Method and system for processing operation data of household energy storage system

Abstract

The invention provides a household energy storage system operation data processing method and system, and relates to the technical field of household energy storage system operation data processing, and the method comprises the steps: obtaining the operation parameters of a battery module in real time, including the voltage, current, temperature, residual electric quantity and total capacity of a single battery, and determining the equivalent resistance of the battery according to the parameters. On this basis, the method can predict the minimum voltage value reached by the battery voltage under different discharge powers, and by comparing the first voltage with the sum of the low-voltage protection threshold and the safety margin as a constraint condition, the method can accurately determine the real-time capability parameters of the battery, including the maximum discharge power and the available energy. Finally, according to the power grid discharge request and the battery real-time capability parameters, the system can execute accurate discharge control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of household energy storage system operation data processing, in particular to a household energy storage system operation data processing method and system. BACKGROUND

[0002] The existing household energy storage system is usually composed of a battery module, a battery management unit (BMU) and an energy management system (EMS). The BMU reduces the terminal voltage of the single battery to a measurable range through a voltage dividing resistor network, and then converts it into a digital signal by a sampling circuit for subsequent state of charge (SOC) estimation, state of health (SOH) evaluation and station scheduling strategy. In the process of long-term continuous operation of the system, the heat generated by repeated charging and discharging of the battery makes the voltage dividing resistor and its welding point continuously experience thermal expansion and contraction. After thousands to tens of thousands of thermal stress cycles, the microstructure of the resistor material appears fatigue, and the contact resistance of the welding point also changes irreversibly, causing a slow but cumulative shift in the actual resistance value of the voltage dividing resistor. This shift is a gradual and systematic error, neither a transient failure nor a random disturbance that can be eliminated by existing transient noise filtering, temperature drift compensation or periodic calibration mechanisms, thus causing the overall voltage sampling value to deviate from the true value, directly weakening the key input accuracy of the SOC algorithm. Since the voltage-charge curve is particularly steep in high-current conditions and near full / empty intervals, this measurement error is further amplified in these scenarios, causing the SOC estimation result to deviate from the true value for a long time.

[0003] The distorted SOC value is passed up to the EMS, making the load prediction and charging / discharging strategy based on the wrong remaining capacity: if the SOC is overestimated, the system misses the low-cost charging window; if the SOC is underestimated, unnecessary grid charging occurs, both of which reduce the economy and accelerate battery aging. At the same time, the SOH evaluation takes the cumulative SOC value as the key input, and the data deviation causes the system to continuously overestimate the available capacity and power capability of the battery. When the energy storage system participates in grid support services, the EMS accepts high-power discharge instructions based on the false high SOH, while the actual battery internal resistance has significantly increased due to long-term non-optimal operation. In the moment of high-current discharge, the battery terminal voltage quickly drops below the protection threshold, triggering the BMU emergency power-off protection, which not only fails to complete the grid scheduling task, but also causes instantaneous power failure on the user side, bringing the risk of breach and safety hazards.

[0004] In view of the above problems, the existing technology needs to be improved. SUMMARY

[0005] The application discloses a household energy storage system operation data processing method, aiming to solve the problems of inaccurate remaining power estimation, suboptimal charging and discharging strategy, battery health degree misjudgment and possible system failure and service interruption caused by voltage measurement deviation due to hardware aging in long-time operation of the existing household energy storage system.

[0006] The technical scheme of the application is as follows: In a first aspect, the application discloses a household energy storage system operation data processing method, the household energy storage system comprising a battery module, the method comprising: acquiring real-time operation parameters of the battery module; the operation parameters comprising battery voltage, battery current, battery temperature, remaining power and total capacity of each single battery; determining battery equivalent resistance according to the battery voltage and the battery current; determining a first voltage as the lowest voltage value reached by the battery voltage under different discharge powers according to the remaining power and the battery equivalent resistance; determining real-time capability parameters of the battery according to the operation parameters, with the constraint that the first voltage is higher than the sum of a low-voltage protection threshold and a safety margin; the real-time capability parameters of the battery comprising maximum discharge power and available energy; and performing discharge control according to a power grid discharge request and the real-time capability parameters of the battery.

[0007] Further, the application also proposes a household energy storage system operation data processing method, wherein determining battery equivalent resistance according to the battery voltage and the battery current comprises: determining the battery equivalent resistance by measuring the ratio of the instantaneous change amount of the battery terminal voltage to the instantaneous change amount of the current; Before determining a first voltage as the lowest voltage value reached by the battery voltage under different discharge powers according to the remaining power and the battery equivalent resistance, the method further comprises: correcting the battery voltage according to the determined measurement deviation of the battery voltage.

[0008] On this basis, the application further proposes a household energy storage system operation data processing method, wherein correcting the battery voltage according to the determined measurement deviation of the battery voltage comprises: comparing each single battery voltage in the battery module with the average voltage of the module to determine the persistence deviation of the each single battery voltage within a certain time period when the battery is in a static working condition; and correcting the corresponding battery voltage according to the persistence deviation to obtain a preliminarily corrected battery voltage. The power grid discharge request comprises a requested discharge power and a requested discharge energy; and performing discharge control according to the power grid discharge request and the real-time capability parameters of the battery comprises: comparing the power grid discharge request and the real-time capability parameters of the battery, and performing discharge control according to the minimum value of the requested discharge power and the maximum discharge power, and the minimum value of the requested discharge energy and the available energy.

[0009] In some preferred embodiments, the application also provides a home energy storage system operation data processing method, which further comprises: determining a first equivalent resistance by using the preliminarily corrected battery voltage and the battery current under the condition of small-amplitude charging and discharging of the battery; comparing the first equivalent resistance with a reference equivalent resistance corresponding to the current battery temperature and the remaining power of the battery to determine a residual deviation; in response to the existence of a nonlinear correlation between the residual deviation and the battery temperature, determining and quantifying a zero-point drift value of the current sensor; and correcting the battery current according to the zero-point drift value to obtain a corrected battery current.

