Design and parametrization analysis of a reduced-order electrochemical model of graphite/LiFePO4 cells for SOC/SOH estimation

Marcicki, James; Canova, Marcello; Conlisk, A. T.; Rizzoni, Giorgio

doi:10.1016/j.jpowsour.2012.12.120

Cited by 210 publications

(90 citation statements)

References 44 publications

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“…5, calibration of the model was necessary. In particular, the electrochemical ESPM was calibrated by tuning parameters related to the battery capacity, Open-Circuit Potentials (OCPs), Ohmic and kinetic behaviors and other electrochemical parameters such as particle radius and volume fractions 38 . As for the thermal model, tunable parameters in the 2D charge balance and energy balance submodels include the entropic coefficients ∂U k /∂ T (k = p, n), the number of jelly roll layers N layer in the battery, the thickness of the current collectors δ k , the planar thermal conductivity k y and k z , etc.…”

Section: Validation Of the Reduced-order Msmd Model Against Numericalmentioning

confidence: 99%

A Reduced-Order Multi-Scale, Multi-Dimensional Model for Performance Prediction of Large-Format Li-Ion Cells

Fan

Pan

Storti

et al. 2016

J. Electrochem. Soc.

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In this paper, a reduced-order, Multi-Scale, Multi-Dimensional (MSMD) model is developed to achieve accurate and fast simulation of the 2D distribution of thermal and electrochemical properties across the surface of a large-format Li-ion battery cell. Since the proposed model aims at supporting long-term simulation, virtual design and optimization studies, minimization of the computational complexity is achieved through analytical Model Order Reduction (MOR) based on a Galerkin projection method. The model is then verified against experimental data and a high-fidelity numerical model at various input current conditions. Results show that the computational complexity of the MSMD model is significantly reduced without sacrificing the accuracy in characterizing the distribution of the electrochemical and thermal properties. Finally, the impact of the applied current and thermal boundary conditions on the distribution of transverse current density is also evaluated. Temperature and temperature gradients are some of the key factors that influence the performance and degradation of Li-ion batteries. Despite in Hybrid Electric Vehicles (HEVs) or Electric Vehicles (EVs) the average pack temperature is regulated by the thermal management systems, thermal gradients may still occur within the pack or across individual cells.1-3 For instance, when cooling plates are in contact with only one side of the cells, the heat generated inside each cell will be dissipated to the cooling plates along a preferential direction. Therefore, thermal gradients in the order of ±5• C are common, and particularly in high-power packs for HEVs or high-performance EVs. 4,5 Thermal gradients on the cell surface are particularly critical in large-format batteries because hot spots may induce localized changes in the physical and chemical properties of the electrodes and electrolyte solution, which in turn can increase the local transverse current density and ultimately cause non-uniform degradation of the electrodes.6,7 Multiscale characterization methods have in fact evidenced that the non-uniform utilization of electrode surface leads to highly uneven degradation. 8,9 Furthermore, cell-to-cell variations in large battery packs (due to packaging constraints) may lead to non-uniform heat transfer in packs and induce further thermal gradients among the various cells. 10,11 This motivates the development of procedures and tools that allow for characterization and prediction of thermal gradients in large-format Lithium ion batteries, and understanding their impact on the performance and durability.Multi-Scale, Multi-Dimensional (MSMD) modeling approaches have been proposed to simulate the distribution of surface temperature and Lithium concentration in large format, prismatic cells. As a first approximation, the rate of heat generation within the cell can be approximated as uniform across the surface.12-16 More recently, this simplification has been abandoned in favor of more rigorous approaches that accurately compute the local heat generation rate ...

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Section: Validation Of the Reduced-order Msmd Model Against Numericalmentioning

confidence: 99%

A Reduced-Order Multi-Scale, Multi-Dimensional Model for Performance Prediction of Large-Format Li-Ion Cells

Fan

Pan

Storti

et al. 2016

J. Electrochem. Soc.

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“…[8][9][10][11][12][13][14][15] In this case, the collected experimental data must be processed with optimization methods in order to reveal unknown parameters and properties. A literature review of the methods applied to Li-ion batteries, i.e., the Parameter Estimation methods (PE), is provided in the first part of this study.…”

Section: 7mentioning

confidence: 99%

An Inverse Method for Estimating the Electrochemical Parameters of Lithium-Ion Batteries

