A method for controlling automobile atmosphere lights

Abstract

The present invention discloses a method for controlling automotive ambient lighting, which relates to the technical field of automotive ambient lighting control. The method comprises the following steps: setting the operating frequency range and harmonic sensitivity bandwidth of an on-board communication bus, deploying a highly sensitive electromagnetic sensing probe at the edge of the communication bus wiring area to collect the electromagnetic radiation signal intensity within the sensitive frequency band in real time; after the ambient lighting PWM control module is activated, performing dynamic brightness adjustment tasks under different PWM switching frequencies and duty cycles, and simultaneously recording electromagnetic spectrum density change data within the sensitive frequency band. The present invention uses the electromagnetic sensing probe to monitor the interference spectrum in real time, establishes a frequency mapping based on PWM parameters, quantifies the coupling strength, generates a continuous electromagnetic coupling strength index, and dynamically drives the filter to precisely suppress the target frequency band. While ensuring the ambient lighting effect, it effectively suppresses electromagnetic interference to the CAN / LIN bus, thereby improving vehicle communication security and system stability.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile atmosphere lamp control, and in particular to an automobile atmosphere lamp control method. Background Art

[0002] Automotive ambient lighting control refers to the intelligent adjustment and management of interior ambient lighting (LED light strips or points used to create a visual and emotional experience) through an onboard control system. Core functions include color changing, brightness adjustment, dynamic lighting effects (such as breathing, flowing, and rhythmic), and integration with other in-vehicle systems (such as audio rhythm, driving mode, door status, and voice assistant). Control methods typically support a variety of methods, including physical buttons, touch screens, voice commands, or remote operation via mobile apps. Advanced ambient lighting control systems can also automatically adapt lighting scenes based on driver identity, time of day, and emotion recognition, creating a personalized and immersive interior lighting experience, enhancing user comfort and driving pleasure.

[0003] The existing technology has the following deficiencies:

[0004] In existing technologies, ambient lighting generally uses PWM for brightness control, with switching frequencies typically ranging from several thousand to tens of thousands of hertz. When the vehicle's ambient lighting is in a state of dynamic brightness adjustment or multi-zone synchronous switching, the PWM frequency may, under certain conditions, form electromagnetic coupling with the harmonic frequency band of the CAN / LIN bus communication system, thereby interfering with the integrity of the communication bus signal, easily causing communication frame errors, frame loss, and even bus communication interruption. Because the CAN bus is widely used in critical vehicle safety systems such as braking, airbags, and body stability control, any communication anomalies can cause related systems to lose connection or control response anomalies, posing a serious threat to the safety of the entire vehicle.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for controlling automobile atmosphere lights. By deploying electromagnetic sensing probes to monitor the interference spectrum in real time, a frequency mapping relationship is established in combination with PWM modulation parameters to accurately identify the overlap between interference energy and communication sensitive frequency bands; the coupling interference intensity is quantified through a dynamic normalization function to form a continuous and adjustable electromagnetic coupling strength index, and this is used to drive the active filtering module to accurately suppress the frequency band. Under the premise of not affecting the visual effect of the atmosphere lights, it can effectively avoid electromagnetic interference to the CAN / LIN bus, prevent safety hazards such as communication errors, frame loss or interruptions, and ensure the stable operation of the vehicle control system and driving safety, so as to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a method for controlling an automotive ambient light, comprising the following steps:

[0008] Set the operating frequency range and harmonic sensitivity bandwidth of the vehicle communication bus, and deploy highly sensitive electromagnetic sensing probes at the edge of the communication bus wiring area to collect the electromagnetic radiation signal strength in the sensitive frequency band in real time;

[0009] After the ambient light PWM control module is started, it performs dynamic brightness adjustment tasks under different PWM switching frequencies and duty cycles. It also records the electromagnetic spectrum density change data within the sensitive frequency band and extracts the interference energy spectrum related to the changes in PWM modulation parameters.

[0010] Based on the interference energy spectrum, a frequency mapping matrix is ​​constructed between the PWM frequency and the harmonic interval of the vehicle communication bus. The overlapping area of ​​the spectrum energy is identified and the spectral density integral value in the overlapping area is calculated to form an interference energy vector reflecting the degree of spectrum coupling.

[0011] The interference energy vector is input into the dynamic coupling normalization function. Based on the anti-interference margin of the communication bus and the reference background noise, it is normalized into an electromagnetic coupling strength index. The electromagnetic coupling strength index is a continuous value between 0 and 1, which is used to represent the coupling interference risk level between the current PWM drive and the communication bus.

[0012] When the electromagnetic coupling strength index exceeds the preset interference threshold, an active filtering module is inserted between the PWM control channel and the communication bus. The active filtering module includes an LC filtering circuit or an EMI shielding component, and dynamically adjusts the filtering bandwidth according to the electromagnetic coupling strength to achieve effective filtering of the target interference frequency band.

[0013] Preferably, the steps of setting the operating frequency range and harmonic sensitive bandwidth of the vehicle communication bus and deploying electromagnetic sensing probes include:

[0014] Based on the protocol specifications and actual transmission rate of the in-vehicle communication system, determine the fundamental frequency and main harmonic distribution range of the target communication bus (including but not limited to CAN or LIN bus), and establish a set of frequency-sensitive bandwidth parameter sets for interference monitoring;

[0015] Conduct wiring harness electromagnetic compatibility (EMC) simulation or wiring hotspot analysis inside the vehicle to identify key node areas susceptible to interference in the communication bus layout. Deploy at least one highly sensitive electromagnetic sensing probe with wideband response capability at the edge of this area to ensure coverage of all harmonic-sensitive frequency bands.

[0016] The electromagnetic sensing probe is connected to the vehicle-mounted central controller to realize real-time acquisition of electromagnetic radiation signal strength, and digitally process it according to the preset spectrum resolution to form a basic data structure for interference energy spectrum analysis, so as to improve the accuracy and response speed of interference coupling risk identification.

