Adaptive control method and system based on module battery state real-time sensing

Abstract

The invention discloses a self-adaptive control method and system based on module battery state real-time sensing, and belongs to the technical field of energy storage and power electronics. The method comprises the following steps: acquiring the charge state, health state, temperature and internal resistance of a battery in real time, and generating a state vector through data fusion; ripple current and voltage fluctuation on the direct current side of the cascade module are collected; calculating a health coefficient based on the state vector, and combining with the ripple current to obtain a real-time ripple stress ratio; dynamically adjusting a filtering parameter according to the health coefficient and the stress ratio, and switching a filtering mode according to a health coefficient threshold; and controlling the active filter to generate compensation current, and injecting the compensation current into a specified node to counteract ripples. The system correspondingly comprises a sensing module, a monitoring module, an evaluation module, a decision-making module and a suppression module. According to the invention, module-level adaptive ripple suppression is realized, the service life of the battery is prolonged, the system loss is reduced, and the reliability and electric energy quality of the cascaded energy storage system are improved.

Description

Technical Field

[0001] This invention belongs to the field of energy storage and power electronics technology, specifically relating to an adaptive control method and system based on real-time sensing of module battery status. Background Technology

[0002] With the rapid development of renewable energy sources such as wind power and photovoltaics, the important role of energy storage as a grid regulation and support device is becoming increasingly prominent. Energy storage is a crucial technology and fundamental equipment for building new power systems. In recent years, new energy storage technologies have gradually developed towards improving the efficiency of energy storage systems and reducing system manufacturing costs. High-voltage cascaded energy storage systems using an H-bridge cascade structure consist of multiple energy storage units. By deparallelizing the battery units, each energy storage unit is discretized and connected to the grid through cascaded power modules. This significantly reduces the battery unit capacity and the number of individual units while effectively improving system capacity and security. High-voltage cascaded energy storage systems are gradually becoming an important direction for future research.

[0003] However, existing cascaded H-bridge energy storage converters generally exhibit significant fluctuations in low-frequency ripple current and voltage on the DC side. This not only accelerates battery aging and poses safety hazards but also reduces system efficiency, increases output voltage and current harmonics, degrades power quality, and affects the overall system reliability. Traditional ripple suppression solutions include passive LC-based filtering with fixed parameters, which suffers from large size, high cost, and limited low-frequency suppression effectiveness; and active filtering with fixed control parameters, which cannot adjust the filtering strength or strategy according to the current battery state to achieve optimal lifespan extension, potentially leading to over-filtering increasing filter losses or under-filtering causing battery aging; and system-level collaborative filtering solutions rely on communication coordination and ignore individual differences among module batteries.

[0004] In summary, existing ripple suppression schemes for cascaded energy storage systems lack dynamic adaptive capabilities based on the battery's own state. There is an urgent need for a module-level adaptive ripple suppression method that can respond to changes in battery state, ensuring suppression effectiveness throughout the entire life cycle of different batteries, and providing guidance for the integrated application of cascaded energy storage systems. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an adaptive control method and system based on real-time sensing of module battery status, which addresses the shortcomings of the prior art. This method solves the technical problem in existing cascaded H-bridge energy storage systems where the ripple suppression scheme cannot dynamically adjust the suppression strategy throughout the battery's life cycle due to fixed control parameters and the inability to sense the battery's own state. This results in unnecessary battery life degradation, low system efficiency, and high safety risks.

[0006] The present invention adopts the following technical solution: An adaptive control method based on real-time sensing of module battery status includes the following steps: The battery's state parameters are acquired in real time, including the energy storage battery's state of charge, state of health, temperature, and internal resistance. The acquired state parameters are then fused to generate a state vector. Collect the ripple current on the DC side of the cascaded module and the voltage fluctuation of the DC bus; Based on the generated state vector, the battery health coefficient is calculated, and the ripple stress margin is evaluated in combination with the ripple current collected in step S2 to obtain the real-time ripple stress ratio. Based on the obtained battery health coefficient and real-time ripple stress ratio, the filtering parameters for ripple suppression are dynamically adjusted, and the filtering mode is switched according to the threshold of the battery health coefficient. Based on the determined filtering parameters and filtering mode, the active filter is controlled to generate a compensation current, which is then injected into the node between the negative terminal of the battery pack and the DC bus capacitor to offset the DC side ripple current.

[0007] Preferably, the real-time acquisition of battery status parameters specifically involves: The battery management system collects key state parameters of the energy storage battery in real time. Specifically, the battery state of charge is calculated by using the ampere-hour integration method combined with the open-circuit voltage correction model. The battery health status is indirectly derived by estimating the internal resistance change based on the capacity decay model online or by analyzing the internal resistance change through electrochemical impedance spectroscopy. The battery temperature is collected by NTC temperature sensors deployed in the key hot areas of the battery module. The dynamic internal resistance value of the battery is output by the electrochemical model built into the battery management system. The local controller synchronously acquires the collected key state parameters through the CAN bus or SPI interface, and generates a state vector after noise elimination by Kalman filtering.

[0008] Preferably, the indirect deduction of health status through electrochemical impedance spectroscopy analysis of internal resistance changes is as follows: The impedance spectrum of the battery is obtained by electrochemical impedance spectroscopy. The charge transfer resistance and double-layer capacitance parameters in the impedance spectrum are extracted, and a correlation model between the parameters and the battery capacity decay rate is established. The health status of the battery is then deduced based on the correlation model.

[0009] Preferably, the acquisition of ripple current on the DC side of the cascaded module and voltage fluctuations on the DC bus specifically includes: A Hall sensor is connected in series at the output of the battery pack, and the second harmonic ripple component is extracted through a bandpass filter to obtain the ripple current; The voltage signal is differentially sampled across the DC-Link capacitor, and the ripple component is separated by a high-pass filter to obtain the voltage fluctuation of the DC bus.

[0010] Preferably, the real-time ripple stress ratio = Irip / I rip_max The ripple stress margin is specifically as follows:

[0011] in, I rip_max This is the maximum ripple current that the battery can withstand. Health coefficient; f ( SOC ) is the adjustment function for SOC; This is the battery's rated current. This represents the ripple current component collected from the DC side.

[0012] Preferably, health coefficient for:

[0013] in, , , The weighting coefficients and , T opt The optimal battery temperature is 25°C. It is the temperature decay factor. For battery health status, This represents the real-time internal resistance value of the battery. This is the reference internal resistance value of the battery. This refers to the battery temperature.

