Intelligent switching control method and system for double-voltage automobile generator

Abstract

The invention discloses an intelligent switching control method and system for a double-voltage automobile generator, and relates to the technical field of automobile electrical control, and the method comprises the following steps: S001, constructing a frequency response baseline of an automobile power distribution network, and calibrating an inductance parameter of a wiring harness and a capacitance parameter of a filter based on frequency response measurement data; and generating an inherent frequency distribution diagram for reflecting the dynamic electrical characteristics. According to the method, the frequency response base line and the harmonic characteristic spectrum are constructed, so that accurate identification and dynamic prediction of the resonance risk are realized; avoiding the inherent frequency by adopting a multi-level control frequency planning strategy; virtual impedance and an active damping mechanism are introduced to improve the switching stability; and closed-loop optimization control is constructed in combination with residual signal feedback, and continuous convergence of the control performance is realized. According to the overall scheme, the stability, robustness and electromagnetic compatibility of the voltage switching process are improved, and safe operation of the double-voltage architecture under complex working conditions is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile electrical control, in particular to a dual-voltage automobile generator intelligent switching control method and system. BACKGROUND

[0002] Dual-voltage automobile generator intelligent switching control refers to when the automobile generator has two different voltage output modes (such as traditional 12-volt low-voltage system and 48-volt high-voltage system), through embedded control strategy and power electronic switching unit, real-time judgment is made on different power demand and operating conditions, and dynamic, smooth and seamless automatic switching is realized between the two voltage outputs. The control not only considers the transient changes of the vehicle electrical load (such as start-stop system, air conditioner compressor, electrically driven auxiliary devices, etc.), but also takes into account the battery charging efficiency, energy distribution balance and the stability of the generator itself. Through intelligent switching, low-voltage power supply can be preferentially maintained during low-power operation of the vehicle to ensure basic functions, and high-voltage output can be switched to in high-power demand or energy-saving mode to improve energy utilization and reduce losses, thereby realizing efficient, safe and intelligent operation of the vehicle electrical system.

[0003] The prior art has the following disadvantages:

[0004] In the prior art, the control strategy of the dual-voltage automobile generator during the intelligent switching process usually relies on fixed frequency or limited bandwidth adjustment. However, when the control frequency in the switching process enters the inherent frequency range of the vehicle electrical system, the harmonic accumulation effect of the electrical network is easily triggered, causing the local disturbance signal to be amplified by several times inside the system. Such harmonic energy is continuously accumulated in the oscillation loop formed by the conductor, inductor and distributed capacitance, and eventually may evolve into global oscillation of the vehicle power network. Such oscillation not only causes voltage fluctuation to exceed the tolerance range of the key control unit, but also may cause abnormal battery charging and discharging, misoperation of the driving actuator and transient failure of the vehicle communication link, and in severe cases, may cause overall instability of the vehicle electrical system, bringing high safety risks.

[0005] The above information disclosed in the background section is only used to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The purpose of the present application is to provide a dual-voltage automobile generator intelligent switching control method and system to solve the problems in the background.

[0007] In order to achieve the above-mentioned purpose, the present application provides the following technical solution: a dual-voltage automobile generator intelligent switching control method, comprising the following steps:

[0008] S001, constructing a frequency response baseline of the automobile power distribution network, based on frequency response measurement data, calibrating inductance parameters of the wire harness and capacitance parameters of the filter, and generating an inherent frequency distribution diagram for reflecting dynamic electrical characteristics;

[0009] S002, under the reference of the obtained inherent frequency distribution diagram, performing a sweep excitation test and a load step response test, obtaining voltage response curves under different excitation conditions, extracting a resonance peak frequency and a half-power bandwidth parameter therefrom, and generating a harmonic accumulation feature map for characterizing resonance behavior;

[0010] S003, constructing a control frequency prediction model using the harmonic accumulation feature map, taking environmental temperature, wire harness length, and load power as input parameters, outputting a frequency interval at risk of resonance, and forming a resonance risk warning window;

[0011] S004, performing control frequency planning through the resonance risk warning window, implementing a spread spectrum control strategy to avoid high-risk frequency intervals, implementing a phase scheduling strategy to reduce the risk of multi-channel synchronous harmonic superposition, and implementing a voltage slope limiting strategy to suppress high-frequency electromagnetic disturbances caused by high-speed switching;

[0012] S005, constructing an active damping control network according to the control frequency planning result, introducing a virtual impedance into a voltage switching control loop, dynamically adjusting output impedance parameters of a voltage regulator, correcting loop gain characteristics, improving phase margin during dynamic switching, and suppressing residual oscillation signals;

[0013] S006, during the operation of the active damping control network, monitoring residual signals and control effectiveness indicators during control, feeding the monitoring data back to the frequency response baseline, dynamically updating resonance risk thresholds, control frequency planning parameters, and virtual impedance parameters, and constructing a stable closed-loop control mechanism capable of continuous convergence.

[0014] Preferably, step S001 comprises:

[0015] The vehicle electrical structure is physically analyzed to clearly identify multiple power supply paths from the generator output end to the vehicle-mounted battery, high-power loads, and low-power loads. The wire length, cross-sectional area, material, shielding structure, connector type, grounding method, and end load parameters are collected and numbered and registered one by one.

[0016] High-precision voltage and current sampling interfaces are arranged at both ends of each power supply path, and electromagnetic shielding protection structures are built to establish a data acquisition infrastructure that can be used for dynamic testing;

[0017] A sinusoidal signal was injected in the range of 10 Hz to 50 kHz using a frequency step excitation method. Multiple rounds of response tests were conducted under three working conditions: static power-on, engine start-up, and high vehicle load. Input and output voltage waveform data were collected.

[0018] Based on the sampled data, the amplitude frequency and phase frequency response curves are calculated respectively, the resonance peak frequency and bandwidth are extracted, the equivalent inductance and filter capacitance parameters corresponding to the conductor structure are deduced, and a complete frequency response distribution diagram is drawn to form a basic dataset of dynamic characteristics for subsequent experimental reference.

[0019] Preferably, step S002 includes:

[0020] Based on the frequency range with obvious peaks or phase abrupt changes in the frequency response distribution diagram, the frequency sweep excitation range and step interval are set, a sinusoidal excitation signal is injected using an adjustable frequency signal generator, and the voltage response waveforms at the input and output terminals are acquired simultaneously.

[0021] Perform a load step response test, rapidly switch current levels under programmable electronic load control, collect transient voltage response characteristics, and repeat the test under no-load, medium load and full load conditions respectively;

[0022] The amplitude ratio and phase difference of the response data of each power supply path are calculated, the resonant peak frequency and half-power bandwidth are extracted, the oscillation period and decay time are identified, and the resonant characteristic parameter set is extracted.

[0023] Based on the power supply path number, the resonant peak frequency, bandwidth, oscillation period, and response amplitude are integrated to draw a harmonic aggregation characteristic spectrum, and the resonant risk level and corresponding load type are marked as input for subsequent frequency control planning steps.

[0024] Preferably, step S003 includes:

[0025] Based on the resonant frequency, bandwidth, response amplitude, and phase jump parameters recorded in the harmonic aggregation characteristic spectrum, combined with the ambient temperature, harness length, and load power during acquisition, a structured response dataset is established and numbered and labeled.

[0026] Quantitative analysis was conducted on the changing trends of each variable and resonance characteristics. A table of response trends of variable changes to frequency shift, bandwidth expansion and amplitude changes was compiled, and a parameter response comparison model corresponding to the path number was established.

[0027] The temperature, wiring harness length and load power obtained during the operation of the vehicle are used as input parameters, substituted into the variable response comparison model, and the resonant peak frequency and corresponding bandwidth are output to determine whether a high-risk resonant frequency range is constituted.

[0028] Based on the frequency center, risk level, half-power bandwidth, and response amplitude, the system outputs resonance risk warning window information, which serves as the input for subsequent frequency control planning. It also generates path numbers and timestamps for traceability and recording.

