Charging and discharging control method and system of energy storage converter

Abstract

The embodiment of the invention provides a charging and discharging control method and system for an energy storage converter, and the method achieves the comprehensive perception of the current actual bearing capacity of a battery through the precise obtaining and fusion of three key vital signs, i.e., a battery health state (SOH), a thermal state characteristic temperature (SOT) and a state of charge (SOC). On the basis, a double-layer dynamic limit value system of a sustainable power boundary and a transient power boundary is defined: the sustainable power boundary and the transient power boundary are based on SOH and SOT, and long-term health and safety of the battery under any working condition are ensured; and the latter provides necessary short-time high-power response capability for the system in combination with moderate relaxation of the SOC state on the basis of the sustainable boundary. And finally, corresponding boundaries are intelligently selected according to the operation mode through correction logic, and fine correction is performed on the power instruction in combination with an SOC safety defense line, so that the optimal dynamic balance of the energy storage system among the performance, the safety and the service life is fundamentally realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent control, in particular to a charge and discharge control method and system of an energy storage converter. BACKGROUND

[0002] With the increasing proportion of renewable energy in the global energy structure, the intermittency and volatility of new energy generation represented by wind and solar energy have brought great challenges to the stable operation of the power grid. Energy storage systems, especially electrochemical energy storage systems, have become key technical equipment for smoothing renewable energy fluctuations and improving the flexibility and reliability of the power grid due to their fast response speed, high energy density, and flexible layout. The charge and discharge control strategy of the energy storage converter, which is the core interface device between the energy storage system and the power grid, directly determines the operation efficiency, safety, and economic benefits of the entire energy storage system. Therefore, constructing an efficient, safe, and intelligent charge and discharge control scheme for the energy storage converter is of great significance for promoting the large-scale application of energy storage technology.

[0003] However, the existing charge and discharge control methods for energy storage converters are generally rough. These methods mostly set a group of static and fixed power limits and protection thresholds based on the rated parameters calibrated by the battery at the factory. This one-size-fits-all control strategy ignores the fact that the battery is a complex electrochemical system, and its internal state is dynamically changing. Specifically, the state of health of the battery will gradually deteriorate with continuous charge and discharge cycles, resulting in an increase in internal resistance and a decrease in actual available capacity. At the same time, the performance and safety boundaries of the battery are highly sensitive to its operating temperature, and its tolerable charge and discharge power will decrease at excessively high or low temperatures. The traditional static control strategy cannot adapt to the aging of the battery in the entire life cycle and the temperature changes in different environments in real time, resulting in two main defects: first, when the battery is in good condition and the temperature is suitable, the fixed power limit may be too conservative, limiting the greater power regulation capacity that the energy storage system should have, reducing its economic benefits in peak shaving, frequency modulation, and other application scenarios; second, when the battery is aging or in extreme temperature conditions, the fixed power limit may be too aggressive, allowing the converter to charge and discharge at a power that exceeds the current actual carrying capacity of the battery, which not only accelerates the irreversible damage to the battery and shortens its service life, but in more serious cases, can even induce lithium precipitation, internal short circuit, and other safety hazards, leading to thermal runaway accidents.

[0004] Therefore, a more efficient and intelligent charge and discharge control method for energy storage converters is desired. SUMMARY

[0005] Embodiments of the present application aim to at least solve one of the technical problems existing in the prior art, and provide a charge and discharge control method and system of an energy storage converter.

[0006] In one aspect, embodiments of the present application provide a method for charge and discharge control of an energy storage converter, comprising: obtaining an optimal estimated SOC, a current thermal state characteristic temperature of the battery, and a current state of health of the battery; calculating a sustainable power boundary based on a rated power of the converter, the current thermal state characteristic temperature of the battery, and the current state of health of the battery to obtain a sustainable charging power limit and a sustainable discharging power limit; calculating a transient power boundary based on the sustainable charging power limit, the sustainable discharging power limit, and the optimal estimated SOC to obtain a transient charging power limit, a transient discharging power limit, and a transient power allowed duration; performing power command arbitration and correction on an EMS power command based on the sustainable charging power limit, the sustainable discharging power limit, the transient charging power limit, and the transient discharging power limit to obtain a power reference value; generating a PWM waveform for driving IGBTs based on the power reference value.

[0007] Optionally, obtaining the optimal estimated SOC, the current thermal state characteristic temperature of the battery, and the current state of health of the battery comprises: obtaining an array of cell temperature sensor readings and a preset temperature safety margin; extracting a highest single cell temperature and an average temperature from the array of cell temperature sensor readings; performing characteristic temperature decision fusion on the highest single cell temperature, a lowest single cell temperature, and the average temperature to obtain the current thermal state characteristic temperature of the battery according to the following formula: ； wherein, the highest single cell temperature is T max, the average temperature is T avg, the preset temperature safety margin is T margin.

[0008] Optionally, obtaining the optimal estimated SOC, the current thermal state characteristic temperature of the battery, and the current state of health of the battery further comprises: calculating a PCS internal SOC estimation value of a current control cycle based on a PCS internal SOC estimation value of a previous control cycle and the current thermal state characteristic temperature of the battery according to the following formula:

[0009] wherein, the PCS internal SOC estimation value of the previous control cycle is SOC prev, a total battery current is I bat, the current thermal state characteristic temperature of the battery is T cur, a control cycle duration is T cycle. The optimal estimated SOC is obtained by weighted fusion of the PCS internal SOC estimation value of the current control cycle and the state of charge uploaded by the BMS.

[0010] Optionally, obtaining the optimal estimated SOC, the current thermal state characteristic temperature of the battery, and the current battery health state further comprises: obtaining a total battery current time series, a start SOC, and an end SOC; integrating the total battery current time series to obtain a cumulative charge amount; based on the cumulative charge amount, the start SOC, and the end SOC, calculating the current actual capacity according to the following formula:

[0011] wherein, the end SOC is, the start SOC is, the cumulative charge amount is; dividing the current actual capacity by the rated capacity of the battery to obtain the current battery health state.

[0012] Optionally, based on the converter rated power, the current thermal state characteristic temperature of the battery, and the current battery health state, calculating the sustainable power boundary to obtain the sustainable charging power limit and the sustainable discharging power limit comprises: based on the current battery health state, calculating an SOH derating factor; based on the current thermal state characteristic temperature of the battery, calculating a SOT charging derating factor and a SOT discharging derating factor; based on the SOH derating factor, the SOT charging derating factor, the SOT discharging derating factor, and the converter rated power, calculating the sustainable charging power limit and the sustainable discharging power limit.

[0013] Optionally, based on the SOH derating factor, the SOT charging derating factor, the SOT discharging derating factor, and the converter rated power, calculating the sustainable charging power limit and the sustainable discharging power limit comprises: calculating the sustainable charging power limit and the sustainable discharging power limit according to the following formula:

[0014]

[0015] wherein, the converter rated power is, the SOH derating factor is, and the SOT charging derating factor and the SOT discharging derating factor are.

