2017 18th International Conference on Electronic Packaging Technology (ICEPT) 2017
DOI: 10.1109/icept.2017.8046695
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Luminous flux modeling for high power LED automotive headlamp module

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Cited by 4 publications
(3 citation statements)
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“…The increase of junction temperature and driving current can deteriorate the luminous efficacy of LEDs. Thus, to predict the luminous flux of LED under different operation conditions, a general luminous flux response surface model [33] is proposed as shown in Eq. (1) (hereby named Model 1):…”
Section: Theories and Methodologies A Luminous Flux Response Surface Modelmentioning
confidence: 99%
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“…The increase of junction temperature and driving current can deteriorate the luminous efficacy of LEDs. Thus, to predict the luminous flux of LED under different operation conditions, a general luminous flux response surface model [33] is proposed as shown in Eq. (1) (hereby named Model 1):…”
Section: Theories and Methodologies A Luminous Flux Response Surface Modelmentioning
confidence: 99%
“…To understand the luminous efficacy decay mechanism of LED in package level and its long-term degradation mechanism, two experiments are firstly designed in this study for LED packages with four general packaging structures, those are the transient thermal and luminous characteristics measurements and accelerated ageing test. In our previous study [33], a universal luminous flux response surface model was developed to relate the luminous flux with junction temperature and driving current effectively. Therefore, the luminous flux response surface model considering the electric-thermal-luminous coupling effect and the Gaussian based SPD model are proposed in this paper to predict the LED package level luminous efficacy decay and long-term degradation mechanisms respectively.…”
Section: Introductionmentioning
confidence: 99%
“…However, for lighting purposes, several studies have verified a strong variation of the optical efficiency, and consequently, of transmitted optical powers, of HPLEDs when faced with factors such as temperature and aging [12]- [15]. Differences between manufacturing batches, etc., which can further aggravate this scenario making even more ineffective the correction of nonlinearities by eventual factory fixed modeling.…”
Section: Introductionmentioning
confidence: 99%