[0029] Preferably, step S004 includes:

[0030] Read the power supply path number, resonance frequency start point, end point, center frequency and bandwidth range recorded in the resonance risk warning window, remove the corresponding frequency band from the control frequency range, and select multiple frequency points in the remaining frequency band for periodic alternating control to avoid harmonic accumulation caused by frequency dwelling phenomenon;

[0031] When there is a need for parallel switching of multiple paths, a phase scheduling strategy is implemented. By setting the trigger delay and frequency priority of control commands, peak control is achieved in the time and frequency dimensions of different paths, reducing the risk of harmonic crossover.

[0032] A voltage slope limiting strategy is implemented, which outputs level commands in segments during the voltage rise and fall process to limit the voltage change rate within a preset threshold, avoids triggering spikes, electromagnetic interference and loop oscillations, and ensures that the voltage change rate is consistent between concurrent paths, thus suppressing common-mode interference.

[0033] Preferably, step S005 includes:

[0034] Based on the control frequency planning results, the frequency distribution range of the current control path during the voltage switching process is confirmed, the frequency segment where residual high-frequency oscillation signals exist is located, and the frequency range where damping needs to be introduced is determined.

[0035] A damping structure consisting of a power bridge circuit, a differential amplifier, and a bandpass filter is connected between the output of the voltage regulator and the input of the load. The bridge arm is triggered to conduct based on the high-frequency components of the voltage, and an equivalent virtual impedance is formed by introducing a resistive load path.

[0036] After the damping structure is introduced, a unit step voltage signal is applied, the open-loop gain characteristics are measured, and the trigger threshold and load parameters are adjusted so that the gain drops by more than 5 dB in the resonant frequency band and the phase margin is increased to more than 40 degrees.

[0037] Voltage switching response waveforms were collected under different load conditions. The oscillation period, peak voltage and duration before and after the introduction of the damping structure were compared to verify its ability to absorb high-frequency disturbance signals.

[0038] The operating parameters of the damping structure are stored using path number, load power, and control frequency as indexes to form a structured configuration group, enabling rapid recall and deployment in repetitive path control scenarios.

[0039] Preferably, step S006 includes:

[0040] During the operation of the active damping structure, the target output voltage, actual output voltage, current change rate and power fluctuation signals are continuously collected with a sampling period of 1 microsecond, and the residual signal waveform is recorded in real time to form a multi-dimensional observation window;

[0041] The acquired residual signals are subjected to frequency deconstruction and feature matching. Combined with the frequency response baseline comparison, the presence of new resonance frequency points or risk frequency range drift is identified, and the frequency risk label of the current path is updated.

[0042] The control frequency planning strategy is adjusted based on the newly added frequency response information, the frequency hopping sequence and frequency interval are reconfigured, and the virtual impedance parameters, including equivalent resistance, response delay and absorption time, are dynamically corrected.

[0043] The updated frequency response parameters, frequency planning sequence, and virtual impedance configuration are solidified into a standard operating configuration unit and written into the frequency response baseline database in real time to build a steady-state closed-loop control mechanism with gradual convergence capability.

[0044] A dual-voltage automotive generator intelligent switching control system includes a frequency response modeling module, a resonance feature identification module, a resonance risk prediction module, a switching frequency control module, an active damping control module, and a closed-loop adaptive optimization module.

[0045] The frequency response modeling module constructs the frequency response baseline of the automotive power distribution network, calibrates the inductance parameters of the wiring harness and the capacitance parameters of the filter based on the frequency response measurement data, and generates an inherent frequency distribution map that reflects the dynamic electrical characteristics.

[0046] The resonance feature identification module, with reference to the obtained inherent frequency distribution map, performs a frequency sweep excitation test and a load step response test to obtain voltage response curves under different excitation conditions. It then extracts the resonance peak frequency and half-power bandwidth parameters from these curves to generate a harmonic aggregation feature spectrum for characterizing resonance behavior.

[0047] The resonance risk prediction module uses harmonic aggregation feature spectrum to build a control frequency prediction model. It takes ambient temperature, harness length and load power as input parameters and outputs the frequency range where resonance risk exists, forming a resonance risk early warning window.

[0048] The switching frequency control module executes control frequency planning through the resonance risk warning window, implements spread spectrum control strategy to avoid high-risk frequency ranges, implements phase scheduling strategy to reduce the risk of multi-channel synchronous harmonic superposition, and implements voltage slope limiting strategy to suppress high-frequency electromagnetic disturbances caused by high-speed switching.

[0049] The active damping control module constructs an active damping control network based on the control frequency planning results, introduces virtual impedance into the voltage switching control loop, dynamically adjusts the output impedance parameters of the voltage regulator, corrects the loop gain characteristics, improves the phase margin during dynamic switching, and suppresses residual oscillation signals.

[0050] The closed-loop adaptive optimization module monitors the residual signal and control performance index during the operation of the active damping control network, feeds the monitoring data back to the frequency response baseline, dynamically updates the resonance risk threshold, control frequency planning parameters and virtual impedance parameters, and constructs a steady-state closed-loop control mechanism that can continuously converge.

[0051] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0052] This invention achieves accurate identification and dynamic prediction of resonance risk by constructing a frequency response baseline and harmonic characteristic spectrum; it avoids exciting the system's inherent frequencies and reduces harmonic superposition effects through a multi-level control frequency planning strategy; further, it improves the stability margin of the voltage regulation loop by introducing an adaptive virtual impedance structure and active damping control mechanism; and finally, it achieves continuous optimization and stable convergence of control performance by combining residual signal feedback and parameter closed-loop update mechanisms. The overall solution achieves synergistic improvements in dynamic control accuracy, electrical system robustness, and electromagnetic compatibility performance, providing technical support for the safe operation of dual-voltage electrical architectures under multiple operating conditions and strong disturbance scenarios. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0054] Figure 1 This is a flowchart of a dual-voltage automotive generator intelligent switching control method according to the present invention.

[0055] Figure 2 This is a schematic diagram of a module of a dual-voltage automotive generator intelligent switching control system according to the present invention. Detailed Implementation

[0056] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0057] This invention provides, for example Figure 1 The intelligent switching control method for a dual-voltage automotive generator, as shown, includes the following steps:

[0058] S001, Construct a frequency response baseline for the automotive power distribution network, calibrate the inductance parameters of the wiring harness and the capacitance parameters of the filter based on the frequency response measurement data, and generate an inherent frequency distribution map that reflects the dynamic electrical characteristics.

[0059] To ensure smooth and undisturbed voltage switching of dual-voltage automotive generators during dynamic operation, a frequency response baseline of the power distribution network needs to be established beforehand. This baseline reveals the voltage response characteristics under different wiring harness structures and load conditions. The specific steps for constructing the frequency response baseline are as follows:

[0060] Conduct a physical analysis of the vehicle's electrical structure to clarify the composition and distribution of various power supply lines. Specifically, this includes the main power supply path from the generator output in the engine compartment to the positive terminal of the vehicle battery; the high-load path from the generator output to high-power components such as the vehicle's AC compressor, electric power steering motor, and electric water pump; and the low-load path from the generator to low-power components such as instruments, central control, and lighting. Each of these paths needs to be measured on-site to determine the conductor length, cross-sectional area, material (e.g., copper or aluminum), shielding type (e.g., single-layer shielding, double-layer shielding, or no shielding), connector type (e.g., plug-in, crimp-in), grounding method (single-point grounding, multi-point grounding), and specific parameters of the end load (rated voltage, current, and input impedance). All paths should be numbered and registered, and voltage and current sampling interfaces should be installed at both ends. High-precision sampling probes with a phase accuracy of no less than 1° and a voltage measurement error of less than ±0.1% should be selected, and an electromagnetic shielding layer should be pre-installed to reduce operating interference and ensure the integrity and accuracy of the collected signals. This step aims to construct a physically observable and accessible measurement structure to support subsequent dynamic experiments.