[0016] Optionally, based on the sustainable charging power limit, the sustainable discharging power limit, and the optimal estimated SOC, the transient power boundary is calculated to obtain the transient charging power limit and the transient discharging power limit, including: Calculate the transient overload coefficient based on the optimal estimated SOC; Based on the transient overload factor, the sustainable charging power limit, and the sustainable discharging power limit, the transient charging power limit and the transient discharging power limit are calculated.

[0017] Optionally, the transient overload factor is calculated based on the optimal estimated SOC, including: calculating the transient overload factor based on the optimal estimated SOC using the following formula:

[0018] in, For peak gain, To achieve the optimal estimate of SOC, The optimal SOC center point, To control the width of the overload SOC range.

[0019] Optionally, power command arbitration and correction are performed on the EMS power command based on the sustainable charging power limit, the sustainable discharging power limit, the transient charging power limit, and the transient discharging power limit to obtain a power reference value, including: When the system operation mode flag is set to normal economic mode, the sustainable charging power limit and the sustainable discharging power limit are assigned to the currently effective charging power limit and the currently effective discharging power limit. When the system operation mode flag is set to emergency support mode, the transient charging power limit and transient discharging power limit are assigned to the currently effective charging power limit and the currently effective discharging power limit. Based on the EMS power command, the current effective charging power limit and the current effective discharging power limit are initially power limited to obtain the intermediate power reference value after boundary limiting. The power reference value is obtained by performing a final power safety derating based on the SOC state on the intermediate power reference value after boundary limiting, based on the optimal estimated SOC.

[0020] On the other hand, embodiments of this disclosure provide a charging and discharging control system for an energy storage converter, comprising: The data acquisition module is used to acquire the optimal estimated SOC, the current thermal state characteristic temperature of the battery, and the current battery health state. The limit calculation module is used to calculate the sustainable power boundary based on the inverter's rated power, the battery's current thermal state characteristic temperature, and the current battery health state to obtain the sustainable charging power limit and the sustainable discharging power limit; The transient calculation module is used to calculate the transient power boundary based on the sustainable charging power limit, the sustainable discharging power limit, and the optimal estimated SOC to obtain the transient charging power limit, the transient discharging power limit, and the allowable duration of transient power. The instruction correction module is used to arbitrate and correct the EMS power instruction based on the sustainable charging power limit, the sustainable discharging power limit, the transient charging power limit, and the transient discharging power limit to obtain a power reference value; The PWM waveform generation module is used to generate the PWM waveform for driving the IGBT based on the power reference value.

[0021] Compared with existing technologies, the charging and discharging control method and system for an energy storage converter provided in this application comprehensively perceives the battery's current actual load-bearing capacity by accurately acquiring and integrating three key vital signs: battery health status, thermal characteristic temperature, and state of charge (SOC). Based on this, a two-layer dynamic limit system is defined: a sustainable power boundary and a transient power boundary. The former, based on SOH and SOT, ensures the long-term health and safety of the battery under any operating condition; the latter, based on the sustainable boundary, appropriately relaxes the SOC to provide the system with the necessary short-term high-power response capability. Finally, through correction logic, the appropriate boundary is intelligently selected according to the operating mode, and the power command is finely corrected in conjunction with the SOC safety defense line, thereby fundamentally achieving the optimal dynamic balance between performance, safety, and lifespan of the energy storage system. Attached Figure Description

[0022] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0023] Figure 1 This is a flowchart of a charging and discharging control method for an energy storage converter according to an embodiment of this application; Figure 2 This is a schematic diagram of the data flow of the charging and discharging control method of the energy storage converter according to an embodiment of this application; Figure 3 This is a flowchart illustrating the charging and discharging control method for an energy storage converter according to an embodiment of this application, which calculates sustainable power boundaries based on the converter's rated power, the battery's current thermal state characteristic temperature, and the current battery health state to obtain sustainable charging power limits and sustainable discharging power limits. Figure 4This is a flowchart illustrating the charging and discharging control method for an energy storage converter according to an embodiment of this application, which arbitrates and corrects EMS power commands based on sustainable charging power limits, sustainable discharging power limits, transient charging power limits, and transient discharging power limits to obtain a power reference value. Figure 5 This is a block diagram of the charge and discharge control system of an energy storage converter according to an embodiment of this application. Detailed Implementation

[0024] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0025] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0026] While this application makes various references to certain modules of the systems according to embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The modules described are merely illustrative, and different aspects of the systems and methods may use different modules.

[0027] Flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0028] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0029] To address the technical problems in the background art, this application proposes a charge and discharge control scheme for an energy storage converter. This scheme solves the problem that existing energy storage converter control strategies, which use static power limits, cannot adapt to the dynamically changing actual load-bearing capacity of batteries throughout their lifespan due to aging and temperature variations, leading to both performance waste and safety risks. Specifically, this scheme first starts from the data source. It periodically updates the actual battery capacity by performing decision fusion on battery temperature sensor array readings, weighted estimation of multi-source SOC information, and integration of historical operating data. This allows for real-time and accurate acquisition of the optimal estimated SOC, thermal state characteristic temperature (SOT), and state of health (SOH) characterizing the battery's current state. Based on this, a two-layer dynamic power boundary model is constructed: on the one hand, it combines SOH and SOT to calculate a sustainable power boundary that ensures long-term healthy battery operation, serving as a benchmark for daily economic operation. On the other hand, based on this sustainable boundary and the current SOC, it calculates a transient power boundary that allows for short-term exceedance, providing performance margin for the system to cope with emergency grid disturbances. Upon receiving the power command from the upper-level EMS, the control system intelligently selects the appropriate power boundary for initial limiting based on whether the current mode is normal economic mode or emergency support mode, forming an intermediate power reference value. Subsequently, this intermediate value undergoes a final safety derating process based on the SOC state to absolutely prevent overcharging and over-discharging. Finally, the safe and accurate power reference value obtained after this multi-level arbitration and correction is used to generate the PWM waveform driving the IGBT, thereby achieving dynamic optimization of the energy storage system's power output while ensuring the safety of the battery throughout its entire life cycle.

[0030] The present application proposes a charging and discharging control method for an energy storage converter. Figure 1 This is a flowchart of a charging and discharging control method for an energy storage converter according to an embodiment of this application. Figure 2 This is a schematic diagram of the data flow in the charge / discharge control method of an energy storage converter according to an embodiment of this application. Figure 1 and Figure 2As shown, the charging and discharging control method for an energy storage converter according to an embodiment of this application includes the following steps: S100, obtaining the optimal estimated SOC, the current thermal state characteristic temperature of the battery, and the current battery health state. S200, calculating the sustainable power boundary based on the converter's rated power, the current thermal state characteristic temperature of the battery, and the current battery health state to obtain a sustainable charging power limit and a sustainable discharging power limit. S300, calculating the transient power boundary based on the sustainable charging power limit, the sustainable discharging power limit, and the optimal estimated SOC to obtain a transient charging power limit, a transient discharging power limit, and a transient power allowable duration. S400, arbitrating and correcting the EMS power command based on the sustainable charging power limit, the sustainable discharging power limit, the transient charging power limit, and the transient discharging power limit to obtain a power reference value. S500, generating a PWM waveform to drive the IGBT based on the power reference value.