[0014] Preferably, the filter parameters for dynamically adjusting ripple suppression specifically include: Based on battery health coefficient Real-time ripple stress ratio Dynamically optimize the compensation gain of the filter and cutoff frequency The details are as follows:

[0015]

[0016] in, For rated gain, The weighting coefficient for the health coefficient. This is the weighting coefficient for ripple stress. Ripple sensitivity factor The rated cutoff frequency, The weighting coefficient for the health coefficient. This is the weighting coefficient for ripple stress. This represents the maximum permissible frequency offset.

[0017] Preferably, switching the filtering mode based on a threshold of the battery health coefficient specifically includes: when K health When the value is greater than 0.9, the high-efficiency mode is enabled, reducing the compensation gain in the filter parameters by 20%. When 0.9> K health When the value is greater than 0.7, the standard mode is enabled, and the compensation gain and cutoff frequency in the filter parameters are kept at the rated parameters. when K health When the value is less than 0.7, protection mode is enabled, and the compensation gain in the filter parameters is increased by 30%.

[0018] Preferably, controlling the active filter to generate compensation current specifically includes: Based on the second harmonic component in the collected ripple current, the current signal generated by the compensation gain adjusted in step S4 is superimposed to synthesize the compensation current reference command. At the same time, a phase-locked loop is used to track the grid frequency to ensure that the compensation current is synchronized with the ripple frequency. High-precision tracking of the compensation current is achieved through a proportional resonant controller. The gain of the resonant controller is dynamically adjusted according to the cutoff frequency adjusted in step S4, and the embedded band-stop filter is used to suppress the interference of switching noise on the compensation current reference command. Space vector modulation is used to drive the active filter to perform energy conversion, generate compensation current and inject it into the node between the negative terminal of the battery pack and the DC bus capacitor, forming a low-impedance ripple current cancellation path.

[0019] Secondly, embodiments of the present invention provide an adaptive control system based on real-time sensing of module battery status, comprising: The sensing module acquires the battery's state parameters in real time, including the energy storage battery's state of charge, health status, temperature, and internal resistance. It also performs data fusion processing on the acquired state parameters to generate a state vector. The monitoring module collects the ripple current on the DC side of the cascaded module and the voltage fluctuation of the DC bus. The evaluation module calculates the battery health coefficient based on the state vector generated by the sensing module, and evaluates the ripple stress margin by combining the ripple current collected by the monitoring module, thus obtaining the real-time ripple stress ratio. The decision module dynamically adjusts the filtering parameters for ripple suppression based on the battery health coefficient and real-time ripple stress ratio obtained from the evaluation module, and switches the filtering mode according to the threshold of the battery health coefficient. The suppression module controls the active filter to generate a compensation current based on the filtering parameters and filtering mode determined by the suppression module. The compensation current is injected into the node between the negative terminal of the battery pack and the DC bus capacitor to cancel the DC side ripple current.

[0020] Preferably, in the sensing module, the real-time acquisition of battery status parameters specifically includes: The battery management system collects key state parameters of the energy storage battery in real time. Specifically, the battery state of charge is calculated by combining the ampere-hour integration method with the open-circuit voltage correction model. The battery health status is indirectly derived by estimating the internal resistance change based on the capacity decay model online or by analyzing the internal resistance change through electrochemical impedance spectroscopy. The battery temperature is collected by NTC temperature sensors deployed in the key hot areas of the battery module. The dynamic internal resistance value of the battery is output by the electrochemical model built into the battery management system. The local controller synchronously acquires the collected key state parameters through the CAN bus or SPI interface, and generates a state vector after noise elimination by Kalman filtering. The specific method of indirectly inferring health status by analyzing changes in internal resistance through electrochemical impedance spectroscopy is as follows: The impedance spectrum of the battery is obtained by electrochemical impedance spectroscopy. The charge transfer resistance and double-layer capacitance parameters in the impedance spectrum are extracted, and a correlation model between the parameters and the battery capacity decay rate is established. The health status of the battery is then deduced based on the correlation model.

[0021] Preferably, the monitoring module, which collects the ripple current on the DC side of the cascaded module and the voltage fluctuation of the DC bus, specifically includes: A Hall sensor is connected in series at the output of the battery pack, and the second harmonic ripple component is extracted through a bandpass filter to obtain the ripple current; The voltage signal is differentially sampled across the DC-Link capacitor, and the ripple component is separated by a high-pass filter to obtain the voltage fluctuation of the DC bus.

[0022] Preferably, in the evaluation module, the real-time ripple stress ratio = I rip / I rip_max The ripple stress margin is specifically as follows:

[0023] in, I rip_max This is the maximum ripple current that the battery can withstand. f ( SOC ) is the adjustment function for SOC; This is the battery's rated current. The ripple current component is collected from the DC side; The health index is specifically:

[0024] in, , , The weighting coefficients and , T opt The optimal battery temperature is 25°C. It is the temperature decay factor. For battery health status, This represents the real-time internal resistance value of the battery. This is the reference internal resistance value of the battery. This refers to the battery temperature.

[0025] Preferably, in the decision module, the filtering parameters for dynamically adjusting ripple suppression specifically include: Based on battery health coefficient Real-time ripple stress ratio Dynamically optimize the compensation gain of the filter and cutoff frequency The details are as follows:

[0026]

[0027] in, For rated gain, The weighting coefficient for the health coefficient. This is the weighting coefficient for ripple stress. Ripple sensitivity factor The rated cutoff frequency, The weighting coefficient for the health coefficient. This is the weighting coefficient for ripple stress. This is the maximum permissible frequency offset; The filtering mode switching based on the battery health coefficient threshold specifically includes: when K health When the value is greater than 0.9, the high-efficiency mode is enabled, reducing the compensation gain in the filter parameters by 20%. When 0.9> K health When the value is greater than 0.7, the standard mode is enabled, and the compensation gain and cutoff frequency in the filter parameters are kept at the rated parameters. when K health When the value is less than 0.7, protection mode is enabled, and the compensation gain in the filter parameters is increased by 30%.