Rajabloo

Jokar

Désilets

et al. 2016

J. Electrochem. Soc.

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This paper is the second part of a two part study on parameter estimation of Li-ion batteries. The methodology was developed in Part I. In Part II, the methodology is tested for LiCoO 2 , LiMn 2 O 4 and LiFePO 4 positive electrode materials. An inverse method combined to a simplified version of the Pseudo-two-Dimensional (P2D) model is used to identify the solid diffusion coefficients (D s,n and D s,p ), the intercalation/deintercalation reaction-rate constants (K n and K p ), the initial SOC (SOC n,0 and SOC p,0 ), and the electroactive surface areas (S n and S p ) of Li-ion batteries. Experimental cell potentials for both low and high discharge rates provide the reference data for minimizing the objective function in the best time interval. For all cases simulated, the numerical predictions show excellent agreement with the experimental data. Lithium-ion (Li-ion) batteries are increasingly employed for energy storage. Their working voltage and energy density are higher than those of similar energy storage technologies. Their service life is longer. They exhibit high energy-to-weight ratios and low selfdischarge. As a result, they have become the preferred energy storage devices in the electronics and the automotive industries.Mathematical modeling of Li-ion batteries is an essential engineering tool for their design and operation. Two different approaches are usually adopted to predict their behavior. These approaches may be divided, broadly speaking, into empirical models and electrochemical models.Empirical models are the simplest mathematical models. They are relatively easy to implement and they provide fast responses. This is why they are mostly suited for control systems used in the high tech industry and in the automotive industry. The scope of applications of empirical models is however narrow. Empirical models ignore the physical phenomena that take place in the cell. Consequently, they cannot predict the life and the capacity fading of the battery. Furthermore, they are only valid for the battery for which they have been developed. [1][2][3] Electrochemical models provide, on the other hand, reliable responses of the battery under a wide range of operating conditions and for different applications. They account for the chemical/ electrochemical kinetics and the transport phenomena. Electrochemical models are unequivocally superior to empirical models. But they are also more complex and require longer computation times.Among the electrochemical models, the Pseudo-two-Dimensional (P2D) model stands out. The P2D model rests on the porous electrode theory, the concentrated solution theory and the use of appropriate kinetics equations.4-6 A simplified and computationally efficient version of the P2D model is the Single Particle Model (SPM). In the SPM, it is assumed that the current distribution along the thickness of the porous electrode remains uniform and that the electrolyte properties are constant. 5,7Both empirical and electrochemical models need to be calibrated in order to simulate faithfully the ...

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“…A more advanced SOH estimation approach that has recently gained attention is the use of a physics-based electrochemical model that solves coupled nonlinear partial differential equations (PDEs) in spatiotemporal coordinates that are related to the conservation of mass and charge in the solid and liquid phases [7][8][9]. Because the parameters used in this model have a physical interpretation, they are directly correlated with battery aging.…”

Section: Introductionmentioning

confidence: 99%

Metamodel for Efficient Estimation of Capacity-Fade Uncertainty in Li-Ion Batteries for Electric Vehicles

2015

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This paper presents an efficient method for estimating capacity-fade uncertainty in lithium-ion batteries (LIBs) in order to integrate them into the battery-management system (BMS) of electric vehicles, which requires simple and inexpensive computation for successful application. The study uses the pseudo-two-dimensional (P2D) electrochemical model, which simulates the battery state by solving a system of coupled nonlinear partial differential equations (PDEs). The model parameters that are responsible for electrode degradation are identified and estimated, based on battery data obtained from the charge cycles. The Bayesian approach, with parameters estimated by probability distributions, is employed to account for uncertainties arising in the model and battery data. The Markov Chain Monte Carlo (MCMC) technique is used to draw samples from the distributions. The complex computations that solve a PDE system for each sample are avoided by employing a polynomial-based metamodel. As a result, the computational cost is reduced from 5.5 h to a few seconds, enabling the integration of the method into the vehicle BMS. Using this approach, the conservative bound of capacity fade can be determined for the vehicle in service, which represents the safety margin reflecting the uncertainty.

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Design and parametrization analysis of a reduced-order electrochemical model of graphite/LiFePO4 cells for SOC/SOH estimation

Cited by 210 publications

References 44 publications

A Reduced-Order Multi-Scale, Multi-Dimensional Model for Performance Prediction of Large-Format Li-Ion Cells

A Reduced-Order Multi-Scale, Multi-Dimensional Model for Performance Prediction of Large-Format Li-Ion Cells

An Inverse Method for Estimating the Electrochemical Parameters of Lithium-Ion Batteries

Metamodel for Efficient Estimation of Capacity-Fade Uncertainty in Li-Ion Batteries for Electric Vehicles

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