[0010] As an optional solution, the application also provides a home energy storage system operation data processing method, wherein the comparison of the voltages of the single batteries in the battery module with the average voltage of the module to determine the persistent deviation of the voltages of the single batteries within a certain time period comprises: obtaining the voltage data of the single batteries under the static condition and determining the first persistent deviation of the voltage data from the historical average voltage within a certain time period; obtaining the overall average of the voltages of all the single batteries under the static condition and determining the second persistent deviation of the overall average from the reference average static voltage within a certain time period; correcting the corresponding battery voltage according to the persistent deviation to obtain a preliminarily corrected battery voltage, which comprises: preliminarily correcting the battery voltage corresponding to all the single batteries according to the second persistent deviation; and secondarily correcting the corresponding battery voltage according to the first persistent deviation.

[0011] Further, the application also provides a home energy storage system operation data processing method, wherein the determination of the first equivalent resistance by using the preliminarily corrected battery voltage and the battery current under the condition of small-amplitude charging and discharging of the battery comprises: obtaining the battery voltage and the battery current under the condition of small-amplitude charging and discharging of the battery and monitoring the change rate of the battery current; adjusting the sampling parameters of the battery voltage and the battery current in response to the change rate of the battery current reaching a preset threshold; the sampling parameters include the sampling frequency and the sampling duration; determining the steady-state voltage change and the steady-state current change according to the sampled battery voltage and battery current data; and determining the first equivalent resistance by using the ratio of the steady-state voltage change to the steady-state current change.

[0012] On the basis of the above, the application further provides a home energy storage system operation data processing method, wherein the determination and quantification of the zero-point drift value of the current sensor in response to the existence of a nonlinear correlation between the residual deviation and the battery temperature comprises: determining the temperature-drift characteristic curve corresponding to the battery temperature; forcibly performing a small-amplitude current pulse discharge in response to the battery being in the static condition; reversely calculating the zero-point drift of the current sensor according to the preliminarily corrected battery voltage; and associating the reversely calculated zero-point drift with a predetermined battery temperature point to update the temperature-drift characteristic curve.

[0013] As a technical improvement, the application further provides a household energy storage system operation data processing method, wherein, in response to the battery being in a static working condition, a small-amplitude current pulse discharge is forced to be carried out, comprising: obtaining the health degree of the current battery and the current battery temperature, adjusting the initial amplitude of the pulse current according to the health degree, and adjusting the pulse duration according to the current battery temperature; monitoring the current battery voltage drop rate and the actual waveform of the current battery current in real time; adjusting the amplitude and pulse duration of the subsequent pulse according to the current battery voltage drop rate and the actual waveform of the current battery current; monitoring the change of the current battery temperature before and after the pulse discharge, and adjusting the interval time between the pulses according to the change of the current battery temperature.

[0014] To perfect the scheme, the application further provides a household energy storage system operation data processing method, wherein, according to the current battery voltage drop rate and the actual waveform of the current battery current, the amplitude and pulse duration of the subsequent pulse are adjusted, comprising: when the current battery voltage drop rate exceeds a preset threshold, stopping the current pulse discharge and isolating the current battery; according to the average voltage drop rate of the remaining healthy single battery in the battery module, the amplitude and duration of the subsequent pulse are re-adjusted.

[0015] In a second aspect, the application further discloses a household energy storage system operation data processing system, the household energy storage system comprising a battery module, the system comprising: an acquisition and determination module, which acquires the running parameters of the battery module in real time; the running parameters comprising the battery voltage, battery current, battery temperature, remaining capacity and battery total capacity of each single battery; determining the battery equivalent resistance according to the battery voltage and battery current; a prediction module, which is used to predict the minimum voltage value of the battery voltage under different discharge powers according to the remaining capacity and battery equivalent resistance, and determine the first voltage; a processing module, which is used to determine the real-time capability parameters of the battery according to the running parameters, with the constraint that the first voltage is higher than the sum of the low-voltage protection threshold and the safety margin; the real-time capability parameters of the battery comprising the maximum discharge power and available energy; and a control module, which is used to perform discharge control according to the grid discharge request and the real-time capability parameters of the battery.

[0016] Advantages The application provides a household energy storage system operation data processing method. The method acquires real-time operation parameters of the battery module, including single battery voltage, current, temperature, remaining capacity and total capacity, and determines the equivalent resistance of the battery according to these parameters. On this basis, the method can predict the minimum voltage value (first voltage) reached by the battery voltage under different discharge powers. By comparing the first voltage with the sum of the low-voltage protection threshold and the safety margin, as a constraint condition, the method can accurately determine the real-time capability parameters of the battery, including the maximum discharge power and available energy. Finally, according to the grid discharge request and the real-time capability parameters of the battery, the system can perform accurate discharge control.

[0017] Through the above technical solution, the application effectively solves the problem of voltage measurement deviation caused by hardware aging in the prior art, which further leads to inaccurate remaining capacity estimation, suboptimal charging and discharging strategy, battery health degree misjudgment and finally may cause system failure and service interruption. Specifically, through the fine processing and prediction of battery operation parameters, the method can more accurately evaluate the real state and available capacity of the battery, avoiding misjudgment and suboptimal decision due to inaccurate data. For example, by accurately predicting the minimum voltage value and combining the safety margin, the system can avoid excessive discharge of the battery, thereby prolonging the battery life and improving the operation safety. At the same time, according to the real-time capability parameters of the battery, the discharge control can be performed to ensure that the system meets the demand of the grid while fully utilizing the available energy of the battery, avoiding the risk of default and sudden power failure of household users due to misjudgment of the health degree. Therefore, the technical solution of the application significantly improves the operation reliability, economic benefit and user experience of the household energy storage system. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0019] Figure 1 is a flowchart of the steps of the household energy storage system operation data processing method disclosed by the embodiments of the application; Figure 2 is a structural schematic diagram of the household energy storage system operation data processing system disclosed by the embodiments of the application. DETAILED DESCRIPTION

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments belong. The terminology used in the description herein is for describing particular embodiments only and is not intended to be limiting of the embodiments. The use herein of terms such as "comprise", "comprises", "comprising", "containing", "contains", "contain" or variations such as "comprises", "comprising", "containing" or "contains" is to be construed as specifying the presence of stated features or components, but does not preclude the presence or addition of one or more other features, components or steps. The use herein of terms such as "first", "second" and "third" or variations such as "first", "second" and "third" is to be construed as specifying a particular order or order of preference, but does not preclude the presence or addition of one or more other features, components or steps.

