Communication power supply core capacity system based on pulse hybrid modulation

Abstract

The application relates to the technical field of communication power supply, in particular to a communication power supply nuclear capacity system based on pulse mixed modulation. The communication power supply nuclear capacity system comprises an electric energy conversion module, a storage battery management module, an energy distribution module, a nuclear capacity function module and an integrated monitoring module, the electric energy conversion module is connected between an AC input side and a DC output side in the communication power supply nuclear capacity system, the storage battery management module is connected with a storage battery pack, the energy distribution module is connected between the DC output side and a load, the nuclear capacity function module is connected between the storage battery pack and the electric energy conversion module, and the integrated monitoring module is used for dynamically adjusting a pulse modulation mode according to a health state evaluation. The application can realize intensive management and control, adjusts the modulation mode by dynamically adapting the storage battery health state, considers efficiency, electromagnetic interference and battery protection, delays battery aging, and the nuclear capacity operation and maintenance is convenient and highly intelligent, and the core load power supply can be accurately guaranteed.

Description

Technical Field

[0001] This application relates to the field of communication power supply technology, and in particular to a communication power supply core capacity system based on pulse hybrid modulation. Background Technology

[0002] As communication networks move towards intensification and intelligence, the power systems of communication base stations and data centers place higher demands on the reliability, energy efficiency, and intelligent operation and maintenance of battery backup power supplies. As the core backup power source, the health status of battery banks directly determines the system's emergency power supply capability, and capacity testing is a key means of assessing and ensuring their health. However, existing communication power supply capacity testing systems still have significant technical bottlenecks: First, most power conversion modules use a single pulse modulation mode, which cannot be dynamically adjusted according to the battery's health status, making it difficult to coordinate and optimize electromagnetic interference control, system efficiency, and battery protection. When battery health declines, aging is accelerated and interference exceeds standards. Second, the subsystems lack a centralized collaborative control mechanism, data interaction is poor, and the linkage between capacity testing and daily power supply is weak, making it difficult to achieve centralized management and control. Third, capacity testing relies on on-site manual operation, with insufficient support for remote control and hardware hot-swapping, resulting in low operation and maintenance efficiency due to low intelligence. Fourth, modulation mode parameter adjustments rely on manual experience, lacking an optimized decision-making mechanism, and cannot adapt to real-time operating conditions such as load rate and voltage ripple, resulting in insufficient system stability and adaptability.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to propose a communication power supply core capacity system based on pulse hybrid modulation, which can dynamically adapt the modulation mode to the health status of the battery, taking into account efficiency, electromagnetic interference and battery protection, and delaying battery aging; the core capacity operation and maintenance is convenient and highly intelligent, and can accurately guarantee the power supply of core loads.

[0005] To achieve the above objectives, one aspect of this application proposes a communication power supply capacity integration system based on pulse hybrid modulation, the communication power supply capacity integration system comprising: A power conversion module is connected between the AC input side and the DC output side of the communication power supply core system. The pulse modulation mode of the power conversion module includes pulse width modulation mode and pulse frequency modulation mode. The power conversion module is used for AC-DC rectification power supply. A battery management module is connected to the battery pack. The battery management module is used to monitor the operating status of the battery pack in real time and output the health status estimate of the battery pack. An energy distribution module is connected between the DC output side and the load. The energy distribution module is used to distribute power and provide power-down protection based on load priority. The capacity function module is connected between the battery pack and the power conversion module. The capacity function module is used to perform capacity testing on the battery pack and to perform capacity charging and discharging of the battery pack together with the power conversion module. An integrated monitoring module is used to dynamically adjust the pulse modulation mode of the power conversion module according to the health status estimate, and to control the power conversion module, the battery management module, the energy distribution module and the core capacity function module connected in communication to perform data interaction.

[0006] In some embodiments, dynamically adjusting the pulse modulation mode of the power conversion module based on the health status estimate includes the following steps: The load rate, weighted ripple factor, health status estimate, and consistency parameters of the power conversion module, as well as the battery pack, are acquired in real time. The target pulse modulation mode is obtained by judging based on the load rate, the weighted ripple factor, the health status estimate, and the consistency parameter. After the target pulse modulation mode generation control command is sent to the power conversion module, the power conversion module is driven to operate in the target pulse modulation mode.

[0007] In some embodiments, determining the target pulse modulation mode based on the load rate, the weighted ripple factor, the health status estimate, and the consistency parameter includes the following steps: When the health status estimate is less than the preset health status threshold, the pulse width modulation mode is used as the target pulse width modulation mode; after the integrated monitoring module controls the power conversion module to adopt or switch to the pulse width modulation mode, the optimal switching frequency and optimal dead time of the pulse width modulation mode are determined by the load rate, the weighted ripple factor and the consistency parameter. When the health status estimate is greater than or equal to the preset health status threshold, the pulse width modulation mode or the pulse frequency modulation mode is taken as the target pulse modulation mode. The optimal switching frequency and optimal dead time of the current target pulse modulation mode are obtained by solving the load rate, the weighted ripple factor and the consistency parameter according to the multi-objective optimization function in the preset collaborative decision rule.

[0008] In some embodiments, the preset collaborative decision-making rule includes the multi-objective optimization function and constraints; The multi-objective optimization function is: ; in, Let represent the multi-objective optimization function to be minimized. Indicates pulse modulation mode, Indicates the switching frequency. This represents the basic weighting coefficient for the efficiency term. This represents the transformation efficiency model. This represents the basic weighting coefficient for the electromagnetic interference term. This represents the electromagnetic interference assessment model. This represents the weighted ripple factor. Indicates load rate, This represents the dynamic health weighting function. Indicates health influencing factors; The formula for calculating the constraint condition is as follows:

[0009] in, Indicates the junction temperature of power devices. Indicates the maximum allowable junction temperature. Indicates the output voltage ripple. Indicates the maximum allowable peak ripple. Indicates pulse modulation mode, Indicates the switching frequency. This indicates the load rate.

