A computationally efficient implementation of a full and reduced-order electrochemistry-based model for Li-ion batteries

Xia, Lu; Najafi, Esmaeil; Li, Z; Bergveld, HJ Henk Jan; Donkers, M.C.F.

doi:10.1016/j.apenergy.2017.09.025

Cited by 42 publications

(39 citation statements)

References 26 publications

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“…To obtain the predicted output of the DFN model for proper parameter estimation, a reliable numerical implementation of the model is needed. The implementation method used in this paper is based on a numerical procedure proposed by Xia et al The main idea for implementing the DFN model can be summarized into the following 3 steps. The first step is a spatial discretization of 3 to 6, by approximating the

\frac{\partial}{\partial x}

term in these equations using a finite‐volume or finite‐difference method, leading to a set of nonlinear differential algebraic equations (DAE) of the form:

({, \begin{matrix} center \\ array \frac{d C}{d t} & array = f (C, ϕ, P) \\ array 0 & array = g (C, ϕ, I_{app}, P), \end{matrix})

where C denotes the spatially discretized concentrations, ϕ denotes the spatially discretized potentials, I app is the applied current and P is the vector of all the model parameters.…”

Section: Model Description and Implementationmentioning

confidence: 99%

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Parameter estimation of an electrochemistry-based lithium-ion battery model using a two-step procedure and a parameter sensitivity analysis

et al. 2018

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Lithium-ion batteries are indispensable in various applications owing to their high specific energy and long service life. Lithium-ion battery models are used for investigating the behavior of the battery and enabling power control in applications. The Doyle-Fuller-Newman (DFN) model is a popular electrochemistry-based model, which characterizes the dynamics in the battery through diffusions in solid and electrolyte and predicts current/voltage response. However, the DFN model contains a large number of parameters that need to be estimated to obtain an accurate battery model. In this paper, a computationally feasible two-step estimation approach is proposed that only uses voltage and current measurements of the battery under consideration. In the two-step procedure, the parameters are divided into 2 groups. The first group contains thermodynamic parameters, which are estimated using low-current discharges, while the second group contains kinetic parameters, which are estimated using a well-designed highly-dynamic pulse (dis-)charge current. A parameter sensitivity analysis is done to find a subset of parameters that can be reliably estimated using current and voltage measurements only. Experimental data are collected for 12 Ah nickel cobalt aluminum pouch lithium-ion cell. The voltage predictions of the identified model are compared with several experimental data sets to validate the model. A root mean square error between model predictions and experimental data smaller than 16 mV is achieved.How to cite this article: Jin N, Danilov DL, Van den Hof PMJ, Donkers MCF. Parameter estimation of an electrochemistry-based lithium-ion battery model using a two-step procedure and a parameter sensitivity analysis. Int J Energy Res.

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\frac{\partial}{\partial x}

term in these equations using a finite‐volume or finite‐difference method, leading to a set of nonlinear differential algebraic equations (DAE) of the form:

({, \begin{matrix} center \\ array \frac{d C}{d t} & array = f (C, ϕ, P) \\ array 0 & array = g (C, ϕ, I_{app}, P), \end{matrix})

where C denotes the spatially discretized concentrations, ϕ denotes the spatially discretized potentials, I app is the applied current and P is the vector of all the model parameters.…”

Section: Model Description and Implementationmentioning

confidence: 99%

“…After obtaining the solution of the dependent variables of the model ( C s , C e , ϕ s , ϕ e ), the cell terminal voltage V ( t ), as the output of the DFN model can be calculated by . More details on the model implementation can be found in Xia et al…”

Section: Model Description and Implementationmentioning

confidence: 99%

Parameter estimation of an electrochemistry-based lithium-ion battery model using a two-step procedure and a parameter sensitivity analysis

et al. 2018

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“…White-box models refer to methods that consider internal reactions and aging mechanisms of the batteries, which include physicochemical, electrochemical, and thermodynamic theories [35]. For instance, Fu et al [36] developed a degradation model based on partial differential equations (PDEs) that estimate the capacity fade using three key parameters: (i) the volume fraction of accessible material in the anode, (ii) ionic and electronic resistance of the solid electrolyte interphase and deposited layers on the electrode surfaces, and (iii) diffusion coefficient of the electrolyte.…”

