of the DFN model need to be solved, as for instance when modelling a thermally coupled pouch or cylindrical cell discharge.…”

Section: Doyle-fuller-newman Model (Dfn)mentioning

confidence: 99%

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A continuum of physics-based lithium-ion battery models reviewed

Planella

Boyce

et al. 2022

Prog. Energy

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Physics-based electrochemical battery models derived from porous electrode theory are a very powerful tool for understanding lithium-ion batteries, as well as for improving their design and management. Different model fidelity, and thus model complexity, is needed for different applications. For example, in battery design we can afford longer computational times and the use of powerful computers, while for real-time battery control (e.g. in electric vehicles) we need to perform very fast calculations using simple devices. For this reason, simplified models that retain most of the features at a lower computational cost are widely used. Even though in the literature we often find these simplified models posed independently, leading to inconsistencies between models, they can actually be derived from more complicated models using a unified and systematic framework. In this review, we showcase this reductive framework, starting from a high-fidelity microscale model and reducing it all the way down to the Single Particle Model (SPM), deriving in the process other common models, such as the Doyle-Fuller-Newman (DFN) model. We also provide a critical discussion on the advantages and shortcomings of each of the models, which can aid model selection for a particular application. Finally, we provide an overview of possible extensions to the models, with a special focus on thermal models. Any of these extensions could be incorporated into the microscale model and the reductive framework re-applied to lead to a new generation of simplified, multi-physics models.

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Section: Doyle-fuller-newman Model To Single Particle Modelmentioning

confidence: 99%

Section: Accepted Manuscript 4 Selecting An Appropriate Modelmentioning

confidence: 99%

Section: Doyle-fuller-newman Model (Dfn)mentioning

confidence: 99%

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A continuum of physics-based lithium-ion battery models reviewed

Planella

Boyce

et al. 2022

Prog. Energy

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“…Solution of the DFN model using DandeLiion We use the highly accurate (secondorder) solver DandeLiion [21], which is available online at [20], to solve the DFN model. This software readily allows the user to simulate the discharges that we investigate here, namely constant current, GITT and drivecycle discharges.…”

Section: Initial Conditionsmentioning

confidence: 99%

“…However, the recent develop ment of linear scaling algorithms, with compiled language and cloudbased data management to solve the DFN model means that this is no longer true. In this context, we mention the package DandeLiion [20,21] which overcomes the aforementioned limitations and provides a standalone solution to solve the DFN model in a userfriendly way.…”

Section: Introductionmentioning

confidence: 99%

Parametrisation and Use of a Predictive DFN Model for a High-Energy NCA/Gr-SiOx Battery

Zulke

Korotkin

Foster

et al. 2021

J. Electrochem. Soc.

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We demonstrate the predictive power of a parametrised Doyle-Fuller-Newman (DFN) model of a commercial cylindrical (21700) lithium-ion cell with NCA/Gr-SiOx chemistry. Model parameters result from the deconstruction of a fresh commercial cell to determine/confirm chemistry and microstructure, and also from electrochemical experiments with half-cells built from electrode samples. The simulations predict voltage proles for (i) galvanostatic discharge and (ii) drive-cycles. Predicted voltage responses deviate from measured ones by <1% throughout at least 95% of a full galvanostatic discharge, whilst the drive cycle discharge is matched to a 1-3% error throughout. All simulations are performed using the online computational tool DandeLiion, which rapidly solves the DFN model using only modest computational resource. The DFN results are used to quantify the irreversible energy losses occurring in the cell and deduce their location. In addition to demonstrating the predictive power of a properly validated DFN model, this work provides a novel simplifed parametrisation work that can be used to accurately calibrate an electrochemical model of a cell.

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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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DandeLiion v1: An Extremely Fast Solver for the Newman Model of Lithium-Ion Battery (Dis)charge

Cited by 27 publications

References 38 publications

A continuum of physics-based lithium-ion battery models reviewed

A continuum of physics-based lithium-ion battery models reviewed

Parametrisation and Use of a Predictive DFN Model for a High-Energy NCA/Gr-SiOx Battery

Porous Electrode Modeling and its Applications to Li‐Ion Batteries

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