[0061] To quantify the electrical response characteristics of the circuit, a frequency excitation experiment was conducted on the aforementioned measurement structure. The experiment employed a frequency step excitation method, using an adjustable frequency AC signal generator to sequentially output sinusoidal signals between 10Hz and 50kHz, continuously scanning in 1Hz steps. The signal was injected into the selected wiring harness through an isolation amplifier circuit, with the signal amplitude stably controlled between 1V and 2V to ensure that it would not trigger load startup and would excite the circuit's frequency response characteristics without interfering with the normal operation of the vehicle control unit. Simultaneously, during each excitation process, the input voltage signal and output response voltage signal were synchronously recorded using a high-speed data acquisition device (sampling rate not less than 1MS / s), with a data recording duration of at least 5 seconds to capture any potential delay response and low-frequency residual vibrations. During data recording, to avoid temperature drift affecting the stability of electrical parameters, the experiment was conducted in a closed, constant-temperature test environment (temperature controlled at 25±2℃). Each circuit was repeatedly tested under the following three typical operating conditions: 1) vehicle stationary but energized state; 2) engine-started operation with load; 3) high-speed vehicle operation accompanied by concurrent startup of a high-power load. The frequency response data under the three states will be grouped and labeled, and bound to the corresponding operating conditions to facilitate subsequent data analysis and modeling.

[0062] The collected voltage response curve data were processed and analyzed to derive the effective values ​​of key electrical parameters for each power supply line. During the analysis, the amplitude ratio and phase difference of the input and output voltages at various frequency points were calculated point-by-point to construct amplitude-frequency response curves and phase-frequency response curves. By identifying the location of the resonant peaks in the curves and combining the resonant frequency and damping ratio formulas, the equivalent inductance value of the line under the current structure was deduced. Then, based on the resonant frequency and response bandwidth, the equivalent capacitance value of the filter circuit was derived. To eliminate the interference of the conductor's own resistance on parameter fitting, corrections were made using DC resistance measurement data to improve the accuracy of parameter extraction. Throughout the process, physical modeling and experimental data comparison were used for verification. For example, the derived equivalent inductance and capacitance values ​​were input into a known first-order series resonant circuit simulation model, and the consistency between the simulated response curve and the actual response curve was compared. If the amplitude deviation was less than 5% and the phase deviation was less than 3°, the parameter modeling was considered effective. Compared with the existing technology that only sets nominal parameters by looking up tables based on wire material and length, this method can reveal complex behaviors such as coupling capacitance and inductance resonance superposition, thus more accurately reflecting the electrical characteristics under real working conditions.

[0063] Based on the inductance and capacitance parameters of each line, a frequency response distribution map is plotted to reflect the natural response behavior of each power supply line in the vehicle across different frequency ranges. This distribution map plots frequency on the x-axis and response amplitude on the y-axis, showing the complete amplitude-frequency response curve for each calibrated path, and marking the resonant frequency, the bandwidth corresponding to the half-power point, the phase transition point, and the peak amplitude position. Furthermore, a color-coded layering method is used to overlay the frequency response distributions of multiple paths, forming a visualized resonance risk map. For cases where multiple paths exhibit amplified responses within the same frequency band, this frequency band should be specifically marked as a high-risk resonance frequency area for reference in subsequent frequency avoidance planning. In addition, this distribution map serves as data input, providing the basis for subsequent harmonic aggregation feature identification, resonance risk prediction, and voltage switching frequency scheduling strategies. This allows for a closed-loop completion of the dynamic modeling process of the vehicle's electrical behavior, significantly improving the predictive ability and vibration suppression capability for switching stability under various operating scenarios.

[0064] This step serves to provide a precise and quantifiable electrical characteristic foundation for the subsequent intelligent switching control of dual-voltage automotive generators. By constructing a frequency response baseline for the automotive power distribution network, the natural response behavior of each power supply line under different frequency excitations can be identified, and key parameters such as equivalent inductance, filter capacitance, resonant frequency, and bandwidth can be extracted. This process not only reveals the composite resonance characteristics caused by the coupling between the wiring harness structure and the load, but also models the dynamic electrical characteristics using measured data rather than nominal parameters, thus overcoming the problem of insufficient reflection of actual operating conditions by traditional static modeling. The resulting frequency response distribution map not only visually identifies the frequency ranges that may cause resonance amplification effects, but also provides input for frequency avoidance, phase scheduling, and active damping adjustment in subsequent control strategies. As the foundation of the entire control method, this step establishes a mapping relationship from physical structure to dynamic response characteristics, which is an important prerequisite for ensuring stability and safety during voltage switching, and also significantly improves the predictability and engineering adaptability of the vehicle's electrical control.

[0065] S002, with reference to the obtained natural frequency distribution map, perform frequency sweep excitation test and load step response test to obtain voltage response curves under different excitation conditions, extract the resonant peak frequency and half-power bandwidth parameters from them, and generate a harmonic aggregation characteristic spectrum to characterize the resonant behavior.

[0066] Based on the established inherent frequency distribution map of the automotive power distribution network, in order to accurately identify voltage switching frequency bands with resonance risk and further clarify the differences in electrical response under different operating conditions, it is necessary to carry out frequency response characteristic enhancement identification work. Specifically, this includes collecting voltage response curves under different excitation methods through frequency sweep excitation tests and load step response tests, then extracting the resonance peak frequency and half-power bandwidth parameters to generate a harmonic aggregation characteristic spectrum to characterize the resonance features of the frequency response. The specific steps are as follows:

[0067] Based on the inherent frequency distribution diagram drawn in the previous stage, frequency ranges with obvious peaks or phase abrupt changes in the amplitude-frequency curve are selected as the focus of the experiment. The range containing high-amplitude response changes is used as the target frequency band, and the experimental excitation frequency range is set. For example, if a power supply path is observed to exhibit continuous amplitude increases in the 3600 Hz to 4200 Hz range in the frequency distribution diagram, the frequency sweep excitation range should be set to 3000 Hz to 5000 Hz to include margins before and after, ensuring complete capture of the resonance characteristics. A 1-volt sine wave signal is generated using a frequency-adjustable signal generator and injected into the input terminal of the power supply path under test through an isolation transformer and voltage injection circuit. A voltage acquisition device is connected to the output terminal, and the voltage at the injection point is measured simultaneously to achieve a relative comparison between the input and output. The entire frequency sweep is performed in a step-by-step manner, with a step interval of 100 Hz. During the sweep, the excitation time at each frequency point is maintained at no less than 2 seconds to ensure that the response reaches a steady state and the waveform is completely acquired. High-speed data acquisition devices with a sampling rate of 1 MHz or higher are used to record the input voltage waveform and output voltage waveform at each frequency point to ensure that high-frequency interference characteristics and high-order harmonic components can be captured.

[0068] Based on the completed frequency sweep excitation test, a load step response test was conducted to simulate the impact of instantaneous load changes on the voltage response of the power supply line during actual operation. A programmable electronic load with constant current absorption capability was selected, and its switching between different current levels was controlled by an external control signal. Starting with an initial current of 3 amps, the load switched to 6 amps within 0.5 milliseconds to simulate the instantaneous load change when a high-power component, such as an electric compressor, starts. During the switching process, voltage and current waveforms were simultaneously acquired to observe whether nonlinear phenomena such as oscillations, spikes, or overshoots occurred in the voltage response. To ensure data comprehensiveness, the step test was repeated under three different power supply conditions: no-load operation, stable operation under medium load, and full-load operation. The same step excitation curve was used for each condition, and the sampling time after each step trigger was no less than 5 seconds, with the sampling frequency remaining consistent with the frequency sweep test. The acquired waveform data was completely saved in time sequence for subsequent response feature extraction.

[0069] After acquiring all raw voltage data from the frequency sweep excitation and load step response, each set of data is processed uniformly. Using the power supply path number as an index, the voltage response amplitude variation and phase delay at the corresponding frequency are analyzed group by group. The maximum response amplitude and its corresponding frequency point for each path during the experiment are extracted as the resonant peak frequency of that path. Further, by finding the frequency points corresponding to the amplitude dropping to 70.7% of the peak value on both sides of the resonant peak frequency, the upper and lower boundary frequencies of the half-power point are determined, and the bandwidth is calculated. The resonant peak frequency and bandwidth are paired to form the resonant characteristic parameter set for that path. In addition, for the oscillating behavior occurring during the load step response, its oscillation amplitude, oscillation period, and decay time are statistically analyzed, and the oscillation frequency is cross-validated with the resonant frequency to identify consistency, further enhancing the accuracy of resonance determination. Compared with existing technologies that rely solely on the frequency sweep response to determine the resonant frequency, this step combines excitation-type and response-type data sources, possessing higher robustness and adaptability, and effectively identifying local resonance phenomena caused by nonlinear characteristics.

