The Lithium-Ion Battery Modeling Challenge

Docimo, Donald J.; Ghanaatpishe, Mohammad; Rothenberger, Michael J.; Rahn, Christopher D.; Fathy, Hosam I.

doi:10.1115/6.2014-jun-5

Cited by 5 publications

(6 citation statements)

References 8 publications

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“…The equivalent circuit model is an empirical model which can simplify the complexity of the electrochemical model. This type of model often needs low-order approximations to fit the model parameters 21 . Stroe et al .…”

Section: Background and Summarymentioning

confidence: 99%

Discharge performance and dynamic behavior of refuellable zinc-air battery

et al. 2019

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Zinc-air batteries (ZABs) are considered a promising energy storage system. A model-based analysis is one of the effective approaches for the study of ZABs. This technique, however, requires reliable discharge data as regards parameter estimation and model validation. This work, therefore, provides the data required for the modeling and simulation of ZABs. Each set of data includes working time, cell voltage, current, capacity, power, energy, and temperature. The data can be divided into three categories: discharge profiles at different constant currents, dynamic behavior at different step changes of discharge current, and dynamic behavior at different random step changes of discharge current. Constant current discharge profile data focus on the evolution of voltage through time. The data of step changes emphasize the dynamic behavior of voltage responding to the change of discharge current. Besides, the data of random step changes are similar to the data of step changes, but the patterns of step changes are random. Such data support the modeling of a zinc-air battery for both theoretical and empirical approaches.

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Section: Background and Summarymentioning

confidence: 99%

Discharge performance and dynamic behavior of refuellable zinc-air battery

et al. 2019

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“…25,26 Table II defines the degradation-related parameters along with their values. 24,26 The negative electrode is composed of lithium graphite, and its reference potential, U re f,n , is defined from Randall et al 27 The positive electrode is composed of LiFePO 4 , and its reference potential, U re f, p , is determined by adding U re f,n to the open-circuit voltage curve of Docimo et al 28 Equations 1a and 1b define the relationship between the input current I (positive for discharge) and the current densities J tot,i , for i = n, p (negative and positive electrode respectively). In the negative electrode, there is a side current density J s that relates to the loss of active material to the SEI layer.…”

Section: Battery Cell Modelmentioning

confidence: 99%

Multivariable State Feedback Control as a Foundation for Lithium-Ion Battery Pack Charge and Capacity Balancing

Docimo

Fathy

2016

J. Electrochem. Soc.

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This article presents a framework for examining the impact of different balancing strategies on cell-to-cell variations in state of charge (SOC), maximum capacity, and resistance within a lithium-ion battery pack. The literature motivates this work by showing that imbalance in both SOC and maximum capacity places limitations on pack-level charge and discharge capabilities. The use of equalization circuits to mitigate differences in SOC and voltage between cells in a battery pack is well-established. The goal of this article, in contrast, is to make the case for the use of state feedback control as a tool for balancing both SOC and maximum charge capacity. Specifically, the article draws on the electrochemical modeling and control theory literature to examine two balancing strategies: voltage balancing and a proposed novel multivariable SOC/capacity balancing strategy. The latter strategy navigates the implicit short-term trade-offs between SOC and capacity balancing by inducing a temporary SOC imbalance between cells to increase the dissipation rate of capacity imbalance. Results indicate that voltage balancing fails to eliminate capacity and resistance imbalance between cells. In contrast, the proposed method is able to eliminate charge, capacity and resistance imbalance within the lifespan of the pack, and extends pack lifespan by 9.2%.

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“…These models are typically obtained through the careful discretization or reduction of higher-order, electrochemistry-based battery models. Examples of methods used for battery model discretization and reduction include finite element and Laplace transform methods, 29 the reduction of single-particle models (SPMs) into simpler equivalent-circuit representations, 30 Padé approximation, 31 integral method analysis (IMA), 11 output linearization, 31 Galerkin z E-mail: hkf2@psu.edu projection, 32 proper orthogonal decomposition, 33 spectral orthogonal collocation, 34 data-driven system identification, 35 etc. Performance of simplified models in terms of reducing computational time is also investigated.…”

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confidence: 99%

How Does Model Reduction Affect Lithium-Ion Battery State of Charge Estimation Errors? Theory and Experiments

Mishra

Garg

Mendoza

et al. 2016

J. Electrochem. Soc.

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This article examines the impact of unmodeled dynamics on the accuracy of model-based lithium-ion battery state of charge (SOC) estimation. The article is motivated by the need for accurate SOC estimation for online battery diagnostics and control. Reduced-order battery models can lessen the computational cost of online SOC estimation. However, this comes at a price: model reduction is known to cause an estimation bias, in addition to the estimation noise typically induced by sensor measurement noise. This article derives analytic expressions for the expected estimation bias for two lithium-ion battery models: a nonlinear equivalent circuit model (ECM), and a nonlinear electrolyte enhanced single particle model. This derivation assumes a least squares SOC estimation law, but can be extended to other estimation laws. The article validates the analytic SOC estimation error expressions using both Monte Carlo simulation and experiments, for two different commercial lithium-ion batteries: a LiFePO4 battery and a LiNixCoyMnzO2 battery. The end result is, to the best of the authors’ knowledge, the first body of experimentally-validated analytic expressions for the expected SOC estimation errors resulting from lithium-ion battery model reduction.

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The Lithium-Ion Battery Modeling Challenge

Cited by 5 publications

References 8 publications

Discharge performance and dynamic behavior of refuellable zinc-air battery

Discharge performance and dynamic behavior of refuellable zinc-air battery

Multivariable State Feedback Control as a Foundation for Lithium-Ion Battery Pack Charge and Capacity Balancing

How Does Model Reduction Affect Lithium-Ion Battery State of Charge Estimation Errors? Theory and Experiments

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