An active suppression method of charging module cluster conducted interference and related device

Abstract

The application discloses an active suppression method of conduction interference of a charging module cluster and related devices, which are used for effectively suppressing conduction interference and maintaining the output stability and reliability of the system when the charging module cluster is in a high-power parallel operation scene. The method comprises the following steps: obtaining the operation parameters of a plurality of parallel charging modules; establishing a switching noise contribution model reflecting the influence degree of each charging module on the conduction interference of the cluster based on the operation parameters; analyzing the switching transient of the plurality of charging modules under the current working condition by using the switching noise contribution model, and calculating the frequency spectrum superposition relationship of the plurality of charging modules under the current working condition; determining a target relative time sequence relationship according to the frequency spectrum superposition relationship; issuing a switching parameter adjustment instruction to the plurality of charging modules according to the target relative time sequence relationship, and synchronously calibrating the switching control time sequence inside the plurality of charging modules, so that the plurality of charging modules perform switching actions according to the calibrated relative time sequence.

Description

Technical Field

[0001] This application relates to the field of power technology, and in particular to an active suppression method and related device for conducted interference in a charging module cluster. Background Technology

[0002] Current high-power DC charging systems, such as DC charging piles and battery swapping station energy cabinets, typically employ a modular architecture consisting of multiple charging modules connected in parallel. Each module contains a front-end power factor correction (PFC) circuit and a rear-end isolated DC / DC converter circuit, both of which utilize high-frequency switching devices for energy conversion. To enhance system reliability and scalability, charging systems often employ dozens or even hundreds of modules working together, achieving a total output power of hundreds of kilowatts or more.

[0003] In existing technologies, the switching control methods for multi-module parallel power supplies typically fall into two categories: one is where each module operates according to a fixed clock, either factory-fixed or with the clock frequency uniformly allocated by the system; the other is where different modules operate with independent oscillation sources, thus forming a completely asynchronous switching mode. Furthermore, to reduce the EMI peak value of a single module, existing technologies also employ methods such as spread spectrum modulation and frequency dithering to perturb the switching frequency.

[0004] The methods described above can improve the EMI performance of power modules to some extent, but they mainly focus on the module's own interference suppression. When the switching clocks of multiple charging modules are highly synchronized, the transient superposition of their switching node currents and voltages will form a high-amplitude EMI peak at a specific frequency, easily leading to excessive conducted interference. Furthermore, the random superposition of switching frequencies will cause EMI energy to spread over a wide bandwidth, resulting in a significant increase in the overall noise floor, making it difficult to meet stringent EMC certification requirements. In other words, existing methods cannot simultaneously ensure both the reliability and EMI performance of the charging system. Summary of the Invention

[0005] This application provides an active suppression method and related apparatus for conducted interference of charging module clusters, which can effectively suppress conducted interference and maintain the output stability and reliability of the system even in high-power parallel operation scenarios.

[0006] The first aspect of this application provides an active suppression method for conducted interference in a charging module cluster, comprising: Obtain the operating parameters of multiple charging modules operating in parallel, including at least the switching frequency, switching duty cycle, internal clock phase reference, and input / output voltage and current parameters; Based on the aforementioned operating parameters, a switching noise contribution model is established to reflect the degree of influence of each charging module on the cluster's conducted interference. The switching noise contribution model is used to characterize the harmonic distribution characteristics of multiple charging modules under different switching transients. The switching noise contribution model is used to analyze the switching transients of the multiple charging modules under the current operating conditions, and the spectral superposition relationship of the multiple charging modules under the current operating conditions is calculated. The target relative timing relationship is determined based on the spectrum superposition relationship, and the target relative timing relationship includes at least the switching phase offset of the plurality of charging modules and the order of switching action triggering; According to the target relative timing relationship, the switch parameter adjustment command is issued to the multiple charging modules, and the switch control timing inside the multiple charging modules is synchronously calibrated so that the multiple charging modules perform switching actions according to the calibrated relative timing.

[0007] Optionally, determining the target relative temporal relationship based on the spectral superposition relationship includes: Based on the aforementioned spectral superposition relationship, initial switching phase offsets are set for the multiple charging modules in the multidimensional phase space, and the initial triggering order is assigned according to the influence of each charging module in the switching noise contribution model and the topological position between the charging modules. The initial switching phase offsets are allocated in the range of 0–360° in an equal division manner or in sequence according to the golden angle. The initial switch phase offset and the initial triggering sequence are iteratively adjusted using a phase optimization algorithm to obtain an optimized phase distribution. The goal of the iterative adjustment is to minimize the peak value of the cluster conducted interference spectrum of the multiple charging modules under the current operating conditions, taking into account the transient harmonic contribution between charging modules, the switching duty cycle and the power level differences. The relative timing relationship of the target is determined based on the switch phase offset and triggering sequence obtained after iterative adjustment.

[0008] Optionally, determining the target relative temporal relationship based on the spectral superposition relationship includes: Based on the spectrum superposition relationship and the quantity, power level, heat load, and location relationship of the multiple charging modules, the multiple charging modules are divided into several subgroups; Based on the relative influence of each subgroup in the switching noise contribution model, the local relative timing parameters within each subgroup are calculated, and a local phase control strategy is implemented on the charging modules within each subgroup to make the switching transients of the charging modules within each subgroup exhibit staggered peak distribution in the spectrum, so as to optimize the spectral superposition relationship within the subgroup. Cross-group timing coordination is performed between subgroups, integrating the local relative timing parameters of each subgroup to generate the target relative timing relationship.

[0009] Optionally, the cross-group timing coordination between subgroups, which integrates the local relative timing parameters of each subgroup to generate a target relative timing relationship, includes: Based on the aforementioned spectral superposition relationship and the local relative timing parameters within each subgroup, the transient superposition effect between subgroups is analyzed, and the relative timing constraints of each subgroup in the overall cluster are determined. Based on the degree of influence of each subgroup in the switching noise contribution model and the relative timing constraints, a cross-group timing offset is assigned to each subgroup. The target relative timing relationship is generated by integrating the local relative timing parameters of each subgroup and the cross-group timing offset.