[0070] All extracted response features, including resonant peak frequency, half-power bandwidth, oscillation decay period, and phase jump position, are summarized and categorized by power supply path number to construct a harmonic aggregation feature map. The horizontal axis of this map represents the frequency range, and the vertical axis represents the response amplitude. Different colors are used to indicate the magnitude of the resonant amplitude for different power supply paths, and resonant regions are highlighted. For cases where multiple paths exhibit overlapping resonant features within the same frequency region, these features are overlaid and statistically analyzed, with frequency aggregation density representing the potential resonance risk level. Furthermore, the map must also indicate the main power supply load type corresponding to each frequency band; for example, indicating that the resonance at 3800 Hz is caused by the high-voltage electric compressor path, and the resonance at 4200 Hz is caused by the LED lighting power supply path. The final harmonic aggregation map not only visually displays the resonance characteristics of different power supply paths but also provides direct input for subsequent frequency planning, spread spectrum scheduling, soft-start limiting, and other control strategies. Thus, the quantitative modeling of resonance features based on measured data has been completed, and a reference basis for accurately identifying frequency risk areas has been constructed. This significantly improves the predictability and safety redundancy of voltage switching control under complex operating conditions, and effectively overcomes the problem of response control instability caused by the lack of a multi-condition resonance identification mechanism in existing technologies.

[0071] The purpose of this step is to, based on the established frequency response baseline of the power distribution network, deeply reveal the dynamic voltage response characteristics of the vehicle's power supply path under different excitation conditions through frequency sweep excitation tests and load step response tests. This allows for the accurate extraction of parameters closely related to resonance behavior, including key indicators such as resonance peak frequency, half-power bandwidth, oscillation duration, and phase abrupt change points. This type of response analysis based on measured data not only identifies hidden resonance sections that are difficult to detect under steady-state conditions but also reflects the differences in the dynamic stability of the electrical structure under different load conditions. Finally, through summary analysis, these characteristic parameters are visualized as harmonic aggregation feature maps, enabling precise location and classification of risky frequency bands in the frequency response. This provides quantifiable and traceable input for subsequent frequency avoidance and dynamic control strategies. Compared to existing methods that rely solely on theoretical models or single-band testing, this step comprehensively integrates both excitation and response testing methods, exhibiting stronger robustness and adaptability, and is a crucial foundation for achieving high-reliability voltage switching control.

[0072] S003 uses harmonic aggregation feature spectrum to construct control frequency prediction model, takes ambient temperature, wire harness length and load power as input parameters, outputs frequency range with resonance risk, and forms resonance risk early warning window;

[0073] To identify and avoid resonance risks during voltage switching under different operating conditions, based on the construction of harmonic aggregation characteristic maps, a method needs to be established to predict the frequency range of resonance risks based on changes in ambient temperature, harness length, and load power. This will enable real-time and adaptive frequency warning capabilities. The specific steps include:

[0074] All resonance parameters in the previously constructed harmonic aggregation feature spectrum were comprehensively organized, and a structured dataset was established. This spectrum clearly identifies the resonance amplitude, phase transition range, half-power bandwidth, and response duration at specific frequency points for different power supply paths. Based on this, each set of data was correlated with the external environmental conditions at the time, specifically including the controlled ambient temperature in the test chamber (accurate to ±0.5 degrees Celsius during data acquisition), the actual physical length of the measured power supply path (measured precisely to 10 mm using a tape measure), and the instantaneous power of the connected load during the test (recorded in 0.1 watt units using a power meter). Each set of test data was labeled with a number; for example, path number A1, temperature 25 degrees Celsius, length 1500 mm, load power 60 watts, resonant frequency 3870 Hz, bandwidth 240 Hz, and amplitude enhancement ratio 2.8 times. A complete data table was established in this manner, providing real, quantitative, and traceable basic data support for the subsequent construction of prediction models.

[0075] After organizing the data structure, the changing trends between each variable and the resonant response were analyzed to establish the correspondence between the dependent variable and the resonant parameters. Taking the harness length variable as an example, the resonant frequencies of the same structural path at lengths of 800 mm, 1000 mm, 1200 mm, and 1500 mm were compared to extract the specific impact values ​​of length changes on the resonant frequency offset direction, offset amount, and bandwidth expansion trend. Again, taking ambient temperature as an example, the resonant peak response of the same path at 25°C, 35°C, and 45°C was analyzed, recording whether the resonant bandwidth expanded and whether the peak amplitude increased or decreased under high-temperature conditions. Regarding the impact of load power, a stepped power input method was used, increasing from 10 watts to 100 watts, recording the changes in resonant amplitude and whether the resonant frequency shifted forward or backward at each power level. For each type of parameter, a repeatable comparison method was used to organize the results into a response trend table, and categorized according to the actual power supply path number, so that each type of path has an independent variable-response comparison model. This process differs from existing methods that rely on theoretical equivalence modeling. Instead, it uses objective response curves formed from measured data to achieve a refined quantitative expression of the impact of variable changes.

[0076] After constructing the variable impact model, it is applied to the prediction task under operating conditions. Specifically, before the vehicle is prepared for dual-voltage switching, the vehicle's temperature data is first acquired through environmental sensors, for example, reading an interior temperature of 38 degrees Celsius. Then, the length of the current power path harness is confirmed by consulting manufacturing configuration information or through sensor networks, for example, 1400 mm. Next, the power consumption of the actual operating load is read, for example, the current power supply load power is 75 watts. These three parameters are then substituted into the established variable response comparison model, and the response data set closest to this temperature, length, and power combination is retrieved. The corresponding resonant frequency position is found through data comparison; for example, the resonant frequency under the current conditions is determined to be 3920 Hz, and the bandwidth is 260 Hz. Based on the safety threshold settings in the established dataset, if the maximum amplitude within this bandwidth increases by more than 2.5 times and the response duration is greater than 10 milliseconds, this frequency band is identified as a high-risk frequency range. In this example, the risk frequency window range is defined as 3790 Hz to 4050 Hz, with 3920 Hz as the center and extending 130 Hz upwards and downwards.

[0077] Based on the identified frequency risk range, a clear resonance risk warning window is generated and output in the form of a data structure as input conditions for the next step of switching control strategies. This warning window should contain at least the following five pieces of information: first, the starting and ending values ​​of the risk frequency (e.g., 3790 Hz to 4050 Hz); second, the location of the resonance peak frequency (e.g., 3920 Hz); third, the half-power bandwidth value (e.g., 260 Hz); fourth, the risk level assessment (e.g., classified as Level 1, Level 2, or Level 3 based on peak amplitude and bandwidth); and fifth, an explanation of the influencing factors (e.g., this prediction is derived from the combined effects of 38 degrees Celsius, 1400 mm harness length, and 75 watt load power). In addition, the path number and warning generation time should be recorded as a traceability basis for subsequent diagnosis and optimization processes.

[0078] The purpose of this step is to construct a method for dynamically predicting the frequency range of resonance risk based on the obtained harmonic aggregation characteristic spectrum, combined with external environmental variables and actual power distribution structure parameters, thereby achieving early warning of resonance risk before voltage switching. By using three key factors that significantly affect electrical characteristics—ambient temperature, harness length, and load power—as input variables, and establishing a variable-response mapping relationship with previously measured response data, it is possible to accurately determine which frequency ranges exhibit high-risk behaviors such as amplification effects, resonance superposition, or abnormal response duration under specific operating conditions. The final output resonance risk warning window not only includes the specific frequency range but also clarifies the risk level, bandwidth range, and causes of the risk, providing a quantitative basis for subsequent frequency planning and control strategy adjustments. This method differs from existing methods that rely on static models or preset frequency tables, possessing the technical advantages of strong real-time performance, high adaptability, and dynamic updates. It is a crucial pre-processing step to ensure safety and stability during dual-voltage switching and also provides precise targets for active vibration damping control.

