Algorithm to Determine the Knee Point on Capacity Fade Curves of Lithium-Ion Cells

Diao, Weiping; Saxena, Saurabh; Han, Bongtae; Pecht, Michael

doi:10.3390/en12152910

Cited by 72 publications

(28 citation statements)

References 12 publications

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“…The occurrence of the "knee" point is only observed in FastCap Cells 3 and 4, as illustrated in Figure 4. This is a common occurrence in lithium-ion batteries [28]; however, it is an understudied topic in EC literature. EoL occurs first by reaching the 30% decrease in capacitance criteria threshold as opposed to the 100% increase in resistance for every sample included in this work.…”

Section: B Accelerated Life-cycle Testing Resultsmentioning

confidence: 99%

“…The method first decomposes the battery capacity degradation curve using the empirical mode decomposition (EMD) and then feeds the decomposed signal to both the LSTM submodel to capture the long-term dependence, and the GPR submodel to generate the uncertainty of each prediction result. One shortcoming of the work, however, is the limited cycle data available that does not include prediction past the knee-point, a problem often encountered in real-life applications [28].…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Degradation Model for Electrochemical Capacitors Operated at High Temperature

et al. 2021

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Electrochemical capacitors (ECs) have only recently been considered as an alternative power source for telemetry sensors of drilling equipment for geothermal or oil and gas exploration. The lifecycle analysis and modelling of ECs is underrepresented in literature in comparison to other storage devices e.g. Li-ion batteries. This paper investigates the degradation of ECs when cycled outside the manufacturerspecified operating temperature envelope and proposes a machine learning-based approach for modelling the degradation. Experimental results show that end of life, defined as a 30% decrease in capacitance, occurs at 1,000 cycles when the environmental temperature exceeds the maximum operating temperature by 30%. The life-cycle test data is then used as an input to a Gaussian process regression (GPR) algorithm to predict the capacitance fade trend. The GPR is validated on a total of nine commercial cells from two different manufacturers, achieving an average root mean squared percent error of less than 2% and a mean calibration score of 93% when referenced to a 95% confidence interval. The model can be utilized to determine the EC degradation rate at a range of operating temperature values.

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Section: B Accelerated Life-cycle Testing Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Machine Learning Degradation Model for Electrochemical Capacitors Operated at High Temperature

et al. 2021

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“…When the capacity is ample, the discharging curve is very stable. Once the voltage drop point is reached, the voltage will rapidly drop suggesting that the remaining capacity will be insufficient, as shown from Figure 2a-d. From the figures, the battery voltage decreases drastically, since the curve goes through a knee point, and eventually drops to the cut-off voltage, at which the battery has been exhausted [43][44][45][46]. Accord to Faraday's law, the capacity is equal to the integral of current over time.…”

Section: Coulombic Efficiency Analysis Of a Battery Under Different Tmentioning

confidence: 99%

Development of a Comprehensive Model for the Coulombic Efficiency and Capacity Fade of LiFePO4 Batteries under Different Aging Conditions

Kuo

2019

Applied Sciences

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In this paper, a comprehensive model for LiFePO4 batteries is proposed to ensure high efficiency and safe operation. The proposed model has a direct correlation between its parameters and the electrochemical principles to estimate the state of charge (SoC) and the remaining capacity of the LiFePO4 battery. This model was based on a modified Thévenin circuit, Butler–Volmer kinetics, the Arrhenius equation, Peukert’s law, and a back propagation neural network (BPNN), which can be divided into two parts. The first part can be represented by the dual exponential terms, responsive to the Coulomb efficiency; the second part can be described by the BPNN, estimating the remaining capacity. The model successfully estimates the SoC of the batteries that were tested with an error of 1.55%. The results suggest that the model is able to accurately estimate the SoC and the remaining capacity in various environments (discharging C rates and temperatures).

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“…Commercial lithium-ion batteries have been the dominant power supply for today's consumer electronics and high-power and energy mobile systems [1,2]. A technical specification sheet (datasheet) is a document that prescribes technical requirements to be fulfilled by a product, process, or service [3], is needed to choose and use a lithium-ion battery.…”

Section: Introductionmentioning

confidence: 99%

Development of an Informative Lithium-Ion Battery Datasheet

2021

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Lithium-ion battery datasheets, also known as specification sheets, are documents that battery manufacturers provide to define the battery’s function, operational limit, performance, reliability, safety, cautions, prohibitions, and warranty. Product manufacturers and customers rely on the datasheets for battery selection and battery management. However, battery datasheets often have ambiguous and, in many cases, misleading terminology and data. This paper reviews and evaluates the datasheets of 25 different lithium-ion battery types from eleven major battery manufacturers. Issues that customers may face are discussed, and recommendations for developing an informative and valuable datasheet that will help customers procure suitable batteries are presented.

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Algorithm to Determine the Knee Point on Capacity Fade Curves of Lithium-Ion Cells

Cited by 72 publications

References 12 publications

A Machine Learning Degradation Model for Electrochemical Capacitors Operated at High Temperature

A Machine Learning Degradation Model for Electrochemical Capacitors Operated at High Temperature

Development of a Comprehensive Model for the Coulombic Efficiency and Capacity Fade of LiFePO4 Batteries under Different Aging Conditions

Development of an Informative Lithium-Ion Battery Datasheet

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