[0010] Optionally, the step of establishing a switching noise contribution model based on the operating parameters to reflect the degree of influence of each charging module on the cluster conducted interference includes: The switching transient voltage and current waveforms of each charging module are sampled, and spectrum analysis is performed based on the switching frequency, the switching duty cycle and the internal clock phase reference to obtain the harmonic amplitude sequence of the charging module under different switching transients. The changes in harmonic amplitude of each charging module under different operating conditions and sampling time periods are analyzed. The harmonic amplitude is weighted according to the input and output voltage and current parameters, switching duty cycle and module power level. The dynamic contribution index of each charging module to the cluster conducted interference in the key frequency range is calculated. The dynamic contribution indicators of each charging module within the key frequency range are organized according to the module number, corresponding frequency sequence, and sampling time order to generate a multi-dimensional data structure switching noise contribution model. Each dimension corresponds to the charging module, frequency, and time period, and each element represents the contribution amplitude of the corresponding charging module at the corresponding frequency and time period.

[0011] Optionally, the active suppression method further includes: The operating parameters are continuously monitored, and it is determined whether the change in the operating parameters exceeds the preset operating condition change threshold. When the change in the operating parameters exceeds the preset operating condition change threshold, the dynamic update of the switching noise contribution model is triggered, and the steps of calculating the spectrum superposition relationship, determining the target relative timing relationship, and issuing the switching parameter adjustment command are re-executed.

[0012] Optionally, after determining the target relative timing relationship based on the spectral superposition relationship, and before issuing switching parameter adjustment instructions to the plurality of charging modules based on the target relative timing relationship, the active suppression method further includes: A controllable micro-perturbation frequency is applied to the switching frequency of each charging module. The amplitude of the micro-perturbation frequency is distributed randomly or in a preset sequence within the range of ±1% to 3%.

[0013] A second aspect of this application provides an active suppression system for conducted interference in a charging module cluster, comprising: The acquisition unit is used to acquire the operating parameters of multiple charging modules that operate in parallel. The operating parameters include at least the switching frequency, switching duty cycle, internal clock phase reference, and input / output voltage and current parameters. A model is established based on the operating parameters to create a switching noise contribution model that reflects the degree of influence of each charging module on the cluster conducted interference. The switching noise contribution model is used to characterize the harmonic distribution characteristics of multiple charging modules under different switching transients. The calculation unit is used to analyze the switching transients of the multiple charging modules under the current operating conditions using the switching noise contribution model, and to calculate the spectral superposition relationship of the multiple charging modules under the current operating conditions. The determining unit is used to determine the target relative timing relationship based on the spectrum superposition relationship, wherein the target relative timing relationship includes at least the switching phase offset of the plurality of charging modules and the order of switching action triggering; The execution unit is used to issue switching parameter adjustment instructions to the plurality of charging modules according to the target relative timing relationship, and to synchronously calibrate the switching control timing inside the plurality of charging modules so that the plurality of charging modules perform switching actions according to the calibrated relative timing.

[0014] A third aspect of this application provides an active suppression device for conducted interference in a charging module cluster, the device comprising: Processor, memory, input / output units, and bus; The processor is connected to the memory, the input / output unit, and the bus; The memory stores a program, which the processor calls to execute the first aspect and any optional method of the first aspect for actively suppressing conducted interference in the charging module cluster.

[0015] The fourth aspect of this application provides a computer-readable storage medium storing a program that, when executed on a computer, performs the active suppression method for conducted interference of a charging module cluster, as described in the first aspect and any optional method of the first aspect.

[0016] As can be seen from the above technical solutions, this application has the following advantages: First, a contribution model for the switching noise of each module is established based on the operating parameters. Then, the spectral superposition relationship of different switching transients is analyzed to obtain a timing optimization basis that reflects the interference coupling strength between modules. Based on the target relative timing relationship determined by the aforementioned spectral superposition rules, the switching transients of each module can form controlled superposition or mutual cancellation in the frequency domain. This allows the system to avoid both narrowband high-amplitude interference peaks in fully synchronous states and broadband noise rises in completely random states. Subsequently, adjustment commands can be issued and the switching actions of each module can be synchronously calibrated to ensure that the optimized phase relationship maintains consistency and dynamic stability within the cluster scale. This enables the charging module cluster to effectively suppress conducted interference in high-power parallel operation scenarios, increasing the probability of passing electromagnetic compatibility tests while maintaining the system's output stability and reliability. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic flowchart of an embodiment of the active suppression method for conducted interference in a charging module cluster provided in this application; Figure 2 A schematic flowchart of an embodiment of the active suppression method for conducted interference in charging module clusters provided in this application, which determines the relative temporal relationship of the target; Figure 3 A schematic flowchart of another embodiment of the active suppression method for conducted interference in charging module clusters provided in this application, which determines the relative temporal relationship of the target; Figure 4 A schematic flowchart of an embodiment of the active suppression method for conducted interference in charging module clusters provided in this application, which establishes a switching noise contribution model; Figure 5 A schematic diagram of an embodiment of the active suppression system for conducted interference in a charging module cluster provided in this application; Figure 6 A schematic diagram of an embodiment of the active suppression device for conducted interference of charging module clusters provided in this application. Detailed Implementation

[0019] This application provides an active suppression method and related apparatus for conducted interference of charging module clusters, which can effectively suppress conducted interference and maintain the output stability and reliability of the system even in high-power parallel operation scenarios.