[0031] Specifically, in step S100, the optimal estimated SOC, the current thermal state characteristic temperature of the battery, and the current battery health state are obtained. It should be understood that the temperature distribution within the battery pack is uneven, and a single temperature measurement point or a simple average value cannot fully reflect its true thermal risk. In particular, localized hot spots may cause safety issues. Simultaneously, the actual capacity of the battery will decrease with increasing usage years. If the control system still calculates based on the factory rated capacity, the power limit will be overly aggressive. Furthermore, the state of charge (SOC) transmitted from the BMS and the SOC estimated internally by the converter through ampere-hour integration each have their advantages and disadvantages; data from a single source cannot simultaneously ensure long-term accuracy and instantaneous responsiveness. Therefore, in the technical solution of this application, the optimal estimated SOC, the current thermal state characteristic temperature of the battery, and the current battery health state are obtained to comprehensively and accurately perceive the real-time vital signs of the battery in three dimensions: thermal state, health state, and charge state. This provides a reliable data foundation for subsequent adaptive power boundary calculations, ensuring that the control strategy is not based on static, outdated factory parameters, but rather closely matches the battery's current actual load-bearing capacity, thereby fundamentally guaranteeing the safety, accuracy, and effectiveness of the entire control scheme.

[0032] More specifically, in this embodiment, obtaining the optimal estimated SOC, the current thermal state characteristic temperature of the battery, and the current battery health state includes: obtaining the readings of the cell temperature sensor array and a preset temperature safety margin; extracting the highest single-cell temperature and average temperature from the cell temperature sensor array readings; calculating the PCS internal SOC estimate for the current control cycle based on the PCS internal SOC estimate from the previous control cycle and the current thermal state characteristic temperature of the battery using the following formula; weighted fusion of the PCS internal SOC estimate for the current control cycle and the state of charge sent by the BMS to obtain the optimal estimated SOC; obtaining the battery total current time series, the initial SOC, and the final SOC; integrating the battery total current time series to obtain the accumulated charge; calculating the current actual capacity based on the accumulated charge, the initial SOC, and the final SOC using the following formula; and dividing the current actual capacity by the battery rated capacity to obtain the current battery health state.

[0033] Specifically, the readings of the cell temperature sensor array and the preset temperature safety margin are obtained. The highest and average individual cell temperatures are extracted from the cell temperature sensor array readings. A characteristic temperature decision fusion is performed on the highest, lowest, and average individual cell temperatures using the following formula to obtain the current thermal state characteristic temperature of the battery:

[0034] in, The highest monomer temperature, The average temperature. As a preset temperature safety margin, To obtain the maximum value.

[0035] It is understandable that in large battery packs, the heat generation and dissipation conditions of individual cells differ, resulting in highly uneven temperature distribution. Relying solely on the average temperature may overlook the safety hazards of localized hot spots, while relying solely on the highest temperature may be overly sensitive to instantaneous disturbances and fail to reflect the overall thermal stress state. Therefore, in the technical solution of this application, the readings of the cell temperature sensor array and the preset temperature safety margin are obtained. Furthermore, the highest single-cell temperature and the average temperature are extracted from the cell temperature sensor array readings. This aggregates the discrete multi-point temperature information into a single characteristic index that can simultaneously characterize the risk of localized hot spots and the overall temperature rise trend. This allows the temperature state value used in subsequent power boundary calculations to sensitively capture the worst-case single-cell temperature to trigger necessary protection, while also avoiding misjudgments by considering the average temperature and safety margin. This achieves accurate and robust assessment of the battery's thermal state, providing a more reliable basis for dynamically adjusting charge and discharge power.

[0036] Specifically, based on the PCS internal SOC estimate from the previous control cycle and the current thermal state characteristic temperature of the battery, the PCS internal SOC estimate for the current control cycle is calculated using the following formula:

[0037] in, This is the PCS internal SOC estimate for the previous control cycle. For the total battery current, The current thermal characteristic temperature of the battery To control the cycle duration.

[0038] It is understandable that the actual usable capacity of a battery is not a constant value, but changes with its operating temperature, especially at low temperatures where capacity decreases. Simply using the rated capacity for ampere-hour integration introduces significant estimation errors due to the neglect of temperature effects, causing the SOC estimate to deviate from the true state. Therefore, in this application's technical solution, a real-time temperature-related correction factor is introduced based on the PCS's internal SOC estimate from the previous control cycle and the battery's current thermal state characteristic temperature. This dynamically adjusts the effective capacity denominator used to calculate SOC changes, building upon the traditional ampere-hour integration method. This allows the SOC estimate obtained internally by the converter using the ampere-hour integration method to compensate for capacity fluctuations caused by temperature changes in real time, improving estimation accuracy over a wide temperature range. This provides a high-quality local data source for subsequent high-reliability fusion with the BMS's state of charge, ultimately improving the accuracy of the entire control system's decision-making.

[0039] Specifically, the optimal estimated SOC is obtained by weighted fusion of the PCS internal SOC estimate and the BMS-supplied SOC for the current control cycle. It should be understood that while the SOC estimated internally by the converter using the ampere-hour integral method can respond quickly to current changes, it suffers from cumulative drift. Conversely, while the SOC supplied by the battery management system has long-term accuracy, its update frequency or data smoothness cannot meet the real-time requirements of the converter's high-dynamic control. Therefore, in this application's technical solution, the optimal estimated SOC is further obtained by weighted fusion of the PCS internal SOC estimate and the BMS-supplied SOC for the current control cycle, thereby constructing a fusion model. This generates an optimal estimated SOC that is both smooth and continuous, and closely tracks the actual state of charge. This provides a high-fidelity, high-reliability decision-making basis for all control links that depend on SOC, such as subsequent transient power boundary calculations and final power safety derating.

[0040] More specifically, in a particular example of this application, firstly, in the control cycle Initially, the control system synchronously acquires two input data: the latest state of charge value received from the BMS communication bus. And the predicted current state of charge calculated by the ampere-hour integral module inside the converter. Secondly, load the preset weighting coefficients. and These two coefficients are stored in non-volatile memory, and their sum is 1. For example, It can be set to 0.7. This can be set to 0.3, indicating a greater trust in the long-term accuracy of the BMS data in the design. Next, the control system performs a weighted average calculation, multiplying the two SOC input values ​​by their corresponding weighting coefficients and then summing the results to generate the fused optimal estimate. The calculation follows the formula: Finally, the optimal estimate is calculated. The value is output to the subsequent power boundary calculation and power arbitration modules as the basis for decision-making in this cycle. At the same time, this value is also used to update the benchmark of the SOC state variables inside the PCS to reduce the prediction deviation in the next cycle.

