Automotive interior lighting has progressed from basic functional illumination to sophisticated aesthetic systems emphasizing chromatic stability under thermal variations. This study enhances an RGB temperature compensation algorithm for LEDs, extending it to an RGBW solution. While several approaches for LED temperature compensation have been proposed in the literature, none have addressed a complete RGBW solution where the white channel is derived and actively adjusted on thermal variations. This research aims to fill this gap by extending an RGB algorithm to RGBW and validating it under realistic automotive conditions. While the proposed compensation strategies are general and may be applied to other LED systems, the automotive interior lighting domain has been selected as a representative case study because it combines stringent chromatic stability requirements (Δ𝑢′𝑣′ ≤0.01) and high industrial relevance. Leveraging Infineon’s LITIX™ LED drivers, experimental results show that the algorithm maintains chromatic stability with deviations below Δ𝑢′𝑣′ =0.00562 in RGB mode and Δ𝑢′𝑣′ =0.0067 in RGBW mode across the tested temperature range. The addition of the white channel improves the color rendering index (CRI) by up to 58.9 points (from 19.7 to 78.6) while preserving color quality. Compared to previous works limited to RGB systems, our approach provides the first practical RGBW compensation algorithm experimentally validated under realistic automotive conditions.
In modern times, interior automotive lighting has experienced a significant transformation, evolving from an exclusively functional component to an essential contributor to the overall driving experience. No longer limited to providing visibility only, interior lighting now plays an essential role in defining the vehicle’s aesthetic, enhancing perceived comfort, and reinforcing brand identity. Once exclusive to high-end vehicles, ambient lighting has steadily permeated the mid- and low-range market segments, demonstrating itself as a new industry standard.
Today, interior lighting has become a true interface between humans and the vehicle. It no longer consists of static or isolated light sources but has evolved into a more complex integrated system that contributes to what is now called the lighting experience. This includes advanced functionalities such as multicolor ambient lighting, animated welcome and goodbye scenarios, driving-mode-based color shifts, and even high-resolution projections on interior surfaces. These elements improve the vehicle’s perceived quality, reinforce its brand identity, and most importantly, can contribute to both driver alertness and passenger comfort [1].
A key technical challenge in this evolution is maintaining color stability under wide temperature variations. In automotive interior systems, LEDs are expected to operate reliably across a broad thermal range (–20 °C to +85 °C) or more. Within this range, chromatic deviations must remain below Δ𝑢′𝑣′ =0.01 to avoid perceivable color shifts. Furthermore, a high color rendering index (CRI) is typically required to ensure both visual comfort and compliance with industry standards. As Light Emitting Diode (LED) technology has evolved, so too has the demand for precision and consistency in light quality, especially in the face of thermal shifts that are typical in automotive environments. Color stability becomes a critical parameter when ensuring that the lighting remains visually appealing and uniform under all operating conditions and temperatures. To address this, temperature compensation algorithms have been developed to mitigate the effects of thermal drifts in RGB and RGBW (Red, Green, Blue, White) LEDs, the latter of which incorporate an additional white channel to enhance the CRI and the overall quality of the emitted light.
The current literature offers a variety of approaches to LED temperature compensation, each targeting specific systems or use cases [2,3,4,5,6,7,8]; recently, also for the specific automotive case [3,9,10,11]. However, none of the reviewed studies provides a complete solution for RGBW systems where the white channel must be actively derived and regulated to maintain color stability under temperature variation.
This work addresses this gap by improving and validating an RGBW temperature compensation algorithm, starting from the implementation of an RGB algorithm and introducing enhancements to support RGBW while maintaining color stability across a wide temperature range. Moreover, the algorithms were optimized to be integrated into the LITIX™ Interior family of LED drivers. Extensive testing and calibration processes were conducted to validate the proposed approach. Therefore, the novelty of this work lies in extending a temperature compensation strategy from RGB to RGBW systems with active regulation of the white channel. Unlike previous studies, the proposed method is experimentally validated under realistic automotive operating conditions, demonstrating chromatic stability (Δ𝑢′𝑣′ <0.01) and improved CRI performance while meeting the stringent requirements of automotive interior lighting.
Although the proposed temperature compensation algorithms are general and can be applied to a wide range of LED-based lighting systems, this work explicitly focuses on automotive interior lighting. The reason is twofold: first, the automotive domain represents a particularly demanding use case, where wide operating temperature ranges, strict requirements on chromatic stability (Δ𝑢′𝑣′ ≤0.01), and high CRI are mandatory for both comfort and safety. Second, this sector is currently experiencing rapid adoption of advanced ambient lighting solutions, making it an industrially relevant and timely context in which to validate the proposed methods. By presenting the approach within this specific application domain, the study provides not only a proof of concept under realistic conditions but also a contribution directly aligned with current industrial needs.
