LED array illumination uniformity optimization method for vehicle-mounted HUD

Abstract

The invention discloses an LED array illuminance uniformity optimization method for a vehicle-mounted HUD, and relates to the technical field of illuminance uniformity optimization of a vehicle-mounted HUD projection light source. According to the method, the illuminance uniformity of an LED array is optimized by setting initial structure parameters of the LED array, establishing an illuminance distribution model and an evaluation function and adopting a cascade iterative optimization strategy fusing a genetic algorithm (GA) and a particle swarm optimization (PSO); and the optimal solution of the LED array spacing is solved, so that the illumination uniformity of the receiving surface is remarkably improved. Experiments show that the method shows excellent performance on LED arrays of different scales, especially for a 5 * 5 array, the illumination uniformity reaches 86.107%, and for a larger-scale array, the illumination uniformity is stabilized at about 88%. According to the method, the limitation of a single optimization algorithm on global search and local convergence precision is effectively solved, and the method has relatively high generalization ability and practicability.

Description

Technical Field

[0001] This invention relates to the field of illuminance uniformity optimization technology for projection light sources in vehicle-mounted HUDs, and particularly to a method for optimizing the illuminance uniformity of LED arrays used in vehicle-mounted HUDs. Background Technology

[0002] With the continuous development of technology, in-vehicle head-up display (HUD) systems are increasingly being used in modern automobiles as a driver assistance tool. In the projection generation unit of an in-vehicle HUD, the LCD screen itself does not emit light and relies on a backlight module for illumination. Therefore, the LED array, as the core component of the backlight source, directly affects the display effect and user experience due to its illuminance uniformity. To meet the high-quality lighting requirements of in-vehicle HUDs, LED backlight modules typically require an illuminance uniformity of over 80%, while their thickness generally needs to be controlled to around 10mm to accommodate ultra-thin packaging designs. However, in practical applications, due to the limitations of LED arrays' luminous characteristics and spatial layout, it is often difficult to directly achieve a highly uniform illumination distribution, which has become one of the key issues restricting the performance improvement of in-vehicle HUDs.

[0003] Currently, methods for improving LED backlight uniformity mainly fall into two categories: one is to improve the shape and brightness distribution of LED light spots by designing freeform lenses; the other is to optimize the spatial layout of LED arrays to improve backlight uniformity. Although the design of freeform lenses can improve the light spot distribution to some extent, its manufacturing difficulty and cost are high, which is not conducive to large-scale promotion. In contrast, improving the spatial layout of LED arrays based on numerical calculation and optimization theory is considered a more practical and economical solution. For example, existing research has proposed using particle swarm optimization (PSO) to optimize circular or square LED arrays and has achieved improved backlight uniformity by constructing an illumination uniformity evaluation function. In addition, some patents have proposed using genetic algorithms to optimize the LED array layout in indoor visible light communication systems, thereby improving the uniformity of light power distribution on the receiving plane. However, these single optimization algorithms still have certain limitations in practical applications. Although PSO has good search capabilities and fast convergence speed, it is prone to getting trapped in local optima in multi-peak optimization problems, leading to search stagnation; while genetic algorithms can avoid premature convergence due to their global search mechanism, their local optimization ability is weak and their convergence accuracy is not high. This trade-off between global search diversity and local convergence accuracy makes it difficult for traditional single optimization algorithms to meet the high requirements of uniformity of contrast in complex scenarios.

[0004] To address the aforementioned issues, there is an urgent need for an optimization method that can balance global search and local optimization capabilities to effectively improve the illuminance uniformity of LED arrays on the receiving surface. Especially in application scenarios with LED arrays of different sizes, how to achieve an optimization strategy that can quickly find the global optimum while accurately adjusting local parameters has become a key problem that urgently needs to be solved in the current technical field. Based on this need, this invention proposes a novel optimization method that integrates genetic algorithms and particle swarm optimization. It aims to significantly improve the illuminance uniformity of the receiving surface through a cascaded iterative optimization strategy, while also possessing strong generalization design capabilities to adapt to the optimization needs of LED arrays of different sizes. Summary of the Invention

[0005] This invention addresses the shortcomings of existing LED array illumination uniformity optimization techniques in automotive head-up display (HUD) backlight modules by proposing a cascaded iterative optimization method that integrates genetic algorithm (GA) and particle swarm optimization (PSO). This method significantly improves illumination uniformity on the receiving surface by precisely optimizing the spatial layout parameters of the LED array, thus solving the problem that a single optimization algorithm struggles to balance global search diversity and local convergence accuracy.