[0041] Specifically, the total battery current time series, initial SOC, and final SOC are obtained; the total battery current time series is integrated to obtain the accumulated charge. Based on the accumulated charge, initial SOC, and final SOC, the current actual capacity is calculated using the following formula:

[0042] in, To end SOC, As the initial SOC, This represents the cumulative charge.

[0043] It is understandable that the State of Health (SOH) of a battery is not constant but continuously decays with increasing charge-discharge cycles, causing its true maximum usable capacity to gradually deviate from the factory rated capacity. If the control system lacks an accurate means of calibrating this actual capacity, all its capacity-based calculations (such as SOH derating and SOC estimation) will be based on errors, thus failing to achieve true adaptive control. Therefore, in the technical solution of this application, the total battery current time series, initial SOC, and final SOC are further obtained. The total battery current time series is integrated to obtain the accumulated charge. Based on the accumulated charge, initial SOC, and final SOC, the current actual capacity is calculated using a formula. This allows for periodic online calibration and quantification of the battery's current actual maximum storage capacity through a complete charge-discharge data segment. In this way, the system can obtain an actual capacity value that accurately reflects the current degree of battery aging. This value is the direct basis for calculating the current state of battery health (SOH), thereby ensuring that the subsequent calculation of the sustainable power boundary can truly reflect the decrease in battery load capacity due to aging. This fundamentally frees the entire power control strategy from dependence on static rated parameters, achieving dynamic adaptation and refined protection for the entire battery life cycle.

[0044] Specifically, the current actual capacity is divided by the battery's rated capacity to obtain the current battery health state. It should be understood that the current actual capacity calculated in the previous process is an absolute physical quantity, such as 1950 Ah, which cannot be directly used as a standardized indicator of aging for subsequent derating factor calculations. The control system needs a relative, normalized percentage to quantify the battery's degradation compared to its brand-new state. Therefore, in the technical solution of this application, the current actual capacity is further divided by the battery's rated capacity to obtain the current battery health state, thereby converting the measured absolute capacity value into a health state (SOH) percentage within the range of 0 to 100%, conforming to industry-standard definitions. This provides a standardized and directly usable input parameter for subsequent calculations of the SOH derating factor in the sustainable power boundary, thus accurately linking the battery's physical capacity degradation with the power derating at the control strategy level, forming a crucial link in the entire adaptive control logic closed loop.

[0045] More specifically, in a particular example of this application, firstly, the task is configured to execute upon meeting specific triggering conditions, such as every 24 hours or after the system detects the completion of a full charge-discharge cycle within a predefined SOC range. Secondly, when the task is triggered, the control system reads two key data points from its internal memory: the current actual capacity value just calculated and stored in the previous process. And the battery rated capacity value, representing the battery's factory specifications, loaded from non-volatile memory during system initialization. Next, the central processing unit performs a floating-point division operation, that is... Finally, the calculated new SOH value is written to a dedicated state variable register. The data in this register will be read by the main control loop program in each control cycle (e.g., every 50 milliseconds) to calculate the sustainable power boundary in real time until the next SOH update task is triggered and refreshes the value.

[0046] Specifically, in step S200, based on the inverter's rated power, the battery's current thermal state characteristic temperature, and the current battery health state, the sustainable power boundary is calculated to obtain the sustainable charging power limit and the sustainable discharging power limit. It should be understood that the inverter's rated power only represents its own hardware capability limit, while the actual sustainable power limit of the entire energy storage system is essentially determined by the long-term aging state and immediate thermal state of the battery, the core electrochemical component. A battery with a low state of health (SOH) or a thermal state characteristic temperature (SOT) in a non-optimal range will experience a sharp increase in internal losses and safety risks, making it unable to withstand the continuous impact of rated power. Therefore, in the technical solution of this application, the sustainable charging power limit and the sustainable discharging power limit are further calculated based on the inverter's rated power, the battery's current thermal state characteristic temperature, and the current battery health state. This quantifies the abstract SOH and SOT state parameters into a specific power operating boundary that can reflect the battery's true health and safety margin in real time. This ensures that the power command output by the inverter is always within the safe range that the battery can withstand over a long period of time. This not only avoids irreversible damage to the battery due to power overload under harsh operating conditions, but also establishes the most basic and core safety baseline for the entire dual-layer power boundary control system. It is the primary prerequisite for achieving refined protection of battery assets throughout their entire life cycle.

[0047] Figure 3 This is a flowchart illustrating the charging and discharging control method for an energy storage converter according to embodiments of this application. It calculates sustainable power boundaries based on the converter's rated power, the battery's current thermal state characteristic temperature, and the current battery health state to obtain sustainable charging power limits and sustainable discharging power limits. (See attached flowchart.) Figure 3 As shown, step S200 includes: S210, calculating the SOH derating factor based on the current battery health state; S220, calculating the SOT charging derating factor and SOT discharging derating factor based on the current thermal characteristic temperature of the battery; and S230, calculating the sustainable charging power limit and sustainable discharging power limit based on the SOH derating factor, SOT charging derating factor, SOT discharging derating factor, and the inverter rated power.

[0048] Specifically, in steps S210 and S220, the SOH derating factor is calculated based on the current battery health state. The SOT charging derating factor and SOT discharging derating factor are calculated based on the current thermal state characteristic temperature of the battery. It should be understood that the long-term aging of the battery (SOH decrease) and the instantaneous temperature (SOT change) have completely different mechanisms and time scales affecting its actual power carrying capacity. The former is a slow, irreversible structural degradation, while the latter is a rapid, reversible conditional limitation. If the two are confused or treated with a single model, their respective limiting effects cannot be accurately decoupled and quantified. Therefore, in the technical solution of this application, the SOH derating factor is further calculated based on the current battery health state, and the SOT charging derating factor and SOT discharging derating factor are calculated based on the current thermal state characteristic temperature of the battery, respectively. This quantifies the two different dimensions of physical constraints—long-term aging and instantaneous thermal conditions—into two independent, standardized derating coefficients. This allows subsequent sustainable power boundary calculations to clearly and accurately reflect the combined effect of the two limiting effects by multiplying these independent derating factors by the rated power, thereby constructing a physically meaningful, multi-dimensional adaptive power derating model.