[0010] In some embodiments, frequency limiting logic is triggered when the health status estimate is less than the preset health status threshold. The frequency limiting logic includes dynamically adjusting the maximum upper limit of the switching frequency and the dynamic constraint range of the switching frequency search range. The maximum switching frequency upper limit is adjusted according to a frequency upper limit function, which is: ; in, This indicates the upper limit of the dynamic maximum switching frequency corresponding to the battery's health status. Indicates the rated maximum permissible switching frequency. Represents the proportionality coefficient and Between 0 and 1, This represents the current health status estimate. This represents the preset health status threshold; The dynamic constraint range for the switching frequency search range is: ; in, This indicates the actual switching frequency of the power conversion module. Indicates the lower limit of the switching frequency. This means that the smaller of the fixed maximum switching frequency and the dynamic maximum switching frequency limit is taken as the final upper limit of the switching frequency.

[0011] In some embodiments, the preset collaborative decision-making rule is optimized through a loss function; the process of performing feedback optimization includes: Obtain historical optimization data over a period of time, including: the pulse modulation mode, the load rate, the weighted ripple factor, the health status estimate, and the consistency parameter; The loss function is constructed based on the historical optimization data. The loss function is minimized using the gradient descent algorithm to obtain the optimized parameters of the preset collaborative decision-making rule; The preset collaborative decision-making rule is optimized by feedback based on the optimization parameters to obtain the optimized preset collaborative decision-making rule; The loss function is: ; in, Represents the total loss function. Indicates the total number of samples collected. Indicates the first Each sampling time, Indicates the first Monitor the rate of degradation in battery health at all times. This represents the balance coefficient between the multi-objective optimization objective and the battery degradation term. Describe the objective function. Indicates the first Time modulation mode, Indicates the first Constant switching frequency, Indicates the first real-time load rate Indicates the first Time-weighted ripple factor, Indicates the first Real-time health status assessment Indicates the first Time consistency parameters.

[0012] In some embodiments, the core capacity function module includes a switching unit, a DC-DC boost discharge module, a DC-DC constant current charging module, and a main control module; The DC-DC boost discharge module adopts a transformer isolation design and supports hot-swapping. The main control module is connected to the integrated monitoring module for communication. The switching unit includes a normally closed contactor, a normally open contactor, and an online diode. In float charging mode, the normally closed contactor is closed and the normally open contactor is open. The battery pack is connected to the rectifier through the normally closed contactor and the online diode, and the rectifier directly float charges the battery pack. When performing capacity testing, the integrated monitoring module controls the switching unit to connect the battery pack to the DC-DC boost discharge module and the DC-DC constant current charging module for charging and discharging.

[0013] In some embodiments, when the battery management module monitors the operating status of the battery pack in real time, it detects an alarm signal from a lagging cell and then amplifies and corrects the health impact factor based on a penalty coefficient to obtain the corrected health impact factor. The formula for calculating the penalty coefficient is as follows: ; in, Indicates the penalty coefficient. Represents the consistency parameter. This indicates the alarm signal for the lagging unit. This represents the penalty magnitude coefficient. Represents the hyperbolic tangent function. This represents the consistency sensitivity coefficient.

[0014] In some embodiments, the integrated monitoring module supports IEC61850 protocol and HTTPS encrypted transmission.

[0015] In some embodiments, when the pulse width modulation mode is used as the pulse modulation mode of the power conversion module, the output voltage is stabilized by adjusting the duty cycle of the switch conduction time. When the pulse frequency modulation mode is used as the pulse modulation mode of the power conversion module, the output voltage is stabilized by reducing the switching frequency or skipping part of the switching cycle.

[0016] The embodiments of this application include at least the following beneficial effects: This application provides a communication power supply capacity system based on pulse hybrid modulation. This solution adopts a power conversion module with both pulse width modulation and pulse frequency modulation modes, combined with an integrated monitoring module that dynamically adjusts the modulation strategy based on the battery health status estimate. This ensures stable and reliable AC-DC rectified power supply while adaptively optimizing the working mode according to the actual battery health status, effectively balancing energy conversion efficiency and electromagnetic interference suppression, reducing the impact of high-frequency ripple and electrical stress on the battery, delaying battery aging, and extending service life. The integrated monitoring module coordinates and controls the power conversion, battery management, energy distribution, and capacity control modules, achieving centralized data interaction and linkage operation of the entire system. This overcomes the drawbacks of traditional independent and decentralized subsystems, improving overall control accuracy and response speed. The capacity control module and the power conversion module work together to complete battery capacity charging and discharging and capacity testing, eliminating the need for additional independent capacity control equipment, simplifying the system structure, and reducing hardware redundancy. The energy distribution module intelligently allocates power according to load priority and performs graded power-down protection, prioritizing power supply to core loads during mains power interruptions or battery discharge, improving the continuity and safety of the communication system's power supply. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of a communication power supply core capacity system based on pulse hybrid modulation provided in an embodiment of this application; Figure 2 This is an architecture diagram of a communication power supply core system based on pulse hybrid modulation; Figure 3 This is a schematic diagram of the battery pack in a steady-current state. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0019] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0020] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] In related technologies, existing communication power supply capacity systems suffer from numerous technical defects. Their power conversion modules often employ a single pulse width modulation (PWM) or pulse frequency modulation (PFM) mode. This modulation mode is fixed and cannot be dynamically switched based on the battery pack's health status. When battery health deteriorates, the system cannot adjust the modulation mode to reduce the impact of electromagnetic interference on the battery, thus accelerating battery aging and shortening its lifespan. Furthermore, the system lacks a multi-objective collaborative optimization mechanism, failing to comprehensively optimize system conversion efficiency, electromagnetic interference assessment values, and battery health impact factors. This leads to blind adjustment of modulation parameters, making it difficult to simultaneously address the multiple requirements of electromagnetic interference suppression, system energy saving, and battery protection. In addition, power conversion... The battery management, capacity testing, and energy distribution modules operate independently with poor coordination, lacking centralized monitoring and control. Data exchange is not smooth, and the linkage between capacity testing and daily power supply is insufficient, making it impossible to achieve centralized management and control. The capacity function module design is imperfect, with most not supporting hot-swapping, and the remote controllability of the capacity testing process is poor, resulting in high operation and maintenance costs. At the same time, this module lacks linkage with the battery health status, making the capacity testing less targeted and effective. Finally, the switching of modulation modes and parameter adjustments lack feedback optimization mechanisms, making it impossible to iteratively optimize decision rules based on long-term operating data such as load, weighted ripple factor, and changes in battery health. The system has weak adaptability and is difficult to adapt to complex and changing operating conditions.