[0021] The implementation details of the technical solutions of the embodiments are described below in detail: The internal voltage dividing resistor for accurately measuring the voltage of a single battery in a conventional existing home energy storage system will produce systematic and progressive resistance value deviation due to physical aging after a long time of operation. This deviation causes the collected battery voltage data to deviate from the true value, thereby affecting the accuracy of the battery state of charge (SOC) estimation. Inaccurate SOC estimation will be propagated upstream, resulting in suboptimal energy management decisions, such as unreasonable charging and discharging plans, which fail to achieve maximum economic benefits or energy efficiency. In addition, long-term SOC estimation errors will also accelerate battery capacity degradation and lead to inaccurate battery state of health (SOH) evaluation. When the system participates in grid support services, this incorrect understanding of the true performance of the battery can cause the system to report a higher discharge power, and when the actual high-power discharge instruction is executed, the battery terminal voltage will drop sharply, triggering low voltage protection and causing service interruption and user inconvenience.

[0022] To this end, the present application proposes a home energy storage system operation data processing method, wherein the home energy storage system comprises a battery module, such as Figure 1 As shown in the figure, the method comprises: S101, acquiring real-time operation parameters of the battery module; the operation parameters comprise the voltage of each single battery, the battery current, the battery temperature, the remaining capacity, and the total capacity of the battery; the equivalent resistance of the battery is determined according to the voltage and the current of the battery; S102, determining the minimum voltage value reached by the battery voltage under different discharge powers as a first voltage according to the remaining capacity and the equivalent resistance of the battery; S103, determining the real-time capability parameters of the battery according to the operation parameters, with the constraint that the first voltage is higher than the sum of the low voltage protection threshold and the safety margin; the real-time capability parameters of the battery comprise the maximum discharge power and the available energy; S104, performing discharge control according to the grid discharge request and the real-time capability parameters of the battery.

[0023] The application can more accurately evaluate the real-time capacity parameters of the battery by real-time acquisition and accurate processing of battery operating parameters, combined with determination of the equivalent resistance of the battery and prediction of the first voltage, so as to perform more accurate and safe discharge control under the request of the power grid discharge, effectively avoid the systematic problems caused by inaccurate data, and improve the operation efficiency and reliability of the household energy storage system.

[0024] The household energy storage system operation data processing method proposed in the application is characterized by a series of refined data processing and prediction steps, which can accurately evaluate the real-time capacity of the battery, and then optimize the discharge control strategy.

[0025] Firstly, the method includes real-time acquisition of the operating parameters of the battery module. These operating parameters are the basis for the system to make subsequent analysis and decision. For example, data can be collected in real time through a sensor array installed inside the battery module. The voltage of each single battery can be measured by high-precision voltage sensors, which are usually directly connected to both ends of each single battery. The battery current can be measured by Hall effect sensors or shunts, which are usually installed in the main circuit of the battery module. The battery temperature can be measured by thermistors or thermocouple sensors, which can be distributed at different positions of the battery module to obtain more comprehensive temperature information. The remaining capacity can be estimated by coulomb counting method or open circuit voltage method, wherein the coulomb counting method calculates the change of electric quantity by integrating the battery current, and the open circuit voltage method matches the static voltage of the battery with the preset voltage-capacity curve. The total capacity of the battery is usually the rated capacity calibrated at the factory, which can also be updated by periodic capacity test.

[0026] Secondly, the equivalent resistance of the battery is determined according to the battery voltage and the battery current. The equivalent resistance of the battery is a simplified model of the internal impedance of the battery, which has important influence on the performance and life of the battery. One way to determine it is to measure the ratio of the instantaneous change of the battery terminal voltage to the instantaneous change of the current when the battery is charging and discharging. For example, when the battery current changes in steps, the corresponding instantaneous drop or rise of the voltage can be recorded, and then the ratio of the voltage change to the current change is calculated. Another way is to establish an equivalent circuit model of the battery, such as Thevenin model or Randles model, and use Kalman filtering algorithm to dynamically estimate the resistance parameter in the model combined with real-time acquisition of voltage and current data.

[0027] Again, based on the remaining capacity, the battery equivalent resistance is predicted to determine the minimum voltage value that the battery voltage will reach under different discharge power, which is determined as the first voltage. This step aims to evaluate the voltage response of the battery under different discharge intensities. One way of prediction is to calculate based on the open-circuit voltage (OCV) curve of the battery and the equivalent resistance model. Specifically, for a given remaining capacity, the corresponding open-circuit voltage can be found from the OCV curve. Then, for different discharge powers, the corresponding discharge current can be calculated. According to Ohm's law, the battery terminal voltage is equal to the open-circuit voltage minus the product of the current and the equivalent resistance. In this way, the minimum value that the battery voltage will reach under different discharge power can be predicted, and it is determined as the first voltage. For example, assuming that the current remaining capacity of the battery is 50%, the corresponding open-circuit voltage is 3.7V, and the equivalent resistance is 0.05 ohms. If it is predicted that the battery voltage will drop to 3.2V under a 10A discharge power, then 3.2V is the first voltage.

[0028] Next, the battery real-time capability parameter is determined according to the operating parameters, with the constraint that the first voltage is higher than the sum of the low-voltage protection threshold and the safety margin. This step is crucial to ensure the safe operation of the battery. The low-voltage protection threshold is the minimum safe voltage set by the battery manufacturer, while the safety margin is to provide additional protection in actual operation. One way of determination is that the system will iteratively increase the discharge power and use the first voltage predicted in the previous step. In each iteration, if the predicted first voltage is still higher than the sum of the low-voltage protection threshold and the safety margin, it is considered that the discharge power is feasible. The system will continue to increase the discharge power until the predicted first voltage is exactly equal to or slightly lower than the constraint condition, at which time the discharge power is determined as the maximum discharge power. At the same time, the available energy can be calculated by subtracting the minimum amount of energy required to meet the safety margin and the low-voltage protection threshold from the current remaining capacity. For example, if the low-voltage protection threshold is 2.8V and the safety margin is 0.1V, the constraint condition is 2.9V. The system will predict whether the battery voltage can remain above 2.9V under different discharge power, thereby determining the maximum discharge power and the available energy.