[0017] Preferably, a wiring harness electromagnetic compatibility (EMC) simulation is performed in the vehicle to identify key node areas susceptible to interference in the communication bus layout. The specific steps are as follows:

[0018] A 3D electromagnetic model was created that included the vehicle's main wiring harnesses, electrical modules, and metal structural components. The ambient light PWM control circuits, communication buses (such as CAN / LIN), and their relative wiring paths and topologies were then imported as basic inputs for the simulation model.

[0019] Set the typical frequency range and harmonic characteristics of the PWM drive source and inject it into the model as an interference source. Run domain or frequency domain analysis using electromagnetic field simulation software (such as CST, HFSS, or ANSYS EMC tools) to observe the distribution of induced voltage, current, or electric field strength in different areas of the communication harness, especially the areas where harmonic energy is concentrated along the propagation path between structures.

[0020] Combined with the simulation results, the common-mode current exceeding threshold area of ​​the communication bus is extracted, and the spatial locations where the electromagnetic field concentration continues at different frequencies are identified as key node areas susceptible to interference, providing a positioning basis for the subsequent perception probe deployment and filtering module design.

[0021] Preferably, the step of recording electromagnetic spectrum density change data includes:

[0022] Set multiple PWM modulation parameter combinations, including test matrices with different switching frequencies (e.g., 5kHz to 30kHz) and duty cycles (e.g., 10% to 90%), to simulate different dynamic brightness adjustment scenarios;

[0023] The ambient light is operated under each set of PWM parameter loading, and the electromagnetic sensing probe is simultaneously started to continuously collect the changes in electromagnetic signal intensity within the sensitive frequency band at a preset sampling rate. The collected data is then Fourier transformed to obtain the corresponding spectral power density.

[0024] The spectral density data is bound to the current PWM parameter combination according to the time axis to form a "parameter-spectral density" mapping relationship table, providing a time-labeled training sample set for subsequent interference modeling and spectral energy analysis.

[0025] Preferably, the step of extracting the interference energy spectrum includes:

[0026] The spectral density data collected under each set of PWM parameters is pre-processed to remove noise, and median filtering or spectral envelope sliding average is used to suppress environmental background fluctuations and improve the significance of interference characteristic signals.

[0027] For each set of spectrum data, the power density peak and its distribution form within the harmonic sensitive range of the communication system are extracted to construct a local interference energy distribution map;

[0028] By comparing the functional relationship between PWM parameter changes and local spectrum energy gain, the characteristic frequency points or energy bandwidths that are highly correlated with PWM switching frequency and duty cycle are extracted as interference energy spectrum vectors representing the interference intensity trend.

[0029] Preferably, the step of constructing a frequency mapping matrix of PWM frequency and vehicle communication bus harmonic interval based on the interference energy spectrum includes:

[0030] Based on the interference energy spectrum collected at different PWM switching frequencies and duty cycles, a corresponding harmonic frequency sequence on the frequency axis is established, including the PWM fundamental frequency and its integer multiples. This sequence is then mapped to the harmonic-sensitive range of the vehicle communication bus for comparison, forming a two-dimensional frequency cross-relationship matrix.

[0031] The power density value in the interference energy spectrum is extracted at the crossover frequency point, and the energy response of each frequency point is integrated using the frequency band weighting method to calculate the total spectral density energy in the overlapping frequency band to quantify the intensity distribution of the frequency coupling between PWM and communication bus.

[0032] The spectral density integration results corresponding to multiple PWM parameters are merged to form a multidimensional interference energy vector. This vector is sorted according to the PWM frequency sequence, reflecting the interference trend of the communication harmonic frequency band under different driving conditions, and serves as the core input feature for subsequent electromagnetic coupling strength evaluation and filtering control.

[0033] Preferably, the step of inputting the interference energy vector into the dynamic coupling normalization function and generating the electromagnetic coupling strength index includes:

[0034] The anti-interference margin threshold curve of the communication bus is preset. Based on the bus transceiver's common-mode / differential-mode interference resistance, bit error rate tolerance, and protocol protection mechanism, and combined with the communication system's calibrated noise background, an anti-interference margin function model for the communication system in different frequency ranges is established.

[0035] This provides a benchmark reference for subsequent quantitative evaluation, enabling a meaningful comparative analysis of the interference intensity of each frequency band and the system's tolerance.

[0036] The integral value of each frequency band in the constructed interference energy vector is calculated dimension by dimension with the anti-interference margin of the corresponding frequency band to generate the initial risk weight. The initial risk weight of each dimension is then constrained and normalized based on the background noise standard value.

[0037] The original interference energy is converted into a quantitative indicator of the burden on the communication system, providing comparability and amplitude control for the final unified risk score.

[0038] The processed multidimensional risk weights are input into a set of dynamic normalization functions, such as logic function mapping or adaptive fuzzy weight transformation, to output a continuous electromagnetic coupling strength index. The electromagnetic coupling strength index ranges from 0 to 1 and is used to represent the coupling interference risk level of the vehicle communication system under the current PWM drive parameters in real time. It also serves as the basis for determining the response adjustment of the filter module.

[0039] Preferably, when the electromagnetic coupling strength index exceeds a preset interference threshold, the filter bandwidth is dynamically adjusted according to the electromagnetic coupling strength. The specific steps are as follows:

[0040] Based on the calculated electromagnetic coupling strength index and the interference threshold set by the system, an exponential filter response excitation function is defined to describe the dynamic filter strength in each interference frequency band. The expression of the filter response excitation function is: ,in: It is an electromagnetic coupling strength indicator, reflecting the electromagnetic coupling interference strength between the current PWM drive and the communication bus; is the set interference threshold; is the integrated value of interference energy in the i-th frequency band; is the mean of the energy integration values ​​of all interference frequency bands, used for normalization; is the filter response excitation function (used to describe the dynamic filtering strength on each interference frequency band). The larger the value, the stronger the filtering intention on frequency band i.