[0023] In view of this, this application provides a communication power supply capacity system based on pulse hybrid modulation. This solution achieves data interoperability and centralized coordination among modules by constructing an integrated collaborative management and control architecture. It dynamically optimizes the modulation mode of the power conversion module based on the battery health status, establishes a multi-objective optimization mechanism and feedback optimization closed loop, and takes into account system conversion efficiency, electromagnetic interference suppression and battery health protection. This improves the intelligence and convenience of capacity testing, reduces operation and maintenance costs, and enhances the reliability and stability of the communication power supply system.

[0024] like Figure 1 As shown, Figure 1 This is a schematic diagram of a communication power supply capacity system based on pulse hybrid modulation provided in an embodiment of this application. Figure 1 The pulse hybrid modulation-based communication power supply capacity system includes a power conversion module, a battery management module, an energy distribution module, a capacity function module, and an integrated monitoring module. The power conversion module connects the AC input and DC output sides of the system, and its pulse modulation modes include pulse width modulation and pulse frequency modulation, used for rectification power supply between AC and DC. The battery management module connects to the battery pack, responsible for real-time monitoring of the battery pack's operating status and outputting a health status estimate. The energy distribution module connects the DC output side to the load, used for power distribution and power-down protection based on load priority. The capacity function module connects the battery pack and the power conversion module, used for performing capacity testing of the battery pack and cooperating with the power conversion module to complete the capacity charging and discharging of the battery pack. The integrated monitoring module dynamically adjusts the pulse modulation mode of the power conversion module based on the health status estimate provided by the battery management module, and simultaneously controls the power conversion module, battery management module, energy distribution module, and capacity function module connected to it for data interaction, enabling coordinated operation of all modules.

[0025] In some embodiments, such as Figure 2 and Figure 3 As shown, Figure 2 The system fully presents the connection relationships and energy flow of each core module. The system's AC input side is equipped with one and two AC input lines, connected to a dual-power automatic transfer switch 210 (ATS) to achieve redundant switching between the two mains power sources, ensuring continuous power supply. The ATS output is connected to multiple parallel AC / DC rectifier modules 240 (corresponding to the core units of the power conversion module). The outputs of these AC / DC rectifier modules are connected to the positive busbar 251 and the negative busbar 252, respectively, forming the system's DC bus to achieve AC-DC rectified power supply, providing a stable DC power source for the load and battery pack.

[0026] On the DC bus side, the positive busbar and negative busbar are connected to the power-down protection unit 253 and the shunt 254, respectively, and are also connected to the battery pack circuit and the energy distribution module 261. The battery pack circuit is equipped with two independent battery packs, namely the first battery pack 271 and the second battery pack 272. Each battery pack is connected to the positive busbar through contactors (K1, K2) and isolation diodes (D1, D2). A DC-DC module (corresponding to the DC-DC boost discharge unit of the core capacity function module) is also configured. The switching between the battery pack and the core capacity circuit is realized through contactors K3, K4 and K5. The negative terminal of the battery pack is connected to the negative busbar through the main contactor K6. The energy distribution module is connected to the DC busbar and the communication load 262, and undertakes the functions of power distribution and power-down protection based on load priority, providing stable power supply for the communication load. The system control side is equipped with an integrated monitoring module 220 and a display screen 230. The control unit is connected to the AC / DC module, ATS, battery pack circuit and energy distribution module to realize centralized coordination control, data acquisition and decision output. The display screen is used for local status display and operation interaction. A fuse F is configured on the negative busbar side to realize overcurrent protection and ensure the safe operation of the system. Figure 1 The architecture fully integrates the entire chain of power conversion, battery management, capacity testing, energy distribution, and centralized monitoring.

[0027] Figure 3 The connection relationships and current flow of the core capacity module, battery pack 324, rectifier 331, and load are clearly displayed, intuitively presenting the system's operating logic under float charging / charging conditions. The core circuit is divided into three main areas: the core capacity module and system control circuit are within the dashed box on the left; the battery pack and switching unit are within the dashed box in the middle; and the rectifier 331, electrical load 332, and DC bus are within the dashed box on the right.

[0028] Right side area: The rectifier, as the core unit of the power conversion module, converts AC power into 53.5V DC power. The positive terminal is connected to the BATC+ terminal 321 (DC bus positive terminal), and the negative terminal is connected to the 0V terminal (DC bus negative terminal). The DC bus supplies power to the electrical load and provides a float charge power source for the battery pack. The current flows out from the positive terminal of the rectifier, supplies power to the load through the BATC+ terminal, and finally flows back to the negative terminal of the rectifier.

[0029] Intermediate area: The positive terminal of the 48V battery pack is connected to the BATD+ terminal 322, and the negative terminal is connected to the BAT- terminal 323. The switching unit includes a normally closed contactor KO313, an online diode DO314, and a normally open contactor Km. In float charging / constant current charging mode, the normally closed contactor KO is closed and the normally open contactor Km315 is open. The battery pack is connected to the BATC+ terminal through KO and DO. The 53.5V DC output from the rectifier is used for float charging of the 48V battery pack through the closed KO and the open DO. The current flows from the BATC+ terminal through KO and DO to the BATD+ terminal, and finally flows into the positive terminal of the battery pack to complete the charging. The negative terminal of the battery pack is connected to the negative terminal of the DC bus through the BAT- terminal to form a complete circuit.