[0079] S004, through the resonance risk warning window, executes control frequency planning, implements spread spectrum control strategy to avoid high-risk frequency range, implements phase scheduling strategy to reduce the risk of multi-channel synchronous harmonic superposition, and implements voltage slope limiting strategy to suppress high-frequency electromagnetic disturbances caused by high-speed switching;

[0080] After obtaining the resonance risk warning window for the current power supply path, to ensure the operational stability of the dual-voltage automotive generator during voltage switching, targeted control frequency planning needs to be implemented based on the risk frequency band. Furthermore, spread spectrum control strategies, phase scheduling strategies, and voltage slope limiting strategies should be implemented on the basis of this planning to avoid resonance-induced frequencies, disperse the concentration trend of excitation energy, and reduce the impact of electromagnetic interference. The specific implementation steps are as follows:

[0081] Before performing voltage switching, read the generated resonance risk warning window information. This information should include the current path number, resonance frequency start point, resonance frequency end point, center resonance frequency value, resonance amplitude amplification factor, half-power bandwidth value, and risk level identifier. Taking path number P12 as an example, its risk frequency window is defined as from 3760 Hz to 4180 Hz, with a center frequency of 3970 Hz and a maximum amplitude amplification of 2.6 times. During the control frequency planning process, this frequency window is used as the shielding interval, and an exclusion range is set in the control frequency generation logic to prohibit all control signal excitation frequencies from falling into this interval. If the current control frequency plan is from 2000 Hz to 5000 Hz, then the frequency band from 3760 Hz to 4180 Hz needs to be removed from the control frequency set, and frequency selection needs to be redone. Instead of using a single fixed frequency, the frequency selection method distributes multiple frequency points within the remaining frequency band, such as 2350 Hz, 3120 Hz, and 4470 Hz. These frequencies are then periodically alternated during switching control to inject control signals. This disperses the excitation energy, avoids prolonged residence at specific frequency points, reduces the possibility of harmonic energy accumulation in narrow-band high-resonance regions, and minimizes the risk of oscillation.

[0082] After completing the frequency point arrangement, to prevent multiple power supply paths from triggering synchronously at the control time, thus causing a superposition effect in certain frequency bands or their integer multiples, a phase scheduling strategy is further implemented. This strategy is executed when multiple voltage switching path control tasks are performed in parallel. For example, if paths P12 and P14 both need to perform voltage switching operations from 12 volts to 48 volts within the same time window, and both of their frequency plans include a 3120 Hz frequency point, excitation overlap may occur. Therefore, the control start time interval for each path needs to be set during execution. Specifically, this can be achieved by adjusting the control command trigger time, delaying the trigger time of path P14 after the trigger command of path P12, for example, by setting the delay to 40 microseconds, to avoid the excitation signals of the control waveforms overlapping in frequency and time. Furthermore, if frequency overlap cannot be completely avoided, the frequency priority order can be adjusted so that path P12 prioritizes the 3120 Hz frequency, while path P14 prioritizes the 4470 Hz frequency. This dual-dimensional scheduling of frequency and time reduces the probability of multiple paths simultaneously stimulating the same resonant point, effectively mitigating the risk of harmonic crossover. This strategy differs from existing uniform periodic driving methods; instead of fixed timing triggering, it independently configures trigger delays and frequency priorities according to path numbers, isolating concurrent switching tasks within the physical electrical channel and improving control redundancy.

[0083] After completing the frequency selection and phase scheduling strategies, a voltage slope limiting strategy is implemented to control the rate of change during the output voltage rise or fall, preventing high-frequency electromagnetic interference, voltage spikes, or loop resonance caused by voltage abrupt changes. In specific operation, for the switching path from 12 volts to 48 volts, the voltage rise rate is set to not exceed 0.1 volts per microsecond; that is, when the target voltage change is 36 volts, the switching time must not be less than 360 microseconds. To achieve a smooth rise, the controller output signal is not issued in step-level form, but rather the voltage target curve is decomposed into segmented level commands with equal amplitude and equal time intervals. For example, the voltage rises by 0.8 volts every 10 microseconds, and this level increment is executed 45 times consecutively to complete one complete voltage rise process. The same method is used in voltage fall operations. For example, when switching from 48 volts to 12 volts, the voltage falls by 0.8 volts every 10 microseconds, and the controller executes a voltage fall curve output to avoid flyback interference when there are large inductive components in the load. Furthermore, in scenarios with multiple concurrent switching tasks, it is required that the voltage change slope values ​​of all paths be consistent to ensure that common-mode interference current is not induced on the common ground loop due to differences in voltage change rates. Compared with traditional hard switching or high-speed PWM control methods, this approach significantly reduces the risks of instantaneous current surges, electromagnetic radiation amplification, and coupling interference, provided that the control target is relatively stable and the response delay is acceptable.

[0084] The purpose of this step is to identify the resonant risk frequency range under current operating conditions, and then formulate and implement a refined control frequency planning strategy. This effectively avoids high-risk frequencies, weakens harmonic excitation sources, and suppresses electromagnetic interference caused by rapid voltage changes during voltage switching. Through analysis of the resonant risk warning window, a spread-spectrum control strategy is first implemented, causing the control signal to dynamically jump between multiple low-risk frequency points, dispersing the signal spectrum energy and avoiding resonant amplification caused by prolonged excitation of a single frequency. Secondly, a phase scheduling strategy is implemented, staggering the control time points and frequency priorities of multiple voltage switching paths to prevent harmonic superposition from different paths on the same frequency band. Finally, a voltage slope limit is applied, precisely controlling the rate of voltage rise or fall to reduce high-frequency oscillations and inductive load flyback effects caused by excessively rapid voltage changes. This step, through comprehensive regulation of three-dimensional parameters—frequency, time, and voltage changes—constructs a shielding mechanism for interference risks. Compared to existing methods using fixed frequencies and hard switching, this approach is more dynamically adaptable and engineering robust, providing a safe and reliable foundation for subsequent stability control and active damping.

[0085] S005, construct an active damping control network based on the control frequency planning results, introduce virtual impedance into the voltage switching control loop, dynamically adjust the output impedance parameters of the voltage regulator, correct the loop gain characteristics, improve the phase margin during dynamic switching, and suppress residual oscillation signals.

[0086] After identifying and planning the avoidance of resonant risk frequencies during voltage switching, a damping structure capable of actively absorbing high-frequency disturbance energy is needed to improve the stability and safety of voltage switching control. This enhances the dynamic response of the control loop, increases phase margin, and suppresses micro-oscillations or control instability caused by residual harmonics. The specific implementation steps of the active damping control structure are as follows:

[0087] Based on the control frequency planning results completed in the previous stage, the frequency distribution range and boundaries of the current control path during voltage switching were confirmed. Taking path number P22 as an example, its frequency planning has avoided the 3920 Hz to 4170 Hz range and uses multiple control frequency points (e.g., 3120 Hz, 4480 Hz, 4750 Hz) for alternating switching control. After executing the above frequency scheduling, real-time measurement of the output voltage waveform revealed a high-frequency small-amplitude oscillation signal with a peak amplitude of approximately 0.4 volts and a frequency between 4400 Hz and 4600 Hz, lasting approximately 40 microseconds. Although this residual fluctuation does not directly cause functional failure, it poses a risk of superposition during repeated loading. Therefore, it was determined that additional damping measures need to be introduced near this frequency range for suppression. The goal is to construct an active damping structure with frequency gating capability and dynamic power absorption capability without affecting the main control objective, and the specific injection location is selected between the voltage regulator output terminal and the load input terminal.