[0006] To achieve the above objectives, the present invention is implemented according to the following technical solution:

[0007] This invention includes the following steps:

[0008] S1: Set the initial structural parameters of the LED array; the initial structural parameters of the LED array include the number of LED array rows N, the distance z between the light source surface and the receiving surface, and the initial distance d0 between the LED arrays. Among them, N is an odd number to ensure the center symmetry of the array, the distance z between the light source surface and the receiving surface is set to a fixed value according to the actual application requirements, such as 10mm, while the initial spacing d0 is determined based on experience or preliminary estimation.

[0009] S2: Establish an LED array illuminance distribution model and evaluation function, and determine the illuminance fitness based on the evaluation function; specifically, this includes the following steps:

[0010] S2.1: Based on the initial structural parameters, establish an LED array illuminance distribution model and calculate the illuminance distribution of the receiving surface;

[0011] S2.2: Define the illuminance uniformity evaluation function in, E represents the average illuminance value of all grids on the receiving surface. σ This represents the standard deviation of illuminance on the receiving surface grid.

[0012] S2.3: The illuminance fitness of the receiving surface is determined by the evaluation function.

[0013] S3: Employing a fusion of genetic algorithm and particle swarm optimization algorithm, the illuminance fitness is calculated iteratively to determine the optimal solution for the LED array spacing, thereby obtaining a highly uniform illuminance distribution value on the receiving surface. Specifically, this includes the following steps:

[0014] S3.1: The maximum illuminance fitness of the fusion genetic algorithm population is calculated iteratively using a fusion genetic algorithm search algorithm, and the initial solution of the LED array spacing is updated.

[0015] S3.2: The illuminance fitness of the population is iteratively calculated using the particle swarm optimization algorithm, and the corresponding LED array spacing solution is updated using the maximum fitness value.

[0016] S3.3: The illuminance fitness is calculated iteratively by fusing the particle swarm optimization algorithm and the genetic algorithm. The cascaded iterative optimization process is repeated until the evaluation function converges. The final LED array spacing solution is then updated to obtain a highly uniform illuminance distribution value on the receiving surface.

[0017] Through the above technical solution, this invention achieves precise optimization of array spacing parameters, exhibiting excellent illuminance uniformity across LED arrays of different sizes. Specifically, when the array size is 5×5, the illuminance uniformity is significantly improved to 86.107%; when the array sizes are 7×7, 9×9, and 11×11, the illuminance uniformity tends to stabilize at around 88%. This method has strong generalization ability and practicality, and is suitable for the design requirements of various automotive HUD backlight modules.

[0018] The beneficial effects of this invention are:

[0019] By employing a cascaded iterative optimization strategy that integrates genetic algorithms and particle swarm optimization (PSO), the optimization accuracy for LED array illumination uniformity is effectively improved. Compared to single optimization algorithms such as simulated annealing (SA), PSO, and genetic algorithms (GA), the proposed GA-PSO algorithm exhibits higher performance in illumination uniformity optimization. Experimental results show that for a 5×5 LED array, the GA-PSO algorithm achieves an illumination uniformity of 86.107%, outperforming other single optimization algorithms. Furthermore, this method demonstrates stable optimization performance across LED arrays of different sizes, meeting the high requirements for illumination uniformity in automotive HUD backlight modules, while also possessing good design flexibility and application prospects. Attached Figure Description

[0020] Figure 1 This is a general flowchart of an embodiment of the present invention;

[0021] Figure 2 This is a flowchart of the optimization algorithm according to an embodiment of the present invention;

[0022] Figure 3This is a graph showing the relationship between array size and illuminance uniformity in an embodiment of the present invention.

[0023] Figure 4 This is a diagram showing the array size parameter N and the illumination uniformity of the receiving surface in an embodiment of the present invention, wherein:

[0024] (a) Illuminance distribution diagram of a 3×3 array; (b) Illuminance curve diagram of a 3×3 array; (c) Illuminance distribution diagram of a 5×5 array; (d) Illuminance curve diagram of a 5×5 array; (e) Illuminance distribution diagram of a 7×7 array; (f) Illuminance curve diagram of a 7×7 array; (g) Illuminance distribution diagram of a 9×9 array; (h) Illuminance curve diagram of a 9×9 array; (i) Illuminance distribution diagram of an 11×11 array; (j) Illuminance curve diagram of an 11×11 array.