[0029] Finally, discharge control is performed according to the grid discharge request and the real-time capability parameters of the battery. This step is the core of achieving intelligent energy management. One way to perform is that the system receives a discharge request from the grid, which usually contains the expected discharge power and discharge energy. Then, the system compares the grid request with the battery real-time capability parameters determined by itself. If the discharge power and discharge energy of the grid request are within the maximum discharge power and available energy range of the battery, the system will perform discharge according to the request. If the grid request exceeds the real-time capability of the battery, the system will adjust according to the actual capability, for example, discharge at the maximum discharge power or only provide energy within the available energy range. For example, if the grid requests a discharge power of 5kW, and the system evaluates the maximum discharge power as 4kW, the system will discharge to the grid at a power of 4kW, and report its maximum available capability to the grid.

[0030] The home energy storage system operation data processing method proposed in this application obtains the operating parameters of the battery module in real time, accurately determines the equivalent resistance of the battery based on these parameters, and then predicts the minimum voltage value (first voltage) under different discharge powers. On this basis, the system can accurately determine the real-time capability parameters of the battery, including the maximum discharge power and available energy, by taking the constraint that the first voltage is higher than the sum of the low-voltage protection threshold and the safety margin. Finally, according to the grid discharge request and these real-time capability parameters, the system can perform precise discharge control.

[0031] Compared with the voltage measurement deviation caused by the aging of the voltage dividing resistor in the prior art, and the inaccurate estimation of the remaining capacity and health degree caused thereby, the application can more accurately reflect the true state of the battery by introducing the determination of the equivalent resistance of the battery and the prediction mechanism of the first voltage. In the prior art, the system may accept a grid discharge instruction that exceeds its true capability due to the false overestimation of the battery health degree, resulting in a sharp drop in the battery terminal voltage and triggering emergency power-off protection. By comparing the first voltage with the sum of the low-voltage protection threshold and the safety margin, the application ensures that the battery voltage can be maintained within a safe range in any discharge scenario, thereby effectively avoiding the risk of over-discharge and accidental power-off. This method not only improves the reliability and safety of the operation of the home energy storage system, but also enables the system to respond more accurately to the grid demand, optimize energy scheduling, and achieve higher economic benefits and energy efficiency. The core innovation of this application is that, through in-depth understanding of the electrochemical characteristics of the battery and real-time data analysis, a more robust and intelligent battery capability evaluation and control framework is constructed, significantly improving the adaptability and performance of the home energy storage system in complex operating environments.

[0032] The application further proposes the above-mentioned home energy storage system operation data processing method, wherein the determination of the equivalent resistance of the battery according to the battery voltage and battery current comprises: The equivalent resistance of the battery is determined by measuring the ratio of the instantaneous change amount of the battery terminal voltage to the instantaneous change amount of the current; the minimum voltage value reached by the battery voltage under different discharge powers is determined as the first voltage according to the remaining power and the equivalent resistance of the battery, and before that, the method further comprises: correcting the battery voltage according to the determined measurement deviation of the battery voltage.

[0033] Specifically, the equivalent resistance of the battery is determined by measuring the ratio of the instantaneous change amount of the battery terminal voltage to the instantaneous change amount of the current, which means that when the current changes instantaneously during the operation of the battery, the battery terminal voltage will also change instantaneously. By accurately capturing these instantaneous change amounts and calculating their ratio, the equivalent resistance of the battery under the current working condition can be dynamically reflected. This method can more accurately reflect the dynamic resistance characteristics of the battery under actual operating conditions, rather than just the static resistance. The instantaneous change amount of the battery terminal voltage and the instantaneous change amount of the current can be obtained by high-frequency sampling.

[0034] Further, the battery voltage is corrected according to the determined measurement deviation of the battery voltage, which means that before the subsequent equivalent resistance determination and first voltage prediction, the real-time acquired battery voltage data is corrected to eliminate or reduce the system error or random error that may exist in the measurement process. For example, this correction can be based on historical data analysis, cross-validation with other sensor data, or comparison with reference values under certain known conditions (such as static) to improve the accuracy and reliability of the battery voltage data.

[0035] The scheme of the present application introduces a measurement deviation correction step of the battery voltage to ensure the accuracy of the battery voltage data relied on subsequent calculations. Specifically, before predicting the minimum voltage value reached by the battery voltage under different discharge powers, the battery voltage is corrected to effectively eliminate measurement errors, making the equivalent resistance determination and first voltage prediction based on this voltage more accurate. At the same time, by measuring the ratio of the instantaneous change amount of the battery terminal voltage to the instantaneous change amount of the current to determine the equivalent resistance of the battery, the internal resistance characteristics of the battery under dynamic working conditions can be more sensitively captured, thereby providing more reliable basic data for subsequent determination of real-time capability parameters of the battery.

[0036] In some preferred embodiments, as a specific implementation, during the operation of the home energy storage system, the operating parameters of the battery module are first collected in real time by a high-precision voltage sensor, including the battery voltage, battery current, battery temperature, remaining capacity, and total capacity of each single battery. In order to eliminate the zero drift or system error that may exist in the sensor, the collected battery voltage is calibrated under the condition that the battery is in a static state or a known reference voltage. For example, the single battery voltage can be compared with a calibrated standard voltage source at regular intervals, and the voltage measurement value can be adjusted according to the comparison result. When determining the equivalent resistance of the battery, the system continuously monitors the instantaneous changes of the battery terminal voltage and current. When the battery is subjected to small-scale charging and discharging or the load is instantaneously changed, the system records the change amount of voltage and current in a very short time, and calculates the ratio as the current equivalent resistance of the battery. In this way, the equivalent resistance value closer to the actual dynamic characteristics of the battery can be obtained. Subsequently, using these corrected battery voltages and dynamically determined battery equivalent resistance, combined with the remaining capacity, the minimum voltage value reached by the battery voltage under different discharge power, i.e. the first voltage, can be accurately predicted.