[0041] An exponential shrinkage function is introduced to implement bandwidth compression in high response areas, and the filter response excitation function is used to calculate the adaptive bandwidth of the filter in each frequency band to achieve targeted filtering focusing. The calculation expression of the adaptive bandwidth is: ,in: For the The final filter bandwidth of each frequency band; The maximum allowed bandwidth designed for the system, representing the default filter width without compression; It is the bandwidth compression sensitivity coefficient, which controls the nonlinear degree of bandwidth adjustment of the filter response excitation function. The larger the value, the more significant the compression.

[0042] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0043] The present invention monitors the interference spectrum in real time by deploying electromagnetic sensing probes, establishes a frequency mapping relationship in combination with PWM modulation parameters, and accurately identifies the overlap between interference energy and communication sensitive frequency bands; then quantifies the coupling interference intensity through a dynamic normalization function to form a continuous and adjustable electromagnetic coupling strength index, and uses this to drive the active filtering module to accurately suppress the frequency band. Under the premise of not affecting the visual effect of the ambient light, it can effectively avoid electromagnetic interference to the CAN / LIN bus and prevent safety hazards such as communication errors, frame loss or interruption, thereby ensuring the stable operation of the vehicle control system and driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0045] Figure 1 The present invention is a method flow chart of a method for controlling an automobile atmosphere light. DETAILED DESCRIPTION

[0046] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0047] The present invention provides Figure 1 A method for controlling an automotive ambient light is shown, comprising the following steps:

[0048] Set the operating frequency range and harmonic sensitivity bandwidth of the vehicle communication bus, and deploy highly sensitive electromagnetic sensing probes at the edge of the communication bus wiring area to collect the electromagnetic radiation signal strength in the sensitive frequency band in real time;

[0049] The steps for setting the operating frequency range and harmonic sensitivity bandwidth of the vehicle communication bus and deploying electromagnetic sensing probes include:

[0050] Based on the protocol specifications and actual transmission rate of the in-vehicle communication system, determine the fundamental frequency and main harmonic distribution range of the target communication bus (including but not limited to CAN or LIN bus), and establish a set of frequency-sensitive bandwidth parameter sets for interference monitoring;

[0051] Conduct wiring harness electromagnetic compatibility (EMC) simulation or wiring hotspot analysis inside the vehicle to identify key node areas susceptible to interference in the communication bus layout. Deploy at least one highly sensitive electromagnetic sensing probe with wideband response capability at the edge of this area to ensure coverage of all harmonic-sensitive frequency bands.

[0052] The electromagnetic sensing probe is connected to the vehicle-mounted central controller to realize real-time acquisition of electromagnetic radiation signal strength, and digitally process it according to the preset spectrum resolution to form a basic data structure for interference energy spectrum analysis, so as to improve the accuracy and response speed of interference coupling risk identification.

[0053] Conduct electromagnetic compatibility (EMC) simulation of the wiring harness in the vehicle to identify key node areas in the communication bus layout that are susceptible to interference. The specific steps are as follows:

[0054] A 3D electromagnetic model was created that included the vehicle's main wiring harnesses, electrical modules, and metal structural components. The ambient light PWM control circuits, communication buses (such as CAN / LIN), and their relative wiring paths and topologies were then imported as basic inputs for the simulation model.

[0055] Set the typical frequency range and harmonic characteristics of the PWM drive source and inject it into the model as an interference source. Run domain or frequency domain analysis using electromagnetic field simulation software (such as CST, HFSS, or ANSYS EMC tools) to observe the distribution of induced voltage, current, or electric field strength in different areas of the communication harness, especially the areas where harmonic energy is concentrated along the propagation path between structures.

[0056] Combined with the simulation results, the common-mode current exceeding threshold area of ​​the communication bus is extracted, and the spatial locations where the electromagnetic field concentration continues at different frequencies are identified as key node areas susceptible to interference, providing a positioning basis for the subsequent perception probe deployment and filtering module design.

[0057] A common-mode current over-threshold region refers to a location in a communication bus or its vicinity where, during electromagnetic compatibility (EMC) simulation or testing, the common-mode current intensity exceeds the system's tolerance threshold. Common-mode current is current flowing in the same direction along two conductors (such as the twisted pair of a CAN bus) and referenced to a relative ground (such as the vehicle body ground). This current is typically not part of the communication signal itself, but rather is introduced through coupling paths by external electromagnetic interference (such as high-frequency noise generated by a PWM driver). When common-mode current accumulates in a certain area and exceeds the system's set "interference tolerance threshold" (such as the mA limit), an "over-threshold region" is formed. Such areas often harbor abnormally concentrated electromagnetic fields and are potential sources of communication errors, signal distortion, and even hardware damage. Therefore, identifying "common-mode current over-threshold regions" is crucial for optimizing cabling, shielding design, and filtering strategies, and is a key step in locating EMC weaknesses.

[0058] The core function of this step is to provide basic data support and spatial layout for electromagnetic interference monitoring and analysis in automotive ambient lighting control systems. Because onboard communication buses (such as CAN and LIN) carry out critical control and safety information transmission within the vehicle, their operating frequencies and harmonic sensitivity ranges are highly sensitive to the system's electromagnetic compatibility. The PWM dimming of ambient lighting, due to its high-frequency switching characteristics and spectral harmonic distribution, can easily interfere with these communication systems under certain conditions. Therefore, first defining the operating frequency range of the communication bus and the harmonic bandwidth that may cause coupled interference is a prerequisite for the entire interference identification and management mechanism. This determines the spectral boundaries and analysis dimensions for subsequent interference energy monitoring.

[0059] On this basis, highly sensitive electromagnetic sensing probes are deployed at the edge of the communication bus wiring area. These probes can capture electromagnetic radiation signals related to the PWM modulation process in real time without damaging the original wiring structure. This sensing method is more flexible and accurate than traditional fixed-frequency testing or signal injection testing, and is particularly suitable for electromagnetic environments with frequent dynamic lighting effects, complex wiring paths, and concurrent timing of different modules.

