Full Cell Parameterization of a High-Power Lithium-Ion Battery for a Physico-Chemical Model: Part I. Physical and Electrochemical Parameters

Schmalstieg, Johannes; Rahe, Christiane; Sauer, Dirk Uwe; Ecker, Madeleine

doi:10.1149/2.0321816jes

Cited by 107 publications

(109 citation statements)

References 49 publications

(59 reference statements)

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“…[1,2] The possibility to also investigate complex and unknown electrochemical systems via impedance spectroscopy turned the DRT method into a popular technique in recent impedance research. [3][4][5][6][7][8][9][10][11][12][13][14][15] However, calculat-ing DRT spectra from noisy experimental impedance data requires mathematical methods which are usually no black-box procedures. In particular, this indicates that certain preassumptions have to be made which strongly affect the calculations result.…”

Section: Introductionmentioning

confidence: 99%

Direct Access to the Optimal Regularization Parameter in Distribution of Relaxation Times Analysis

2020

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The distribution of times (DRT) method has become an important extension for electrochemical impedance investigations. Calculating the DRT is particularly advantageous for complex systems, as it yields an increased resolution in frequency domain. However, the determination of the DRT from experimental impedance data requires special mathematical effort. An often‐used method for the calculation of the DRT is the Tikhonov regularization. The quality of the result of this method depends on the choice of a suitable regularization parameter. This parameter has to be selected by the user. In a former work (N. Schlüter, S. Ernst, U. Schröder, ChemElectroChem 2019, 6, 6027–6037), we showed a method that helps to find the best regularization parameter for given impedance data. However, this test was based on repetitive impedance measurements, which have to be performed at least twice. In this paper, we optimize our previous method to assure the determination of an optimal regularization parameter from just one single impedance spectrum. Eventually, this revision saves measurement time and makes our test also applicable for less time stable measurements.

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Section: Introductionmentioning

confidence: 99%

Direct Access to the Optimal Regularization Parameter in Distribution of Relaxation Times Analysis

2020

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“…Modeling of the thermal aspects of lithium-ion battery operation states relies on model parameterization [47][48][49][50], validation of key performance variables [21,47,51,52] and the correct interpretation of model deviations [53,54], as well as behavioral indicators [55]. Therefore, the model accuracy strongly depends on the accurate modeling of each of the aforementioned physical phenomena that influences the flux of heat within and around the battery cell.…”

Section: Of 26mentioning

confidence: 99%

The Impact of Environmental Factors on the Thermal Characteristic of a Lithium–ion Battery

Liebig¹,

Kirstein²,

Geißendörfer³

et al. 2020

Batteries

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To draw reliable conclusions about the thermal characteristic of or a preferential cooling strategy for a lithium–ion battery, the correct set of thermal input parameters and a detailed battery layout is crucial. In our previous work, an electrochemical model for a commercially-available, 40 Ah prismatic lithium–ion battery was validated under heuristic temperature dependence. In this work the validated electrochemical model is coupled to a spatially resolved, three dimensional (3D), thermal model of the same battery to evaluate the thermal characteristics, i.e., thermal barriers and preferential heat rejection patterns, within common environment layouts. We discuss to which extent the knowledge of the batteries’ interior layout can be constructively used for the design of an exterior battery thermal management. It is found from the study results that: (1) Increasing the current rate without considering an increased heat removal flux at natural convection at higher temperatures will lead to increased model deviations; (2) Centralized fan air-cooling within a climate chamber in a multi cell test arrangement can lead to significantly different thermal characteristics at each battery cell; (3) Increasing the interfacial surface area, at which preferential battery interior and exterior heat rejection match, can significantly lower the temperature rise and inhomogeneity within the electrode stack and increase the batteries’ lifespan.

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“…At the cell level, two methods have been proposed in the literature for the electrochemical models' parametrization. In the first method, the cell geometry and physico-chemical properties are measured by performing cell teardown and conducting extensive experimental elaborations [16,17].…”

Section: Background and Research Gapmentioning

confidence: 99%

Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications

et al. 2020

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Large-scale introduction of electric vehicles (EVs) to the market sets outstanding requirements for battery performance to extend vehicle driving range, prolong battery service life, and reduce battery costs. There is a growing need to accurately and robustly model the performance of both individual cells and their aggregated behavior when integrated into battery packs. This paper presents a novel methodology for Lithium-ion (Li-ion) battery pack simulations under actual operating conditions of an electric mining vehicle. The validated electrochemical-thermal models of Li-ion battery cells are scaled up into battery modules to emulate cell-to-cell variations within the battery pack while considering the random variability of battery cells, as well as electrical topology and thermal management of the pack. The performance of the battery pack model is evaluated using transient experimental data for the pack operating conditions within the mining environment. The simulation results show that the relative root mean square error for the voltage prediction is 0.7–1.7% and for the battery pack temperature 2–12%. The proposed methodology is general and it can be applied to other battery chemistries and electric vehicle types to perform multi-objective optimization to predict the performance of large battery packs.

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Full Cell Parameterization of a High-Power Lithium-Ion Battery for a Physico-Chemical Model: Part I. Physical and Electrochemical Parameters

Cited by 107 publications

References 49 publications

Direct Access to the Optimal Regularization Parameter in Distribution of Relaxation Times Analysis

Direct Access to the Optimal Regularization Parameter in Distribution of Relaxation Times Analysis

The Impact of Environmental Factors on the Thermal Characteristic of a Lithium–ion Battery

Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications

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