[0037] The application further proposes that the above-mentioned battery voltage corrected according to the determined measurement deviation of the battery voltage comprises: comparing the voltage of each single battery in the battery module with the average voltage of the module under the condition that the battery is in a static state, determining the persistent deviation of the voltage of each single battery within a certain time period, and correcting the corresponding battery voltage according to the persistent deviation to obtain a preliminary corrected battery voltage. The grid discharge request includes a request discharge power and a request discharge energy; and the discharge control performed according to the grid discharge request and the real-time capability parameters of the battery comprises: comparing the grid discharge request and the real-time capability parameters of the battery, and performing discharge control according to the minimum value of the request discharge power and the maximum discharge power, and the minimum value of the request discharge energy and the available energy.

[0038] Specifically, under the condition that the battery is in a static state, the battery module does not perform charging and discharging operation within a period of time, or the charging and discharging current is extremely small, and it can be considered that the internal chemical reaction of the battery is in a relatively balanced state. Under this condition, by comparing the voltage of each single battery in the battery module with the average voltage of the module, the voltage deviation of the single battery caused by individual differences or long-term drift can be effectively identified. The persistent deviation refers to the deviation that the difference between the voltage of the single battery and the average voltage of the module remains relatively stable or shows a certain trend within a certain time period. By correcting this persistent deviation, a more accurate preliminary corrected battery voltage can be obtained.

[0039] The grid discharge request can be understood as an energy scheduling instruction issued by the grid to the household energy storage system, and its specific content includes a requested discharge power and a requested discharge energy. The requested discharge power refers to the instantaneous power that the grid expects the energy storage system to provide at a certain time, while the requested discharge energy refers to the total energy that the grid expects the energy storage system to provide within a period of time. When performing discharge control, the requested discharge power of the grid is compared with the maximum discharge power of the battery, and the minimum of the two is taken as the actual discharge power. At the same time, the requested discharge energy of the grid is compared with the available energy of the battery, and the minimum of the two is taken as the actual discharge energy. This comparison and minimum taking method aims to ensure that the discharge operation can respond to the grid demand and strictly comply with the real-time capability limit of the battery itself, avoiding overloading or excessive discharge.

[0040] The scheme of the present application can effectively identify and correct the voltage measurement deviation of single batteries caused by long-term use or manufacturing differences by correcting the voltage deviation under the battery resting condition. Under the resting condition, the internal electrochemical reaction of the battery tends to be stable, and the voltage measurement at this time can better reflect the true state of the battery. By comparing with the average voltage of the module, the interference of external dynamic factors can be excluded, so that the persistent deviation of the single battery can be accurately quantified and corrected. This correction mechanism makes the subsequent calculation of various parameters based on the battery voltage (such as the remaining capacity, the battery equivalent resistance, etc.) more accurate, laying a foundation for the accurate determination of the real-time capability parameters of the battery.

[0041] In addition, by explicitly defining the composition of the grid discharge request (requested discharge power and requested discharge energy), and performing fine comparison with the real-time capability parameters of the battery (maximum discharge power and available energy), and selecting the minimum value as the actual execution parameter, the scheme of the present application ensures the safety and rationality of the discharge control. This mechanism can effectively prevent overloading or damage caused by the grid request exceeding the actual bearing capacity of the battery, while also protecting the health of the battery and prolonging its service life to the maximum extent under the premise of meeting the grid demand.

[0042] Through the above technical solutions, the present application can significantly improve the accuracy and reliability of the data processing of the household energy storage system. Specifically, by fine correcting the voltage of the single battery under the resting condition, the measurement deviation can be effectively eliminated, and the accuracy of the battery state estimation can be improved, thereby providing a more reliable data basis for subsequent energy management and fault diagnosis. At the same time, by double-constraining and optimizing matching the grid discharge request with the real-time capability parameters of the battery, it can ensure that the discharge operation is always within the safe working range of the battery, effectively avoiding potential risks such as overcharging and overdischarging, thereby prolonging the service life of the battery and improving the operation safety and economic benefits of the entire energy storage system.

[0043] In some preferred embodiments, it is assumed that the battery modules in a home energy storage system are in a resting state at night. At this time, the system continuously monitors the voltage of all single batteries in the module. If it is found that the voltage of a single battery is continuously lower than the average voltage of the module for several hours, and the deviation value exceeds the preset threshold, the system will determine that the single battery has a persistent deviation. For example, if the average voltage of the module is 3.6V, and the voltage of a single battery is continuously 3.55V, the system will record and use this 0.05V deviation to preliminarily correct the voltage of the single battery, making it closer to the true value. In another example, when the grid issues a discharge request, for example, the request discharge power is 5kW, and the request discharge energy is 10kWh. At this time, the system will determine the real-time capability parameters of the battery according to the real-time operating parameters, assuming that the maximum discharge power is 4kW and the available energy is 8kWh. According to the scheme of the present application, the system will compare the request discharge power 5kW with the maximum discharge power 4kW, and take the minimum value 4kW as the actual discharge power; at the same time, compare the request discharge energy 10kWh with the available energy 8kWh, and take the minimum value 8kWh as the actual discharge energy. Thus, the system will discharge to the grid with a power of 4kW until 8kWh of energy is discharged, thereby meeting part of the demand of the grid while ensuring that the battery operates within a safe range and avoiding damage that may be caused by exceeding the maximum discharge capacity of the battery.

[0044] In some embodiments of the present application described above, the accuracy of the battery parameters is improved by correcting the battery voltage. However, in actual operation, the zero drift problem of the battery current sensor, especially under different temperature conditions, can cause persistent deviation in the current measurement value. If the above problem is not solved, even if the battery voltage is preliminarily corrected, the accuracy of the battery equivalent resistance and the subsequent real-time capability parameters of the battery may still be affected, thereby reducing the reliability of the entire home energy storage system operation data processing method. In this regard, the present application further proposes a method for improving the accuracy of battery current measurement by identifying and correcting the zero drift of the current sensor, thereby improving the overall precision of the battery parameter determination.