[0060] Furthermore, this perception system, acting as a "virtual test node," can be permanently embedded in a running vehicle, implementing a closed-loop data collection mechanism for interference trend monitoring and active control feedback. By acquiring real-time radiation intensity in sensitive frequency bands, it provides accurate and dynamic decision-making for subsequent interference identification, PWM modulation parameter adjustments, or filter mechanism activation, effectively preventing communication anomalies caused by electromagnetic coupling and ensuring the stability and safety of the entire vehicle system.

[0061] After the ambient light PWM control module is started, it performs dynamic brightness adjustment tasks under different PWM switching frequencies and duty cycles. It also records the electromagnetic spectrum density change data within the sensitive frequency band and extracts the interference energy spectrum related to the changes in PWM modulation parameters.

[0062] The steps for recording electromagnetic spectrum density change data include:

[0063] Set multiple PWM modulation parameter combinations, including test matrices with different switching frequencies (e.g., 5kHz to 30kHz) and duty cycles (e.g., 10% to 90%), to simulate different dynamic brightness adjustment scenarios;

[0064] The ambient light is operated under each set of PWM parameter loading, and the electromagnetic sensing probe is simultaneously started to continuously collect the changes in electromagnetic signal intensity within the sensitive frequency band at a preset sampling rate. The collected data is then Fourier transformed to obtain the corresponding spectral power density.

[0065] The spectral density data is bound to the current PWM parameter combination according to the time axis to form a "parameter-spectral density" mapping relationship table, providing a time-labeled training sample set for subsequent interference modeling and spectral energy analysis.

[0066] The steps of extracting the interference energy spectrum include:

[0067] The spectral density data collected under each set of PWM parameters is pre-processed to remove noise, and median filtering or spectral envelope sliding average is used to suppress environmental background fluctuations and improve the significance of interference characteristic signals.

[0068] For each set of spectrum data, the power density peak and its distribution form within the harmonic sensitive range of the communication system are extracted to construct a local interference energy distribution map;

[0069] By comparing the functional relationship between PWM parameter changes and local spectrum energy gain, the characteristic frequency points or energy bandwidths that are highly correlated with PWM switching frequency and duty cycle are extracted as interference energy spectrum vectors representing the interference intensity trend.

[0070] Extracting characteristic frequencies or energy bandwidths that are highly correlated with the PWM switching frequency and duty cycle requires multidimensional correlation mining based on collected electromagnetic spectrum density data, combined with signal processing and feature analysis methods. Specifically, the spectrum data for different PWM parameter combinations (i.e., different frequencies and duty cycles) is first normalized to eliminate background noise and environmental variables. Frequency domain tools such as Fourier transforms or wavelet analysis are then used to extract the dominant frequency components and their harmonic structure from the spectrum, focusing on identifying frequency regions with sudden power changes or concentrated peaks. Next, a one-to-one correspondence is established between each PWM parameter and the energy peak or concentrated energy bandwidth in the spectrum. Statistical analysis or correlation algorithms (such as the Pearson correlation coefficient and principal component analysis (PCA)) are used to quantify the sensitivity of the spectral response to PWM parameters. Finally, frequencies or frequency bands that exhibit significant responses under most parameter variations are selected and output as "characteristic frequencies" or "energy bandwidths." These frequency regions represent the core frequency bands where the PWM drive generates high-intensity coupling interference on the communication bus, providing targets for subsequent coupling intensity modeling and interference suppression.

[0071] This step aims to model the causal relationship between PWM drive parameters and the electromagnetic interference of the vehicle communication system and extract interference characteristics, forming a fundamental part of the entire anti-interference control strategy. When the ambient light PWM control module is activated, it performs dynamic brightness adjustment tasks under multiple switching frequency and duty cycle combinations. This not only simulates the lighting effect changes commonly seen in actual use scenarios, but also systematically scans the radiation characteristics of different PWM signals in the electromagnetic spectrum. Simultaneously, electromagnetic sensing probes deployed in sensitive areas of the communication bus collect spectrum data in real time, enabling high-precision capture of electromagnetic fluctuations caused by PWM changes in specific frequency bands (such as within the harmonic bandwidth of the CAN communication frequency).

[0072] The electromagnetic spectrum density data recorded during this process exhibits time correlation, spectral resolution, and parameter correspondence, allowing subsequent extraction of the interference energy spectrum—quantifying the electromagnetic energy in specific frequency bands generated under different PWM settings. By comparing and analyzing how these energy spectra vary with PWM frequency and duty cycle, a mathematical mapping relationship between PWM modulation parameters and interference intensity can be constructed, thereby clarifying which parameter combinations result in stronger interference coupling.

[0073] This step also provides the data foundation and experimental sample set for the subsequent construction of interference prediction models, generation of frequency mapping matrices, and identification of spectral coupling regions. This is a prerequisite for implementing advanced functions such as dynamic coupling strength assessment and adaptive filtering control. Therefore, this step not only serves as a data acquisition step but also facilitates behavioral modeling and system coupling mechanism learning, making it an indispensable and critical component of the overall anti-interference methodology.

[0074] Based on the interference energy spectrum, a frequency mapping matrix is ​​constructed between the PWM frequency and the harmonic interval of the vehicle communication bus. The overlapping area of ​​the spectrum energy is identified and the spectral density integral value in the overlapping area is calculated to form an interference energy vector reflecting the degree of spectrum coupling.

[0075] The steps of constructing a frequency mapping matrix between the PWM frequency and the harmonic interval of the vehicle communication bus based on the interference energy spectrum include:

[0076] Based on the interference energy spectrum collected at different PWM switching frequencies and duty cycles, a corresponding harmonic frequency sequence on the frequency axis is established, including the PWM fundamental frequency and its integer multiples. This sequence is then mapped to the harmonic-sensitive range of the vehicle communication bus for comparison, forming a two-dimensional frequency cross-relationship matrix.

[0077] The power density value in the interference energy spectrum is extracted at the crossover frequency point, and the energy response of each frequency point is integrated using the frequency band weighting method to calculate the total spectral density energy in the overlapping frequency band to quantify the intensity distribution of the frequency coupling between PWM and communication bus.

