Modeling capacity fade in lithium-ion cells

Liaw, Bor Yann; Jungst, Rudolph G.; Nagasubramanian, Ganesan; Case, Herbert L; Doughty, D.H.

doi:10.1016/j.jpowsour.2004.08.017

Cited by 192 publications

(101 citation statements)

References 14 publications

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“…This high increase of internal resistance is due to the different degradation mechanisms explained earlier. These effects can be also observed in the decreasing of the capacity value of the cell during its lifetime as seen in Figure 3 [69,70].…”

Section: Discussionmentioning

confidence: 74%

Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion

et al. 2016

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Abstract:In this paper, advanced equivalent circuit models (ECMs) were developed to model large format and high energy nickel manganese cobalt (NMC) lithium-ion 20 Ah battery cells. Different temperatures conditions, cell characterization test (Normal and Advanced Tests), ECM topologies (1st and 2nd Order Thévenin model), state of charge (SoC) estimation techniques (Coulomb counting and extended Kalman filtering) and validation profiles (dynamic discharge pulse test (DDPT) and world harmonized light vehicle profiles) have been incorporated in the analysis. A concise state-of-the-art of different lithium-ion battery models existing in the academia and industry is presented providing information about model classification and information about electrical models. Moreover, an overview of the different steps and information needed to be able to create an ECM model is provided. A comparison between begin of life (BoL) and aged (95%, 90% state of health) ECM parameters (internal resistance (R o ), polarization resistance (R p ), activation resistance (R p2 ) and time constants (τ) is presented. By comparing the BoL to the aged parameters an overview of the behavior of the parameters is introduced and provides the appropriate platform for future research in electrical modeling of battery cells covering the ageing aspect. Based on the BoL parameters 1st and 2nd order models were developed for a range of temperatures (15˝C, 25˝C, 35˝C, 45˝C). The highest impact to the accuracy of the model (validation results) is the temperature condition that the model was developed. The 1st and 2nd order Thévenin models and the change from normal to advanced characterization datasets, while they affect the accuracy of the model they mostly help in dealing with high and low SoC linearity problems. The 2nd order Thévenin model with advanced characterization parameters and extended Kalman filtering SoC estimation technique is the most efficient and dynamically correct ECM model developed.

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Section: Discussionmentioning

confidence: 74%

Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion

et al. 2016

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“…For instance, a prediction of life expectancy of a battery cell remains difficult. To date, battery life prediction can be achieved only via laboratory testing with proper aging protocols [Broussely et al, 2001;Spotnitz et al, 2002;Ramadass et al, 2004Liaw et al, 2004Liaw et al, , 2005. To permit accurate life prediction, a large set of test conditions is usually required, which is very time consuming and labor intensive.…”

Section: Battery Modeling and Simulationmentioning

confidence: 99%

“…To enable timely life prediction of a cell under any given conditions, we believe that high fidelity battery modeling and simulation is an effective approach to allow such a knowledge transfer and life prediction. We have thus developed a unique empirical approach to model battery behavior using an equivalent circuit model Liaw et al, 2004Liaw et al, , 2005 with resistance mapping technique . Figure 13 presents the equivalent circuit model that we used.…”

Section: Battery Modeling and Simulationmentioning

confidence: 99%

Vehicle Evaluation, Battery Modeling, and Fleet-testing Experiences in Hawaii

Dubarry

Vuillaume

Liaw

et al. 2007

JAEV

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“…In [13], the authors proposed a method to describe the battery capacity under particular conditions in which battery capacity testing took considerable amounts of time. Similarly, a significant amount of time is required to test several cycles in [12], in which an artificial neural network (ANN) method of testing cycles was proposed.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Real-Time Estimation Method Based on Hidden Markov Models for Battery System States of Health

Piao¹,

Li²,

Lu³

et al. 2016

Journal of Power Electronics

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A new method is proposed based on a hidden Markov model (HMM) to estimate and analyze battery states of health. Battery system health states are defined according to the relationship between internal resistance and lifetime of cells. The source data (terminal voltages and currents) can be obtained from vehicular battery models. A characteristic value extraction method is proposed for HMM. A recognition framework and testing datasets are built to test the estimation rates of different states. Test results show that the estimation rates achieved based on this method are above 90% under single conditions. The method achieves the same results under hybrid conditions. We can also use the HMMs that correspond to hybrid conditions to estimate the states under a single condition. Therefore, this method can achieve the purpose of the study in estimating battery life states. Only voltage and current are used in this method, thereby establishing its simplicity compared with other methods. The batteries can also be tested online, and the method can be used for online prediction.

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Modeling capacity fade in lithium-ion cells

Cited by 192 publications

References 14 publications

Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion

Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion

Vehicle Evaluation, Battery Modeling, and Fleet-testing Experiences in Hawaii

Analysis of Real-Time Estimation Method Based on Hidden Markov Models for Battery System States of Health

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