This paper is structured as follows: Section 2 begins by introducing some recent works related to the state-of-the-art. Section 3 starts from an overview on the theoretical background related to colorimetry and temperature dependence of LEDs (Section 3.1). Specifically, Section 3.1.1 provides a general overview of color theory. The colorimetry section first introduces color representation through chromaticity diagrams, followed by a focus on the white color, explaining blackbody radiation and the Planckian locus. Part of the chapter is dedicated to color mixing, where a mathematical procedure to obtain a color from a set of primaries (in this case, LEDs) is illustrated. Finally, the topic of color rendering is briefly addressed, with a focus on the CRI. Section 3.1.2 provides a brief explanation on how the LEDs are influenced by temperature, from both electrical and optical points of view.
After the theoretical hints, Section 3 focuses on the RGB temperature compensation algorithm, described in Section 3.2.1. It illustrates the characterization and calibration of the LED used and then details the functioning of the algorithm, both from a theoretical and logical point of view. Section 3.2.2 explains the motivations that led to the evolution toward the improved RGBW version. Finally, Section 3.3 describes the hardware, software and instruments for the implementation, the measurements and the validation of the algorithm.
In Section 4, the results obtained from the temperature validation of both the RGB and RGBW algorithms are presented. Section 5 discusses the obtained results and outlines possible future improvements. Section 6 draws conclusions.
Recent literature offers a range of approaches to temperature compensation in LED systems. Tao and Liu [2] propose a temperature prediction model (PT-model) and a compensation algorithm on RGB LEDs. Their model uses physical laws such as Fourier’s law of heat conduction and thermal resistance equations to estimate the junction temperature of the LEDs, taking into account multiple factors, including self-heating of the LED and other electronic components, heat transfer through the PCB, and input power to the system. In this way, the temperature is not measured directly on the LED chip, but the internal sensor of the MCU is used. The compensation is then performed by adjusting the final PWM, taking into account variations in luminous flux and chromaticity caused by temperature and aging. Aging has been modeled through an accelerated test.
Hong and Liu [3] likely present an evolution of the previous work by introducing an improved model tailored for low-cost automotive LED chips. Their approach refines the earlier model by optimizing it for deployment on microcontrollers, replacing floating-point arithmetic with a 16.16 fixed-point format to significantly reduce computational overhead. The improved model achieves temperature prediction errors within ±5 °C. Furthermore, a new chromaticity compensation strategy is introduced, where PWM duty cycles are further compensated for correcting a chromaticity shift at low brightness due to the reduced 𝑇𝑜𝑛 time.
Both works present interesting alternative approaches to temperature estimation that are not based on forward voltage measurements and implement compensation strategies that yielded good results in their respective validation tests. However, these methods are limited to RGB LEDs only and are therefore not directly applicable to systems with RGBW LEDs, where the white channel plays a crucial role in overall luminous output and color rendering.
Among the few studies explicitly addressing RGBW systems, Liu et al. [7] offer an interesting solution based on spectral modeling and multi-objective optimization. Their method relies on experimentally derived spectral power distribution of the four LEDs across temperature points, from which a model is constructed. The authors then use a non-dominated sorting genetic algorithm (NSGA-II) to optimize color output based on correlated color temperature (CCT), color fidelity index (Rf), and color gamut index (Rg). During the real-time operation, the temperature is measured via a sensor on the LED board, and compensation is applied by querying a precomputed lookup table stored in the microcontroller. This compensation method proves to be highly accurate, with deviations of less than 10 K in CCT and less than 4% in Rf and Rg within the (2000–7000) K range. However, their solution assumes complete knowledge of spectral behavior and does not specify how the RGB and white channels should be derived in real time from a known color point.
In [4], Ren et al. present a new RGBW color mixing method for LED stage lighting. The method integrates white LED with traditional RGB to better emulate natural light by aligning with the blackbody radiation curve (Planckian locus). Using MATLAB® simulations and field testing, the authors optimize mixing coefficients to achieve better CRI and continuous color temperature (CT) adjustment (from 2500 K to 8875 K). An optimization algorithm (Fmincon) is used to fit chromaticity coordinates to the blackbody curve while maximizing CRI. The proposed RGBW method represents a significant improvement in LED stage lighting, especially for high-CRI, adjustable white light generation. It is particularly well-suited for theatrical and performance environments demanding accurate color rendering, but its effectiveness drops at lower CTs and demands careful calibration and component choice.
A complementary line of research is emerging from the use of machine learning techniques to predict and control LED behavior under thermal stress. Merenda et al. [5] demonstrate that neural networks can be employed directly on embedded systems to predict junction