[0020] In the active suppression method for conducted interference of charging module clusters provided in this application, the charging module cluster refers to a power supply system composed of multiple (two or more) power conversion units connected in parallel. In this system, each power conversion unit typically shares an input or output DC bus, and achieves high-power power output and regulation through cooperative work. It should be noted that the charging module cluster described in this application should be understood as a broad power electronics application architecture, rather than being limited to a specific hardware form. It can be manifested as a combination of multiple charging modules integrated inside a single high-power DC charging pile, or as a group of charging power units dynamically allocated according to power demand by a split charger; in addition, DC microgrid systems with multi-module parallel connection characteristics, parallel PCS energy storage converter systems, or server power arrays, as long as they involve multiple switching power supply modules operating in parallel and there is a risk of conducted interference coupling, are all included in the scope of the charging module cluster described in this application. This application does not limit the specific number of charging modules, individual power levels, circuit topology, or physical arrangement of the charging modules included in the charging module cluster.

[0021] Please see Figure 1 , Figure 1 An embodiment of the active suppression method for conducted interference in charging module clusters provided in this application includes: 101. Obtain the operating parameters of multiple charging modules operating in parallel. The operating parameters include at least the switching frequency, switching duty cycle, internal clock phase reference, and input / output voltage and current parameters. The main control unit in the charging module cluster establishes a real-time data connection with each parallel-operating charging module. Each charging module uses its internal digital signal processor (DSP) or microcontroller (MCU) to read the current values ​​of the underlying control registers to obtain its own operating parameters. Among the obtained operating parameters, the switching frequency refers to the fundamental frequency at which the power devices of the charging module switch at the current moment; the switching duty cycle refers to the ratio of the on-time of the power device to the total cycle within one switching cycle, which is directly read from the comparator register of the PWM generator; the internal clock phase reference refers to the time difference or count deviation of the current count value of the PWM counter of each charging module relative to the system synchronization reference signal (such as a synchronization pulse or zero-crossing signal); and the input and output voltage and current parameters are real-time operating data acquired through voltage and current transformers and analog-to-digital converters (ADCs). In actual execution, when the central controller issues a synchronization sampling command, each charging module needs to simultaneously latch the current PWM register state (to obtain the frequency and duty cycle) and the local clock count value (to obtain the phase reference). For the input and output voltage and current parameters, the instantaneous value at the time of the command or the average value within the control cycle is collected.

[0022] These operating parameters form the basis for establishing module noise characteristics and performing spectral comparisons. Among them, the switching frequency and duty cycle determine the frequency domain distribution characteristics of the interference noise generated by a single charging module. According to the Fourier series principle, changes in the duty cycle directly alter the amplitude coefficients of each harmonic, leading to differentiated noise energy distributions at different frequency points. Input and output voltage and current parameters determine the baseline intensity of the interference noise. Heavier loads (higher currents) or higher voltages result in larger di / dt and dv / dt during power device switching, leading to higher interference amplitudes. The internal clock phase reference is the most often overlooked but crucial parameter for cluster interference, characterizing the degree of time-domain alignment between different charging modules. In parallel systems, the superposition effect of multiple noise sources depends on their relative phases; in-phase superposition leads to a surge in peak values, while out-of-phase superposition cancels them out.

[0023] 102. Based on the operating parameters, establish a switching noise contribution model that reflects the degree of influence of each charging module on the cluster conducted interference. The switching noise contribution model is used to characterize the harmonic distribution characteristics of multiple charging modules under different switching transients. In a charging module cluster, although the hardware structures of each module are identical, subtle differences in the switching duty cycle result in inconsistent harmonic distribution characteristics among them. For example, even harmonics are extremely low at a duty cycle of 0.5, while they may be significant at a duty cycle of 0.3. Experience or bus voltage alone cannot distinguish the differences in the contribution of different charging modules to interference at specific frequencies. Without establishing a model for independent calculation, the system will be unable to differentiate the spectral differences between different charging modules at specific frequencies.

[0024] Based on this, in this embodiment, the operating parameters obtained in step 101 can be used to construct a switching noise contribution model reflecting the degree of influence of each charging module on the cluster's conducted interference using a preset analytical algorithm. Specifically, this switching noise contribution model treats the switching transients of the charging modules as excitation sources and uses Fourier transform or discrete time-domain analysis to convert the PWM pulse waveform in the time domain into spectral data in the frequency domain. Through this switching noise contribution model, the system can deterministically obtain the spectral characteristics of each charging module as an independent interference source under the current operating conditions, quantifying the noise amplitude and distribution characteristics of each individual module.

[0025] 103. Analyze the switching transients of multiple charging modules under the current operating conditions using the switching noise contribution model, and calculate the spectral superposition relationship of multiple charging modules under the current operating conditions. The switching noise contribution model generated in step 102 is used, combined with the internal clock phase reference values ​​of each charging module obtained in step 101, to synthesize and calculate the electromagnetic interference characteristics of the cluster under the current operating conditions. Specifically, this calculation process performs vector operations in the frequency domain based on the superposition principle of linear systems. For each target frequency point, the noise generated by each charging module is regarded as a vector signal with a specific amplitude and a specific phase. The amplitude data comes from the switching noise contribution model, and the phase data is determined by the internal clock phase reference value and harmonic order of each module. The spectral superposition relationship is the complex sum of the noise vectors of all parallel charging modules at each frequency point. This calculation process traverses the entire frequency band of interest, and finally generates synthetic spectral data reflecting the conducted interference intensity at the total output of the cluster. This data can express which frequency points' noise energy is superimposed and enhanced due to phase consistency under the current switching sequence, and which frequency points' noise energy cancels each other out.

[0026] By analyzing the synthesized spectral data, it is possible to identify which modules are superimposed under what phase relationship to form the most prominent harmonic peak. This analysis result can provide a clear direction for adjustment and optimization target for subsequent timing optimization.