[0049] More specifically, in a concrete example of this application, firstly, the SOH derating factor is calculated. The control system reads the current battery health state (SOH) value as 85% and calculates it according to a preset SOH derating function. This function is defined as follows: when SOH is above 90%, the derating factor is 1. When SOH is between 80% and 90%, the factor linearly decreases from 1 to 0. When SOH is below 80%, the factor is 0. Therefore, for an SOH of 85%, the SOH derating factor is... The value is calculated as (85-80) / (90-80) = 0.5. Next, the SOT derating factor is calculated. The control system reads the current thermal characteristic temperature (SOT) as 45°C and calculates it according to the preset SOT discharge derating function. This function is defined as follows: when the SOT is in the optimal range of 15°C to 35°C, the factor is 1. When the SOT is in the high-temperature derating range of 35°C to 50°C, the factor linearly decreases from 1 to 0. Therefore, for an SOT of 45°C, the SOT discharge derating factor is... The SOH derating factor is calculated as (50-45) / (50-35) ≈ 0.33. The SOT charging derating factor is calculated using the same logic based on another set of temperature threshold functions for charging characteristics. Finally, the calculated SOH derating factor of 0.5 and SOT discharging derating factor of 0.33 are passed to the next step to calculate the final sustainable discharge power limit.

[0050] Specifically, in step S230, based on the SOH derating factor, SOT charging derating factor, SOT discharging derating factor, and converter rated power, the sustainable charging power limit and the sustainable discharging power limit are calculated. The sustainable charging power limit and the sustainable discharging power limit are calculated using the following formula:

[0051]

[0052] in, The rated power of the converter, The SOH deflator, and SOT charging derating factor and SOT discharging derating factor.

[0053] It should be understood that the SOH derating factor and SOT derating factor calculated in the aforementioned steps are only independent, standardized proportional coefficients, while the rated power of the converter is a fixed hardware upper limit. This scattered information has not yet been integrated into a final power limit in kilowatts that can be directly executed by the control system. Therefore, in the technical solution of this application, based on the SOH derating factor, SOT charging derating factor, SOT discharging derating factor, and the rated power of the converter, sustainable charging power limit and sustainable discharging power limit are calculated. This allows the SOH derating factor, representing the long-term aging effect, and the SOT derating factor, representing the immediate thermal limitation effect, to act together on the system's basic power capability through multiplication, thereby integrating multi-dimensional state constraints into a single power boundary with clear physical meaning. In this way, a sustainable power limit that dynamically and adaptively adjusts according to the battery health state and real-time temperature can be generated. This limit is both the direct basis for power command arbitration under conventional economic mode and the basis for calculating transient power boundaries. It fundamentally solves the problem of the disconnect between traditional static limits and the actual battery carrying capacity, forming the cornerstone of the entire refined control strategy.

[0054] Specifically, in step S300, based on the sustainable charging power limit, the sustainable discharging power limit, and the optimal estimated SOC, a transient power boundary is calculated to obtain the transient charging power limit, the transient discharging power limit, and the allowable duration of transient power. It should be understood that relying solely on the sustainable power boundary set to protect the long-term health of the battery will make the energy storage system insufficient in the face of emergency situations requiring rapid, high-power responses, such as sudden changes in grid frequency, due to its inherent conservatism. This limits the energy storage system's ability to participate in high-value grid ancillary services and its overall performance. Therefore, in the technical solution of this application, a transient power boundary is further calculated based on the sustainable charging power limit, the sustainable discharging power limit, and the optimal estimated SOC to obtain the transient charging power limit, the transient discharging power limit, and the allowable duration of transient power. This constructs a higher power operating space, associated with the battery's current state of charge and allowing for short-term exceedance, on top of the sustainable boundary that ensures basic safety. This enables energy storage systems to possess a layered, dynamic power response capability, allowing them to adhere to sustainable boundaries to ensure lifespan under normal economic conditions, while in emergency support modes they can invoke transient boundaries to provide maximum performance. This enhances the system's flexibility and grid support value without sacrificing long-term asset health, achieving a dynamic balance between safety and performance.

[0055] More specifically, in the embodiments of this application, the transient power boundary is calculated based on the sustainable charging power limit, the sustainable discharging power limit, and the optimal estimated SOC to obtain the transient charging power limit and the transient discharging power limit, including: calculating the transient overload coefficient based on the optimal estimated SOC; and calculating the transient charging power limit and the transient discharging power limit based on the transient overload coefficient, the sustainable charging power limit, and the sustainable discharging power limit.

[0056] Specifically, based on the optimal estimated SOC, the transient overload factor is calculated using the following formula:

[0057] in, For peak gain, To achieve the optimal estimate of SOC, The optimal SOC center point, To control the width of the overload SOC range.

[0058] It is understandable that the battery's terminal voltage responds differently to current changes under different states of charge (SOCs), especially when the SOC is close to its upper or lower limits. Even a brief surge in current can cause the voltage to reach the protection threshold, triggering a system trip. Therefore, the permissible transient overload capacity is not a fixed value but is closely related to the current SOC. Thus, in the technical solution of this application, a transient overload coefficient is calculated using a formula based on the optimal estimated SOC. This allows for a nonlinear and precise mathematical modeling of the battery's transient overload capacity and its real-time state of charge using a Gaussian function centered on the optimal SOC. This generates an intelligent overload coefficient that dynamically changes with the SOC. This coefficient peaks when the battery's SOC is in its healthiest intermediate region and smoothly decays to zero near the extremes. This ensures that the energy storage system releases its maximum transient power potential only within the safe window with the most sufficient voltage margin, achieving a refined and dynamic synergy between performance and safety.

[0059] Specifically, transient charging power limits and transient discharging power limits are calculated based on the transient overload coefficient, the sustainable charging power limit, and the sustainable discharging power limit. It should be understood that the calculated transient overload coefficient is only a dimensionless scaling factor, while the sustainable power boundary is an independent power reference value. These two have not yet been combined into a final transient power limit in kilowatts that the control system can directly adopt in emergency mode. Therefore, in the technical solution of this application, the transient charging power limit and transient discharging power limit are further calculated based on the transient overload coefficient, the sustainable charging power limit, and the sustainable discharging power limit. This allows the transient overload capacity determined by the SOC to be applied multiplicatively to the sustainable power baseline jointly determined by SOH and SOT. This generates a final transient power boundary that inherits the protection of the battery's long-term health and immediate thermal state from the sustainable boundary, while also adding the additional performance margin allowed within the safe SOC window. This provides the power command arbitration module with the high power limit input required in emergency mode.

[0060] More specifically, in a particular example of this application, the module first obtains three key input data from the system's internal data bus or shared memory: the calculated sustainable charging power limit. (e.g., 80.0 kW), sustainable discharge power limit (82.5 kW), and the transient overload factor associated with the current SOC. (1.194). Next, the controller's arithmetic logic unit (ALU) performs two independent floating-point multiplication operations to calculate the transient charging power limit and the transient discharging power limit, respectively. The specific calculation process is as follows:

[0061] Finally, the calculated transient charging power limit of 95.5 kW and transient discharging power limit of 98.5 kW are written into a designated register or memory address for use by the subsequent power command arbitration and correction module when the system is determined to enter emergency support mode.