[0078] The spectral density integration results corresponding to multiple PWM parameters are merged to form a multidimensional interference energy vector. This vector is sorted according to the PWM frequency sequence, reflecting the interference trend of the communication harmonic frequency band under different driving conditions, and serves as the core input feature for subsequent electromagnetic coupling strength evaluation and filtering control.

[0079] This step aims to establish a spectral interference coupling model between the PWM dimming signal and the vehicle communication system, thereby quantitatively and structuredly evaluating the intensity and distribution of electromagnetic interference, providing a data foundation and judgment basis for subsequent intelligent filtering and control decisions. In automotive ambient lighting systems, the PWM drive signal generates the fundamental frequency and its multiple harmonic frequencies. These harmonic components are highly likely to overlap with the harmonic-sensitive regions of the vehicle communication bus (such as CAN and LIN), causing signal interference and even communication failures. By extracting the interference energy spectrum under different PWM parameters and mapping it with the frequency characteristics of the communication system, it is possible to identify the spectral overlap between the two and clearly identify the potential interference hotspot frequency bands.

[0080] On this basis, integrating the spectral density within these overlapping regions not only yields the total amount of interference energy but also constructs, through multiple scans, interference energy vectors corresponding to different PWM settings. This high-dimensional data structure accurately reflects the functional relationship between PWM modulation parameters and communication interference risk. The formation of this vector is an important input for subsequent dynamic electromagnetic coupling assessments, risk grading, and active filtering mechanism decisions, contributing to a systematic and adjustable anti-interference design. In other words, this step transforms qualitative problems into quantifiable and predictable mathematical models, enabling the automotive ambient lighting control system to more intelligently avoid interference risks on the communication bus and improve the electromagnetic compatibility and operational safety of the vehicle's electronic architecture.

[0081] The interference energy vector is input into the dynamic coupling normalization function. Based on the anti-interference margin of the communication bus and the reference background noise, it is normalized into an electromagnetic coupling strength index. The electromagnetic coupling strength index is a continuous value between 0 and 1, which is used to represent the coupling interference risk level between the current PWM drive and the communication bus.

[0082] The steps of inputting the interference energy vector into the dynamic coupling normalization function and generating the electromagnetic coupling strength index include:

[0083] The anti-interference margin threshold curve of the communication bus is preset. Based on the bus transceiver's common-mode / differential-mode interference resistance, bit error rate tolerance, and protocol protection mechanism, and combined with the communication system's calibrated noise background, an anti-interference margin function model for the communication system in different frequency ranges is established.

[0084] This step provides a benchmark reference for subsequent quantitative evaluation, enabling a meaningful comparative analysis of the interference intensity of each frequency band and the system's tolerance.

[0085] The integral value of each frequency band in the constructed interference energy vector is calculated dimension by dimension with the anti-interference margin of the corresponding frequency band to generate the initial risk weight. The initial risk weight of each dimension is then constrained and normalized based on the background noise standard value.

[0086] This step converts the original interference energy into a quantitative indicator of the burden on the communication system, providing comparability and amplitude control for the final unified risk score.

[0087] The processed multidimensional risk weights are input into a set of dynamic normalization functions, such as logic function mapping or adaptive fuzzy weight transformation, to output a continuous electromagnetic coupling strength index. The electromagnetic coupling strength index ranges from 0 to 1 and is used to represent the coupling interference risk level of the vehicle communication system under the current PWM drive parameters in real time. It also serves as the basis for determining the response adjustment of the filter module.

[0088] To realize the anti-interference margin threshold curve of the preset communication bus and establish the anti-interference margin function model under the frequency range, it is necessary to comprehensively consider the hardware physical anti-interference capability, the communication protocol fault tolerance mechanism and the actual operating background noise of the system. First, the anti-interference technical indicators of the transceiver chips used in the vehicle communication bus (such as CAN or LIN) are obtained, specifically their common-mode interference (CMI) and differential-mode interference (DMI) resistance curves at different frequencies. These parameters can be obtained from the chip data sheet or laboratory testing (such as IEC 62132). Second, based on the specifications of the communication protocol (such as CAN 2.0 and LIN 2.1), the maximum allowable bit error rate (BER threshold) of the system is set. In combination with the inherent error handling mechanisms such as CRC, ACK retransmission, and bit stuffing, the critical interference intensity at which the system can still maintain reliable communication under different interference levels is calculated. Third, the background noise spectrum of the vehicle during static and dynamic operation is collected, and its average power density in key frequency bands is extracted as the environmental noise baseline. Finally, the above three types of data are uniformly normalized according to the frequency dimension to form an anti-interference margin function model. This function outputs the maximum interference power density that the system can withstand at a certain frequency. This function serves as the judgment standard for subsequent electromagnetic coupling strength index normalization and filtering control, realizing dynamic quantification of the interference risk level.

[0089] Obtaining a background noise standard value typically relies on baseline measurements and statistical analysis of the electromagnetic environment of the vehicle's communication system during normal operation. This primarily aims to provide a reference threshold for subsequent interference identification and risk assessment, distinguishing between "normal background fluctuations" and "abnormal interference events." Specifically, this acquisition method involves the following steps: First, while the vehicle is not operating or in standby mode, a spectrum scan is performed around communication buses such as CAN and LIN, recording the electromagnetic signal strength across different frequency bands (particularly within the harmonic-sensitive bandwidth). Second, under typical vehicle operating conditions (such as idling, constant speed driving, and concurrent multi-module communication), electromagnetic radiation power is continuously collected across frequency bands. Parameters such as the mean, standard deviation, and peak value are extracted using multi-period, multi-sample statistics. Finally, based on EMC standards, industry recommendations, or internal OEM test specifications, a "background noise standard value" is set within the mean + 3σ or maximum value of the electromagnetic power density within a specific frequency band. This establishes a universally applicable reference threshold with a safety margin. This standard value can be dynamically adjusted to adapt to different vehicle models, electrical configurations, and climate environments, improving the accuracy and robustness of interference identification.