[0030] Left side area: The core capacity function module integrates a high-frequency DC / DC boost discharge module 311 and a high-frequency DC / DC constant current charging module 312. The input terminals of the two modules are connected to the BATD+ terminal 322 via a normally open contactor Km315, and the output terminals are connected to the BATC+ terminal 321. It also includes other related control, drive, communication, and display circuits, as well as a normally open contactor KC316 for controlling the on / off state of the negative circuit of the battery pack. During constant current charging, the DC / DC constant current charging module is connected to the circuit to provide precise constant current charging to the battery pack, realizing the charging recovery process of the core capacity test. The system power is taken from the battery pack, ensuring that the system control circuit operates normally and the load power supply is uninterrupted when the mains power is interrupted.

[0031] In some embodiments, the communication power supply core capacity system based on pulse hybrid modulation is composed of five core units deeply integrated together: a power conversion module, an integrated monitoring module, a battery management module, an energy distribution module, and a core capacity function module. This achieves end-to-end coordination of communication power supply, battery core capacity, status monitoring, energy scheduling, and operation and maintenance management, completely breaking down the technical barriers of traditional separate designs for communication power supply and core capacity devices. The power conversion module, as the system's energy conversion hub, connects the AC input side and DC output side of the communication power supply core capacity system. It employs high-frequency switching power supply technology and incorporates two sets of control logic: pulse width modulation (PWM) and pulse frequency modulation (PFM), forming a hybrid modulation architecture. It can dynamically and seamlessly switch between the two modes according to the instructions of the integrated monitoring module. In PWM mode, the switching frequency is fixed, and the output voltage is stabilized by adjusting the duty cycle, offering advantages such as fast dynamic response, concentrated output ripple spectrum, and controllable electromagnetic interference characteristics, making it suitable for normal power supply scenarios. In PFM mode, voltage stabilization is achieved by reducing the switching frequency or skipping cycles, significantly reducing switching losses under light loads and improving energy efficiency, making it suitable for special operating conditions such as core capacity charging and discharging. The module's digital signal controller can smoothly transition between modes within several switching cycles, synchronously adjust the drive waveform, avoid drastic fluctuations in output voltage and current, and also undertake AC-DC rectification power supply functions. It works in conjunction with the battery capacity control module to provide a charging and discharging circuit for battery capacity testing.

[0032] The integrated monitoring module, serving as the system's intelligent decision-making center, communicates with the power conversion module, battery management module, energy distribution module, and core capacity module, undertaking the core functions of centralized coordination control and end-to-end data interaction. The module continuously collects multi-dimensional data from each unit: from the power conversion module, it obtains current load rate, input / output voltage and current, and module temperature; from the battery management module, it obtains the battery pack health status estimate, individual cell voltage / temperature / internal resistance, and consistency indicators; and it collects the input AC weighted ripple factor through sensors. Based on this data, the integrated monitoring module incorporates preset collaborative decision-making rules, constructing an optimization function with system conversion efficiency, electromagnetic interference assessment value, and battery health impact factors as core objectives. This function is dynamically solved by combining real-time load rate, weighted ripple factor, and battery health status estimate to determine the optimal pulse modulation mode, switching frequency, dead time, and other key control parameters for the power conversion module. The core linkage mechanism of the integrated monitoring module uses battery health status estimation as the decision-making core: A preset health status threshold is set. When the battery health status is good, the optimization algorithm prioritizes system energy efficiency, allowing switching to PFM mode or high switching frequency operation under light load. When the health status estimation is below the threshold, the weight of reducing electrical stress is significantly increased, forcing the decision engine to output an operating point with better electromagnetic interference characteristics. This controls the power conversion module to switch to PWM mode and slows down the switching edge rate and attenuates high-frequency harmonics by reducing the switching frequency and increasing the dead time, providing a low-noise, low-ripple, and gentle power supply environment for the battery, delaying battery aging, and achieving preventative maintenance. Simultaneously, the integrated monitoring module supports the IEC61850 standard protocol and HTTPS encrypted transmission, realizing remote telemetry, remote signaling, remote control, and remote adjustment functions. It incorporates safety mechanisms such as KD testing, over / under voltage protection, and dual-machine linkage protection, ensuring that the load power supply is not affected during faults, guaranteeing system operational reliability.

[0033] The battery management module is directly connected to the battery pack. It monitors the voltage and surface temperature of each individual battery cell in real time through a high-precision acquisition circuit, periodically measures the internal resistance, and calculates and outputs the health status estimate of the battery pack based on an advanced algorithm model. At the same time, it determines whether there are any lagging cells, providing core data support for the optimization decision-making of the integrated monitoring module and the triggering of capacity testing. It is the basic unit of the system's battery health management.

[0034] The energy distribution module connects the DC output side and the communication load, undertaking the function of fine-grained energy scheduling: the energy distribution module has remote branch control function, and each DC output branch switch can be remotely turned on and off, which facilitates remote operation and maintenance and fault isolation; at the same time, it has built-in priority power-down protection logic, continuously monitors the DC bus voltage, and when the mains power is interrupted or the battery discharges to the low voltage alarm point, it remotely disconnects non-core load branches in batches according to the preset load priority from low to high, giving priority to ensuring the longest backup power supply time for core communication equipment, and realizing on-demand power distribution.