[0062] Specifically, in step S400, the EMS power command is arbitrated and corrected based on the sustainable charging power limit, sustainable discharging power limit, transient charging power limit, and transient discharging power limit to obtain a power reference value. It should be understood that since the two sets of power boundaries calculated by the system (sustainable and transient), the power command issued by the upper-level energy management system, and the absolute safety red line determined by the battery's state of charge are independent and potentially conflicting control constraints, without a clear and orderly arbitration mechanism, it will be impossible to generate a unique, clear, and absolutely safe final execution command under multiple objectives. Therefore, in the technical solution of this application, the EMS power command is further arbitrated and corrected based on the sustainable charging power limit, sustainable discharging power limit, transient charging power limit, and transient discharging power limit to obtain a power reference value. This includes assigning the sustainable charging power limit and sustainable discharging power limit to the currently effective charging power limit and currently effective discharging power limit when the system operating mode flag is in the normal economic mode. When the system operating mode flag is set to emergency support mode, transient charging power limits and transient discharging power limits are assigned to the currently effective charging power limits and discharging power limits. Based on the EMS power command, initial power limiting is applied to the currently effective charging power limits and discharging power limits to obtain an intermediate power reference value after boundary limiting. Finally, based on the optimal estimated SOC, a final power safety derating is applied to the intermediate power reference value after boundary limiting to obtain the power reference value. This constructs a multi-level, layered power command processing pipeline. This pipeline can dynamically select strategies according to the system's macroscopic operating mode and sequentially perform boundary constraints and final safety checks on the power commands. This ensures that the final generated power reference value is the optimal solution after comprehensively balancing the upper-level scheduling intent, long-term battery health, immediate performance potential, and absolute safety boundaries. This converges all dispersed control logic and state information into a single, conflict-free, precise command that can directly drive the converter, providing the ultimate guarantee for the closed-loop and effective execution of the entire adaptive control scheme.

[0063] Figure 4 This is a flowchart illustrating the power command arbitration and correction process for EMS power commands based on sustainable charging power limits, sustainable discharging power limits, transient charging power limits, and transient discharging power limits, according to an embodiment of this application, to obtain a power reference value.Figure 4 As shown, step S400 includes: S410, when the system operation mode flag is in normal economic mode, assigning the sustainable charging power limit and the sustainable discharging power limit to the currently effective charging power limit and the currently effective discharging power limit. S420, when the system operation mode flag is in emergency support mode, assigning the transient charging power limit and the transient discharging power limit to the currently effective charging power limit and the currently effective discharging power limit. S430, based on the EMS power command, performing initial power limiting on the currently effective charging power limit and the currently effective discharging power limit to obtain an intermediate power reference value after boundary limiting. S440, based on the optimal estimated SOC, performing a final power safety derating on the intermediate power reference value after boundary limiting based on the SOC state to obtain the power reference value.

[0064] Specifically, in steps S410 and S420, when the system operation mode flag is in the normal economic mode, the sustainable charging power limit and the sustainable discharging power limit are assigned to the currently effective charging power limit and the currently effective discharging power limit. When the system operation mode flag is in the emergency support mode, the transient charging power limit and the transient discharging power limit are assigned to the currently effective charging power limit and the currently effective discharging power limit. It should be understood that since the system has calculated two sets of power boundaries serving different objectives in parallel—namely, the sustainable power boundary aimed at long-term asset health and the transient power boundary aimed at high-performance emergency response—without a clear decision-making mechanism, the control system will be unable to select the only applicable constraint under specific operating conditions, thus rendering the dual-boundary design meaningless. Therefore, in the technical solution of this application, when the system operation mode flag is in the normal economic mode, the sustainable charging power limit and the sustainable discharging power limit are further assigned to the currently effective charging power limit and the currently effective discharging power limit. When the system operating mode flag is set to emergency support mode, the transient charging power limit and transient discharging power limit are assigned to the currently effective charging power limit and discharging power limit, respectively. This establishes a dynamic routing mechanism based on the system's macroscopic operating mode. This mechanism selectively outputs a single, currently effective power limit based on the state of the mode flag. This enables the entire control strategy to possess macroscopic scenario awareness and adaptive switching capabilities. It ensures that the energy storage system prioritizes battery life protection during normal economic operation, while decisively switching to a high-performance output mode when the grid requires emergency support. This transforms the dual-boundary design concept into executable control behavior, achieving an optimal balance between economy and functionality in different application scenarios.

[0065] More specifically, in a concrete example of this application, the boundary selection process based on operating mode is implemented as a conditional judgment and assignment module that executes within each control cycle. First, at the beginning of the power command arbitration process, this module reads a critical input state from the internal data bus or shared memory: the system operating mode flag, Mode_Flag. This flag is updated in real-time by the upper-level monitoring logic based on the grid status or external dispatch commands. Simultaneously, the module also obtains four calculated power boundary values: sustainable charging power limit, sustainable discharging power limit (82.5 kW), transient charging power limit, and transient discharging power limit (98.5 kW). Second, the controller's central processing unit executes a conditional judgment statement, such as a switch-case structure, to parse the value of Mode_Flag. In this example, the system detects an abnormal grid frequency, and Mode_Flag is set to 1, representing emergency support mode. Therefore, the program flow enters the branch corresponding to this mode and performs an assignment operation: assigning the value of the transient charging power limit to the working variable. The transient discharge power limit of 98.5 kW was assigned to the working variable. Finally, the updated working variables. and (Its value is 98.5 kW in this cycle) is output to the next stage of the power command arbitration pipeline, namely the initial power limiting module, as a dynamic reference for command comparison.

[0066] Specifically, in step S430, based on the EMS power command, initial power limiting is applied to the currently effective charging power limit and the currently effective discharging power limit to obtain an intermediate power reference value after boundary limiting. It should be understood that since the power command issued by the upper-level energy management system only reflects the needs of the grid side or economic dispatch level, it does not consider the dynamic power boundary calculated by the energy storage converter based on the real-time battery status. Therefore, the command value may far exceed the actual safe tolerance range of the current battery. Therefore, in the technical solution of this application, further initial power limiting is applied to the currently effective charging power limit and the currently effective discharging power limit based on the EMS power command to obtain an intermediate power reference value after boundary limiting, thereby establishing a mandatory power command filter. This filter aligns the external original command with the internally calculated boundary representing the battery's true capability for the first time. This ensures that regardless of the value of the upper-level command, the power command processed in this step will never exceed the safe operating boundary determined by the battery's SOH, SOT, and system operating mode. This completes the first and most crucial layer of power protection for the battery, providing a pre-purified and reasonable power command benchmark for subsequent processing.

[0067] More specifically, in a particular example of this application, the initial power limiting process is implemented as a comparison and selection module that executes in each control cycle. First, this module receives two key inputs: the raw power command from the upper-level energy management system. Its value is 600 kW. And the currently effective discharge power limit determined by the previous mode selection step. Its value is 98.5 kW. Secondly, the controller's arithmetic logic unit determines... The sign of the value is used to determine if it is positive, i.e., a discharge command. Next, a minimum value comparison operation is performed, i.e. Substituting the specific values, the operation becomes... Finally, the result of this comparison, 98.5 kW, was determined as the intermediate power reference value after boundary limiting. It is then output to the last stage of the power command arbitration pipeline, namely the final power safety derating module based on the SOC state.

