An Estimation Method for the Efficiency of Light-Emitting Diode (LED) Devices

Xue, Tao; Yang, Bin

doi:10.6113/jpe.2016.16.2.815

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Many research teams have realized the importance of characterizing LEDs in multiple domains of operation. Mostly, these studies used empirical and sometimes rough approximations to obtain model parameters from measured data, see, e.g., [13][14][15][16][17]. In [13], an emphasis was placed on modelling LEDs' efficiency.…”

Section: The Wider Backgroundmentioning

confidence: 99%

“…The authors of [14] with their ambitious target design of retrofitted LED lamps, used the photo-electro-thermal theory, which in our view is a relatively rough, empirical way of describing LED behavior. The authors of [15][16][17] in their work relied on LED characterization tools that have been introduced to the SSL industry as a result of our team's pioneering work in multi-domain characterization [18,19]. In [15][16][17] (that are representative examples of the literature in the field), certain aspects of the junction temperature affected LED operating parameters were investigated, such as the electrical power [15], efficiency [16], and luminous flux [17].…”

Section: The Wider Backgroundmentioning

confidence: 99%

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Multi-Domain Modelling of LEDs for Supporting Virtual Prototyping of Luminaires

et al. 2019

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This paper presents our approaches to chip level multi-domain LED (light emitting diode) modelling, targeting luminaire design in the Industry 4.0 era, to support virtual prototyping of LED luminaires through luminaire level multi-domain simulations. The primary goal of such virtual prototypes is to predict the light output characteristics of LED luminaires under different operating conditions. The key component in such digital twins of a luminaire is an appropriate multi-domain model for packaged LED devices that captures the electrical, thermal, and light output characteristics and their mutual dependence simultaneously and consistently. We developed two such models with this goal in mind that are presented in detail in this paper. The first model is a semi analytical, quasi black-box model that can be implemented on the basis of the built-in diode models of spice-like circuit simulators and a few added controlled sources. Our second presented model is derived from the physics of the operation of today’s power LEDs realized with multiple quantum well heterojunction structures. Both models have been implemented in the form of visual basic macros as well as circuit models suitable for usual spice circuit simulators. The primary test bench for the two circuit models was an LTspice simulation environment. Then, to support the design of different demonstrator luminaires of the Delphi4LED project, a spreadsheet application was developed, which ensured seamless integration of the two models with additional models representing the LED chips’ thermal environment in a luminaire. The usability of our proposed models is demonstrated by real design case studies during which simulated light output characteristics (such as hot lumens) were confirmed by luminaire level physical tests.

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Section: The Wider Backgroundmentioning

confidence: 99%

Section: The Wider Backgroundmentioning

confidence: 99%

Multi-Domain Modelling of LEDs for Supporting Virtual Prototyping of Luminaires

et al. 2019

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“…The direct method measures the temperature of a heatsink attached to an LED, and the differential method measures the temperature of two identical heatsinks. The studies were described in [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent LED Certification System in Mass Production

Malykhina

Tarkhov

Shkodyrev

et al. 2021

Sensors

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It is impossible to effectively use light-emitting diodes (LEDs) in medicine and telecommunication systems without knowing their main characteristics, the most important of them being efficiency. Reliable measurement of LED efficiency holds particular significance for mass production automation. The method for measuring LED efficiency consists in comparing two cooling curves of the LED crystal obtained after exposure to short current pulses of positive and negative polarities. The measurement results are adversely affected by noise in the electrical measuring circuit. The widely used instrumental noise suppression filters, as well as classical digital infinite impulse response (IIR), finite impulse response (FIR) filters, and adaptive filters fail to yield satisfactory results. Unlike adaptive filters, blind methods do not require a special reference signal, which makes them more promising for removing noise and reconstructing the waveform when measuring the efficiency of LEDs. The article suggests a method for sequential blind signal extraction based on a cascading neural network. Statistical analysis of signal and noise values has revealed that the signal and the noise have different forms of the probability density function (PDF). Therefore, it is preferable to use high-order statistical moments characterizing the shape of the PDF for signal extraction. Generalized statistical moments were used as an objective function for optimization of neural network parameters, namely, generalized skewness and generalized kurtosis. The order of the generalized moments was chosen according to the criterion of the maximum Mahalanobis distance. The proposed method has made it possible to implement a multi-temporal comparison of the crystal cooling curves for measuring LED efficiency.

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