[0035] The capacity-controlled module connects the battery pack and the power conversion module. It consists of a switching unit, a transformer-isolated DC-DC boost discharge module, and a main control module, and is used for controlled remote capacity-controlled testing of the battery pack. The switching unit includes a normally closed contactor KO, a normally open contactor Km, and an online diode DO. The DC-DC boost discharge module adopts a transformer-isolated design to ensure that module failure during capacity-controlled discharge will not endanger the online load. It also supports hot-swapping, allowing for replacement and maintenance without power interruption, significantly improving operational efficiency. In float charging mode, normally closed contactor KO is closed and normally open contactor Km is open, allowing the battery pack to be directly online and float charged directly by the rectifier. The system power is drawn from the battery pack, ensuring uninterrupted power supply to the load when the mains power is interrupted. Upon receiving a remote or local capacity testing command, the integrated monitoring module controls the switching unit to open normally closed contactor KO and close normally open contactor Km through the main control module, switching the battery pack into the capacity testing circuit. The DC-DC boost discharge module boosts the battery power and then achieves constant current discharge through the power conversion module feedback circuit or an independent load. The module monitors voltage, current, and temperature throughout the process. Once the termination voltage threshold is reached, the discharge automatically stops, and the system enters a three-stage steady current charging recovery program. Data is automatically recorded and uploaded to the remote monitoring platform throughout the process, completing the battery capacity and health status assessment.

[0036] This system is physically integrated into a single cabinet or rack-mount device. Internally, it connects various functional modules to a unified hardware backplane and communication network, integrating communication power supply and battery capacity verification functions. Utilizing flexible modulation technology, it balances power supply stability with accurate capacity verification, achieving streamlined operation and maintenance. It features four core operating modes: In normal power supply mode, AC mains power is rectified and regulated to -48V DC by the power conversion module. One path powers the communication load through the energy distribution module, while the other path float-charges the battery bank on the DC bus. In backup discharge mode, when mains power is interrupted, the battery bank seamlessly switches over via a switching unit to power the load, ensuring uninterrupted communication. In capacity testing mode, test commands are initiated remotely or locally, and the integrated monitoring module coordinates all modules to complete constant current battery discharge, charging recovery, and health assessment. In intelligent operation and maintenance mode, the system collects operational data in real time, analyzes and makes decisions locally, and uploads the data to a remote master station, achieving intelligent operation and maintenance throughout the entire lifecycle.

[0037] In some embodiments, the pulse hybrid modulation-based communication power supply system achieves adaptive adjustment of the pulse modulation mode of the power conversion module through four stages: data acquisition, intelligent decision-making, instruction execution, and closed-loop optimization. This achieves a globally optimal balance between system efficiency, electromagnetic interference suppression, and battery health protection. Through quantitative calculation and multi-objective optimization functions, the battery health status and power conversion behavior are deeply linked to form an intelligent closed-loop control logic.

[0038] In the real-time data acquisition and quantitative calculation link, the integrated monitoring module synchronously acquires multi-dimensional operating parameters and performs standardized numerical calculation. First, the current total output current of the power conversion module is obtained and the rated output current of the system , through the calculation formula: ; the load factor L is obtained through calculation; meanwhile, the input voltage is sampled and subjected to Fast Fourier Transform (FFT), to extract the switching frequency fundamental wave and the amplitude of the frequency band of its second harmonic frequency band amplitude , through the calculation formula of weighted ripple factor: ; the weighted ripple factor R is obtained, wherein represents the frequency band weight coefficient, represents the rated input voltage. In addition, the module receives the state of health estimation H output by the battery management module (0<H≤1, where 1 represents a brand-new battery), and calculates the consistency parameter U through the ratio of the single-cell voltage standard deviation to the average voltage , and the calculation formula is: ; the consistency parameter provides complete quantitative input for subsequent decision-making.

[0039] In some embodiments, in the collaborative decision-making link based on the multi-objective optimization function, the integrated monitoring module determines the target pulse modulation mode of the power conversion module through preset collaborative decision-making rules based on the load factor, the weighted ripple factor and the battery state of health estimation. The preset collaborative decision-making rules include a multi-objective optimization function and constraint conditions. First, the multi-objective optimization function is defined: ; wherein, represents the multi-objective optimization function to be minimized; represents the pulse modulation mode; represents the switching frequency; represents the basic weight coefficient of the efficiency term; represents the conversion efficiency model, which is a binary function fitted based on historical data or a physical model, describing the given load rate , pulse modulation mode and switching frequency the estimated efficiency value under; represents the basic weight coefficient of the electromagnetic interference term, used to balance the inherent contradiction between efficiency and EMI; This represents the electromagnetic interference assessment model, along with the weighted ripple factor. Positive correlation, and also the switching frequency functions (usually) , For PFM mode, the bandwidth widening effect needs to be considered, and the average frequency or worst-case frequency may be used for calculation. (These are topology-related constants.) Indicates the weighted ripple factor; Indicates load rate; This represents a health impact factor, which is related to pulse modulation mode. and switching frequency The relevant function, used to quantify the potential stress of power supply noise on the battery, can be modeled as follows: ,in ; The health impact factor is an increasing function due to the bandwidth characteristics of PFM. ; The dynamic health weight function representing core innovation is defined as: ; in, For positive integers, To prevent small amounts from being removed, this function will increase significantly as the battery's health status declines and consistency deteriorates, thereby increasing the decision weight of health impact factors.

[0040] The formula for calculating constraints is: ; in, Indicates the junction temperature of power devices. Indicates the maximum allowable junction temperature. Indicates the output voltage ripple. Indicates the maximum allowable peak ripple. Indicates pulse modulation mode, Indicates the switching frequency. This indicates the load rate.