[0068] Specifically, in step S440, the intermediate power reference value after boundary limiting is subjected to a final power safety derating based on the SOC state based on the optimal estimated SOC to obtain the power reference value.

[0069] It is understandable that while the intermediate power reference value after the preceding limiting steps has considered the battery's health and thermal state, it does not account for the sharp increase in the battery's voltage sensitivity to current changes when the state of charge (SOC) approaches its upper or lower limits. Even the limited power could cause the voltage to exceed the protection threshold, leading to unexpected system shutdown. Therefore, in the technical solution of this application, the intermediate power reference value after boundary limiting is further dated based on the optimal estimated SOC to obtain the final power reference value. This constructs a highest-priority safety defense line, which uses a derating function related to the SOC extreme value to perform a final, mandatory reduction of the power command. This ensures that the final power reference value issued to the converter is an absolutely safe command that will not cause battery overcharging or over-discharging under any circumstances. It constitutes the final closed loop of the entire multi-level arbitration mechanism, guaranteeing the ultimate operational safety and reliability of the system.

[0070] More specifically, in a concrete example of this application, the final power safety derating is implemented as a computation module executed at the end of the power command arbitration pipeline. First, this module acquires three key input data points: an intermediate power reference value from the preceding initial power limiting module. Its value is 98.5 kW. The current optimal estimated SOC value. Its value is 12%. And the SOC protection parameters loaded from non-volatile memory, including the lower discharge limit. The starting point for 10% and the discharge buffer zone It is 15%. Secondly, the controller's central processing unit determines... If the value is positive, it indicates that a discharge operation is in progress, and a comparison is made. Based on the protection parameters, a 12% SOC value was determined to fall within the discharge buffer range of [10%, 15%]. Therefore, the system calls the corresponding linear derating function for this range to calculate the SOC derating factor. The calculation formula is as follows: = ( - ) / ( - ) Substituting the specific values, we get = (12 - 10) / (15 - 10) = 2 / 5 = 0.4. Next, the system performs a floating-point multiplication operation, converting the intermediate power reference value... With the calculated SOC depreciation factor Multiply them to obtain the final power reference value. The specific calculation is as follows: = 98.5 kW × 0.4 = 39.4 kW. Finally, the power reference value of 39.4 kW after final safety derating is output as the final and absolutely safe power command for driving the IGBT of the converter in this control cycle.

[0071] Specifically, in step S500, a PWM waveform for driving the IGBTs is generated based on the power reference value. It should be understood that since the final power reference value calculated in all the preceding steps is an abstract instruction existing in the digital controller, it cannot directly drive the physical power conversion circuit composed of IGBTs. There is a gap that must be bridged between the digital control domain and the physical power domain. Therefore, in the technical solution of this application, a PWM waveform for driving the IGBTs is further generated based on the power reference value. This accurately translates the digital power instruction, which represents the final control intention and has undergone multi-level arbitration and correction, into a set of pulse width modulation signals that can directly control the high-frequency on / off switching of each IGBT in the converter. In this way, the wisdom of the entire complex control algorithm is ultimately transformed into the physical driving behavior of the power semiconductor devices. By precisely controlling the amplitude, frequency, and phase of the converter output voltage, energy exchange completely consistent with the target power value is achieved on the grid side. This is the final point of the entire control scheme from software decision-making to hardware execution.

[0072] More specifically, in a particular example of this application, the generation process of the PWM waveform is implemented within the digital signal processor of the converter through a standard current-decoupled dual-loop control structure based on a synchronously rotating dq coordinate system. First, the control structure obtains its main instruction input, namely the final power reference value determined by the preceding steps. Its value is 39.4 kW, and a reactive power reference value is also obtained. (For example, 0 kVar to achieve unity power factor operation). Secondly, in the power outer loop, the power reference value... Compared with the actual active power calculated by sampling the grid voltage and current The error is compared and then processed by a proportional-integral regulator to generate a reference value for the active current component. Similarly, the error in reactive power generates a reference value for the reactive current component. Next, in the inner current loop, the current reference value is... and Compare with the actual current components obtained after coordinate transformation. and The error is then compared and passed through a set of proportional-integral regulators to generate the final dq-axis voltage command. and Subsequently, the dq-axis voltage command undergoes an inverse Park transformation and is converted back to a sinusoidal voltage reference waveform in a three-phase stationary coordinate system. , , Finally, these three sinusoidal reference waveforms are fed into the space vector pulse width modulation module. This module calculates in real time the precise turn-on and turn-off times required to drive the six IGBT switches of the three-phase bridge arm by comparing them with the carrier wave or by sector judgment, thereby generating six high-frequency PWM drive signals. These signals are finally applied to the gate of the IGBT through the isolation drive circuit.

[0073] In summary, the charge / discharge control method for the energy storage converter according to the embodiments of this application is explained. It comprehensively perceives the battery's current actual load-bearing capacity by accurately acquiring and fusing three key vital signs: battery health status, thermal characteristic temperature, and state of charge (SOC). Based on this, a two-layer dynamic limit system is defined: a sustainable power boundary and a transient power boundary. The former, based on SOH and SOT, ensures the long-term health and safety of the battery under any operating condition. The latter, based on the sustainable boundary, appropriately relaxes the SOC state to provide the system with the necessary short-term high-power response capability. Finally, through correction logic, the appropriate boundary is intelligently selected according to the operating mode, and the power command is finely corrected in conjunction with the SOC safety defense line, thereby fundamentally achieving the optimal dynamic balance between performance, safety, and lifespan of the energy storage system.

[0074] Furthermore, a charging and discharging control system for an energy storage converter is also provided.

[0075] Figure 5 This is a block diagram of the charge and discharge control system of an energy storage converter according to an embodiment of this application. Figure 5 As shown, the charging and discharging control system 500 of the energy storage converter according to an embodiment of this application includes: a data acquisition module 510, used to acquire the optimal estimated SOC, the current thermal state characteristic temperature of the battery, and the current battery health state; a limit calculation module 520, used to calculate the sustainable power boundary based on the converter rated power, the current thermal state characteristic temperature of the battery, and the current battery health state to obtain the sustainable charging power limit and the sustainable discharging power limit; a transient calculation module 530, used to calculate the transient power boundary based on the sustainable charging power limit, the sustainable discharging power limit, and the optimal estimated SOC to obtain the transient charging power limit, the transient discharging power limit, and the transient power allowable duration; a command correction module 540, used to arbitrate and correct the EMS power command based on the sustainable charging power limit, the sustainable discharging power limit, the transient charging power limit, and the transient discharging power limit to obtain a power reference value; and a PWM waveform generation module 550, used to generate a PWM waveform for driving the IGBT based on the power reference value.