[0027] 104. Determine the target relative timing relationship based on the spectrum superposition relationship. The target relative timing relationship includes at least the switching phase offset of multiple charging modules and the order of switching action triggering. The core of step 104 is to adjust the relative phase relationship of each charging module based on the distribution of interference energy in the frequency domain, so as to effectively reduce the frequency points with high amplitude in the spectrum. Based on this spectral superposition relationship, a target relative timing relationship that can reduce the amplitude of cluster conducted interference is determined. This target relative timing relationship includes at least two core parameters: switching phase offset and the order of switching action triggering. The switching phase offset is used to adjust the phase delay of the PWM carrier signal of each charging module relative to the system's main synchronization reference, so that the main harmonic components among the charging modules are distributed in the frequency domain, thereby reducing the interference amplification caused by in-phase superposition. The order of switching action triggering is defined as the sequence in which the switching cycles of each charging module are started on the time axis, so as to ensure that the actual execution process strictly conforms to the phase offset planning.

[0028] In practical implementation, by analyzing the amplitude and gain direction of the synthesized spectrum at key frequency points, the frequency points with the greatest impact on cluster conducted interference can be identified, and the phase contribution of each charging module at these frequency points can be further tracked. Based on the vector superposition direction and the interference enhancement law at that frequency point, the direction of the phase offset adjustment that causes the amplitude to decrease can be derived in reverse. Based on determining the phase offset, the triggering order of the switching actions between charging modules is further determined according to the difference in superposition contribution of modules at different frequency points. The triggering order is used to change the degree of overlap of transient current and voltage between modules in the time domain, thereby further controlling the superposition energy of the spectrum. For example, for a charging module that contributes significantly at the main harmonic, its triggering order can be appropriately delayed, causing the phase of its generated main harmonic to shift relative to other modules, thus weakening the enhancement effect of in-phase superposition.

[0029] By establishing the target relative timing relationship, the frequency domain energy can be redistributed by adjusting the time domain phase without changing the hardware structure, thereby reducing the overall conducted interference peak of the cluster.

[0030] 105. Based on the target relative timing relationship, issue switching parameter adjustment instructions to multiple charging modules, and synchronously calibrate the switching control timing within multiple charging modules so that multiple charging modules perform switching actions according to the calibrated relative timing.

[0031] Based on the determined target relative timing relationship, the main control unit of the charging module cluster generates switching timing adjustment instructions containing parameters specific to each charging module and sends them to the corresponding charging modules. These adjustment instructions include at least a switching phase offset setting, a trigger sequence identifier, and a matching calibration synchronization mark. Because each charging module's local crystal oscillator frequency has objective errors, such as frequency deviations caused by temperature drift or aging, if online correction is not performed according to a unified calibration synchronization mark, the preset phase difference will accumulate errors over time, causing the originally designed destructive interference to fail. Therefore, after receiving the instructions, each charging module needs to perform online correction on key control quantities such as the internal clock reference source, PWM carrier phase, modulation wave reference point, and soft-start synchronization point to forcibly align the switching action times of each charging module. It should be noted that, to ensure the stability of the calibration process, a gradual compensation strategy can be adopted for timing correction, gradually approaching the target relative phase over several switching cycles to avoid transient disturbances.

[0032] After the synchronization calibration is completed, all charging modules perform actual switching actions according to the updated relative timing relationship, so that they maintain the same operating mode as the target phase offset and triggering sequence throughout the entire operation, thereby achieving the purpose of global spectrum reconstruction and active suppression of conducted interference.

[0033] In this embodiment, firstly, a contribution model of the switching noise of each module is established based on the operating parameters. Then, the spectral superposition relationship of different switching transients is analyzed to obtain a timing optimization basis that reflects the interference coupling strength between modules. Based on the target relative timing relationship determined by the above spectral superposition law, the switching transients of each module can form controlled superposition or mutual cancellation in the frequency domain. This allows the system to avoid both narrowband high-amplitude interference peaks in a fully synchronous state and broadband noise rise in a completely random state. Subsequently, adjustment commands can be issued and the switching actions of each module can be synchronously calibrated to ensure that the optimized phase relationship maintains consistency and dynamic stability within the cluster scale. This enables the charging module cluster to effectively suppress conducted interference in high-power parallel operation scenarios, increasing the probability of passing electromagnetic compatibility tests while maintaining the system's output stability and reliability.

[0034] In some specific embodiments, the suppression strategy can be dynamically updated in response to changes in load and power grid environment. Specifically, this includes: continuously monitoring operating parameters and determining whether the change in operating parameters exceeds a preset operating condition change threshold; when the change in operating parameters exceeds the preset operating condition change threshold, triggering a dynamic update of the switching noise contribution model, and re-executing the steps of calculating the spectrum superposition relationship, determining the target relative timing relationship, and issuing switching parameter adjustment instructions.

[0035] After the charging module cluster enters steady-state operation, the operating parameters of each charging module can be continuously acquired. The real-time acquired parameter values ​​are compared with the baseline parameter values ​​locked during the last model calculation, and the difference or rate of change between the two is calculated. This change is then compared with a preset operating condition change threshold. This threshold is usually set as the boundary of a physical quantity that will significantly affect the harmonic distribution (e.g., load power change exceeding 5%, or input voltage fluctuation exceeding 10V). If the change does not exceed the threshold, the system maintains the current timing control strategy to maintain system stability. Once it is determined that the change exceeds the threshold, a dynamic update mechanism is triggered. This involves re-executing the steps of calculating the spectrum superposition relationship, determining the target relative timing relationship, and issuing switching parameter adjustment instructions based on the latest operating parameters, generating a target relative timing relationship adapted to the current new operating condition, and completing a new round of synchronization calibration. Through the above dynamic update mechanism, this embodiment enables the entire active suppression strategy to adaptively adjust in real time according to changes in the cluster's operating conditions, thereby ensuring the stability and effectiveness of conducted interference suppression under actual operating conditions.

[0036] In some specific embodiments, after determining the target relative timing relationship based on the spectrum superposition relationship, and before issuing switching parameter adjustment instructions to multiple charging modules based on the target relative timing relationship, it is also possible to: apply a controllable micro-perturbation frequency to the switching frequency of each charging module, wherein the amplitude of the micro-perturbation frequency is distributed randomly or in a preset sequence within the range of ±1% to 3%.