[0028] Preferably, in the suppression module, controlling the active filter to generate the compensation current specifically includes: Based on the second harmonic component in the ripple current collected by the monitoring module, the current signal generated by the compensation gain adjusted by the decision module is superimposed to synthesize the compensation current reference command. At the same time, a phase-locked loop is used to track the grid frequency to ensure that the compensation current is synchronized with the ripple frequency. High-precision tracking of the compensation current is achieved through a proportional resonant controller. The gain of the resonant controller is dynamically adjusted according to the cutoff frequency adjusted by the decision module, and the embedded band-stop filter is used to suppress the interference of switching noise on the compensation current reference command. Space vector modulation is used to drive the active filter to perform energy conversion, generate compensation current and inject it into the node between the negative terminal of the battery pack and the DC bus capacitor, forming a low-impedance ripple current cancellation path.

[0029] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the adaptive control method based on real-time sensing of module battery status described above.

[0030] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described adaptive control method based on real-time sensing of module battery status.

[0031] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the aforementioned adaptive control method based on real-time sensing of module battery status.

[0032] In a sixth aspect, embodiments of the present invention provide an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-described adaptive control method based on real-time sensing of module battery status.

[0033] Compared with the prior art, the present invention has at least the following beneficial effects: An adaptive control method based on real-time sensing of module battery status is proposed. A module-level adaptive control framework is constructed, overcoming the limitations of traditional fixed-parameter filtering. By dynamically matching the battery status with the ripple suppression strategy in real time, dynamic matching of state and strategy is achieved. Compared to existing system-level solutions, this method eliminates the need for cross-module communication, avoiding uneven suppression effects caused by individual battery differences. Furthermore, by precisely injecting compensation current into key nodes, it fundamentally solves the problem of DC-side ripple damage to the battery, laying the foundation for full-lifecycle battery protection.

[0034] Furthermore, the accuracy of SOC calculation is improved by ampere-hour integration and open-circuit voltage correction, avoiding the accumulation of errors from a single algorithm; the capacity decay model / EIS derivation uses a dual-path approach to obtain SOH, adapting to different operating conditions; NTC sensor fixed-point temperature acquisition ensures data representativeness; Kalman filtering eliminates noise and ensures the reliability of the state vector, providing high-quality data input for subsequent stress assessment, and solving the control inaccuracy problem caused by low accuracy and high noise interference in traditional parameter acquisition.

[0035] Furthermore, EIS technology is used to achieve non-destructive testing of the battery's internal state. Compared with the capacity decay model, it can capture changes in micro-parameters such as internal resistance and double-layer capacitance earlier, and predict the battery aging trend in advance. By establishing an impedance parameter-capacity decay rate correlation model, abstract impedance data is transformed into intuitive SOH values, improving the operability and accuracy of health status assessment and solving the problems of lagging traditional SOH assessment and dependence on charge-discharge cycles.

[0036] Furthermore, the series Hall sensor ensures full acquisition of ripple current, and the 100Hz center frequency bandpass filter accurately extracts the second harmonic core ripple component, avoiding interference from irrelevant frequencies; DC-Link capacitor differential sampling combined with 50Hz cutoff frequency high-pass filtering effectively separates the ripple component in voltage fluctuations, providing accurate ripple data for subsequent stress assessment, and solving the problems of low accuracy and susceptibility to fundamental signal interference in traditional ripple monitoring.

[0037] Furthermore, by introducing a health coefficient and a SOC adjustment function, the maximum ripple current that the battery can withstand is dynamically corrected, avoiding the shortcomings of traditional fixed thresholds that ignore differences in battery state. The real-time ripple stress ratio intuitively reflects the current threat level of ripple to the battery, providing a quantitative basis for adjusting filter parameters, ensuring that the ripple suppression strategy neither excessively depletes the filter nor causes battery damage, and achieving on-demand suppression.

[0038] Furthermore, by using weighting coefficients to balance the impact of various state parameters on the health level, the one-sidedness of single-parameter assessment is avoided; by introducing temperature decay factors and reference internal resistance, the temperature deviation and internal resistance decay rate are transformed into quantifiable health coefficients, which intuitively reflect the overall health status of the battery; the optimal temperature setting of 25℃ fits the actual working characteristics of the battery, providing a scientific threshold basis for subsequent mode switching, and solving the problem of traditional health assessment having a single dimension and lacking quantitative standards.

[0039] Furthermore, by using dual-variable control parameters of health coefficient and ripple stress ratio, precise control can be achieved where the worse the health status and the greater the ripple, the more significant the parameter adjustment range becomes. The typical value ranges of parameters such as weight coefficient and ripple sensitivity factor are clearly defined to improve the feasibility of the solution. Compared with fixed parameter filtering, the suppression effect can be optimized in real time according to the battery status and ripple intensity, achieving a dynamic balance between protecting the battery and reducing filter loss.

[0040] Furthermore, precise mode switching is achieved through dual thresholds of 0.7 and 0.9: When the device is in good health, an efficient mode is activated to reduce wear and tear while maintaining economic efficiency; in a moderately healthy state, a standard mode is used to balance protection and efficiency; and in an aging state, a protection mode is activated to strengthen suppression and ensure safety. This avoids the problems of traditional single-mode systems that either result in excessive wear or insufficient protection, achieving optimal efficiency-protection matching throughout the entire lifecycle.

[0041] Furthermore, the accuracy of the command is ensured by superimposing the second harmonic component to compensate the gain, and the PLL tracks the grid frequency to ensure that the compensation current is synchronized with the ripple; the proportional resonant controller combined with band-stop filtering achieves high-precision current tracking and suppresses switching noise interference; the SVPWM drive and specific node injection design form a low-impedance cancellation path, improve the ripple cancellation efficiency, and solve the problems of low accuracy and poor cancellation effect of traditional compensation current.

[0042] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0043] In summary, this invention achieves a leap from fixed filtering to intelligent adaptive filtering by real-time linkage between battery status and ripple stress, ultimately significantly improving the safety, economy, and lifespan of cascaded energy storage systems.

[0044] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0045] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the cascaded energy storage module ripple suppression system of the present invention; Figure 3 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 4 This is a block diagram of a chip provided according to an embodiment of the present invention.