[0041] The decision variables are the modulation mode M (PWM or PFM) and the switching frequency. The constraints include module junction temperature not exceeding the upper limit and output ripple not exceeding the maximum allowable value. The optimal switching frequency and objective function value for both modes are solved using a hierarchical enumeration method, and the mode with the smaller function value is selected as the objective mode. When the health status estimate H is lower than the preset health status threshold... When this occurs, the system triggers dynamic frequency limiting logic. This logic includes dynamically adjusting the maximum switching frequency limit and dynamically constraining the switching frequency search range. The maximum switching frequency limit is adjusted according to a frequency limit function, which is: ,in This indicates the upper limit of the dynamic maximum switching frequency corresponding to the battery's health status. Indicates the rated maximum permissible switching frequency. Represents the proportionality coefficient and Between 0 and 1, This indicates an estimate of the current health status. This represents the preset health status threshold. Lowering the maximum switching frequency upper limit adjusts the dynamic constraint range of the switching frequency search range to... ;in, This indicates the actual switching frequency of the power conversion module. Indicates the lower limit of the switching frequency. This means taking the smaller of the fixed maximum switching frequency and the dynamic maximum switching frequency upper limit as the final upper limit of the switching frequency, thereby forcibly reducing electromagnetic interference.

[0042] In some embodiments, in the specific implementation of the multi-objective optimization function, a multi-objective optimization function is constructed with conversion efficiency, an electromagnetic interference assessment value calculated based on the input-side weighted ripple factor, and a health impact factor negatively correlated with the current health status estimate as objective terms. The current load rate, input-side voltage, weighted ripple factor, and current health status estimate are used as inputs to solve the multi-objective optimization function to obtain the target pulse modulation mode and the corresponding switching frequency and dead time parameters. Conversion efficiency model The characteristic curves of the power conversion module can be obtained by looking up tables or fitting formulas. For example, they can be modeled as load factor. and switching frequency Bivariate functions: ; in, These represent the parameters obtained by fitting data for PWM and PFM modes respectively. Efficiency parameters for different modes are obtained by fitting characteristic curves; electromagnetic interference evaluation values ​​are adopted using: ; in, It reflects the spectrum broadening characteristics of PFM mode; This represents an exponent greater than 0. Indicates reference frequency. Health impact factors are simplified to... A more refined model can include weighting for specific harmonic frequencies. The objective function is then solved using the above model. When the BMS detects a lagging cell, if the cell voltage is lower than the group average voltage by more than [a certain amount]... It will issue a Boolean alarm signal. ;otherwise, When the battery management module detects a lagging cell and outputs an alarm signal, it amplifies and corrects the health impact factor based on a penalty coefficient, resulting in a corrected health impact factor. The formula for calculating the penalty coefficient is: ; in, Indicates the penalty coefficient. Represents the consistency parameter. This indicates an alarm signal indicating a lagging unit. This represents the penalty magnitude coefficient. Represents the hyperbolic tangent function. This represents the consistency sensitivity coefficient. When... hour, The value is significantly greater than 1, and varies with inconsistency. It increases and then further increases. This relates to health impact factors. Revised to Alternatively, or more directly, modify the health-related weights in the objective function as follows: The forced decision-making process favors the low electromagnetic stress operating point, providing emergency protection for the battery. This also means that once a lagging individual cell alarm occurs, regardless of the overall SOH value... However, the weight of battery health in the optimization objective will be drastically amplified, such as by multiplying it by a coefficient. This causes the optimization results to be extremely biased towards the lowest possible electromagnetic stress operating point, i.e., selecting the PWM mode and using the lowest possible switching frequency, thus achieving mandatory protection.

[0043] In some embodiments, during the control command generation and smooth execution phase, the integrated monitoring module determines the optimal modulation mode based on the decision. With switching frequency The parameters are converted into configuration parameters recognizable by the driver chip. If a modulation mode switch is required, the switching frequency is gradually changed to the target value within milliseconds to tens of milliseconds according to a smooth transition function. The duty cycle is adjusted synchronously to suppress voltage and current disturbances. Finally, the parameters are written to the digital controller of the power conversion module via SPI, I2C, or parallel bus to complete the control closed loop. This process quantifies the battery state into control constraints through a dynamic health weighting function. When the battery health is poor, some efficiency is proactively sacrificed to delay battery aging with a lower noise and gentler power supply environment, thus achieving preventative protection.

[0044] Specifically, control commands are generated based on the target pulse modulation mode and sent to the power conversion module to drive it to switch to or maintain operation under the target pulse modulation mode. The decision output... This is converted to a register configuration value recognizable by the power conversion module driver chip or a comparison threshold for the PWM controller. If a mode switch is required, the controller does not immediately change the registers, but rather follows a smooth transition function. Over a period of milliseconds to tens of milliseconds, the frequency is gradually changed from the current value to the target value. Simultaneously, the duty cycle is adjusted to minimize output voltage and current disturbances. The final configuration parameters are written to the digital controller of the power conversion module via SPI, I2C, or a parallel bus to complete the control closed loop. This implementation process establishes a dynamic health weight function... The multi-objective optimization function incorporates real-time health information of the battery. This is quantitatively and adaptively transformed into hard constraints and soft optimization guidelines for the modulation behavior of the power converter. When the battery condition is poor, As the value increases, the optimization model will automatically sacrifice some efficiency or dynamic performance in exchange for a more moderate (lower) performance. The power output characteristics (value) are analyzed to proactively protect the battery life. The entire process is clearly quantified, easily implemented as an algorithm in embedded systems, and provides a full, clear, and verifiable technical disclosure for the patent.

[0045] In some embodiments, the system collects the actual pulse modulation mode, key control parameters, corresponding load and weighted ripple factor, and subsequent battery pack health status estimates over a period of time; it then uses this data to perform feedback optimization on the parameters of a preset collaborative decision-making rule or multi-objective optimization function. In the closed-loop feedback optimization process, the system periodically collects operational data samples. ,in This represents the change in the estimated health status of the battery, with the goal of learning and controlling behavior. Changes in battery health The long-term impact. After accumulating sufficient samples, a training set is constructed and a loss function is defined, with the goal of making decisions that are conducive to delaying health decline. A loss function related to health decline is defined as follows: ; in, Represents the total loss function. Indicates the total number of samples collected. Indicates the first Each sampling time, Indicates the first Monitor the rate of degradation in battery health at all times. This represents the balance coefficient between the multi-objective optimization objective and the battery degradation term. Describe the objective function. Indicates the first Time modulation mode, Indicates the first Constant switching frequency, Indicates the first real-time load rate Indicates the first Time-weighted ripple factor, Indicates the first Real-time health status assessment Indicates the first Time consistency parameters.