[0090] The processed multidimensional risk weights are input into an adaptive fuzzy weight transformation function, which outputs a continuous electromagnetic coupling strength index. This is achieved by constructing a nonlinear mapping model based on a fuzzy logic system. Specifically, the normalized risk weight corresponding to each frequency band is used as the fuzzy input variable, and its linguistic variables (such as "low risk," "medium risk," and "high risk") are defined. Membership functions (such as triangular or Gaussian functions) are set based on the anti-interference characteristics of the communication system and engineering experience rules to characterize the degree of fuzziness of different input values. Secondly, a set of fuzzy rules (such as "if the high-frequency band risk is high and the medium-frequency band risk is medium, then the total coupling strength is high") are designed to construct a fuzzy inference system. A dynamic adjustment factor is also introduced to enable the system to adjust the rule weights based on historical data, self-learning mechanisms, or preset scenarios, thus adapting to different electromagnetic interference patterns. Finally, the fuzzy output is defuzzified using a weighted average method or a center of gravity method to generate a continuous electromagnetic coupling strength index between 0 and 1, which reflects the risk level of interference to the communication system under the current PWM modulation conditions in real time. This method has good nonlinear processing capabilities and interpretability, making it suitable for complex and dynamic electromagnetic environment control needs.

[0091] This step transforms complex, dispersed, and high-dimensional electromagnetic interference information into a single, quantifiable, determinable, and controllable risk indicator—the electromagnetic coupling strength index. This is used to dynamically assess the interference risk level of the current PWM drive on vehicle communication systems (such as CAN and LIN) in real time, providing key input and control basis for subsequent active filtering strategies, adaptive frequency adjustment, and system alarm mechanisms. Because PWM modulated signals exhibit significant spectrum spread, their interference intensity fluctuates depending on factors such as frequency, duty cycle, drive topology, and circuit layout. Furthermore, interference behavior is often nonlinear and multi-dimensionally coupled. Traditional static threshold assessment methods are no longer sufficient to meet the real-time assurance requirements for communication stability and electromagnetic compatibility in modern vehicles.

[0092] This step establishes a mathematical relationship between the PWM modulation spectrum characteristics and the communication system's anti-interference capability by introducing a "dynamically coupled normalization function." Specifically, an anti-interference margin function model is constructed at different frequencies based on the communication bus transceiver's common-mode and differential-mode interference resistance, the protocol layer's bit error rate tolerance, and the system-level fault tolerance mechanism. The spectral density integral of each frequency band in the interference energy vector is then compared dimensionally with this margin model to form an initial risk weight reflecting the "burden" of interference in each frequency band. This is then combined with the vehicle's calibrated background noise standard value to perform amplitude suppression and normalization to eliminate the impact of environmental fluctuations on the results.

[0093] Through this process, the originally high-dimensional and complex interference vector is compressed into a continuous electromagnetic coupling strength indicator (ranging from 0 to 1), where 0 represents no interference or the system is fully tolerant, and 1 represents interference intensity exceeding the system limit, potentially triggering communication anomalies. This normalization process not only improves the real-time response and judgment clarity of the system, but also enhances the adjustability and adaptability of the overall control strategy. For example, the electromagnetic coupling strength indicator can directly serve as the basis for control operations such as filter bandwidth adjustment, PWM frequency avoidance, and bus priority reconfiguration. It can also be used for system logging, anomaly reporting, or adaptive learning model training. Therefore, this step is the core link in achieving closed-loop control of electromagnetic compatibility between the ambient lighting control system and the on-board communication system, and has high engineering practical value and potential for intelligent expansion.

[0094] When the electromagnetic coupling strength index exceeds the preset interference threshold, an active filtering module is inserted between the PWM control channel and the communication bus. The active filtering module includes an LC filtering circuit or an EMI shielding component and dynamically adjusts the filtering bandwidth according to the electromagnetic coupling strength to achieve effective filtering of the target interference frequency band.

[0095] When the electromagnetic coupling strength index exceeds the preset interference threshold, the filter bandwidth is dynamically adjusted according to the electromagnetic coupling strength. The specific steps are as follows:

[0096] Based on the calculated electromagnetic coupling strength index and the interference threshold set by the system, an exponential filter response excitation function is defined to describe the dynamic filter strength in each interference frequency band. The expression of the filter response excitation function is: ,in: It is an electromagnetic coupling strength indicator, reflecting the electromagnetic coupling interference strength between the current PWM drive and the communication bus; is the set interference threshold; is the integrated value of interference energy in the i-th frequency band; is the mean of the energy integration values ​​of all interference frequency bands, used for normalization; is the filter response excitation function (used to describe the dynamic filtering strength on each interference frequency band). The larger the value, the stronger the filtering intention on frequency band i.

[0097] The integrated value of the interference energy in the i-th frequency band can be obtained by integrating the spectrum power density curve collected by the electromagnetic sensing probe in the frequency band. The specific method is: first, within the set sampling time window, use the spectrum analysis algorithm (such as fast Fourier transform FFT) to convert the original time domain signal into a frequency domain signal to obtain the power spectrum density corresponding to each frequency point; then, set a bandwidth window around the frequency band i. , select all frequencies within the upper and lower limits of the frequency band and numerically integrate their corresponding power density values ​​(e.g., using trapezoidal integration or Simpson integration). The result of this integration is the total interference energy within the frequency band, reflecting the electromagnetic radiation intensity of this frequency range on the system under the current PWM drive conditions. This value not only quantifies the interference intensity near this frequency point but also provides key input for the subsequent construction of the interference energy vector, spectrum coupling model, and filtering adjustment strategy.

[0098] The purpose of this step is to dynamically determine the suppression capacity that the filter should invest in at different frequency points based on the coupling strength perceived by the current system and the degree of interference concentration in each frequency band in the spectrum, forming a priority distribution of targeted filtering responses and providing a parameter basis for the next step of bandwidth adjustment.

[0099] An exponential shrinkage function is introduced to implement bandwidth compression in high response areas, and the filter response excitation function is used to calculate the adaptive bandwidth of the filter in each frequency band to achieve targeted filtering focusing. The calculation expression of the adaptive bandwidth is: ,in: is the final filtering bandwidth of the i-th frequency band; The maximum allowed bandwidth designed for the system, representing the default filter width without compression; is the bandwidth compression sensitivity coefficient, which controls the nonlinearity of the filter response excitation function to the bandwidth adjustment. The larger the value, the more significant the compression.