[0046] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0049] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0050] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0051] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0052] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0053] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0054] This invention provides an adaptive control method based on real-time sensing of module battery status. By integrating an adaptive suppression device within each cascaded module and combining it with local real-time battery status sensing capabilities, the method dynamically adjusts filter parameters or operating modes to achieve personalized optimal suppression of ripple current in the module's battery, maximizing battery life and improving safety. This invention addresses the DC-side ripple problem in cascaded energy storage systems, achieving three core effects through module-level real-time battery status sensing and adaptive control: First, it enhances ripple suppression precision, with high synchronization between compensation current and ripple, resulting in over 30% higher cancellation efficiency compared to traditional LC filtering, effectively reducing battery aging rates and extending lifespan by 20%-30%; second, it improves system efficiency, with health-state-based mode switching reducing filter losses by 15%-20%, balancing protection and economy; and third, it offers strong application adaptability, eliminating the need for cross-module communication, adapting to batteries in different health states, and directly integrating into H-bridge cascaded energy storage systems, improving system reliability and power quality, and providing technical support for new power system energy storage applications.

[0055] Please see Figure 1 This invention discloses an adaptive control method based on real-time sensing of module battery status, which achieves accurate and reasonable ripple suppression by dynamically adjusting filter parameters, including the following steps: S1. Module Battery Status Awareness – Real-time acquisition of the module's battery status parameters, including the energy storage battery's state of charge (SOC), state of health (SOH), temperature, and internal resistance. Specific steps are as follows: S101. Real-time acquisition of key state parameters of the energy storage battery in this module through the built-in Battery Management System (BMS): Calculation of the battery's State of Charge (SOC) using the ampere-hour integration method combined with an open-circuit voltage correction model; online estimation of the battery's State of Health (SOH) based on a capacity decay model, or indirect derivation through electrochemical impedance spectroscopy (EIS) analysis of internal resistance changes; acquisition of battery temperature by deploying NTC temperature sensors in key hot zones of the battery module. T The dynamic internal resistance value of the battery is output through the electrochemical model built into the BMS. R internal .

[0056] S102. The local controller synchronously acquires the above parameters through the CAN bus or SPI interface, and generates a state vector after noise elimination by Kalman filtering to achieve data fusion.

[0057] S2. DC-side ripple monitoring—Collects the ripple current on the DC side of the cascaded module and the voltage fluctuation of the DC bus. The specific steps are as follows: S201. Ripple Current Monitoring: A high-precision Hall sensor (bandwidth ≥ 10kHz) is connected in series at the battery pack output. The second harmonic ripple component is extracted through a bandpass filter (center frequency 100Hz, bandwidth ±20Hz). I rip ; S202, DC bus voltage fluctuation detection: The voltage signal is differentially sampled across the DC-Link capacitor of the module, and the ripple component is separated by a high-pass filter. The cutoff frequency is set to 50Hz.

[0058] S3. Status and Ripple Stress Assessment – ​​Based on the acquired module battery status parameters, assess the battery's current health condition and its ability to withstand ripple stress. The specific steps are as follows: S301. Battery health coefficient calculation: Based on the current health status, internal resistance and temperature of the energy storage battery, assess the overall health level of the energy storage battery, and calculate the health coefficient according to the following formula.

[0059] (1) in, , , The weighting coefficients and , T opt The optimal battery temperature is 25°C. This is the temperature decay factor.

[0060] S302. Ripple stress margin assessment: Based on the correlation between energy storage battery life and ripple interference, assess the maximum ripple stress that can be withstood under the current battery health level, so as to guide subsequent ripple current suppression. The ripple stress margin is calculated according to the following formula.

[0061] (2) in, I rip_max This is the maximum ripple current that the battery can withstand. f ( SOC The function is the SOC adjustment function (1 for SOC=50%, reduced to 0.7 for extreme SOC), and the output decision quantity is the real-time ripple stress ratio. = I rip / I rip_maxThis is used to trigger adaptive strategies.

[0062] S4. Adaptive Filter Parameter Decision – Based on the assessed battery health coefficient K health and real-time ripple stress ratio The specific steps for dynamically adjusting the filter parameters for ripple suppression and switching the filter mode are as follows: S401. When the battery ages or the ripple exceeds the limit, the compensation gain and cutoff frequency of the dynamically optimized filter are calculated using the following formula.

[0063] (3) in, For rated gain, This is the weighting factor for the health index (typical value 0.3-0.6). This is the weighting factor for ripple stress (typically 0.2-0.4). This is the ripple sensitivity factor (typical value 0.25, controlling the rate of increase of the exponent). (4) in, f c0 The rated cutoff frequency, This is the weighting factor for the health coefficient (typical value 0.4). This is the weighting factor for ripple stress (typically 0.15). This represents the maximum permissible frequency offset.

[0064] S402, Mode Switching Mechanism The filter switches to a preset working mode based on a health status threshold, as follows: when K health When the value is >0.9, it indicates that the battery is in good health. At this time, the high-efficiency mode is activated, with a gain reduction of 20% to prioritize reducing switching losses. When 0.9> K health When the value is >0.7, the standard mode is enabled, and the system operates with the rated parameters. when K health When the value is less than 0.7, it indicates that the battery has experienced high-temperature aging. At this time, the protection mode is activated, which increases the gain by 30% and enhances the filtering performance.

[0065] S5. Filter Closed-Loop Suppression—Based on the state parameters, the built-in active filter of this module is controlled to generate a compensation current to cancel the ripple component in real time. The specific steps are as follows: S501, Compensation Current Command Generation: Based on the collected ripple second harmonic component, the compensation gain dynamically decided by the local controller is superimposed.K p The generated current signal is used to synthesize a compensation current reference command; a phase-locked loop (PLL) is used to track the grid frequency to ensure that the compensation current is synchronized with the ripple frequency.

[0066] S502, Current Tracking Control: High-precision tracking of the compensation current is achieved through a proportional resonant controller, based on the decision cutoff frequency. f c The gain of the resonant controller is dynamically adjusted, and a band-stop filter is embedded in the controller to suppress the interference of switching noise on the reference signal.

[0067] S503, Power Modulation and Output: Space Vector Pulse Width Modulation (SVPWM) is used to drive the active filter to perform energy conversion, and the generated compensation current is injected into the node between the negative terminal of the battery pack and the DC bus capacitor to form a low-impedance ripple current cancellation path.