[0046] The loss function is minimized using the gradient descent algorithm, and the parameter set in the decision model is updated. : ;in This represents the fundamental balance weighting coefficient between system conversion efficiency and electromagnetic interference. This represents the basic weighting coefficient for the electromagnetic interference assessment item. This represents the fixed baseline coefficient of the dynamic health weighting function. All represent constant coefficients in the dynamic health weighting function used to adjust the degree of influence of health status and battery consistency on decision weights, and k represents the proportional coefficient for dynamically limiting the maximum switching frequency when the battery health status is low. This represents the set of parameters currently used by the model before the parameter iteration update. This represents the new set of parameters obtained after iterative optimization. The model learning rate controls the step size for parameter updates in each iteration. The loss function represents the loss function with respect to the parameter set. The gradient reflects the rate and direction of change of the loss function with respect to the model parameters. The loss function is constructed with the goal of delaying battery health degradation and improving the overall control effect. The optimized parameters are applied to the real-time decision logic, enabling the system to continuously iterate and optimize based on long-term operating data, forming a self-learning and adaptive intelligent control mechanism, and further improving the matching degree between the control strategy and the field operating conditions.

[0047] In some embodiments, the pulse hybrid modulation-based communication power supply capacity system uses an integrated monitoring module as its core to construct a collaborative management and control architecture. This architecture enables centralized communication, coordinated control, and data interaction among four major modules: power conversion, battery management, energy distribution, and capacity control, achieving integrated management and control. The power conversion module adopts a hybrid modulation architecture of PWM and PFM. The integrated monitoring module can dynamically control the switching between the two modes based on the health status estimate provided by the battery management module, achieving adaptive matching between the modulation mode and the battery health status. The system establishes a linkage mechanism between battery health status and electromagnetic interference control. When the health status estimate is lower than a preset threshold, the integrated monitoring module controls the power conversion module to switch to a mode with better electromagnetic interference characteristics. By reducing the switching frequency and / or increasing the dead time, the damage of electromagnetic interference to aging batteries is reduced. Simultaneously, a multi-objective optimization function is constructed with system conversion efficiency, electromagnetic interference assessment value, and battery health impact factor as objective terms. Combining real-time load rate, input-side weighted ripple factor, and battery health status estimate, a hierarchical enumeration method is adopted to solve the problem: the outer layer enumerates two modes, PWM and PFM, and the inner layer finds the optimal switching frequency within the dynamic constraint interval through gradient descent or golden section method. Finally, the mode with the minimum objective function value and corresponding parameters are selected to achieve multi-objective collaborative optimization.

[0048] The capacity testing module consists of a switching unit, a transformer-isolated DC-DC boost-discharge module, and a main control module. It supports hot-swapping and can remotely control the entire capacity testing process via an integrated monitoring module, eliminating the need for on-site shutdown and improving maintenance convenience and safety. The system incorporates a feedback optimization mechanism, collecting long-term operational data to optimize parameters of preset collaborative decision-making rules and multi-objective optimization functions. It also introduces a penalty coefficient for lagging individual cell alarms into the battery health impact factors, enhancing the targeted nature of decision-making. The energy distribution module implements priority-based energy distribution and power-down protection, coordinated with the integrated monitoring module to ensure stable power supply to core loads and prevent damage from load anomalies and electromagnetic interference. This application effectively addresses the shortcomings of existing systems, such as poor coordination and inability to adapt to changes in battery health. Through dynamic modulation mode switching and multi-objective optimization functions, it balances efficiency, electromagnetic interference, and battery health, delaying battery aging and reducing energy consumption. Remote capacity testing and feedback optimization enhance intelligence, reduce maintenance costs, and comprehensively improve system reliability and practicality.

[0049] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0050] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0051] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0052] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0053] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0054] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above 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; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0055] The units described above 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.

[0056] 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.

[0057] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A communication power supply capacity system based on pulse hybrid modulation, characterized in that, The communication power supply capacity system includes: A power conversion module is connected between the AC input side and the DC output side of the communication power supply core system. The pulse modulation mode of the power conversion module includes pulse width modulation mode and pulse frequency modulation mode. The power conversion module is used for AC-DC rectification power supply. A battery management module is connected to the battery pack. The battery management module is used to monitor the operating status of the battery pack in real time and output the health status estimate of the battery pack. An energy distribution module is connected between the DC output side and the load. The energy distribution module is used to distribute power and provide power-down protection based on load priority. The battery pack is connected between the battery pack and the power conversion module. The battery pack is used to perform capacity testing on the battery pack and to perform capacity charging and discharging of the battery pack together with the power conversion module. An integrated monitoring module is used to dynamically adjust the pulse modulation mode of the power conversion module according to the health status estimate, and to control the power conversion module, the battery management module, the energy distribution module and the core capacity function module connected in communication to perform data interaction.

2. The communication power supply capacity system based on pulse hybrid modulation according to claim 1, characterized in that, The step of dynamically adjusting the pulse modulation mode of the power conversion module based on the health status estimate includes the following steps: The load rate, weighted ripple factor, health status estimate, and consistency parameters of the power conversion module, as well as the battery pack, are acquired in real time. The target pulse modulation mode is obtained by judging based on the load rate, the weighted ripple factor, the health status estimate, and the consistency parameter. After the target pulse modulation mode generation control command is sent to the power conversion module, the power conversion module is driven to operate in the target pulse modulation mode.