Section: White-box Methodsmentioning

confidence: 99%

A Circular Economy of Electrochemical Energy Storage Systems: Critical Review of SOH/RUL Estimation Methods for Second-Life Batteries

Montoya-Bedoya¹,

Sabogal-Moncada²,

Garcia-Tamayo³

et al. 2020

Green Energy and Environment

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Humanity is facing a gloomy scenario due to global warming, which is increasing at unprecedented rates. Energy generation with renewable sources and electric mobility (EM) are considered two of the main strategies to cut down emissions of greenhouse gasses. These paradigm shifts will only be possible with efficient energy storage systems such as Li-ion batteries (LIBs). However, among other factors, some raw materials used on LIB production, such as cobalt and lithium, have geopolitical and environmental issues. Thus, in a context of a circular economy, the reuse of LIBs from EM for other applications (i.e., second-life batteries, SLBs) could be a way to overcome this problem, considering that they reach their end of life (EoL) when they get to a state of health (SOH) of 70-80% and still have energy storage capabilities that could last several years. The aim of this chapter is to make a review of the estimation methods employed in the diagnosis of LIB, such as SOH and remaining useful life (RUL). The correct characterization of these variables is crucial for the reassembly of SLBs and to extend the LIBs operational lifetime.

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“…They obtained not only accurate results but also a speed‐up factor about 10 to 15 against finite volume method (FVM). In addition, Xia et al applied a ROM based on POD‐DEIM (discrete empirical interpolation method) to electrochemical equations of lithium‐ion battery and achieved a speed‐up factor of about 7 via full‐order model.…”

Section: Introductionmentioning

confidence: 99%

Reduced‐order modeling of lead‐acid battery using cluster analysis and orthogonal cluster analysis method

Shahbazi

Esfahanian

2019

Int J Energy Res

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Summary Real‐time fast simulation of lead‐acid battery (LAB) plays an important role in monitoring, control, optimization, and many other engineering fields. Hence, any improvement toward a reduction in computational time of LAB simulation while maintaining the accuracy of results is of practical interest. Reduced‐order modeling (ROM) is one of the promising tools, which is computationally cost‐effective along with producing accurate results. In this study, ROM is employed in a transient one‐dimensional simulation of LABs within discharge to investigate the variation of battery parameters, eg, cell voltage, acid concentration, and state of charge (SoC). Accordingly, three reduced‐order models are implemented, namely, proper orthogonal decomposition (POD), cluster analysis (CA), and orthogonal cluster analysis (OCA), wherein the latter one is a new hybrid model of POD and CA methods proposed in the present work. The results reveal that ROM of LAB reduces the simulation time significantly (speed‐up factor about 7‐12) and provide good consistency comparing with previous experimental and numerical studies (less than 1% relative error for cell voltage, acid concentration, and SoC). In addition, the results indicate that the new hybrid method inherits the advantages of both POD and CA methods, ie, enhances the speed of POD method by 8% to 24% and accuracy of CA method by 17% to 65%.

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A computationally efficient implementation of a full and reduced-order electrochemistry-based model for Li-ion batteries

Cited by 42 publications

References 26 publications

Parameter estimation of an electrochemistry-based lithium-ion battery model using a two-step procedure and a parameter sensitivity analysis

Parameter estimation of an electrochemistry-based lithium-ion battery model using a two-step procedure and a parameter sensitivity analysis

A Circular Economy of Electrochemical Energy Storage Systems: Critical Review of SOH/RUL Estimation Methods for Second-Life Batteries

Reduced‐order modeling of lead‐acid battery using cluster analysis and orthogonal cluster analysis method

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