[0076] As described above, the charge / discharge control system 500 of the energy storage converter according to the embodiments of this application can be implemented in various wireless terminals, such as servers with charge / discharge control algorithms for the energy storage converter. In one possible implementation, the charge / discharge control system 500 of the energy storage converter according to the embodiments of this application can be integrated into the wireless terminal as a software module and / or a hardware module. For example, the charge / discharge control system 500 of the energy storage converter can be a software module in the operating system of the wireless terminal, or it can be an application developed for the wireless terminal. Of course, the charge / discharge control system 500 of the energy storage converter can also be one of many hardware modules of the wireless terminal.

[0077] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A charging and discharging control method for an energy storage converter, characterized in that, include: Obtain the optimal estimated SOC, the current thermal state characteristic temperature of the battery, and the current battery health state; Based on the inverter's rated power, the battery's current thermal state characteristic temperature, and the current battery health state, the sustainable power boundary is calculated to obtain the sustainable charging power limit and the sustainable discharging power limit. Based on the sustainable charging power limit, the sustainable discharging power limit, and the optimal estimated SOC, the transient power boundary is calculated to obtain the transient charging power limit, the transient discharging power limit, and the allowable duration of transient power. Based on the sustainable charging power limit, sustainable discharging power limit, transient charging power limit, and transient discharging power limit, the EMS power command is arbitrated and corrected to obtain the power reference value; Based on the power reference value, a PWM waveform for driving the IGBT is generated.

2. The charging and discharging control method for the energy storage converter according to claim 1, characterized in that, Obtain the optimal estimated SOC, the current thermal state characteristic temperature of the battery, and the current battery health state, including: Obtain readings from the cell temperature sensor array and the preset temperature safety margin; Extract the highest and average individual cell temperatures from readings of the cell temperature sensor array; The highest single-cell temperature, lowest single-cell temperature, and average temperature are fused using the following formula to obtain the current thermal state characteristic temperature of the battery: ； in, The highest monomer temperature, The average temperature. This is a preset temperature safety margin.

3. The charging and discharging control method for the energy storage converter according to claim 2, characterized in that, Obtaining the optimal estimated SOC, the current thermal characteristic temperature of the battery, and the current battery health status also includes: Based on the PCS internal SOC estimate from the previous control cycle and the current thermal state characteristic temperature of the battery, the PCS internal SOC estimate for the current control cycle is calculated using the following formula: in, This is the estimated internal SOC value of the PCS in the previous control cycle. For the total battery current, The current thermal characteristic temperature of the battery To control cycle duration; The optimal estimated SOC is obtained by weighted fusion of the PCS internal SOC estimate and the BMS-sent state of charge estimate during the current control cycle.

4. The charging and discharging control method for the energy storage converter according to claim 3, characterized in that, Obtaining the optimal estimated SOC, the current thermal characteristic temperature of the battery, and the current battery health status also includes: Obtain the battery total current time series, starting SOC, and ending SOC; The cumulative charge is obtained by integrating the total battery current over time. Based on the accumulated charge, initial SOC, and final SOC, the current actual capacity is calculated using the following formula: in, To end SOC, As the initial SOC, This refers to the accumulated charge. The current actual capacity is divided by the battery's rated capacity to obtain the current battery health status.

5. The charging and discharging control method for the energy storage converter according to claim 1, characterized in that, Based on the inverter's rated power, the battery's current thermal state characteristic temperature, and the current battery health state, sustainable power boundaries are calculated to obtain sustainable charging power limits and sustainable discharging power limits, including: Calculate the SOH derating factor based on the current battery health status; Calculate the SOT charging derating factor and SOT discharging derating factor based on the current thermal state characteristic temperature of the battery. Based on the SOH derating factor, SOT charging derating factor, SOT discharging derating factor, and converter rated power, calculate the sustainable charging power limit and the sustainable discharging power limit.

6. The charging and discharging control method for the energy storage converter according to claim 5, characterized in that, Based on the SOH derating factor, SOT charging derating factor, SOT discharging derating factor, and converter rated power, the sustainable charging power limit and sustainable discharging power limit are calculated, including: calculating the sustainable charging power limit and sustainable discharging power limit using the following formula, wherein the formula is: in, The rated power of the converter, The SOH deflator, and SOT charging derating factor and SOT discharging derating factor.

7. The charging and discharging control method for the energy storage converter according to claim 1, characterized in that, Based on the sustainable charging power limit, the sustainable discharging power limit, and the optimal estimated SOC, the transient power boundary is calculated to obtain the transient charging power limit and the transient discharging power limit, including: Calculate the transient overload coefficient based on the optimal estimated SOC; Based on the transient overload factor, the sustainable charging power limit, and the sustainable discharging power limit, the transient charging power limit and the transient discharging power limit are calculated.

8. The charging and discharging control method for the energy storage converter according to claim 7, characterized in that, The transient overload factor is calculated based on the optimal estimated SOC, including: calculating the transient overload factor based on the optimal estimated SOC using the following formula: in, For peak gain, To achieve the optimal estimate of SOC, The optimal SOC center point, To control the width of the overload SOC range.

9. The charging and discharging control method for the energy storage converter according to claim 1, characterized in that, Based on sustainable charging power limits, sustainable discharging power limits, transient charging power limits, and transient discharging power limits, the EMS power command is arbitrated and corrected to obtain a power reference value, including: When the system operation mode flag is set to normal economic mode, the sustainable charging power limit and the sustainable discharging power limit are assigned to the currently effective charging power limit and the currently effective discharging power limit. When the system operation mode flag is set to emergency support mode, the transient charging power limit and transient discharging power limit are assigned to the currently effective charging power limit and the currently effective discharging power limit. Based on the EMS power command, the current effective charging power limit and the current effective discharging power limit are initially power limited to obtain the intermediate power reference value after boundary limiting. The power reference value is obtained by performing a final power safety derating based on the SOC state on the intermediate power reference value after boundary limiting, based on the optimal estimated SOC.

10. A charging and discharging control system for an energy storage converter, characterized in that, include: The data acquisition module is used to acquire the optimal estimated SOC, the current thermal state characteristic temperature of the battery, and the current battery health state. The limit calculation module is used to calculate the sustainable power boundary based on the inverter's rated power, the battery's current thermal state characteristic temperature, and the current battery health state to obtain the sustainable charging power limit and the sustainable discharging power limit; The transient calculation module is used to calculate the transient power boundary based on the sustainable charging power limit, the sustainable discharging power limit, and the optimal estimated SOC to obtain the transient charging power limit, the transient discharging power limit, and the allowable duration of transient power. The instruction correction module is used to arbitrate and correct the EMS power instruction based on the sustainable charging power limit, the sustainable discharging power limit, the transient charging power limit, and the transient discharging power limit to obtain a power reference value; The PWM waveform generation module is used to generate the PWM waveform for driving the IGBT based on the power reference value.

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