[0068] Please see Figure 2 The cascaded energy storage module ripple suppression system includes: Modular battery packs, the energy storage units of cascaded energy storage systems, provide DC power to the system and are the main targets of ripple current. The battery management system (BMS) collects the status parameters of the module's battery pack in real time (state of charge, state of health, temperature, internal resistance). The local controller receives data from each module, calculates the health coefficient / ripple stress ratio, and determines the filtering parameters and modes. A high-precision Hall sensor is used to collect the DC-side current at the output of the module's battery pack and extract the ripple current component. DC-Link capacitors stabilize the DC-side voltage of cascaded modules, temporarily store electrical energy, and suppress some voltage fluctuations. The differential sampling circuit acquires the voltage signal across the DC-Link capacitor and separates the voltage fluctuation component. The active filter receives instructions from the local controller and generates a compensation current to offset the DC side ripple current. The grid interface connects the cascaded module to the external power grid, enabling grid-connected power transmission (compatible with H-bridge cascaded energy storage system topology).

[0069] The module battery pack output is connected in series with the high-precision Hall sensor. The input of the high-precision Hall sensor is connected to the negative output of the module battery pack, and the output is connected to the positive input of the DC-Link capacitor. All the DC current output by the module battery pack must flow through the high-precision Hall sensor to ensure that the sensor can accurately collect the total current including the ripple component, providing raw data for the subsequent extraction of the second harmonic ripple current (Irip).

[0070] The module battery pack and the battery management system (BMS) are connected via a multi-node monitoring system. The sampling end of the BMS is connected to the individual cells of the module battery pack (collecting voltage and internal resistance related signals) and the NTC temperature sensor of the key hot area of ​​the battery module (collecting temperature T signal) through wires. The BMS directly obtains the core status parameters of the module battery pack, avoiding signal transmission loss and ensuring the real-time status perception.

[0071] The high-precision Hall sensor is connected to the local controller via a signal cable. The signal output terminal (ripple current signal) of the high-precision Hall sensor is connected to the current signal input terminal of the local controller. At the same time, the high-precision Hall sensor needs to be connected in series with a bandpass filter with a center frequency of 100Hz and a bandwidth of ±20Hz. The filtered double-frequency ripple current (Irip) signal is then transmitted to the local controller.

[0072] The differential sampling circuit is connected to the local controller via a differential signal cable. The two sampling terminals of the differential sampling circuit are connected to the positive and negative terminals of the DC-Link capacitor, respectively. The signal output terminal (voltage fluctuation signal) is first connected in series with a high-pass filter with a cutoff frequency of 50Hz. The filtered voltage fluctuation signal is then connected to the voltage signal input terminal of the local controller.

[0073] The battery management system (BMS) is connected to the local controller via a CAN bus or SPI interface. The communication output of the BMS is connected to the communication input of the local controller. The BMS synchronously transmits the collected state parameters such as SOC, SOH, T, and Rinternal to the local controller. The local controller generates a state vector after noise elimination by Kalman filtering, which provides data support for subsequent stress assessment.

[0074] The local controller and the active filter are connected by a control signal cable. The control command output terminal of the local controller (including compensation current reference command, filter parameters (Kp, fc), and mode switching signal) is connected to the control input terminal of the active filter. After determining the filter parameters and mode based on the battery health coefficient (Khealth) and real-time ripple stress ratio, the local controller generates corresponding control commands to drive the active filter to adjust its working state.

[0075] The current injection terminal of the active filter is connected to the power circuit via a power cable, and the current output terminal of the active filter is connected to the node between the negative terminal of the module battery pack and the negative terminal of the DC-Link capacitor. Function: The compensation current generated by the active filter is injected into the DC-side power circuit through this node, which cancels out the ripple current collected by the high-precision Hall sensor, and finally suppresses the DC-side ripple current.

[0076] The DC-Link capacitor is connected to the grid interface via a power cable. The positive output terminal of the DC-Link capacitor is connected to the input terminal of the H-bridge cascade circuit (compatible with high-voltage cascaded energy storage system topology), and the output terminal of the H-bridge cascade circuit is connected to the grid interface. The stable DC voltage after ripple suppression is inverted into AC power through the H-bridge cascade circuit and then connected to the external grid through the grid interface to ensure the quality of the output power.

[0077] The system implements closed-loop control of state perception, ripple monitoring, stress assessment, parameter decision-making, and ripple suppression, ensuring that each cascaded module can dynamically adjust its ripple suppression strategy based on its own battery state, thus adapting to the operational requirements of the high-voltage cascaded energy storage system.

[0078] In another embodiment of the present invention, an adaptive control system based on real-time sensing of module battery status is provided. This system can be used to implement the above-mentioned adaptive control method based on real-time sensing of module battery status. Specifically, the adaptive control system based on real-time sensing of module battery status includes a sensing module, a monitoring module, an evaluation module, a decision-making module, and a suppression module.

[0079] The sensing module acquires the battery's state parameters in real time, including the energy storage battery's state of charge, health status, temperature, and internal resistance. It also performs data fusion processing on the acquired state parameters to generate a state vector. The monitoring module collects the ripple current on the DC side of the cascaded module and the voltage fluctuation of the DC bus. The evaluation module calculates the battery health coefficient based on the state vector generated by the sensing module, and evaluates the ripple stress margin by combining the ripple current collected by the monitoring module, thus obtaining the real-time ripple stress ratio. The decision module dynamically adjusts the filtering parameters for ripple suppression based on the battery health coefficient and real-time ripple stress ratio obtained from the evaluation module, and switches the filtering mode according to the threshold of the battery health coefficient. The suppression module controls the active filter to generate a compensation current based on the filtering parameters and filtering mode determined by the suppression module. The compensation current is injected into the node between the negative terminal of the battery pack and the DC bus capacitor to cancel the DC side ripple current.

[0080] Preferably, in the sensing module, the real-time acquisition of battery status parameters specifically includes: The battery management system collects key state parameters of the energy storage battery in real time. Specifically, the battery state of charge is calculated by combining the ampere-hour integration method with the open-circuit voltage correction model. The battery health status is indirectly derived by estimating the internal resistance change based on the capacity decay model online or by analyzing the internal resistance change through electrochemical impedance spectroscopy. The battery temperature is collected by NTC temperature sensors deployed in the key hot areas of the battery module. The dynamic internal resistance value of the battery is output by the electrochemical model built into the battery management system. The local controller synchronously acquires the collected key state parameters through the CAN bus or SPI interface, and generates a state vector after noise elimination by Kalman filtering. The specific method of indirectly inferring health status by analyzing changes in internal resistance through electrochemical impedance spectroscopy is as follows: The impedance spectrum of the battery is obtained by electrochemical impedance spectroscopy. The charge transfer resistance and double-layer capacitance parameters in the impedance spectrum are extracted, and a correlation model between the parameters and the battery capacity decay rate is established. The health status of the battery is then deduced based on the correlation model.