[0037] After determining the target relative timing relationship based on the aforementioned spectral superposition relationship, and before issuing switching parameter adjustment commands to multiple charging modules, a controllable micro-perturbation frequency can be applied to the switching frequency of each charging module. The amplitude of the micro-perturbation frequency can vary within the range of ±1% to 3%, and its variation can be generated based on a random distribution or modulated according to a preset sequence distribution. By introducing this micro-perturbation frequency, the spectral components between adjacent switching cycles are slightly diffused in a local range, thereby further reducing the concentrated harmonic energy peaks without changing the overall switching strategy and energy transfer characteristics. This discretizes the spectral energy near key frequency points, effectively pre-dispersing noise energy.

[0038] In step 104, an optimization algorithm can be used to determine the target relative temporal relationship. See also... Figure 2 , Figure 2 An embodiment of the active suppression method for conducted interference in charging module clusters provided in this application, which determines the relative temporal relationship of a target, includes: 201. Based on the spectral superposition relationship, initial switching phase offsets are set for multiple charging modules in the multi-dimensional phase space. The initial triggering order is assigned according to the influence of each charging module in the switching noise contribution model and the topological position between the charging modules. The initial switching phase offsets are assigned in the range of 0–360° in an equal division manner or in sequence according to the golden angle. First, based on the spectral superposition relationship analysis, the harmonic phase difference and superposition trend of each charging module at key frequency points are analyzed to identify phase combinations that are prone to peak resonance. Then, all charging modules are mapped to a multi-dimensional phase space, where each dimension corresponds to the PWM carrier phase of one module. Within this multi-dimensional phase space, the starting point for optimization needs to be determined, namely the initial switching phase offset and the initial triggering sequence.

[0039] Two strategies can be used for the initial switching phase offset: the equal division strategy or the golden angle strategy. The equal division strategy divides the 0–360° range into intervals equal to the number of modules, with each module corresponding to a unique phase offset angle. This strategy is suitable for scenarios where the operating parameters of charging modules are highly consistent. The golden angle strategy assigns phases to each module sequentially according to the golden ratio (approximately 137.5°), resulting in a more uniform distribution of switching transients in the frequency domain and avoiding harmonic phase concentration in consecutive modules. This strategy is suitable for scenarios with a large number of charging modules and significant parameter differences. Specifically, the initial phase offset for each module is determined based on the principle of avoiding harmonic peak enhancement and reducing the superposition energy in key frequency bands.

[0040] The initial triggering order can be allocated based on the interference weight of each module in the switching noise contribution model and its physical topology location. Modules with larger weights can be prioritized or postponed to ensure that the initial configuration is reasonably distributed across the spectrum. The usual strategy is to prioritize the high-weight modules that contribute the most to the interference weight and arrange them at the maximum interval on the time axis, or to force adjacent modules to be electrically staggered.

[0041] 202. The initial switch phase offset and initial trigger sequence are iteratively adjusted using a phase optimization algorithm to obtain an optimized phase distribution. The goal of the iterative adjustment is to minimize the peak value of the cluster conducted interference spectrum of multiple charging modules under the current operating conditions, taking into account the transient harmonic contribution between charging modules, the switching duty cycle and the power level differences. Using the initial phase offset and triggering sequence generated in step 201 as input, that is, treating the phase offset and triggering sequence of each module as optimization variables, the phase optimization algorithm is called for iterative calculation. The specific optimization algorithm can be based on gradient descent, particle swarm optimization, or genetic algorithms, etc., and is not limited here. The optimization objective is to minimize the peak point with the largest amplitude in the synthesized spectrum, while considering the transient harmonic contributions between modules, the difference in PWM duty cycle, and the difference in power level of each module. Since the PWM waveform under different duty cycles has different harmonic zeros and peak distributions, the phase optimization algorithm will simulate phase adjustment and calculate the vector superposition results under different phase combinations.

[0042] By evaluating the spectral superposition effect of each iterative combination in the multidimensional phase space, the phase offset and triggering order are gradually adjusted until the preset convergence condition or the upper limit of the number of iterations is reached. The resulting phase distribution can effectively reduce the in-phase superposition effect at a specific frequency point and achieve reasonable dispersion of noise energy in the time and frequency domains, thereby obtaining an optimized phase distribution.

[0043] 203. Determine the relative timing relationship of the target based on the switch phase offset and triggering sequence obtained after iterative adjustment.

[0044] The optimized phase distribution, i.e., phase offset and trigger sequence, is summarized to generate target relative timing parameters for each charging module. This target relative timing relationship includes the phase delay angle of the PWM signals of each module and the order of switching actions triggered on the time axis.

[0045] In this embodiment, an initial switching phase offset is set for each charging module in a multi-dimensional phase space and iteratively adjusted using an optimization algorithm. Specifically, by equally dividing the initial phase or allocating it at a golden angle, the switching transients of the modules are dispersed in the frequency domain, avoiding severe in-phase superposition interference peaks in the initial stage. Furthermore, the phase offset and triggering sequence are iteratively adjusted using a phase optimization algorithm, which minimizes the cluster conducted interference peak at key frequency points while considering the transient harmonic contributions, duty cycles, and power level differences between modules. The resulting target relative timing relationship ensures that each charging module operates strictly according to the calculated phase and triggering sequence in physical execution. Thus, without changing the hardware structure, frequency domain energy redistribution is achieved by adjusting the time-domain phase, significantly reducing the overall conducted interference level of the cluster and improving the EMI performance and reliability of the system.