[0025] Figure 5 The images show a comparison of the uniform light distribution effects of a single optimization algorithm and the GA-PSO algorithm on a 5×5 LED rectangular array, respectively.

[0026] (a) Illuminance distribution map optimized by SA algorithm; (b) Illuminance curve map optimized by SA algorithm; (c) Illuminance distribution map optimized by GA algorithm; (d) Illuminance curve map optimized by GA algorithm; (e) Illuminance distribution map optimized by PSO algorithm; (f) Illuminance curve map optimized by PSO algorithm; (g) Illuminance distribution map optimized by GA-PSO algorithm; (h) Illuminance curve map optimized by GA-PSO algorithm. Detailed Implementation

[0027] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions herein are used to explain the present invention, but are not intended to limit the present invention.

[0028] This invention provides a method for optimizing the illuminance uniformity of an LED array for vehicle-mounted HUDs, combined with... Figure 1 To be continued Figure 5 The specific implementation process of the present invention is described in detail using the data in Tables 1 and 2. The following content is based on actual application scenarios to ensure the operability and completeness of the technical solution.

[0029] In this example, the initial structural parameter setting of the LED array in step S1 specifically includes the following steps:

[0030] S1. Based on product requirements or industry specifications and LED light-emitting parameters, set the number of LED array rows N, the distance between the light source surface and the receiving surface z, and the initial distance between LED arrays d0.

[0031] In this example, step S2, establishing the LED array illuminance distribution model and evaluation function, and determining the illuminance fitness based on the evaluation function, specifically includes the following sub-steps:

[0032] S2.1 Establish an LED array illuminance distribution model. Ideally, the light emission of the LED chip is very similar to that of a near-Lambertian light source, and its intensity distribution is shown below:

[0033] I(θ) = I0·cos m (θ) (1)

[0034]

[0035] In equations (1) and (2), θ is the emission angle. 1 / 2 I0 is the emission half-angle, and I0 is the emission intensity distribution perpendicular to the normal direction of the light source plane. The m value parameter depends on the emission half-angle.

[0036] Assuming the LED is fixed on a plane, called the S-plane (z = 0), and the plane receiving the LED illumination is called the receiving plane, called the T-plane. The distance between the S-plane containing the light source and the T-plane is z. Assuming there is any point A on the T-plane with coordinates (x, y, z), and an LED on the S-plane with coordinates (X, Y, 0), the approximate irradiance distribution produced by the LED on the receiving plane can be calculated by the following formula:

[0037]

[0038] Since LEDs are incoherent light sources, the illuminance produced by an array of n LEDs at the receiving surface (x,y,z) is:

[0039]

[0040] In the formula, (X i ,Y i (0) is the coordinate of the i-th LED in the LED array.

[0041] In this invention, for ease of calculation, the number of array rows N is taken as an odd number. In an N×N LED array, the coordinates of a single LED are represented as follows:

[0042]

[0043] Based on the luminous characteristics of near-Lambertian light sources, an illuminance distribution model for the LED array on the receiving surface is derived:

[0044]

[0045] S2.2 Define the illuminance uniformity evaluation function. Divide the receiving surface into P×Q grids, and the illuminance value of each grid can be calculated. Based on the illuminance values ​​of the receiving surface grids, construct the evaluation function as shown in the following formula:

[0046]

[0047] In the formula, The average irradiance value across all grids on the receiving surface is:

[0048]

[0049] E σ The standard deviation of illuminance for all grid cells can be calculated using the following formula:

[0050]

[0051] Clearly, the smaller the evaluation function value, the better the uniformity.

[0052] S2.3 Determine the illuminance fitness in step S3 based on the evaluation function.

[0053] In this example, step S3, which employs a fusion of the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm, calculates the illuminance fitness through interactive iteration to solve for the optimal solution d3 of the LED array spacing, thereby obtaining a highly uniform illuminance distribution value on the receiving surface, specifically includes the following sub-steps:

[0054] S3.1. The GA search algorithm is used to iteratively calculate the maximum illuminance fitness of the GA population, and the initial solution of the LED array spacing is updated to d1.

[0055] Generate a value related to the array spacing d i The initial GA population is set, where each individual represents a potential solution. Relevant parameters are set, including: number of particles (PopulationSize = 100), maximum number of iterations per round (MaxGenerations = 50), search boundary [lb, ub] = [0.1, 10], and crossover probability (CrossoverFraction = 0.7).

[0056] According to the formula The fitness of each individual is evaluated; a higher fitness indicates a better solution. Here, i represents the i-th solution in the population. E represents the average illuminance value of all grids on the receiving surface. σ This represents the standard deviation of illuminance on the receiving surface grid.