3. The communication power supply capacity system based on pulse hybrid modulation according to claim 2, characterized in that, The step of determining the target pulse modulation mode based on the load rate, the weighted ripple factor, the health status estimate, and the consistency parameter includes the following steps: When the health status estimate is less than the preset health status threshold, the pulse width modulation mode is used as the target pulse width modulation mode; after the integrated monitoring module controls the power conversion module to adopt or switch to the pulse width modulation mode, the optimal switching frequency and optimal dead time of the pulse width modulation mode are determined by the load rate, the weighted ripple factor and the consistency parameter. When the health status estimate is greater than or equal to the preset health status threshold, the pulse width modulation mode or the pulse frequency modulation mode is taken as the target pulse modulation mode. The optimal switching frequency and optimal dead time of the current target pulse modulation mode are obtained by solving the load rate, the weighted ripple factor and the consistency parameter according to the multi-objective optimization function in the preset collaborative decision rule.

4. The communication power supply capacity system based on pulse hybrid modulation according to claim 3, characterized in that, The preset collaborative decision-making rules include the multi-objective optimization function and constraints; The multi-objective optimization function is: ； in, Let represent the multi-objective optimization function to be minimized. Indicates pulse modulation mode, Indicates the switching frequency. This represents the basic weighting coefficient for the efficiency term. This represents the transformation efficiency model. This represents the basic weighting coefficient for the electromagnetic interference term. This represents the electromagnetic interference assessment model. This represents the weighted ripple factor. Indicates load rate, This represents the dynamic health weighting function. Indicates health influencing factors; The formula for calculating the constraint condition is as follows: in, Indicates the junction temperature of power devices. Indicates the maximum allowable junction temperature. Indicates the output voltage ripple. Indicates the maximum allowable peak ripple. Indicates pulse modulation mode, Indicates the switching frequency. This indicates the load rate.

5. The communication power supply capacity system based on pulse hybrid modulation according to claim 3, characterized in that, When the health status estimate is less than the preset health status threshold, frequency limiting logic is triggered. The frequency limiting logic includes dynamically adjusting the maximum upper limit of the switching frequency and the dynamic constraint range of the switching frequency search range. The maximum switching frequency upper limit is adjusted according to a frequency upper limit function, which is: ； in, This indicates the upper limit of the dynamic maximum switching frequency corresponding to the battery's health status. Indicates the rated maximum permissible switching frequency. Represents the proportionality coefficient and Between 0 and 1, This represents the current health status estimate. This represents the preset health status threshold; The dynamic constraint range for the switching frequency search range is: ； in, This indicates the actual switching frequency of the power conversion module. Indicates the lower limit of the switching frequency. This means that the smaller of the fixed maximum switching frequency and the dynamic maximum switching frequency limit is taken as the final upper limit of the switching frequency.

6. The communication power supply capacity system based on pulse hybrid modulation according to claim 3, characterized in that, The preset collaborative decision-making rule is optimized through feedback using a loss function; The feedback optimization process includes: Obtain historical optimization data over a period of time, including: the pulse modulation mode, the load rate, the weighted ripple factor, the health status estimate, and the consistency parameter; The loss function is constructed based on the historical optimization data. The loss function is minimized using the gradient descent algorithm to obtain the optimized parameters of the preset collaborative decision-making rule; The preset collaborative decision-making rule is optimized by feedback based on the optimization parameters to obtain the optimized preset collaborative decision-making rule; The loss function is: ； in, Represents the total loss function. Indicates the total number of samples collected. Indicates the first Each sampling time, Indicates the first Monitor the rate of degradation in battery health at all times. This represents the balance coefficient between the multi-objective optimization objective and the battery degradation term. Describe the objective function. Indicates the first Time modulation mode, Indicates the first Constant switching frequency, Indicates the first real-time load rate Indicates the first Time-weighted ripple factor, Indicates the first Real-time health status assessment Indicates the first Time-consistency parameters.

7. The communication power supply capacity system based on pulse hybrid modulation according to claim 1, characterized in that, The core capacity functional module includes a switching unit, a DC-DC boost discharge module, a DC-DC constant current charging module, and a main control module; The DC-DC boost discharge module adopts a transformer isolation design and supports hot-swapping. The main control module is connected to the integrated monitoring module for communication. The switching unit includes a normally closed contactor, a normally open contactor, and an online diode; In float charging mode, the normally closed contactor is closed and the normally open contactor is open. The battery pack is connected to the rectifier through the normally closed contactor and the online diode, and the rectifier directly float charges the battery pack. When performing capacity testing, the integrated monitoring module controls the switching unit to connect the battery pack to the DC-DC boost discharge module and the DC-DC constant current charging module for charging and discharging.

8. The communication power supply capacity system based on pulse hybrid modulation according to claim 1, characterized in that, When the battery management module monitors the operating status of the battery pack in real time, it detects an alarm signal from a lagging cell and then amplifies and corrects the health impact factor based on a penalty coefficient to obtain the corrected health impact factor. The formula for calculating the penalty coefficient is as follows: ； in, Indicates the penalty coefficient. Represents the consistency parameter. This indicates the alarm signal for the lagging unit. This represents the penalty magnitude coefficient. Represents the hyperbolic tangent function. This represents the consistency sensitivity coefficient.

9. The communication power supply capacity system based on pulse hybrid modulation according to claim 1, characterized in that, The integrated monitoring module supports IEC61850 protocol and HTTPS encrypted transmission.

10. The communication power supply capacity system based on pulse hybrid modulation according to claim 1, characterized in that, When the pulse width modulation mode is used as the pulse modulation mode of the power conversion module, the output voltage is stabilized by adjusting the duty cycle of the switch conduction time. When the pulse frequency modulation mode is used as the pulse modulation mode of the power conversion module, the output voltage is stabilized by reducing the switching frequency or skipping part of the switching cycle.

Images