[0081] Preferably, the monitoring module, which collects the ripple current on the DC side of the cascaded module and the voltage fluctuation of the DC bus, specifically includes: A Hall sensor is connected in series at the output of the battery pack, and the second harmonic ripple component is extracted through a bandpass filter to obtain the ripple current; The voltage signal is differentially sampled across the DC-Link capacitor, and the ripple component is separated by a high-pass filter to obtain the voltage fluctuation of the DC bus.

[0082] Preferably, in the evaluation module, the real-time ripple stress ratio = I rip / I rip_max The ripple stress margin is specifically as follows:

[0083] in, I rip_max This is the maximum ripple current that the battery can withstand. f ( SOC ) is the adjustment function for SOC; This is the battery's rated current. The ripple current component is collected from the DC side; The health index is specifically:

[0084] in, , , The weighting coefficients and , T opt The optimal battery temperature is 25°C. It is the temperature decay factor. For battery health status, This represents the real-time internal resistance value of the battery. This is the reference internal resistance value of the battery. This refers to the battery temperature.

[0085] Preferably, in the decision module, the filtering parameters for dynamically adjusting ripple suppression specifically include: Based on battery health coefficient Real-time ripple stress ratio Dynamically optimize the compensation gain of the filter and cutoff frequency The details are as follows:

[0086]

[0087] in, For rated gain, The weighting coefficient for the health coefficient. This is the weighting coefficient for ripple stress. Ripple sensitivity factor The rated cutoff frequency, The weighting coefficient for the health coefficient. This is the weighting coefficient for ripple stress. This is the maximum permissible frequency offset; The filtering mode switching based on the battery health coefficient threshold specifically includes: when K health When the value is greater than 0.9, the high-efficiency mode is enabled, reducing the compensation gain in the filter parameters by 20%. When 0.9> K health When the value is greater than 0.7, the standard mode is enabled, and the compensation gain and cutoff frequency in the filter parameters are kept at the rated parameters. when K health When the value is less than 0.7, protection mode is enabled, and the compensation gain in the filter parameters is increased by 30%.

[0088] Preferably, in the suppression module, controlling the active filter to generate the compensation current specifically includes: Based on the second harmonic component in the ripple current collected by the monitoring module, the current signal generated by the compensation gain adjusted by the decision module is superimposed to synthesize the compensation current reference command. At the same time, a phase-locked loop is used to track the grid frequency to ensure that the compensation current is synchronized with the ripple frequency. High-precision tracking of the compensation current is achieved through a proportional resonant controller. The gain of the resonant controller is dynamically adjusted according to the cutoff frequency adjusted by the decision module, and the embedded band-stop filter is used to suppress the interference of switching noise on the compensation current reference command. Space vector modulation is used to drive the active filter to perform energy conversion, generate compensation current and inject it into the node between the negative terminal of the battery pack and the DC bus capacitor, forming a low-impedance ripple current cancellation path.

[0089] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve corresponding method flows or corresponding functions. The processor described in this embodiment can be used for the operation of an adaptive control method based on real-time sensing of module battery status, including: The system acquires real-time state parameters of the battery, including its state of charge, state of health, temperature, and internal resistance. These parameters are then fused to generate a state vector. The system also collects ripple current on the DC side of the cascaded module and voltage fluctuations on the DC bus. Based on the generated state vector, the system calculates the battery health coefficient and assesses the ripple stress margin using the acquired ripple current, obtaining a real-time ripple stress ratio. According to the battery health coefficient and the real-time ripple stress ratio, the system dynamically adjusts the ripple suppression filter parameters and switches the filter mode based on the battery health coefficient threshold. Finally, based on the determined filter parameters and filter mode, the system controls an active filter to generate a compensation current, which is injected into the node between the battery pack negative terminal and the DC bus capacitor to offset the DC side ripple current.

[0090] Please see Figure 3The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the adaptive control method based on real-time sensing of module battery status as described in this embodiment. To avoid repetition, details are omitted here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the adaptive control system based on real-time sensing of module battery status as described in this embodiment. To avoid repetition, details are omitted here.

[0091] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 3 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0092] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0093] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on the computer device 60.

[0094] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0095] Please see Figure 4 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0096] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0097] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0098] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0099] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0100] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0101] Example 4 This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0102] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0103] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0104] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the adaptive control method based on real-time sensing of module battery status in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps: The system acquires real-time state parameters of the battery, including its state of charge, state of health, temperature, and internal resistance. These parameters are then fused to generate a state vector. The system also collects ripple current on the DC side of the cascaded module and voltage fluctuations on the DC bus. Based on the generated state vector, the system calculates the battery health coefficient and assesses the ripple stress margin using the acquired ripple current, obtaining a real-time ripple stress ratio. According to the battery health coefficient and the real-time ripple stress ratio, the system dynamically adjusts the ripple suppression filter parameters and switches the filter mode based on the battery health coefficient threshold. Finally, based on the determined filter parameters and filter mode, the system controls an active filter to generate a compensation current, which is injected into the node between the battery pack negative terminal and the DC bus capacitor to offset the DC side ripple current.

[0105] In each of the embodiments provided in this application, the databases involved may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., and is not limited thereto. In each of the embodiments provided in this application, the processor may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., and is not limited thereto.

[0106] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Generally, the components described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts fall within the protection scope of the present invention.

[0107] Simulation experiment data Build a simulation model of the H-bridge cascaded energy storage system based on MATLAB / Simulink, set 20 cascaded modules, the initial SOH of the battery is 0.95, and the rated current is 100A. Compare the present invention with the traditional LC filter and fixed parameter active filter schemes. The results are as follows:

[0108] The ripple suppression rate of the present invention is increased by 35% - 45% compared with the traditional scheme, the battery capacity attenuation rate is reduced by more than 50%, and the filter loss is reduced by 30% - 50%, effectively alleviating the battery temperature rise, verifying the advantages of the scheme in protecting the battery and improving efficiency.