[0057] Using sequential selection, individuals with higher fitness are chosen from the current population to serve as parents. The selected parent solution d... i1 and di2 A new solution is generated through a crossover operation, as shown in the following equation:

[0058] d i =p×d i1 +(1-p)×d i2 (10)

[0059] In the formula, p represents a random number between (0,1).

[0060] For the constrained optimization problem with the current search boundary [lb, ub], an improved mutation function is used to perform the mutation operation. The boundary constraints for each solution are known:

[0061] ld≤d i ≤ud (11)

[0062] Define the maximum feasible perturbation range for the i-th solution:

[0063]

[0064] Therefore, for the i-th solution d i The following mutations are applied:

[0065]

[0066] Where, γ i ~U(-α,α) is a uniformly distributed random number, and α<1. The fitness of the new solution generated after crossover and mutation operations is calculated and compared with the solution with the worst fitness in the current population. If a better solution is found, it is replaced. This updates the initial solution d1 for the LED array spacing, and one-quarter of the high-fitness solutions are input into the PSO algorithm.

[0067] S3.2. The illuminance fitness of the population is calculated iteratively using the PSO algorithm, and the corresponding LED array spacing solution d2 is updated with the maximum fitness value.

[0068] Several solutions are randomly generated within the range [ld, ud], and together with some solutions input from the GA population, they are used to construct the initial particles of the PSO population. The current position of each particle is set as the historical best solution pbest, and its fitness value is calculated to find the population best solution gbest.

[0069] Velocity updates are performed, influenced by the inertia factor w, the individual cognitive factor c1 (prompting particles to review their own experience), the collective cognitive factor c2 (prompting particles to move closer to the global optimum), the current velocity, the individual historical best solution pbest, and the collective best solution gbest. t represents the iteration number. The formula for particle update velocity is as follows:

[0070] v i (t+1)=w×vi (t)+c1×r1×(pbest i (t)-x i (t))+c2×r2×(gbest i (t)-x i (t))(14)

[0071] The position is updated, and the particle's position is adjusted according to the updated velocity, as shown in the following formula:

[0072] d i (t+1)=d i (t)+v i (t) (15)

[0073] After each update, the fitness of a particle is calculated based on its current position and compared with its historical best solution. If the fitness is higher, the best solution for that particle is updated. After all particles have been updated, the swarm optimal solution is the optimal LED array spacing d2 generated in this iteration.

[0074] S3.3. The illuminance fitness is calculated iteratively using the PSO algorithm and the GA algorithm. The cascaded iterative optimization process is repeated until the evaluation function converges. The final LED array spacing solution d3 is updated to obtain the final high uniformity illuminance distribution value of the receiving surface.

[0075] After each iteration of the PSO particle swarm optimization (PSO) population, half of the high-fitness particles are selected from the current PSO population and linearly cross-paired to generate a new solution.

[0076] x new =αx a +(1-α)x b ,α~U(0,1) (16)

[0077] The fitness of the new solution is calculated and compared with the worst-fitting solution in the GA population. If it is better, it is input into the GA algorithm. After each round of optimization, the GA population selects one-quarter of the solutions with higher fitness to replace the low-fitting solutions in the PSO population, and continues particle swarm optimization. This cascaded iterative optimization process is repeated until the evaluation function is reached. The solution is obtained by solving for the optimal array spacing d3 until convergence.

[0078] Example verification:

[0079] To meet the requirements of ultra-thin backlighting, the distance between the receiving surface and the light source surface is set to 10mm, and the LED emission angle θ is set to 60°, with a luminous flux of 40lm per LED. An evaluation function is constructed based on the irradiance calculation formula for a square array. In Matlab, the spacing d between LEDs in arrays of different sizes is optimized using a fusion genetic-particle swarm optimization (GA-PSO) algorithm to obtain the minimum value of the evaluation function.

[0080] The optical simulation software Zemax was used to verify LED arrays with different numbers of rows N. A three-dimensional model of the LEDs was built in the software, ensuring that the emission angle of each LED conformed to a Lambertian distribution. The optimization results of the GA-PSO algorithm were applied to the array, and finally, a receiving surface was added. Based on the illuminance data obtained from the simulation results of the receiving surface, the software was used to process and calculate the data. The simulation calculation results are shown in Table 1, which lists the number of array rows N, the optimized spacing d, and the illuminance uniformity of the target area.