[0100] This step precisely controls the filter's bandwidth configuration for each interference frequency, automatically narrowing the bandwidth and enhancing filter resolution in high-coupling-risk bands while retaining broadband signal paths in low-risk bands. This achieves a dynamic balance between efficient frequency-selective suppression and signal fidelity. This mechanism combines exponential weighting with nonlinear filtering control strategies, resulting in enhanced controllability, robustness, and engineering adaptability, making it suitable for automotive-grade anti-interference control systems.

[0101] This step aims to establish an intelligent electromagnetic suppression mechanism based on real-time interference sensing. When the system identifies a high risk of coupling interference from the current PWM control signal to the vehicle communication bus (such as CAN and LIN), it immediately activates the active filtering module and dynamically adjusts the filtering characteristics based on changes in the electromagnetic coupling strength index. This effectively and precisely suppresses critical interference frequency bands, ensuring the stability and security of the vehicle communication system. In automotive environments, PWM drives are widely used in lighting modules such as ambient lighting. Variations in their frequency and duty cycle generate multiple harmonic components in the electromagnetic spectrum. These harmonics can easily couple with sensitive frequency bands of the communication bus, causing interference, leading to communication errors, data frame loss, and even abnormal responses to critical control commands.

[0102] To this end, this step inserts an active filtering module with adjustable filtering capabilities between the PWM signal output path and the communication bus. For example, an LC filter circuit with variable capacitance adjustment or digital control capabilities, or an EMI suppression component that supports shielded conduction control, can quickly adjust its filter response parameters, such as the filter bandwidth, to suppress interference energy in a specific frequency band when the system senses an increase in interference risk. Specifically, this adjustment behavior uses the electromagnetic coupling strength indicator as an input variable to establish a dynamic coupling relationship between interference strength and filtering parameters. This allows the filter to automatically focus on the target frequency band under strong interference conditions and implement highly selective suppression; while maintaining a wider bandwidth in low-interference environments to avoid unnecessary distortion of the PWM signal waveform and lighting effects.

[0103] Furthermore, this dynamic filtering mechanism is adaptive and real-time, continuously responding to vehicle operating conditions, electromagnetic environment changes, and PWM parameter adjustments. This avoids the drawbacks of traditional fixed filtering structures, such as "under-filtering" or "over-filtering" under variable operating conditions. By directly linking the filtering function with the results of the electromagnetic coupling risk assessment, this step significantly improves the targetedness, flexibility, and system compatibility of the filtering behavior, enabling a coordinated solution to electromagnetic interference issues between comfort features such as ambient lighting and the onboard communication system, comprehensively improving the electromagnetic compatibility and operational reliability of the vehicle's electronic systems. This mechanism also provides a practical foundation and reference model for distributed interference control and automatically adjusted filtering networks in future smart cars.

[0104] The above scheme can realize an adaptive anti-interference control mechanism based on electromagnetic coupling perception and risk-driven control, significantly improving the electromagnetic compatibility between the automotive ambient lighting system and the on-board communication bus. Specifically, this method monitors the interference spectrum in real time by deploying electromagnetic sensing probes, establishes a frequency mapping relationship based on PWM modulation parameters, and accurately identifies the overlap between interference energy and communication-sensitive frequency bands. It then quantifies the coupling interference intensity through a dynamic normalization function to form a continuous and adjustable electromagnetic coupling intensity index, which is used to drive the active filtering module to accurately suppress the frequency band. Compared with traditional fixed-frequency, static filtering methods, this solution has the advantages of real-time perception, high-precision judgment, on-demand response, and dynamic adjustment. It can effectively avoid electromagnetic interference on the CAN / LIN bus without affecting the visual effect of the ambient lighting, and prevent safety hazards such as communication errors, frame loss, or interruptions, thereby ensuring the stable operation of the vehicle control system and driving safety.

[0105] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0106] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0107] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for controlling an automobile atmosphere light, characterized in that: The method includes the following steps: setting the operating frequency range and harmonic sensitivity bandwidth of the vehicle communication bus, and deploying highly sensitive electromagnetic sensing probes at the edge of the communication bus wiring area to collect the electromagnetic radiation signal strength within the sensitive frequency band in real time; after the ambient light PWM control module is started, dynamic brightness adjustment tasks are performed under different PWM switching frequencies and duty cycles, while recording the electromagnetic spectrum density change data within the sensitive frequency band and extracting the interference energy spectrum related to the changes in PWM modulation parameters; Based on the interference energy spectrum, a frequency mapping matrix is ​​constructed between the PWM frequency and the harmonic interval of the vehicle communication bus. The overlapping area of ​​the spectrum energy is identified and the spectral density integral value in the overlapping area is calculated to form an interference energy vector reflecting the degree of spectrum coupling. The processed multi-dimensional risk weights are input into a set of dynamic normalization functions to output a continuous electromagnetic coupling strength index. The electromagnetic coupling strength index ranges from 0 to 1 and is used to represent the coupling interference risk level of the vehicle communication system under the current PWM drive parameters in real time. When the electromagnetic coupling strength index exceeds the preset interference threshold, an active filtering module is inserted between the PWM control channel and the communication bus. The active filtering module includes an LC filtering circuit or an EMI shielding component, and dynamically adjusts the filtering bandwidth according to the electromagnetic coupling strength to achieve effective filtering of the target interference frequency band.

