Research on Color Mixing and Optimization of RGBW-LEDs Based on Genetic Algorithm

XIONG Chen-yu; WU Yu-xiang; WEN Shang-sheng

doi:10.3788/fgxb20173802.0254

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Research on Color Mixing and Optimization of RGBW-LEDs Based on Genetic Algorithm

更新时间：2020-08-12

- Research on Color Mixing and Optimization of RGBW-LEDs Based on Genetic Algorithm
- Chinese Journal of Luminescence Vol. 38, Issue 2, Pages: 254-265(2017)
- 作者机构：
  
  1. 华南理工大学发光材料与器件国家重点实验室, 广东广州 510640
  2. 华南理工大学自动化科学与工程学院, 广东广州 510640
- 作者简介：
- 基金信息：
  
  Supported by Science and Technology Project of Guangdong Province(2011A081301017,2012A080304001,2012A080304012,2013B090600048)
- DOI：10.3788/fgxb20173802.0254
  CLC： TN206;TN312+.8
- Received：14 August 2016，
  
  Revised：21 September 2016，
  
  Published：05 February 2017
- 稿件说明：
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熊晨雨, 吴玉香, 文尚胜. 基于遗传算法的RGBW混光优化研究[J]. 发光学报, 2017,38(2): 254-265

XIONG Chen-yu, WU Yu-xiang, WEN Shang-sheng. Research on Color Mixing and Optimization of RGBW-LEDs Based on Genetic Algorithm[J]. Chinese Journal of Luminescence, 2017,38(2): 254-265
熊晨雨, 吴玉香, 文尚胜. 基于遗传算法的RGBW混光优化研究[J]. 发光学报, 2017,38(2): 254-265 DOI： 10.3788/fgxb20173802.0254.

XIONG Chen-yu, WU Yu-xiang, WEN Shang-sheng. Research on Color Mixing and Optimization of RGBW-LEDs Based on Genetic Algorithm[J]. Chinese Journal of Luminescence, 2017,38(2): 254-265 DOI： 10.3788/fgxb20173802.0254.

摘要

根据RGBW四色混光原理，采用脉冲宽度调制（PWM）调光方式，建立目标色坐标与占空比的关系方程，采用遗传算法对混合光的光通量和一般显色指数进行多元约束下寻优，在3 571~11 082 K色温范围内选取13组目标色温，采用Matlab软件进行优化仿真，最后通过实验验证。结果表明：采用遗传算法对混合光进行光通量最大化寻优，能够使混合光的光通量最高可达166.062 lm；采用遗传算法对一般显色指数进行最大化寻优，能够使混合光的一般显色指数最高可达88.3。为了保证混合光光源同时兼具高光通量和高显色性能，采用遗传算法对光通量和一般显色指数同时寻优，并采用Matlab进行模拟实验，结果表明：通过同时优化光通量和一般显色指数，能够保证混合光的一般显色指数大于82时兼具适用于大多数照明场所的高光通量。

Abstract

A relationship equation between the target chromaticity coordinate and the duty cycle based on the principle of RGBW color mixing and pulse width modulation (PWM) was established. The multi-constraint optimization simulation of the luminous flux and the general color index of mixed light based on genetic algorithm was carried out on Matlab program by selecting thirteen groups of target color temperature in the temperature range from 3 571 to 11 082 K

which was further verified by the experiments. The results show that the luminous flux can reach up to 166.062 lm under the maximum optimization of luminous flux

while

can reach up to 88.3 under the maximum optimization of general color index. Finally

the genetic algorithm was used to simultaneously optimize the luminous flux and general color index. It can ensure that the mixed light has high luminous flux to satisfy the demand of the current lighting manufacturing while the general color index exceeds 82.

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Keywords

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