[0088] After determining the access location, a voltage-response driven damping structure is constructed to form an equivalent virtual impedance characteristic. This structure mainly consists of a drive execution circuit, a voltage sampling line, a current feedback loop, and a control signal coupler. During implementation, a power bridge circuit is first connected in parallel at the regulator output, and the bridge arm conduction state is activated through an independent control loop. Simultaneously, a high-bandwidth differential amplifier is connected in series in the line to detect abnormal signals such as voltage rising edges, falling edges, and transient spikes in real time. All signals must be processed by a bandpass filter circuit to extract the high-frequency components of the target frequency band (4400 Hz to 4600 Hz in this case) as the trigger source for the drive bridge arm conduction signal. When the amplitude of the signal in this frequency band exceeds a set threshold (e.g., 0.3 volts), the bridge arm sends a pulse command through the controller to activate the resistive load path, forming a current absorption path, thereby effectively introducing damping characteristics into the physical circuit. To ensure response speed, the drive signal delay must not exceed 3 microseconds, and the impedance switching response time must not exceed 10 microseconds. The design goal of this structure is to achieve damping behavior with strong frequency selectivity, high energy absorption efficiency, and fast response speed.

[0089] After introducing the active damping structure, the open-loop gain curve of the original voltage regulator needs to be tested and adjusted to verify and optimize the loop's dynamic response capability. Specifically, first, in the undamped state, a unit step voltage signal is applied, and the output response curve is measured. The amplitude-frequency and phase-frequency characteristics of the loop are recorded, with particular attention paid to the phase change gradient, gain change amplitude, and oscillation waveform in the 4400 Hz to 4600 Hz frequency band. Then, the response characteristics after introducing the damping structure are remeasured under the same input signal excitation, and the differences between the two sets of data are compared. When a gain drop of more than 5 dB occurs near the resonant frequency, and the phase margin increases from the original 15 degrees to more than 40 degrees, the damping structure is considered effective in that frequency band. If the gain does not reach the target drop, the bridge arm trigger threshold can be adjusted to extend the damping absorption time, or the load resistance parameters can be adjusted to make its equivalent impedance more closely match the resonant characteristics at the target frequency. This process is a crucial step to ensure the damping effect and needs to be tested under different load conditions (light load, medium load, and full load) to ensure stability.

[0090] After completing gain correction and structural debugging, the voltage response waveforms throughout the entire voltage switching process were compared and verified. Starting from the switching command trigger, voltage response data was collected over a period of 200 microseconds before and after the switch. The residual oscillation waveform characteristics under the same switching frequency conditions before and after the addition of the damping structure were compared and analyzed, including whether there was significant suppression in the frequency range, peak amplitude, duration, and energy distribution of the residual signal. For example, in path P22 during the switching operation, before the introduction of the damping structure, a 3-cycle oscillation was detected with a peak voltage of 0.6 volts and a residual time of 80 microseconds; after the introduction, the oscillation period decreased to 1 cycle, the peak voltage was compressed to 0.2 volts, and the residual time was shortened to 30 microseconds, indicating that the damping structure has a significant absorption and attenuation effect in the control loop and meets the engineering safety assessment standards. This verification data can serve as a basis for technical decisions and as input for subsequent closed-loop feedback adjustment stages.

[0091] After the damping structure configuration and testing are successful, its operating parameters are fixed as the default damping strategy for the current path number and operating environment, facilitating subsequent calls and rapid deployment. Specific parameters include: frequency response range (4400 Hz to 4600 Hz), analog impedance values ​​(equivalent resistance 3.2 ohms, equivalent inductance 17 microhenries), bridge arm drive delay (3 microseconds), voltage threshold trigger point (0.3 volts), and maximum allowable power absorption (15 watts). All parameters are bound to the path number, load power level, and control frequency planning scheme to form a structured configuration group, stored in the control data table. The next time the same path voltage switching operation is executed, only the configuration group needs to be called; there is no need to repeatedly build and debug the damping structure, enabling rapid deployment.

[0092] The purpose of this step is to effectively absorb the high-frequency oscillation energy remaining during voltage switching by constructing an active damping structure with dynamic response capabilities after frequency planning and interference avoidance, thereby further improving the stability and response accuracy of the control loop. This structure introduces a voltage sampling and control circuit with frequency recognition capabilities. Upon detecting minute fluctuations within a specific frequency range, it automatically adjusts the equivalent output impedance, forming a virtual impedance with energy absorption characteristics in the control loop. This physically weakens the high-frequency oscillations caused by non-ideal control signals. Simultaneously, this damping structure, combined with gain characteristic adjustment and phase margin optimization measures, can achieve gain compression and phase enhancement at key frequency points, significantly improving the stability margin of the control loop. Finally, through residual oscillation signal verification and parameter solidification, a reusable damping strategy template is formed, enabling rapid adaptation and dynamic suppression of resonance risks under different power supply paths and load conditions. This step overcomes the limitations of traditional passive filtering in dealing with high-frequency resonance, enhances the electromagnetic compatibility and control robustness of voltage switching control in complex automotive environments, and is the core technical link in achieving deep stability in the entire control strategy.

[0093] S006. During the operation of the active damping control network, the residual signal and control performance index of the control process are monitored, the monitoring data are fed back to the frequency response baseline, the resonance risk threshold, control frequency planning parameters and virtual impedance parameters are dynamically updated, and a steady-state closed-loop control mechanism that can continuously converge is constructed.

[0094] To improve the voltage switching stability of dual-voltage automotive generators under complex operating conditions, it is necessary to monitor the control residual signal and actual control performance throughout the operation of the active damping control structure. Based on feedback data, the resonant frequency threshold, control frequency planning strategy, and response parameters of the virtual impedance structure are dynamically updated. Ultimately, a closed-loop control mechanism is constructed that can automatically correct its control configuration according to the operating state and possesses gradual convergence capability, ensuring that the control performance remains within the optimal range. The specific implementation of this optimized closed-loop control process is as follows:

[0095] During the continuous operation of the active damping control structure, a high-resolution data acquisition path is used to perform full-process sampling and monitoring of the voltage switching process. The sampling start time is set 50 microseconds before the voltage switching control command trigger point, and the end time is extended to 300 microseconds after the switching is completed, with a total sampling duration of 350 microseconds. The data sampling period is set to 1 microsecond to ensure that key information such as oscillation start, excitation intensity, peak amplitude, response delay, and decay period can be fully acquired. The acquired signal content includes: (1) the target output voltage setting curve; (2) the actual output voltage transient waveform; (3) the output current rise slope and peak current change rate; (4) the power fluctuation during the switching period; and (5) the residual signal curve, i.e., the real-time error between the actual output and the target output. Within the sample period, each type of signal is written to the buffer array in real time, which can be used for subsequent calculation, comparison, and trend evaluation. The multi-parameter, time-complete observation window established in this step is a prerequisite for realizing data closed-loop control.

[0096] The acquired residual signal data is subjected to frequency deconstruction and response feature matching. Combined with the existing frequency response baseline, the system is compared to confirm whether there is any drift, widening, intensity enhancement, or formation of new risk frequency bands in the resonance risk range. Taking path number P28 as an example, its original frequency response baseline shows a gain amplification effect of more than 3 dB between 4300 Hz and 4480 Hz, belonging to the medium-risk range. During the latest handover, the sampled waveform shows abnormal behavior at 4700 Hz, with a frequency maintenance time exceeding 60 microseconds, a sudden phase drop of 30 degrees, and a residual signal fluctuation amplitude reaching 0.5 volts. Verification shows that this frequency band was not marked in the original frequency response baseline, indicating that it is a newly added unstable frequency point during operation. This frequency point is then added to the risk frequency range of path P28 and set to a high-risk level, expanding the effective range of the frequency response baseline. Simultaneously, the 4520 Hz to 4700 Hz segment is marked as a "potential coupling band" for shielding during frequency planning.