[0046] For large-scale charging piles containing dozens or even hundreds of modules, directly performing global phase optimization across all permutations results in an exponential increase in computational complexity, making it difficult to meet real-time requirements. Therefore, in step 104, the relative temporal relationship of the target can be determined through subgrouping and cross-group phase control strategies, decomposing the global optimization problem into several small-scale local problems. Please refer to [link to relevant documentation]. Figure 3 , Figure 3 Another embodiment of the active suppression method for conducted interference in charging module clusters provided in this application, which determines the relative temporal relationship of the target, includes: 301. Based on the spectrum superposition relationship and the number, power level, heat load and location relationship of multiple charging modules, the multiple charging modules are divided into several subgroups; The system first analyzes the harmonic contribution characteristics of each charging module at key frequency points based on spectral superposition relationships, including harmonic amplitude, phase distribution, and cross-module superposition trends, thereby identifying module sets with significant coupling characteristics in the same frequency band. Modules with strong superposition effects at key frequency points, potentially contributing to cluster harmonic peaks, are preferentially grouped into the same subgroup for centralized local phase control optimization within the subgroup. Modules with significantly different contribution patterns on the spectrum are better suited to be split into different subgroups, enhancing independence and decoupling effects during cross-group scheduling. After initial screening based on spectral superposition relationships, the impact of module number, power level, heat load, and physical location on the grouping structure is further considered. Based on this, the modules are divided into several subgroups, ensuring a moderate number of modules and a balanced distribution of power level and heat load within each subgroup. The purpose of subgrouping is to decompose the clustering problem into a local optimization problem, reducing computational complexity while ensuring controllable dispersion of transient interference in the local spectrum within each subgroup.

[0047] In practical implementation, location relationships need to be considered. Modules that are physically close or share the same DC bus or filter branch should be grouped into the same subgroup, as physical proximity means the lowest coupling impedance and the most direct interference superposition effect. Secondly, considering power levels and thermal load conditions, modules with similar operating conditions should be categorized, or high-load and low-load modules should be mixed and grouped to balance energy within the group. Simultaneously, referring to spectral superposition relationships, the set of modules currently contributing primarily to co-channel interference should be identified and included in specific groups for centralized management. Finally, a unique group ID should be assigned to each subgroup, and a master-slave relationship or coordination mechanism should be established within the group.

[0048] 302. Based on the relative influence of each subgroup in the switching noise contribution model, calculate the local relative timing parameters within each subgroup, implement a local phase control strategy for the charging modules within the subgroup, so that the switching transients of the charging modules within each subgroup are staggered in the spectrum, in order to optimize the spectrum superposition relationship within the subgroup. Based on the relative influence of each subgroup in the switching noise contribution model, i.e., the noise amplitude weight, local relative timing parameters are calculated. These parameters specifically include the phase offset and triggering order of each module within the subgroup relative to the subgroup's reference clock. During this calculation, the module's power level, duty cycle difference, and local spectral contribution weight are used as constraints. The phase positions of high-influence modules are adjusted first to ensure a reasonable distribution of switching actions within the subgroup in the time domain and to achieve the expected peak-shaving effect in the frequency domain. The final generated local relative timing parameters quantitatively characterize the timing parameters when the subgroup reaches its minimum interference state. The calculation of these local relative timing parameters can specifically employ equal phase division, golden angle, or iterative adjustments based on local optimization algorithms; these details will not be elaborated upon here.

[0049] Based on the local relative timing parameters, a local phase control strategy is implemented for the charging modules within the subgroup, adjusting their switching phase offset and triggering sequence to ensure that the switching transients of each charging module exhibit a staggered distribution in the frequency domain. This staggered distribution means that the interference amplitudes at the same frequency point will not completely overlap in phase, thereby reducing the peak EMI within the subgroup. The goal of the local phase control strategy is to disperse the high-amplitude switching subharmonic energy in the synthesized noise spectrum within the subgroup across a wider frequency band, or to cancel out the main harmonic components, thus achieving a staggered distribution. This process does not consider the influence of external charging modules within the subgroup. By implementing local phase control and forcing the modules within the group to operate at staggered peaks, the effective value (RMS) of the ripple current flowing through the common impedance can be significantly reduced, thereby directly reducing the differential-mode interference intensity at the source.

[0050] 303. Perform cross-group timing coordination between subgroups, integrate the local relative timing parameters of each subgroup, and generate the target relative timing relationship.

[0051] After completing local phase control within each subgroup, cross-group timing coordination is required. Specifically, this involves integrating the calculated local relative timing parameters within each subgroup, considering the transient superposition effects between different subgroups, and generating the target relative timing relationship for the entire cluster. Cross-group coordination can further break potential phase synchronization phenomena between subgroups, redistributing the overall spectral energy of the cluster and thus reducing the amplitude of conducted interference at key frequency points.

[0052] In some specific embodiments, step 303 further includes: analyzing the transient superposition effect between subgroups based on the spectral superposition relationship and the local relative timing parameters within each subgroup, and determining the relative timing constraints of each subgroup in the overall cluster; assigning cross-group timing offsets to each subgroup according to the degree of influence of each subgroup in the switching noise contribution model and the relative timing constraints; and integrating the local relative timing parameters and cross-group timing offsets of each subgroup to generate the target relative timing relationship.

[0053] Specifically, after determining the local relative timing parameters within each subgroup, since the switching actions of different subgroups in the cluster may still overlap in phase on the time axis, thus forming enhanced transient interference at specific frequency points, it is also necessary to analyze the relative timing of each subgroup in the overall cluster. Using the spectral superposition data generated in step 103, the local synthetic spectrum of each subgroup is treated as an independent vector source. The complex superposition of the local spectra of all subgroups is calculated for each key frequency point in the spectrum. The amplitude direction of the spectral vector sum is then analyzed to identify which subgroups overlap in phase at that frequency point, leading to an increase in peak value. Based on the analysis results, phase adjustment constraints between subgroups, i.e., relative timing constraints, are determined to disperse or cancel out the main harmonics and high-amplitude harmonics between each subgroup as much as possible in the complex plane. After determining the relative timing constraints, specific cross-group timing offsets are further calculated to adjust the distribution of the switching phases of each subgroup within the overall cluster. Specifically, the interference contribution weight of each subgroup at key frequency points can be obtained through a switching noise contribution model. Based on relative timing constraints and contribution weights, a cross-group phase offset or trigger delay time (i.e., a cross-group timing offset) is assigned to each subgroup, causing the switching actions of high-weight subgroups to be staggered relative to other subgroups in time. Finally, for the charging modules within each subgroup, the local relative timing parameters are superimposed with the cross-group timing offset to calculate the absolute switching phase of the charging module relative to the global synchronization signal, thus determining the absolute phase value and trigger time of each charging module relative to the system's global master clock.