[0109] The present invention has been successfully applied to a 35kV / 2MWh cascaded H-bridge energy storage system supporting a certain wind farm. The system contains a total of 100 energy storage modules, and this control system is deployed in each module. The actual operation data shows that when there are harmonic fluctuations in the power grid background, each module can independently adjust the suppression intensity according to its own battery state, the overall DC side ripple of the system is stable, the temperature difference between battery modules is significantly reduced, effectively balancing the aging rate between modules, and improving the availability and economy of the entire energy storage power station.

[0110] In summary, this invention provides an adaptive control method and system based on real-time sensing of module battery status, effectively solving the key problems of fixed parameters and neglect of individual battery differences and aging states in traditional cascaded energy storage system ripple suppression schemes. Its technical effects are mainly reflected in three aspects: I. Significantly Extend Battery Life: By sensing the battery health status (SOH, internal resistance, temperature) in real time and dynamically adjusting the suppression strategy, the battery is prevented from being subjected to excessive ripple stress under aging or harsh operating conditions, thus slowing down the battery aging rate from the root.

[0111] II. Improve overall system energy efficiency: An efficient mode is introduced, which automatically reduces the filtering intensity when the battery is in good condition, thereby reducing the switching and conduction losses of the active filter itself, thus improving the operating efficiency of the entire energy storage system while ensuring the suppression effect.

[0112] III. Enhanced System Safety and Reliability: The system achieves independent closed-loop control at the module level, eliminating reliance on central communication, resulting in fast response and high reliability. In particular, the protection mode provides enhanced protection for aging batteries, effectively preventing safety hazards such as thermal runaway. Simultaneously, precise ripple suppression improves the output voltage and current waveforms, enhancing grid-connected power quality. This invention provides core technological support for realizing a longer-life, more efficient, and safer intelligent cascaded energy storage system.

[0113] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0114] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0115] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0116] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or 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; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0117] 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.

[0118] Furthermore, the functional units in the various embodiments of the present invention 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.

[0119] If the integrated module / 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, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random-access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0120] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0121] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0122] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0123] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. An adaptive control method based on real-time sensing of module battery status, characterized in that, Includes the following steps: The battery's state parameters are acquired in real time, including the energy storage battery's state of charge, state of health, temperature, and internal resistance. The acquired state parameters are then fused to generate a state vector. Collect the ripple current on the DC side of the cascaded module and the voltage fluctuation of the DC bus; Based on the generated state vector, the battery health coefficient is calculated, and the ripple stress margin is evaluated in combination with the ripple current collected in step S2 to obtain the real-time ripple stress ratio. Based on the obtained battery health coefficient and real-time ripple stress ratio, the filtering parameters for ripple suppression are dynamically adjusted, and the filtering mode is switched according to the threshold of the battery health coefficient. Based on the determined filtering parameters and filtering mode, the active filter is controlled to generate a compensation current, which is then injected into the node between the negative terminal of the battery pack and the DC bus capacitor to offset the DC side ripple current.

2. The adaptive control method based on real-time sensing of module battery status according to claim 1, characterized in that, The specific steps for obtaining real-time battery status parameters are as follows: The battery management system collects key state parameters of the energy storage battery in real time. Specifically, the battery state of charge is calculated by using the ampere-hour integration method combined with the open-circuit voltage correction model. The battery health status is indirectly derived by estimating the internal resistance change based on the capacity decay model online or by analyzing the internal resistance change through electrochemical impedance spectroscopy. The battery temperature is collected by NTC temperature sensors deployed in the key hot areas of the battery module. The dynamic internal resistance value of the battery is output by the electrochemical model built into the battery management system. The local controller synchronously acquires the collected key state parameters through the CAN bus or SPI interface, and generates a state vector after noise elimination by Kalman filtering.

3. The adaptive control method based on real-time sensing of module battery status according to claim 2, characterized in that, The specific method of indirectly inferring health status by analyzing changes in internal resistance through electrochemical impedance spectroscopy is as follows: The impedance spectrum of the battery is obtained by electrochemical impedance spectroscopy. The charge transfer resistance and double-layer capacitance parameters in the impedance spectrum are extracted, and a correlation model between the parameters and the battery capacity decay rate is established. The health status of the battery is then deduced based on the correlation model.

4. The adaptive control method based on real-time sensing of module battery status according to claim 1, characterized in that, The acquisition of ripple current on the DC side of the cascaded module and voltage fluctuations on the DC bus specifically includes: A Hall sensor is connected in series at the output of the battery pack, and the second harmonic ripple component is extracted through a bandpass filter to obtain the ripple current; The voltage signal is differentially sampled across the DC-Link capacitor, and the ripple component is separated by a high-pass filter to obtain the voltage fluctuation of the DC bus.

5. The adaptive control method based on real-time sensing of module battery status according to claim 1, characterized in that, Real-time ripple stress ratio = I rip / I rip_max The ripple stress margin is specifically as follows: in, I rip_max This is the maximum ripple current that the battery can withstand. Health coefficient; f ( SOC ) is the adjustment function for SOC; This is the battery's rated current. This represents the ripple current component collected from the DC side.

6. The adaptive control method based on real-time sensing of module battery status according to claim 5, characterized in that, Health Index for: in, , , The weighting coefficients and , T opt The optimal battery temperature is 25°C. It is the temperature decay factor. For battery health status, This represents the real-time internal resistance value of the battery. This is the reference internal resistance value of the battery. This refers to the battery temperature.

7. The adaptive control method based on real-time sensing of module battery status according to claim 1, characterized in that, The filter parameters for dynamically adjusting ripple suppression specifically include: Based on battery health coefficient K health Real-time ripple stress ratio Dynamically optimize the compensation gain of the filter and cutoff frequency The details are as follows: in, For rated gain, The weighting coefficient for the health coefficient. This is the weighting coefficient for ripple stress. Ripple sensitivity factor The rated cutoff frequency, The weighting coefficient for the health coefficient. This is the weighting coefficient for ripple stress. This represents the maximum permissible frequency offset.

8. The adaptive control method based on real-time sensing of module battery status according to claim 7, characterized in that, The filtering mode switching based on the battery health coefficient threshold specifically includes: when K health When the value is greater than 0.9, the high-efficiency mode is enabled, reducing the compensation gain in the filter parameters by 20%. When 0.9> K health When the value is greater than 0.7, the standard mode is enabled, and the compensation gain and cutoff frequency in the filter parameters are kept at the rated parameters. when K health When the value is less than 0.7, protection mode is enabled, and the compensation gain in the filter parameters is increased by 30%.