[0097] Based on the updated frequency response results and residual trends, the current control frequency planning strategy and active damping structure configuration parameters were revised. Regarding control frequency planning, firstly, the newly added 4700 Hz frequency was removed from the frequency hopping candidate list; secondly, the frequency hopping paths were rearranged within the available frequency bands, for example, selecting three sets of frequency points from 3120 Hz, 3550 Hz, 3980 Hz, and 4470 Hz to construct a switching control frequency sequence; furthermore, the control frequency hopping interval was increased from every 100 Hz to every 250 Hz to further dilute the spectral density and reduce frequency overlap points. Regarding virtual impedance parameters, for the detected new risk frequency band of 4700 Hz, the equivalent analog impedance value in the active damping structure was adjusted from the original 2.5 ohms to 3.8 ohms to enhance absorption capability; the injection delay was reduced from 3 microseconds to 1.5 microseconds to ensure a sufficiently fast response time; and the absorption duration was set to 120 microseconds to cover the complete cycle of the oscillation wave. In addition, the impedance response curve of the controller is adjusted so that it shows an impedance increase trend in the frequency range of 4500 Hz to 4750 Hz, thereby forming a dynamic energy suppression band.

[0098] All monitored data, along with frequency response updates, control frequency planning parameter adjustment records, and virtual impedance configuration parameters, are consolidated into a standardized operational configuration unit and written back to the frequency response baseline database in real time. This database, indexed by path number, includes fields such as corresponding load type, operating temperature, battery terminal voltage, line length, historical resonant frequency range, frequency hopping sequence parameters, virtual impedance response parameters, oscillation residual quantification index, and phase margin change trend. Taking path P28 as an example, its current updates include: a newly identified high-risk frequency of 4700 Hz, frequency hopping sequence adjusted to 3120 / 3550 / 3980 Hz, simulated impedance increased to 3.8 ohms, response delay set to 1.5 microseconds, and absorption time of 120 microseconds. All parameters are archived in a structured manner for direct loading during subsequent executions, eliminating the need for re-detection and debugging. By accumulating five rounds of operational records, a trend tracking chart can be generated to determine whether control stability is gradually converging. If indicators such as a continuous decrease in oscillation residuals and a continuous increase in control performance scores are observed, it indicates that the current parameter configuration has entered the optimal range and has the ability to be continuously optimized. Conversely, if these changes occur, an advanced parameter tuning process is triggered to comprehensively reconstruct the frequency channel or excitation sequence.

[0099] The purpose of this step is to establish a sustainable, self-optimizing closed-loop control mechanism, enabling the dual-voltage automotive generator to perceive resonance risks in real time, actively adjust control parameters, and continuously optimize control performance during dynamic switching. During the operation of the active damping structure, the system monitors residual signals in real time during the switching process using high-precision sampling methods, extracts deviation data between the control target and the actual response, and identifies frequency risks by combining frequency, phase, and amplitude characteristics. After the identification results are fed back to the frequency response baseline, the resonance frequency threshold and frequency avoidance strategy can be dynamically updated, further adjusting the control frequency planning and virtual impedance parameters to form a parameter set that highly matches the actual operating state. All updates are stored in a structured manner in the control configuration dataset for subsequent retrieval or trend tracking. Through this mechanism, the system continuously corrects itself and approaches the optimal control boundary during operation, achieving closed-loop convergence from the initial configuration to a stable operating state. Compared with traditional static control strategies, this step endows the control structure with dynamic adaptability, self-learning ability, and engineering stability, and is a key technical path to ensure that voltage switching maintains efficient, safe, and disturbance-free operation under varying environments and load conditions.

[0100] The aforementioned intelligent switching control method for dual-voltage automotive generators effectively solves the harmonic accumulation and system resonance problems caused by traditional fixed-frequency or bandwidth-limited control strategies during electrical switching. This method achieves accurate identification and dynamic prediction of resonance risks by constructing a frequency response baseline and harmonic characteristic spectrum; avoids exciting the system's inherent frequencies and reduces harmonic superposition effects through a multi-level control frequency planning strategy; further enhances the stability margin of the voltage regulation loop by introducing an adaptive virtual impedance structure and active damping control mechanism; and finally, combines residual signal feedback and parameter closed-loop update mechanisms to achieve continuous optimization and stable convergence of control performance. The overall solution achieves synergistic improvements in dynamic control accuracy, electrical system robustness, and electromagnetic compatibility performance, providing technical support for the safe operation of dual-voltage electrical architectures under multiple operating conditions and strong disturbance scenarios.

[0101] This invention provides, for example Figure 2 The dual-voltage automotive generator intelligent switching control system shown includes a frequency response modeling module, a resonance feature identification module, a resonance risk prediction module, a switching frequency control module, an active damping control module, and a closed-loop adaptive optimization module.

[0102] The frequency response modeling module constructs the frequency response baseline of the automotive power distribution network, calibrates the inductance parameters of the wiring harness and the capacitance parameters of the filter based on the frequency response measurement data, and generates an inherent frequency distribution map that reflects the dynamic electrical characteristics.

[0103] The resonance feature identification module, with reference to the obtained inherent frequency distribution map, performs a frequency sweep excitation test and a load step response test to obtain voltage response curves under different excitation conditions. It then extracts the resonance peak frequency and half-power bandwidth parameters from these curves to generate a harmonic aggregation feature spectrum for characterizing resonance behavior.

[0104] The resonance risk prediction module uses harmonic aggregation feature spectrum to build a control frequency prediction model. It takes ambient temperature, harness length and load power as input parameters and outputs the frequency range where resonance risk exists, forming a resonance risk early warning window.

[0105] The switching frequency control module executes control frequency planning through the resonance risk warning window, implements spread spectrum control strategy to avoid high-risk frequency ranges, implements phase scheduling strategy to reduce the risk of multi-channel synchronous harmonic superposition, and implements voltage slope limiting strategy to suppress high-frequency electromagnetic disturbances caused by high-speed switching.

[0106] The active damping control module constructs an active damping control network based on the control frequency planning results, introduces virtual impedance into the voltage switching control loop, dynamically adjusts the output impedance parameters of the voltage regulator, corrects the loop gain characteristics, improves the phase margin during dynamic switching, and suppresses residual oscillation signals.

[0107] The closed-loop adaptive optimization module monitors the residual signal and control performance index during the operation of the active damping control network, feeds the monitoring data back to the frequency response baseline, dynamically updates the resonance risk threshold, control frequency planning parameters and virtual impedance parameters, and constructs a steady-state closed-loop control mechanism that can continuously converge.

[0108] The present invention provides a dual-voltage automotive generator intelligent switching control method, which is implemented through the aforementioned dual-voltage automotive generator intelligent switching control system. For details of the specific method and process of the dual-voltage automotive generator intelligent switching control system, please refer to the aforementioned embodiment of the dual-voltage automotive generator intelligent switching control method, which will not be repeated here.

[0109] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A dual voltage automotive generator intelligent switching control method, characterized by, The method comprises the following steps: S001, constructing a frequency response baseline of the automobile power distribution network, calibrating the inductance parameters of the wire harness and the capacitance parameters of the filter based on the frequency response measurement data, and generating an inherent frequency distribution map for reflecting dynamic electrical characteristics; S002, under the reference of the obtained inherent frequency distribution map, performing a sweep excitation test and a load step response test, obtaining voltage response curves under different excitation conditions, extracting a resonance peak frequency and a half-power bandwidth parameter therefrom, and generating a harmonic accumulation characteristic map for characterizing resonance behavior; S003, constructing a control frequency prediction model using the harmonic accumulation characteristic map, taking the ambient temperature, the wire harness length and the load power as input parameters, outputting a frequency interval at risk of resonance, and forming a resonance risk early warning window; S004, performing control frequency planning through the resonance risk early warning window, implementing a spread spectrum control strategy for avoiding a high-risk frequency interval, implementing a phase scheduling strategy for reducing the risk of multi-channel synchronous harmonic superposition, and implementing a voltage slope limiting strategy for suppressing high-frequency electromagnetic disturbances caused by high-speed switching; S005, constructing an active damping control network according to the control frequency planning result, introducing a virtual impedance into a voltage switching control loop, dynamically adjusting the output impedance parameters of the voltage regulator, correcting the loop gain characteristics, improving the phase margin in the dynamic switching process, and suppressing residual oscillation signals; S006, during the operation of the active damping control network, monitoring residual signals and control efficiency indicators in the control process, feeding the monitoring data back to the frequency response baseline, dynamically updating resonance risk threshold values, control frequency planning parameters and virtual impedance parameters, and constructing a stable closed-loop control mechanism capable of continuous convergence.