[0045] In this regard, the present application further proposes a home energy storage system operation data processing method, which further comprises: determining a first equivalent resistance using the preliminarily corrected battery voltage and battery current under a small amplitude charge-discharge condition of the battery; comparing the first equivalent resistance with a reference equivalent resistance corresponding to the current battery temperature and the remaining battery capacity of the battery to determine a residual deviation; determining and quantifying a zero drift value of the current sensor in response to the existence of a nonlinear correlation between the residual deviation and the battery temperature; and correcting the battery current according to the zero drift value to obtain a corrected battery current.

[0046] Specifically, the small-amplitude charge-discharge condition of the battery refers to an operation mode in which the battery is in a non-static state but the charge-discharge current fluctuates slightly. In this condition, an instantaneous equivalent resistance can be calculated using the preliminarily corrected battery voltage and the currently measured battery current, which is defined as a first equivalent resistance. The determination of the first equivalent resistance can be based on Ohm's law or a more complex battery model, and is obtained by monitoring the change relationship between the voltage and the current.

[0047] After the first equivalent resistance is determined, it needs to be compared with a reference equivalent resistance of the battery under the current operating conditions. The reference equivalent resistance is obtained in advance through experiments, modeling or table lookup, and reflects the theoretical or calibrated equivalent resistance value of the battery under a specific battery temperature and remaining capacity. By comparing the actually measured first equivalent resistance with the reference equivalent resistance, a residual deviation can be obtained. The residual deviation reflects the difference between the current measurement and the theoretical value, which may include the influence of the zero-point drift of the current sensor.

[0048] In practical applications, there can be a nonlinear relationship between the residual deviation and the battery temperature. For example, the zero-point drift value of the current sensor often exhibits a specific nonlinear law with the change of temperature. Therefore, by analyzing the relationship between the residual deviation and the battery temperature, the zero-point drift value of the current sensor can be determined and quantified. This can be achieved by establishing a temperature-drift characteristic curve, using a machine learning algorithm or fitting based on an empirical model. Once the zero-point drift value is determined, the original measured battery current can be corrected according to the zero-point drift value, thereby obtaining a more accurate corrected battery current.

[0049] The scheme of the present application can solve the problem of inaccurate measurement caused by the zero-point drift of the current sensor because it introduces a systematic calibration mechanism. First, in the small-amplitude charge-discharge condition of the battery, a first equivalent resistance is calculated using the preliminarily corrected battery voltage and the current battery current, which provides basic data for subsequent deviation analysis. Second, by comparing the first equivalent resistance with the reference equivalent resistance under the same battery temperature and remaining capacity, the residual deviation in the measurement system can be effectively identified. It is precisely because of the nonlinear relationship between the residual deviation and the battery temperature that the present application can further accurately determine and quantify the zero-point drift value of the current sensor by analyzing this relationship. Finally, the battery current is corrected according to the obtained zero-point drift value, thereby eliminating the influence of the current sensor's own error on the measurement results, making the battery current data more real and reliable.

[0050] In some preferred embodiments, the following is described by a specific example. Assume that during the operation of the home energy storage system, the battery module is in a small amplitude charging and discharging state, for example, small power energy scheduling or maintaining charging is being carried out. At this time, the system will obtain the preliminarily corrected battery voltage and the current measured battery current in real time. For example, when the battery temperature is 25℃ and the remaining capacity is 50%, the first equivalent resistance obtained by calculation is 10 milliohms. At the same time, by consulting the preset battery characteristic curve or database, it is found that the reference equivalent resistance at 25℃ and 50% remaining capacity should be 9.5 milliohms. Thus, it can be determined that there is a residual deviation of 0.5 milliohms. Further, the system will analyze the relationship between the residual deviation of 0.5 milliohms and the current 25℃ battery temperature. If it is found that there is a specific nonlinear correlation between the deviation and the temperature, for example, by a pre-established temperature-drift characteristic model, it can be deduced that there is a +0.1 ampere zero point drift of the current sensor. Based on this, the system will correct all subsequent battery current measurements by -0.1 amperes, thereby obtaining more accurate battery current data. For example, if the original measured current of the sensor is 5.1 amperes, the corrected true current value will be 5.0 amperes. In this way, even in the case of zero point drift of the current sensor, the system can continuously provide high-precision battery current data, ensuring the accuracy of subsequent battery state estimation and control decisions.

[0051] Specifically, in the process of correcting the battery voltage according to the determined measurement deviation of the battery voltage described above, in order to more accurately identify and correct the persistent deviation of the battery voltage, the present application proposes a hierarchical correction strategy.

[0052] The comparison of the voltage of each single battery in the battery module with the average voltage of the module determines the persistent deviation of the voltage of each single battery within a certain time period, comprising: The voltage data of each single battery under static working conditions is obtained, and the first persistent deviation of the voltage of each single battery from its historical average voltage within a certain time period is determined. The overall average of all single battery voltages under static working conditions is obtained, and the second persistent deviation of the overall average from the reference average static voltage within a certain time period is determined. The preliminary corrected battery voltage obtained by correcting the corresponding battery voltage according to the persistent deviation, comprising: preliminarily correcting the battery voltage corresponding to all single batteries according to the second persistent deviation; and secondarily correcting the corresponding battery voltage according to the first persistent deviation.

