Control oriented 1D electrochemical model of lithium ion battery

Smith, Kandler; Rahn, Christopher D.; Wang, Chao Yang

doi:10.1016/j.enconman.2007.03.015

Cited by 365 publications

(226 citation statements)

References 20 publications

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“…However, due to non-linearity in parameter dependence, difficulty in online parameter estimation led to greater prediction error. In comparison, Smith et al derived an impedance model [10] under assumptions of linear model behaviour and decoupling reaction current from electrolyte concentration. This impedance model is further reduced to low order single-input-multiple-output (SIMO) state variable models (SVM).…”

Section: Introductionmentioning

confidence: 99%

Reduction of high fidelity lithium-ion battery model via data-driven system identification

Sumisławska¹,

Phillip²,

Marinescu³

et al. 2013

Hybrid and Electric Vehicles Conference 2013 (HEVC 2013)

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Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. CURVE is the Institutional Repository for Coventry University AbstractThe battery management system of a hybrid electric vehicle requires a computationally simple yet accurate model of the battery. In this paper a reduced order battery model is developed using a stochastic top-down approach. Firstly a pseudo-2D, multi-particle electrochemical model, considered as a surrogate for the real system, is used to obtain the observational data. Then the model structure is inferred directly from the data. The dependencies between the states and the model parameters are analysed, which results in a 5 th order piecewise state dependent parameter model which can describe the nonlinear relationship between the current, the voltage and the state of charge of the battery.

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Section: Introductionmentioning

confidence: 99%

Reduction of high fidelity lithium-ion battery model via data-driven system identification

Sumisławska¹,

Phillip²,

Marinescu³

et al. 2013

Hybrid and Electric Vehicles Conference 2013 (HEVC 2013)

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“…Also, Smith et al 12 (p2568) and Plett 14 (p218) assumed that it is reasonable to decouple the electrolyte potential from the electrolyte concentration by neglecting the concentration term of the electrolyte ) unless CC License in place (see abstract). ecsdl.org/site/terms_use address.…”

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confidence: 99%

“…Smith et al, 12 and Plett et al 14 used Taylor series to linearize nonlinear Bulter-Volmer equation, which only applies when the overpotential is close to zero volt. Also, Smith et al 12 (p2568) and Plett 14 (p218) assumed that it is reasonable to decouple the electrolyte potential from the electrolyte concentration by neglecting the concentration term of the electrolyte charge conservation equation. Models with these two assumptions lose the nonlinear features between the electrolyte potential and concentration, and introduce errors in the simulation results.…”

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confidence: 99%

“…Due to the numerical complexity of these models, various reduced-order models (ROMs) have been proposed as substitutes for either the single particle (SP) or the P2D full-order model (FOM) in practical online simulation applications. [6][7][8][9][10][11][12][13][14][15][16] Also, ROMs have been published to simulate capacity fading 17,18 and for use in parameter estimations. [19][20][21][22][23][24] The single particle model dates back to 1979, when Atlung et al 2 presented a relatively simple cell model for the Li/TiS 2 couple.…”

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confidence: 99%

“…constant current/voltage modes), but lose accuracy if the input signal varies rapidly with time (i.e., high-frequency current pulses). Smith et al 12 developed a ROM by a control-oriented method: by linearizing the material thermodynamics and electrochemical kinetics, the P2D model is first converted to a single-input transfer function in the Laplace-domain, and then a state variable model (SVM) is derived using approximated poles and gains. Smith's SVM approach provides excellent convergence at high current rates (up to 50 C) and has been adopted into the multi-scale multi-dimensional framework 47,48 to model the distributed thermal behavior over large-format cell geometry.…”

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confidence: 99%

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Nonlinear State-Variable Method for Solving Physics-Based Li-Ion Cell Model with High-Frequency Inputs

Jin

White

2017

J. Electrochem. Soc.

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A nonlinear state-variable method is presented and used to solve the pseudo-2D (P2D) Li-ion cell model under high-frequency input current and temperature signals. The physics-based governing equations are formulated into a nonlinear state variable method (NSVM), in which the mass transfer variables are evaluated using a 1 st order exponential integrator approach at each discrete time point and the electrochemical kinetics (Butler-Volmer) equations are solved by either an iterative or an explicit method. This procedure provides an accurate, computationally efficient method to develop physics-based simulations of the performance of a dual-foil Li-ion cell during practical drive cycles. Physics-based Li-ion cell models are useful tools for analyzing the battery cell performances, characterizing material properties, supporting cell design, and developing battery management systems (BMS). 5 in their work, a dual-foil Li-ion cell was represented by a pseudo-two-dimensional domain; and therefore, this model is usually referred as the "pseudo-2D model (P2D)".3 This model contains several coupled transport phenomena described by nonlinear partial differential equations (PDEs). Due to the numerical complexity of these models, various reduced-order models (ROMs) have been proposed as substitutes for either the single particle (SP) or the P2D full-order model (FOM) in practical online simulation applications.6-16 Also, ROMs have been published to simulate capacity fading 17,18 and for use in parameter estimations. presented an approximate solution to the spherical diffusion problem with a fixed flux which provides high accuracy with only a few terms in a series when compared to the analytic solution with at least 100 terms. This approximate solution provides significant savings in computation time.In order to predict the performance of lithium ion cells operating at higher rates, more complicated models were developed. In 1982, West et al. 4 presented a Nernst-Planck model for porous insertion electrodes for a Li/TiS 2 cell. They pointed out the importance of coupling the transport of lithium ions between the insertion electrode and the electrolyte. In 1993, Mao and White 35 presented a Nernst-Planck model of the Li/TiS 2 cell which included transport of lithium ions through the separator and demonstrated quantitatively how the utilization of the TiS 2 electrode could be improved by decreasing the thickness of the separator. Also in 1993, Doyle et al. 36 published a concentrated solution theory model for a lithium/polymer/TiS 2 cell. Shortly after that in 1994 Fuller et al.5 published a concentrated solution theory model for a dual lithium ion insertion cell ("dual foil"), which is known as "pseudo-2D model (P2D)". In 1996, Doyle et al. 37 published a comparison of their model predictions to experimental data from a Bellcore plastic lithium ion cell. Unfortunately, the computation burden associated with using the program dual foil is too great to use their program for extensive simulations needed for parameter estimation an...

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2013

Battery Systems Engineering

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Control oriented 1D electrochemical model of lithium ion battery

Cited by 365 publications

References 20 publications

Reduction of high fidelity lithium-ion battery model via data-driven system identification

Reduction of high fidelity lithium-ion battery model via data-driven system identification

Nonlinear State-Variable Method for Solving Physics-Based Li-Ion Cell Model with High-Frequency Inputs

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