[0081] Table 1. Uniform light data for square arrays of different sizes

[0082]

[0083] The relationship curve between the number of array rows and illuminance uniformity, as shown in the figure. Figure 3 As shown in the figure, the effect diagrams of different structural parameters and lighting uniformity are as follows: Figure 4 As shown.

[0084] It can be seen that the inflection point of the curve is reached when the array size is 5×5. Compared with the 3×3 array, the illuminance uniformity is significantly improved, reaching over 86%, resulting in a better illumination distribution. When the algorithm is applied to 7×7, 9×9, and 11×11 arrays, the illuminance uniformity tends to stabilize, remaining at around 88%. This result demonstrates the generalization ability of the GA-PSO algorithm in optimizing LED arrays of different sizes. The GA-PSO algorithm maintains search depth while balancing stability and practicality.

[0085] To verify the superiority of the fusion genetic-particle swarm optimization algorithm over a single optimization algorithm in terms of optimization accuracy, simulated annealing (SA), particle swarm optimization (PSO), genetic algorithm (SA), and fusion genetic-particle swarm optimization (GA-PSO) were used to optimize a 5×5 rectangular LED array. The quantization performance comparison of the four optimization algorithms is shown in Table 2.

[0086] Table 2 Comparison of the optimization performance of four algorithms for 5×5 LED arrays

[0087]

[0088] The visualization of the uniformity effect is shown in the comparison chart, such as... Figure 4 As shown.

[0089] The technical solutions of the present invention are not limited to the specific embodiments described above. Any technical modifications made in accordance with the technical solutions of the present invention fall within the protection scope of the present invention.

Claims

1. A method for optimizing the illuminance uniformity of an LED array for vehicle-mounted HUDs, characterized in that, Includes the following steps: S1: Set the initial structure parameters of the LED array; S2: Establish an LED array illuminance distribution model and evaluation function, and determine the illuminance fitness based on the evaluation function; S3: By combining genetic algorithm and particle swarm optimization algorithm, the illuminance fitness is calculated through interactive iteration to find the optimal solution for the LED array spacing and obtain the highly uniform illuminance distribution value of the receiving surface.

2. The method for optimizing the illuminance uniformity of an LED array for vehicle-mounted HUDs according to claim 1, characterized in that: The initial structural parameters of the LED array in step S1 include the number of LED array rows N, the distance z between the light source surface and the receiving surface, and the initial distance d0 between the LED arrays.

3. The method for optimizing the illuminance uniformity of an LED array for vehicle-mounted HUDs according to claim 2, characterized in that: Step S2 specifically includes the following steps: S2.1: Based on the initial structural parameters, establish an LED array illuminance distribution model and calculate the illuminance distribution of the receiving surface; S2.2: Define the illuminance uniformity evaluation function in, E represents the average illuminance value of all grids on the receiving surface. σ This represents the standard deviation of illuminance on the receiving surface grid. S2.3: The illuminance fitness of the receiving surface is determined by the evaluation function.

4. The method for optimizing the illuminance uniformity of an LED array for vehicle-mounted HUDs according to claim 3, characterized in that: Step S3 specifically includes the following steps: S3.1: The maximum illuminance fitness of the fusion genetic algorithm population is calculated iteratively using a fusion genetic algorithm search algorithm, and the initial solution of the LED array spacing is updated. S3.2: The illuminance fitness of the population is iteratively calculated using the particle swarm optimization algorithm, and the corresponding LED array spacing solution is updated using the maximum fitness value. S3.3: The illuminance fitness is calculated iteratively by fusing the particle swarm optimization algorithm and the genetic algorithm. The cascaded iterative optimization process is repeated until the evaluation function converges. The final LED array spacing solution is then updated to obtain a highly uniform illuminance distribution value on the receiving surface.

5. The method for optimizing the illuminance uniformity of an LED array for vehicle-mounted HUDs according to claim 3, characterized in that: Step S2.1 specifically includes: The light intensity distribution is shown below: I(θ)=I0·cos m (i)(1) Illuminance distribution model of LED array on receiving surface: In equations (1) and (2), θ is the emission angle. 1 / 2 I0 is the emission half-angle, and I0 is the emission intensity distribution perpendicular to the normal direction of the light source plane. The m value parameter depends on the emission half-angle.

6. The method for optimizing the illuminance uniformity of an LED array for vehicle-mounted HUDs according to claim 5, characterized in that: In step S2.2 Specifically: E σ The standard deviation of illuminance for all grids is calculated using the following formula: The smaller the evaluation function value, the better the uniformity.

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