Numerical Modeling of Damage Evolution Phenomenon in Solid-State Lithium-Ion Batteries

Behrou, Reza; Maute, Kurt

doi:10.1149/2.1101712jes

Cited by 42 publications

(30 citation statements)

References 60 publications

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“…Danilov, et al [7] developed an isothermal model for thin-film solid-state batteries (TFSSB), which includes diffusion and migration of ions in the electrolyte, the charge-transfer kinetics at the electrolyte/electrode interface and Li-ion diffusion in the intercalation cathode. A number of other modeling approaches followed [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Modeling of all-solid-state thin-film Li-ion batteries: Accuracy improvement

et al. 2019

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Thin-film Solid-State Batteries (TFSSB) is one of most promising and quickly developing fields in modern electrochemical energy storage. Modeling these devices is interesting from theoretical and practical point of view. This paper represents a simulation approach for TFSSB which overcome a major drawback of available mathematical models, i.e. decline in accuracy of the models at high current rates. A onedimensional electrochemical model, including charge transfer kinetics on the electrolyte-electrode interface, diffusion and migration in electrolyte as well as diffusion in intercalation electrode has been developed and the simulation results are compared to experimental voltage-capacity measurements. A new definition of diffusion coefficient as a function of concentration, based on the experimental measurements, is used to improve the performance of the model. The simulation results fit the available experimental data at low and high discharge currents up to 5 mAh cm -2 . The models show that the cathode diffusion constant is a prime factor limiting the rate capability for TFSSB in particular for ultrafast charging applications.

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Section: Introductionmentioning

confidence: 99%

Modeling of all-solid-state thin-film Li-ion batteries: Accuracy improvement

et al. 2019

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“…A strong correlation between battery temperature and battery degradation is reported for battery performance in the literature [2][3][4][5][6][7]. Therefore, the research on battery degradation induced by temperature effects is a vital multidisciplinary field with research branches in battery temperature monitoring and regulation [3,4,8], battery performance prediction [9][10][11][12][13][14][15] and optimization [8,16,17], as well as battery system design [1,[18][19][20]. The shared aim is to decrease the impact of temperature-induced fault mechanisms and battery cell performance flaws within the associated battery system, while, as the applications demand, safety objectives and durability ambitions are met [17,21,22].…”

Section: Introductionmentioning

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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“…The state and output equations can be deduced from Equation (2). Meanwhile, we set r = [r 1 , r 2 ] T and v as the process noise and measurement noise.…”

Section: Battery Model and Parameter Identificationmentioning

confidence: 99%

“…Overcharge/discharge should be strictly avoided in the use of lithium batteries. Otherwise, the battery service life will be significantly reduced [2,3]. Thus, accurate estimation of battery state of charge (SOC) becomes an important issue in lithium battery applications.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of LFP Battery SOC Estimation Using Auxiliary Particle Filter

Liu

et al. 2019

Energies

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State of charge (SOC) estimation of lithium batteries is one of the most important unresolved problems in the field of electric vehicles. Due to the changeable working environment and numerous interference sources on vehicles, it is more difficult to estimate the SOC of batteries. Particle filter is not restricted by the Gaussian distribution of process noise and observation noise, so it is more suitable for the application of SOC estimation. Three main works are completed in this paper by taken LFP (lithium iron phosphate) battery as the research object. Firstly, the first-order equivalent circuit model is adapted in order to reduce the computational complexity of the algorithm. The accuracy of the model is improved by identifying the parameters of the models under different SOC and minimum quadratic fitting of the identification results. The simulation on MATLAB/Simulink shows that the average voltage error between the model simulation and test data was less than 24.3 mV. Secondly, the standard particle filter algorithm based on SIR (sequential importance resampling) is combined with the battery model on the MATLAB platform, and the estimating formula in recursive form is deduced. The test data show that the error of the standard particle filter algorithm is less than 4% and RMSE (root mean square error) is 0.0254. Thirdly, in order to improve estimation accuracy, the auxiliary particle filter algorithm is developed by redesigning the importance density function. The comparative experimental results of the same condition show that the maximum error can be reduced to less than 3.5% and RMSE is decreased to 0.0163, which shows that the auxiliary particle filter algorithm has higher estimation accuracy.The intrinsic parameter measurement method is to select suitable battery parameters that can directly characterize the SOC for direct measurement, and then estimate the battery SOC through these parameters. The commonly used intrinsic parameters include open circuit voltage (OCV), impedance spectrum [7], electromotive force (EMF), and internal resistance (IR). The advantage of this method is that the battery SOC can be directly obtained through the mapping relationship between the SOC and the battery intrinsic parameter, and the disadvantage is that the estimation accuracy relies excessively on the accuracy of the mapping relationship between SOC and intrinsic parameters of battery. Holger [8] summarized the SOC estimation method based on the impedance spectrum. Waag [9] confirmed that the impedance parameter of a Li-ion battery changes negatively as the battery ages.Computer intelligence methods found in literature mainly include the neural network model, genetic algorithm and particles swarm optimization. The advantage of these methods is that they do not require a thorough understanding of battery internal mechanism, but they do require large amounts of data for the machine learning process. Sheikhan et al. [10] used an artificial neural network to realize feedback correction of the traditional time-integration m...

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Numerical Modeling of Damage Evolution Phenomenon in Solid-State Lithium-Ion Batteries

Cited by 42 publications

References 60 publications

Modeling of all-solid-state thin-film Li-ion batteries: Accuracy improvement

Modeling of all-solid-state thin-film Li-ion batteries: Accuracy improvement

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

Evaluation of LFP Battery SOC Estimation Using Auxiliary Particle Filter

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