[0054] In step 102, the spectral characteristics and dynamic contribution of each charging module can be quantified, and a switching noise contribution model can be established accordingly. Please refer to [link / reference]. Figure 4 , Figure 4 An embodiment of the active suppression method for conducted interference in charging module clusters provided in this application, which establishes a switching noise contribution model, includes: 401. Sample the switching transient voltage and current waveforms of each charging module, and perform spectrum analysis based on the switching frequency, switching duty cycle and internal clock phase reference to obtain the harmonic amplitude sequence of the charging module under different switching transients. The transient voltage and current waveforms of each charging module are sampled, including voltage and current spikes generated during the switching on and off of the power devices. Then, the collected waveforms are analyzed by combining the switching frequency, switching duty cycle, and internal clock phase reference obtained in step 101. Spectrum analysis can be performed using discrete Fourier transform or time-domain window analysis methods to convert the time-domain PWM waveform into frequency-domain data, obtaining the amplitude distribution of each charging module at each harmonic frequency point. This yields the noise characteristics of a single charging module under different switching transients, quantifying the amplitude and distribution of different harmonics in the frequency domain.

[0055] 402. Analyze the changes in harmonic amplitude of each charging module under different operating conditions and sampling time periods, and perform weighted processing on the harmonic amplitude according to the input and output voltage and current parameters, switching duty cycle and module power level, and calculate the dynamic contribution index of each charging module to the cluster conducted interference in the key frequency range. The harmonic amplitude variations of each charging module under different operating conditions and sampling time periods were analyzed. Combined with the input voltage, current, output voltage and current, duty cycle, and module power level obtained in step 101, each harmonic amplitude was weighted. The weighting considered the influence of power level on switching transient current di / dt and voltage dv / dt, the influence of duty cycle differences on the amplitude of each harmonic, and the adjustment effect of input and output voltage and current on the noise reference intensity. Through weighting, the dynamic contribution index of each charging module within the key frequency range (e.g., 150kHz-30MHz) was calculated. This index reflects the actual contribution of the module to the total conducted interference of the cluster at a specific frequency and under specific operating conditions.

[0056] 403. Organize the dynamic contribution indicators of each charging module in the key frequency range according to the module number, corresponding frequency sequence and sampling time order to generate a multi-dimensional data structure switching noise contribution model, where each dimension corresponds to the charging module, frequency and time period respectively, and each element represents the contribution amplitude of the corresponding charging module at the corresponding frequency and time period.

[0057] The dynamic contribution indicators obtained in step 402 are organized according to module number, corresponding frequency sequence, and sampling time order to generate a multi-dimensional data structure switching noise contribution model. Each dimension of the model corresponds to the charging module, frequency, and time period, respectively. Each element value represents the contribution amplitude of a specific module at a specific time and for a specific frequency. Through the switching noise contribution model, the specific contribution of each module to the cluster conducted interference at different times and frequencies can be clearly quantified, and it can be quickly indexed and called by subsequent steps.

[0058] The active suppression system for conducted interference in the charging module cluster provided in this application is described in detail below. Please refer to [link / reference]. Figure 5 , Figure 5 Another embodiment of the active suppression system for conducted interference in a charging module cluster provided in this application includes: The acquisition unit 501 is used to acquire the operating parameters of multiple charging modules that work in parallel. The operating parameters include at least the switching frequency, switching duty cycle, internal clock phase reference, and input and output voltage and current parameters. Unit 502 is established to establish a switching noise contribution model based on operating parameters to reflect the degree of influence of each charging module on the cluster conducted interference. The switching noise contribution model is used to characterize the harmonic distribution characteristics of multiple charging modules under different switching transients. The calculation unit 503 is used to analyze the switching transient of multiple charging modules under the current operating conditions using the switching noise contribution model, and to calculate the spectral superposition relationship of multiple charging modules under the current operating conditions. The determining unit 504 is used to determine the target relative timing relationship based on the spectrum superposition relationship. The target relative timing relationship includes at least the switching phase offset of multiple charging modules and the order of switching action triggering. The execution unit 505 is used to issue switching parameter adjustment instructions to multiple charging modules according to the target relative timing relationship, and to synchronously calibrate the switching control timing inside the multiple charging modules so that the multiple charging modules perform switching actions according to the calibrated relative timing.

[0059] In this embodiment, the functions of each unit are the same as described above. Figures 1 to 4 The steps in the method embodiments shown correspond to those in the examples, and will not be repeated here.

[0060] This application also provides an active suppression device for conducted interference in a charging module cluster; please refer to [link / reference]. Figure 6 , Figure 6 One embodiment of the active suppression device for conducted interference in a charging module cluster provided in this application includes: Processor 601, memory 602, input / output unit 603, bus 604; The processor 601 is connected to the memory 602, the input / output unit 603, and the bus 604; The memory 602 stores a program, and the processor 601 calls the program to execute an active suppression method for conducted interference of any of the charging module clusters mentioned above.

[0061] This application also relates to a computer-readable storage medium on which a program is stored, which, when run on a computer, causes the computer to execute an active suppression method for conducted interference of any of the charging module clusters described above.