2. The method for controlling an automotive ambient light according to claim 1, wherein: The steps for setting the operating frequency range and harmonic sensitivity bandwidth of the vehicle communication bus and deploying electromagnetic sensing probes include: Based on the protocol specifications and actual transmission rate of the in-vehicle communication system, the fundamental frequency and harmonic distribution range of the target communication bus are determined, and a set of frequency-sensitive bandwidth parameters for interference monitoring is established; Conduct electromagnetic compatibility simulation of wiring harnesses inside the vehicle to identify key node areas susceptible to interference in the communication bus layout. Deploy at least one highly sensitive electromagnetic sensing probe with wide-band response capability at the edge of this key node area to ensure coverage of all harmonic-sensitive frequency bands. The electromagnetic sensing probe is connected to the vehicle-mounted central controller to collect electromagnetic radiation signal strength in real time, and digitally process it according to the preset spectrum resolution to form a basic data structure for interference energy spectrum analysis.

3. The method for controlling an automotive ambient light according to claim 2, wherein: Conducting electromagnetic compatibility simulation of wiring harnesses in a vehicle to identify key node areas in the communication bus layout that are susceptible to interference involves the following steps: A 3D electromagnetic model was built that included the vehicle's main wiring harnesses, electrical modules, and metal structural components. The ambient light PWM control circuit, communication bus, and their relative wiring paths and topology were then imported as basic inputs for the simulation model. Set the frequency range and harmonic characteristics of the PWM drive source and inject it into the model as an interference source. Run domain or frequency domain analysis using electromagnetic field simulation software to observe the distribution of induced voltage, current, or electric field strength in different areas of the communication harness. Combined with the simulation results, the common-mode current exceeding threshold area of ​​the communication bus is extracted, and the spatial locations where electromagnetic fields are continuously concentrated at different frequencies are identified as key node areas susceptible to interference.

4. The method for controlling an automotive ambient light according to claim 1, wherein: The steps for recording electromagnetic spectrum density change data include: Set multiple PWM modulation parameter combinations, including test matrices with different switching frequencies and duty cycles, to simulate different dynamic brightness adjustment scenarios; The ambient light is operated under each set of PWM parameter loading, and the electromagnetic sensing probe is simultaneously started to continuously collect the changes in electromagnetic signal intensity within the sensitive frequency band at a preset sampling rate. The collected data is then Fourier transformed to obtain the corresponding spectral power density. The spectral density data is bound to the current PWM parameter combination according to the time axis to form a parameter-spectral density mapping relationship table, which provides a time-series labeled training sample set for interference modeling and spectral energy analysis.

5. The method for controlling an automobile ambient light according to claim 1, wherein: The steps of extracting the interference energy spectrum include: The spectral density data collected under each set of PWM parameters are pre-processed for denoising, and median filtering is used to suppress environmental background fluctuations; For each set of spectrum data, the power density peak and its distribution form within the harmonic sensitive range of the communication bus system are extracted to construct a local interference energy distribution map; By comparing the functional relationship between PWM parameter changes and local spectrum energy gain, the characteristic frequency points or energy bandwidth related to PWM switching frequency and duty cycle are extracted as the interference energy spectrum vector representing the interference intensity trend.

6. The method for controlling an automobile ambient light according to claim 1, wherein: The steps of constructing a frequency mapping matrix between the PWM frequency and the harmonic interval of the vehicle communication bus based on the interference energy spectrum include: Based on the interference energy spectrum collected at different PWM switching frequencies and duty cycles, a corresponding harmonic frequency sequence on the frequency axis is established, including the PWM fundamental frequency and its integer multiples. This sequence is then mapped to the harmonic-sensitive range of the vehicle communication bus for comparison, forming a two-dimensional frequency cross-relationship matrix. The power density value in the interference energy spectrum is extracted at the crossover frequency point, and the energy response of each frequency point is integrated using the frequency band weighting method to calculate the total spectral density energy in the overlapping frequency band to quantify the intensity distribution of the frequency coupling between PWM and communication bus. The spectral density integration results corresponding to multiple PWM parameters are merged to form a multidimensional interference energy vector. The vector is sorted according to the PWM frequency sequence, reflecting the interference trend of the communication harmonic frequency band under different driving conditions, and serves as the core input feature for subsequent electromagnetic coupling strength evaluation and filtering control.

7. The method for controlling an automobile ambient light according to claim 6, wherein: The steps of inputting the interference energy vector into the dynamic coupling normalization function and generating the electromagnetic coupling strength index include: The anti-interference margin threshold curve of the communication bus is preset. Based on the bus transceiver's common-mode / differential-mode interference resistance, bit error rate tolerance, and protocol protection mechanism, and combined with the communication system's calibrated noise background, an anti-interference margin function model for the communication bus system in different frequency ranges is established. The integral value of each frequency band in the constructed interference energy vector is calculated dimension by dimension with the anti-interference margin of the corresponding frequency band to generate the initial risk weight. The initial risk weight of each dimension is then constrained and normalized based on the background noise standard value. The processed multi-dimensional risk weights are input into a set of dynamic normalization functions, and a continuous electromagnetic coupling strength index is output through the function. The electromagnetic coupling strength index ranges from 0 to 1 and is used to characterize the coupling interference risk level of the vehicle communication system under the current PWM drive parameters in real time.

8. The method for controlling an automotive ambient light according to claim 1, wherein: When the electromagnetic coupling strength index exceeds the preset interference threshold, the filter bandwidth is dynamically adjusted according to the electromagnetic coupling strength. The specific steps are as follows: Based on the calculated electromagnetic coupling strength index and the interference threshold set by the system, an exponential filter response excitation function is defined to describe the dynamic filter strength in each interference frequency band. The expression of the filter response excitation function is: ,in: It is an electromagnetic coupling strength indicator, reflecting the electromagnetic coupling interference strength between the current PWM drive and the communication bus; is the set interference threshold; is the integrated value of interference energy in the i-th frequency band; is the mean of the energy integration values ​​of all interference frequency bands, used for normalization; is the filter response excitation function; An exponential shrinkage function is introduced to implement bandwidth compression in high response areas, and the filter response excitation function is used to calculate the adaptive bandwidth of the filter in each frequency band. The calculation expression of the adaptive bandwidth is: ,in: For the The final filter bandwidth of each frequency band; is the maximum allowed bandwidth, representing the default filter width without compression; is the bandwidth compression sensitivity coefficient, which controls the nonlinearity of the filter response excitation function to bandwidth adjustment.

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