2. The dual voltage automotive generator intelligent switching control method of claim 1, wherein, Step S001 comprises: physically analyzing the vehicle electrical structure, clearly defining multiple power supply paths from the generator output end to the vehicle-mounted storage battery, high-power loads and low-power loads, collecting wire length, cross-sectional area, material, shielding structure, connector type, grounding method and end load parameters one by one, and numbering and registering them respectively; high-precision voltage and current sampling interfaces are arranged at both ends of each power supply path, and electromagnetic shielding protection structures are built, and a data acquisition infrastructure that can be used for dynamic testing is established; a frequency step excitation method is used to inject a sinusoidal signal in the range of 10 Hz to 50 kHz, and multiple response tests are performed under three working conditions of static power-on, engine starting and vehicle high load, and input and output voltage waveform data are collected; based on the sampling data, amplitude-frequency and phase-frequency response curves are calculated, resonance peak frequency and bandwidth are extracted, equivalent inductance and filter capacitance parameters corresponding to the wire structure are back calculated, and a complete frequency response distribution map is drawn, forming a dynamic characteristic basic data set for subsequent test reference.

3. The dual voltage automotive generator intelligent switching control method of claim 1, wherein, Step S002 comprises: according to the frequency interval with obvious wave peaks or phase mutations in the frequency response distribution map, setting the sweep excitation range and step interval, using an adjustable frequency signal generator to inject a sinusoidal excitation signal, and synchronously collecting input and output voltage response waveforms; Perform load step response test, quickly switch current level under programmable electronic load control, collect transient voltage response characteristics, repeat test in no-load power-on, medium load and full load state respectively; Calculate the amplitude ratio and phase difference of the response data of each power supply path, extract the resonance peak frequency and half-power bandwidth, identify the oscillation period and decay time, and extract the resonance characteristic parameter group; According to the power supply path number, integrate the resonance peak frequency, bandwidth, oscillation period and response amplitude, draw the harmonic accumulation characteristic map, and mark the resonance risk level and corresponding load type as the input basis for the subsequent control frequency planning step.

4. The dual voltage automotive generator smart switching control method of claim 1, wherein, Step S003 includes: According to the resonance frequency, bandwidth, response amplitude and phase jump parameters recorded in the harmonic accumulation characteristic map, combined with the environmental temperature, wire length and load power at the time of collection, a structured response data set is established, and the number identification is completed; Quantitative analysis is performed on the change trend between each variable and the resonance characteristics, the response trend table of variable change on frequency shift, bandwidth expansion and amplitude change is sorted out, and the parameter response comparison model corresponding to the path number is established; Take the temperature, wire length and load power obtained during the whole vehicle running process as input parameters, substitute them into the variable response comparison model, output the resonance peak frequency and the corresponding bandwidth, and judge whether it constitutes a high-risk resonance frequency interval; According to the frequency center, risk level, half-power bandwidth and response amplitude, output the resonance risk warning window information, which is used as the input basis for the subsequent control frequency planning, and is accompanied by generating path number and time stamp for traceability record.

5. The dual voltage automotive generator smart switching control method of claim 1, wherein, Step S004 includes: Read the power supply path number, resonance frequency start point, end point, center frequency and bandwidth range recorded in the resonance risk warning window, exclude the corresponding frequency band from the control frequency range, and select multiple frequency points in the remaining frequency band for periodic alternate control to avoid the phenomenon of frequency residence causing harmonic accumulation; When multiple paths exist and parallel switching is required, implement a phase scheduling strategy, set control command trigger time delay and frequency priority to ensure that different paths achieve staggered control in time and frequency dimensions, and reduce the risk of harmonic cross superposition; Implement a voltage slope limiting strategy, output level instructions in segments during voltage rise and fall, limit the voltage change rate within the preset threshold, avoid exciting sharp peaks, electromagnetic interference and loop oscillation, and ensure that the voltage change rate between concurrent paths is consistent to suppress common mode interference.

6. The dual voltage automotive generator smart switching control method of claim 1, wherein, Step S005 includes: According to the control frequency planning result, confirm the frequency distribution range of the current control path in the voltage switching process, locate the frequency band with residual high-frequency oscillation signal, and determine the frequency interval that needs to introduce damping; Connect the damping structure composed of power bridge circuit, differential amplifier and band-pass filter between the output end of the voltage regulator and the input end of the load, trigger the bridge arm conduction based on the voltage high-frequency component, and introduce a resistance load path to form an equivalent virtual impedance; After introducing the damping structure, apply a unit step voltage signal, measure the open-loop gain characteristics, adjust the trigger threshold and load parameters, so that the gain in the resonance frequency band decreases by more than 5 decibels and the phase margin increases to more than 40 degrees. The voltage switching response waveforms are collected under different load states, and the oscillation period, peak voltage and duration before and after the introduction of the damping structure are compared to verify its absorption capacity for high-frequency disturbance signals; The damping structure operating parameters are stored with path number, load power and control frequency as indexes to form a structured configuration group for fast calling and deployment in repeated path control scenarios.

7. The dual voltage automotive generator smart switching control method of claim 1, wherein, Step S006 includes: During the operation of the active damping structure, the target output voltage, actual output voltage, current rate of change and power fluctuation signal are continuously collected at a sampling period of 1 microsecond, and the residual signal waveform is recorded in real time to form a multi-dimensional observation window; The collected residual signals are frequency-decomposed and feature-matched to identify whether there are new resonance frequency points or risk frequency interval drifts by comparing with the frequency response baseline, and the frequency risk identifier of the current path is updated; The control frequency planning strategy is adjusted according to the new frequency response information, the frequency hopping sequence and frequency interval are reconfigured, and the virtual impedance parameters are dynamically corrected; The updated frequency response parameters, frequency planning sequence and virtual impedance configuration are solidified as standard operation configuration units and written into the frequency response baseline database in real time to build a stable closed-loop control mechanism with step-by-step convergence capability.

8. A dual voltage automotive generator intelligent switching control system for implementing the dual voltage automotive generator intelligent switching control method of any one of claims 1-7, wherein, It includes frequency response modeling module, resonance feature identification module, resonance risk prediction module, switching frequency regulation and control module, active damping control module and closed-loop adaptive optimization module: The frequency response modeling module builds the frequency response baseline of the automobile power distribution network, calibrates the inductance parameters of the wire harness and the capacitance parameters of the filter based on the frequency response measurement data, and generates the inherent frequency distribution map for reflecting the dynamic electrical characteristics; The resonance feature identification module performs sweep excitation test and load step response test under the reference of the obtained inherent frequency distribution map, obtains voltage response curves under different excitation conditions, extracts resonance peak frequency and half-power bandwidth parameters from them, and generates harmonic accumulation feature map for characterizing resonance behavior; The resonance risk prediction module uses the harmonic accumulation feature map to build a control frequency prediction model, takes environmental temperature, wire harness length and load power as input parameters, outputs frequency intervals with resonance risk, and forms a resonance risk warning window; The switching frequency regulation and control module performs control frequency planning through the resonance risk warning window, implements spread spectrum control strategy to avoid high-risk frequency intervals, implements phase scheduling strategy to reduce the risk of multi-channel synchronous harmonic superposition, and implements voltage slope limiting strategy to suppress high-frequency electromagnetic disturbance caused by high-speed switching; The active damping control module constructs an active damping control network according to the control frequency planning result, introduces a virtual impedance into the voltage switching control loop, dynamically adjusts the output impedance parameters of the voltage regulator, corrects the loop gain characteristics, improves the phase margin in the dynamic switching process, and suppresses the residual oscillation signal. The closed-loop adaptive optimization module monitors residual signals and control performance indicators during the active damping control network process, feeds the monitoring data to the frequency response baseline, dynamically updates resonance risk thresholds, control frequency planning parameters, and virtual impedance parameters, and builds a stable closed-loop control mechanism that can continuously converge.

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