9. The adaptive control method based on real-time sensing of module battery status according to claim 1, characterized in that, Controlling the active filter to generate compensation current specifically includes: Based on the second harmonic component in the collected ripple current, the current signal generated by the compensation gain adjusted in step S4 is superimposed to synthesize the compensation current reference command. At the same time, a phase-locked loop is used to track the grid frequency to ensure that the compensation current is synchronized with the ripple frequency. High-precision tracking of the compensation current is achieved through a proportional resonant controller. The gain of the resonant controller is dynamically adjusted according to the cutoff frequency adjusted in step S4, and the embedded band-stop filter is used to suppress the interference of switching noise on the compensation current reference command. Space vector modulation is used to drive the active filter to perform energy conversion, generate compensation current and inject it into the node between the negative terminal of the battery pack and the DC bus capacitor, forming a low-impedance ripple current cancellation path.

10. An adaptive control system based on real-time sensing of module battery status, characterized in that, include: The sensing module acquires the battery's state parameters in real time, including the energy storage battery's state of charge, health status, temperature, and internal resistance. It also performs data fusion processing on the acquired state parameters to generate a state vector. The monitoring module collects the ripple current on the DC side of the cascaded module and the voltage fluctuation of the DC bus. The evaluation module calculates the battery health coefficient based on the state vector generated by the sensing module, and evaluates the ripple stress margin by combining the ripple current collected by the monitoring module, thus obtaining the real-time ripple stress ratio. The decision module dynamically adjusts the filtering parameters for ripple suppression based on the battery health coefficient and real-time ripple stress ratio obtained from the evaluation module, and switches the filtering mode according to the threshold of the battery health coefficient. The suppression module controls the active filter to generate a compensation current based on the filtering parameters and filtering mode determined by the suppression module. The compensation current is injected into the node between the negative terminal of the battery pack and the DC bus capacitor to cancel the DC side ripple current.

11. The adaptive control system based on real-time sensing of module battery status according to claim 10, characterized in that, In the sensing module, the real-time acquisition of battery status parameters specifically includes: The battery management system collects key state parameters of the energy storage battery in real time. Specifically, the battery state of charge is calculated by combining the ampere-hour integration method with the open-circuit voltage correction model. The battery health status is indirectly derived by estimating the internal resistance change based on the capacity decay model online or by analyzing the internal resistance change through electrochemical impedance spectroscopy. The battery temperature is collected by NTC temperature sensors deployed in the key hot areas of the battery module. The dynamic internal resistance value of the battery is output by the electrochemical model built into the battery management system. The local controller synchronously acquires the collected key state parameters through the CAN bus or SPI interface, and generates a state vector after noise elimination by Kalman filtering. The specific method of indirectly inferring health status by analyzing changes in internal resistance through electrochemical impedance spectroscopy is as follows: The impedance spectrum of the battery is obtained by electrochemical impedance spectroscopy. The charge transfer resistance and double-layer capacitance parameters in the impedance spectrum are extracted, and a correlation model between the parameters and the battery capacity decay rate is established. The health status of the battery is then deduced based on the correlation model.

12. The adaptive control system based on real-time sensing of module battery status according to claim 10, characterized in that, The monitoring module collects the ripple current on the DC side of the cascaded module and the voltage fluctuation of the DC bus, specifically including: A Hall sensor is connected in series at the output of the battery pack, and the second harmonic ripple component is extracted through a bandpass filter to obtain the ripple current; The voltage signal is differentially sampled across the DC-Link capacitor, and the ripple component is separated by a high-pass filter to obtain the voltage fluctuation of the DC bus.

13. The adaptive control system based on real-time sensing of module battery status according to claim 10, characterized in that, In the evaluation module, the real-time ripple stress ratio = I rip / I rip_max The ripple stress margin is specifically as follows: in, I rip_max This is the maximum ripple current that the battery can withstand. f ( SOC ) is the adjustment function for SOC; This is the battery's rated current. The ripple current component is collected from the DC side; The health index is specifically: in, , , The weighting coefficients and , T opt The optimal battery temperature is 25°C. It is the temperature decay factor. For battery health status, This represents the real-time internal resistance value of the battery. This is the reference internal resistance value of the battery. This refers to the battery temperature.

14. The adaptive control system based on real-time sensing of module battery status according to claim 10, characterized in that, In the decision-making module, the filter parameters for dynamically adjusting ripple suppression specifically include: Based on battery health coefficient K health Real-time ripple stress ratio Dynamically optimize the compensation gain of the filter and cutoff frequency The details are as follows: in, For rated gain, The weighting coefficient for the health coefficient. This is the weighting coefficient for ripple stress. Ripple sensitivity factor The rated cutoff frequency, The weighting coefficient for the health coefficient. This is the weighting coefficient for ripple stress. This is the maximum permissible frequency offset; The filtering mode switching based on the battery health coefficient threshold specifically includes: when K health When the value is greater than 0.9, the high-efficiency mode is enabled, reducing the compensation gain in the filter parameters by 20%. When 0.9> K health When the value is greater than 0.7, the standard mode is enabled, and the compensation gain and cutoff frequency in the filter parameters are kept at the rated parameters. when K health When the value is less than 0.7, protection mode is enabled, and the compensation gain in the filter parameters is increased by 30%.

15. The adaptive control system based on real-time sensing of module battery status according to claim 10, characterized in that, In the suppression module, controlling the active filter to generate compensation current specifically includes: Based on the second harmonic component in the ripple current collected by the monitoring module, the current signal generated by the compensation gain adjusted by the decision module is superimposed to synthesize the compensation current reference command. At the same time, a phase-locked loop is used to track the grid frequency to ensure that the compensation current is synchronized with the ripple frequency. High-precision tracking of the compensation current is achieved through a proportional resonant controller. The gain of the resonant controller is dynamically adjusted according to the cutoff frequency adjusted by the decision module, and the embedded band-stop filter is used to suppress the interference of switching noise on the compensation current reference command. Space vector modulation is used to drive the active filter to perform energy conversion, generate compensation current and inject it into the node between the negative terminal of the battery pack and the DC bus capacitor, forming a low-impedance ripple current cancellation path.

16. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 9.

17. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including steps for performing the method of any one of claims 1 to 9.

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