[0053] The standing condition refers to a state where the battery module is not in charging or discharging, i.e., the battery current is close to zero or fluctuates within a small range, at which time the electrochemical reaction inside the battery tends to be stable, and the voltage measurement is less affected by external dynamic factors. In this condition, obtaining the voltage data of each single battery can more accurately reflect the true voltage state of the battery. The first persistent deviation refers to the long-term and stable difference between the voltage value of a single battery in the standing state and the average voltage value of the battery itself in the historical standing condition. This deviation may be caused by factors such as battery aging, internal impedance changes, etc. By continuously monitoring and calculating within a certain time period, the influence of transient fluctuations can be excluded, and a more reliable individual deviation can be obtained. The second persistent deviation refers to the difference between the overall average value of all single battery voltages in the standing state of the entire battery module and a preset, ideal or calibrated reference average standing voltage. This deviation may reflect systematic measurement errors of the entire module, overall drift caused by environmental temperature changes, or sensor calibration problems. The preliminary correction is a unified adjustment of the voltage of all single batteries based on the second persistent deviation. The purpose is to eliminate the systematic deviation of the entire module, so that the overall voltage level of the module is closer to the true value. The secondary correction is to further adjust the voltage of each single battery according to the first persistent deviation on the basis of the preliminary correction. The purpose is to eliminate the individual deviation of each single battery, so that the voltage value of each single battery more accurately reflects its true state.

[0054] The scheme of the present application can more comprehensively and finely correct the measurement deviation of the battery voltage by introducing the first persistent deviation and the second persistent deviation and adopting a hierarchical correction method. Specifically, the determination of the second persistent deviation and the preliminary correction aim to solve the problem of systematic voltage drift of the entire battery module, such as unified deviation caused by overall calibration error of the measurement device or environmental factors. By preliminarily correcting the voltage of all single batteries, the voltage reference of the entire module can be effectively pulled back to a more accurate level. On this basis, the determination of the first persistent deviation and the secondary correction focus on solving the individual difference problem of single batteries, such as voltage deviation caused by different aging degrees of batteries, internal characteristic differences, etc. This two-stage correction mechanism enables the system to consider both the overall and individual aspects, thereby obtaining more accurate single battery voltage data. As a result, the accuracy and reliability of the subsequent determination of the equivalent resistance of the battery, the prediction of the remaining capacity, and the calculation of the real-time capability parameters of the battery based on these voltage data can be significantly improved.

[0055] In some embodiments of the present application, a scheme is proposed for determining the first equivalent resistance using the preliminarily corrected battery voltage and battery current under small-amplitude charging and discharging conditions of the battery. However, in the implementation process, if the battery voltage and battery current are simply used for calculation, the determination of the first equivalent resistance may be affected by measurement noise, transient response or current fluctuation, resulting in inaccurate determination of the first equivalent resistance, and further affecting the accuracy of the subsequent real-time battery capability parameters.

[0056] To this end, the present application further proposes that the step of determining the first equivalent resistance using the preliminarily corrected battery voltage and battery current under small-amplitude charging and discharging conditions of the battery includes: obtaining the battery voltage and battery current under small-amplitude charging and discharging conditions of the battery, and monitoring the battery current change rate; adjusting the sampling parameters of the battery voltage and battery current in response to the battery current change rate reaching a preset threshold; the sampling parameters include sampling frequency and sampling duration; determining the steady-state voltage change and the steady-state current change according to the sampled battery voltage and battery current data; and determining the first equivalent resistance using the ratio of the steady-state voltage change to the steady-state current change.

[0057] Specifically, obtaining the battery voltage and battery current under small-amplitude charging and discharging conditions of the battery, and monitoring the battery current change rate, means that when the battery module is in a small-amplitude charging or discharging state, the system continuously collects the battery voltage and battery current data of each single battery. At the same time, the obtained battery current data is analyzed in real time to calculate its change in unit time, i.e. the battery current change rate. The battery current change rate can be understood as the rate of change of current with time, which is used to evaluate the dynamics of the battery working condition.

[0058] In response to the battery current change rate reaching a preset threshold, adjusting the sampling parameters of the battery voltage and battery current, the sampling parameters including sampling frequency and sampling duration, means that when the monitored battery current change rate exceeds the pre-set threshold, it indicates that the battery may be in a dynamic change or unstable working condition. In order to more accurately capture the transient response of the battery or ensure the representativeness of the data under a certain working condition, the system will dynamically adjust the sampling parameters of the battery voltage and battery current. For example, the sampling frequency can be increased to obtain more intensive instantaneous data, or the sampling duration can be extended to collect enough data samples for averaging or trend analysis, so as to better reflect the true state of the battery.

[0059] According to the sampled battery voltage and battery current data, the steady-state voltage change and the steady-state current change are determined, which refers to the system processing and analyzing the data after adjusting the sampling parameters and obtaining new battery voltage and battery current data. The purpose is to identify and extract the voltage and current change of the battery in a relatively stable state. The steady-state voltage change refers to the net change value of the battery voltage when the battery operating condition transitions from one stable state to another stable state; the steady-state current change refers to the net change value of the battery current under the same operating condition. These steady-state changes can effectively eliminate the interference of transient fluctuations and noise.

[0060] The ratio of the steady-state voltage change and the steady-state current change is used to determine the first equivalent resistance, which refers to dividing the steady-state voltage change by the steady-state current change after obtaining the optimized sampling and processing of the steady-state voltage change and the steady-state current change. The ratio obtained is determined as the first equivalent resistance. This calculation method is based on Ohm's law and can more accurately reflect the internal equivalent resistance of the battery by measuring and calculating under relatively stable conditions.

[0061] The scheme of the present application monitors the battery current change rate in real time and adjusts the sampling parameters of the battery voltage and battery current according to the dynamic state, ensuring that the collected data can more accurately reflect the actual state of the battery under small amplitude charging and discharging conditions of the battery. It is precisely because the sampling frequency and sampling duration can be adjusted in time when the current change rate reaches the preset threshold, thereby avoiding data distortion caused by insufficient or improper sampling under unstable conditions. On this basis, by determining the steady-state voltage change and the steady-state current change, and using the ratio to calculate the first equivalent resistance, the influence of transient noise and fluctuations is effectively filtered out, making the determined equivalent resistance value more accurate and reliable.