[0062] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0063] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0064] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0065] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0066] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for actively suppressing conducted interference in a charging module cluster, characterized in that, The active inhibition method includes: Obtain the operating parameters of multiple charging modules operating in parallel, including at least the switching frequency, switching duty cycle, internal clock phase reference, and input / output voltage and current parameters; Based on the aforementioned operating parameters, a switching noise contribution model is established to reflect the degree of influence of each charging module on the cluster's conducted interference. The switching noise contribution model is used to characterize the harmonic distribution characteristics of multiple charging modules under different switching transients. The switching noise contribution model is used to analyze the switching transients of the multiple charging modules under the current operating conditions, and the spectral superposition relationship of the multiple charging modules under the current operating conditions is calculated. The target relative timing relationship is determined based on the spectrum superposition relationship, and the target relative timing relationship includes at least the switching phase offset of the plurality of charging modules and the order of switching action triggering; According to the target relative timing relationship, the switch parameter adjustment command is issued to the multiple charging modules, and the switch control timing inside the multiple charging modules is synchronously calibrated so that the multiple charging modules perform switching actions according to the calibrated relative timing. Determining the target relative temporal relationship based on the spectral superposition relationship includes: Based on the spectrum superposition relationship and the quantity, power level, heat load, and location relationship of the multiple charging modules, the multiple charging modules are divided into several subgroups; Based on the relative influence of each subgroup in the switching noise contribution model, the local relative timing parameters within each subgroup are calculated, and a local phase control strategy is implemented on the charging modules within each subgroup to make the switching transients of the charging modules within each subgroup exhibit staggered peak distribution in the spectrum, so as to optimize the spectral superposition relationship within the subgroup. Cross-group timing coordination is performed between subgroups, integrating the local relative timing parameters of each subgroup to generate the target relative timing relationship.

2. The active suppression method according to claim 1, characterized in that, The cross-group timing coordination between subgroups integrates the local relative timing parameters of each subgroup to generate a target relative timing relationship, including: Based on the aforementioned spectral superposition relationship and the local relative timing parameters within each subgroup, the transient superposition effect between subgroups is analyzed, and the relative timing constraints of each subgroup in the overall cluster are determined. Based on the degree of influence of each subgroup in the switching noise contribution model and the relative timing constraints, a cross-group timing offset is assigned to each subgroup. The target relative timing relationship is generated by integrating the local relative timing parameters of each subgroup and the cross-group timing offset.

3. The active suppression method according to claim 1, characterized in that, The establishment of a switching noise contribution model based on the operating parameters, reflecting the degree of influence of each charging module on the cluster's conducted interference, includes: The switching transient voltage and current waveforms of each charging module are sampled, and spectrum analysis is performed based on the switching frequency, the switching duty cycle and the internal clock phase reference to obtain the harmonic amplitude sequence of the charging module under different switching transients. The changes in harmonic amplitude of each charging module under different operating conditions and sampling time periods are analyzed. The harmonic amplitude is weighted according to the input and output voltage and current parameters, switching duty cycle and module power level. The dynamic contribution index of each charging module to the cluster conducted interference in the key frequency range is calculated. The dynamic contribution indicators of each charging module within the key frequency range are organized according to the module number, corresponding frequency sequence, and sampling time order to generate a multi-dimensional data structure switching noise contribution model. Each dimension corresponds to the charging module, frequency, and time period, and each element represents the contribution amplitude of the corresponding charging module at the corresponding frequency and time period.

4. The active suppression method according to claim 1, characterized in that, The active inhibition method further includes: The operating parameters are continuously monitored, and it is determined whether the change in the operating parameters exceeds a preset threshold for change in operating conditions. When the change in the operating parameters exceeds the preset operating condition change threshold, the dynamic update of the switching noise contribution model is triggered, and the steps of calculating the spectrum superposition relationship, determining the target relative timing relationship, and issuing the switching parameter adjustment command are re-executed.

5. The active suppression method according to any one of claims 1 to 4, characterized in that, After determining the target relative timing relationship based on the spectral superposition relationship, and before issuing switching parameter adjustment instructions to the plurality of charging modules based on the target relative timing relationship, the active suppression method further includes: A controllable micro-perturbation frequency is applied to the switching frequency of each charging module. The amplitude of the micro-perturbation frequency is distributed randomly or in a preset sequence within the range of ±1% to 3%.

6. An active suppression system for conducted interference in a charging module cluster, characterized in that, The system includes: The acquisition unit is used to acquire the operating parameters of multiple charging modules that operate in parallel. The operating parameters include at least the switching frequency, switching duty cycle, internal clock phase reference, and input / output voltage and current parameters. A model is established based on the operating parameters to create a switching noise contribution model that reflects the degree of influence of each charging module on the cluster conducted interference. The switching noise contribution model is used to characterize the harmonic distribution characteristics of multiple charging modules under different switching transients. The calculation unit is used to analyze the switching transients of the multiple charging modules under the current operating conditions using the switching noise contribution model, and to calculate the spectral superposition relationship of the multiple charging modules under the current operating conditions. The determining unit is used to determine the target relative timing relationship based on the spectrum superposition relationship, wherein the target relative timing relationship includes at least the switching phase offset of the plurality of charging modules and the order of switching action triggering; An execution unit is used to issue switching parameter adjustment instructions to the plurality of charging modules according to the target relative timing relationship, and to synchronously calibrate the switching control timing inside the plurality of charging modules so that the plurality of charging modules perform switching actions according to the calibrated relative timing. The determining unit is specifically used for: Based on the spectrum superposition relationship and the quantity, power level, heat load, and location relationship of the multiple charging modules, the multiple charging modules are divided into several subgroups; Based on the relative influence of each subgroup in the switching noise contribution model, the local relative timing parameters within each subgroup are calculated, and a local phase control strategy is implemented on the charging modules within each subgroup to make the switching transients of the charging modules within each subgroup exhibit staggered peak distribution in the spectrum, so as to optimize the spectral superposition relationship within the subgroup. Cross-group timing coordination is performed between subgroups, integrating the local relative timing parameters of each subgroup to generate the target relative timing relationship.

7. An active suppression device for conducted interference in a charging module cluster, characterized in that, The device includes: Processor, memory, input / output units, and bus; The processor is connected to the memory, the input / output unit, and the bus; The memory stores a program, which the processor invokes to perform the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains a program that, when executed on a computer, performs the method as described in any one of claims 1 to 5.

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