[0062] In some preferred embodiments, assuming that the battery module in the home energy storage system is undergoing small-scale charging and discharging. The system first acquires battery voltage and battery current data at a regular frequency (for example, 1 Hz), and calculates the battery current rate of change in real time. When the battery current is monitored to change more than a preset threshold (for example, 0.2 A / s) in a short time (for example, within 0.1 seconds), the system determines that the battery is in a dynamic changing condition. At this time, in order to obtain more accurate data, the system will immediately adjust the sampling parameters: increase the sampling frequency to 10 Hz, and extend the sampling duration to 5 seconds. After the 5-second sampling is completed, the system analyzes the 50 voltage and current data points, identifies the relatively stable voltage and current interval through methods such as moving average or least squares, and calculates the steady-state voltage change (for example, 0.05 V) and steady-state current change (for example, 0.5 A) in the interval. Finally, using the ratio of 0.05 V to 0.5 A, the first equivalent resistance is determined to be 0.1 ohm. This dynamic adjustment of the sampling strategy and calculation based on steady-state data ensures that even in complex conditions of small-scale charging and discharging, high-precision battery equivalent resistance can be obtained.

[0063] The application further proposes a more refined zero-point drift determination and quantification method, which constructs a temperature-drift characteristic curve and combines forced pulse discharge for calibration to improve the measurement accuracy of the current sensor.

[0064] In response to the existence of a nonlinear relationship between the residual deviation and the battery temperature, the zero-point drift value of the current sensor is determined and quantified, including: According to the interval in which the battery temperature is located, a temperature-drift characteristic curve corresponding to the battery temperature is determined; In response to the battery being in a resting condition, a small-amplitude current pulse discharge is forced; and the zero-point drift of the current sensor is calculated reversely according to the preliminarily corrected battery voltage; The reversely calculated zero-point drift is associated with a predetermined battery temperature point, and the temperature-drift characteristic curve is updated.

[0065] Specifically, according to the interval in which the battery temperature is located, a temperature-drift characteristic curve corresponding to the battery temperature is determined, which means that before the calibration of the zero-point drift of the current sensor, the system will select or generate a preset curve describing the relationship between the zero-point drift of the current sensor and the temperature according to the range in which the current battery temperature is located. The curve can be pre-calibrated through experiments and stored in the system memory, and is used to preliminarily estimate the possible zero-point drift at different temperatures. The purpose is to provide an initial reference model for subsequent accurate calibration.

[0066] In response to the battery being in a resting state, a small-amplitude current pulse discharge is forced; the zero-point drift of the current sensor is calculated in reverse according to the preliminarily corrected battery voltage, which can be understood as when the battery is in a resting state, i.e. without external charging and discharging current, the battery voltage change at this time is mainly caused by internal self-discharge or temperature change, and is relatively stable. Under this condition, by actively applying a known small-amplitude current pulse discharge, the instantaneous change of the battery voltage can be accurately observed. Since the actual current at this time is known (i.e. pulse current), and the battery voltage has been preliminarily corrected, the actual zero-point drift of the current sensor at this time can be calculated in reverse by Ohm's law or battery model, using the change amount of the preliminarily corrected battery voltage and the known pulse current. The purpose is to accurately measure the deviation of the sensor by introducing a known disturbance under controlled conditions.

[0067] In practical applications, the zero-point drift calculated in reverse is associated with the predetermined battery temperature point, and the temperature-drift characteristic curve is updated, specifically, the accurate zero-point drift value obtained by the above-mentioned forced pulse discharge will be associated with the battery temperature at the time of measurement. These new data points will be used to update or optimize the previously determined temperature-drift characteristic curve. By continuously calibrating and updating at different temperature points, the temperature-drift characteristic curve can more accurately reflect the nonlinear drift characteristics of the current sensor in the entire working temperature range. The purpose is to improve the accuracy of the temperature-drift model through iterative learning and calibration, so as to realize more accurate zero-point drift compensation.

[0068] The scheme of the present application first determines the corresponding temperature-drift characteristic curve according to the interval of the battery temperature when there is a nonlinear association between the residual deviation and the battery temperature, providing a preliminary model for the quantification of zero-point drift. Subsequently, by forcing a small-amplitude current pulse discharge when the battery is in a resting state, a controllable current disturbance is introduced, and the zero-point drift of the current sensor is calculated in reverse using the preliminarily corrected battery voltage. This method takes advantage of the relatively stable characteristics of the battery in a resting state, and by precisely controlling the external excitation, the measurement of the zero-point drift is more accurate. Finally, the zero-point drift calculated in reverse is associated with the predetermined battery temperature point, and is used to update the temperature-drift characteristic curve. It is precisely due to this iterative and calibration mechanism that the temperature-drift characteristic curve can dynamically adapt to the actual operating environment of the battery, thereby more accurately capturing the nonlinear zero-point drift of the current sensor, effectively solving the problem that the traditional method cannot accurately quantify the zero-point drift under nonlinear association.

[0069] As a specific implementation, the following is described through a specific example. Assume that during the operation of the home energy storage system, the system detects that there is a significant nonlinear correlation between the residual deviation of the battery module and the battery temperature. For example, when the battery temperature is 25°C, the zero-point drift of the current sensor is +5 mA; while at 0°C, the zero-point drift may become -10 mA, and there is not a simple linear relationship between the two. At this time, the system will first obtain an initial zero-point drift estimation value from the preset temperature-drift characteristic curve according to the current battery temperature (for example, 25°C). Subsequently, when the battery module enters the resting working condition, the system will forcibly perform a current pulse discharge with a short duration and a small amplitude (for example, 1 A, lasting 100 ms). During the pulse discharge, the system will high-frequency sample the preliminary corrected battery voltage and the actual pulse current. By analyzing the drop of the battery voltage and the known pulse current, the system can accurately calculate the actual zero-point drift value of the current sensor at the current 25°C, for example, +4.8 mA. This accurately calculated zero-point drift value +4.8 mA will be associated with the predetermined battery temperature point of 25°C and used to update the temperature-drift characteristic curve. When the battery temperature changes to 0°C, the system will again perform a small-amplitude current pulse discharge in the resting working condition and reversely calculate the actual zero-point drift value at 0°C, for example, -9.5 mA. This value is also used to update the temperature-drift characteristic curve. By performing such accurate calibration and updating at multiple key temperature points, the temperature-drift characteristic curve will become more fine and accurate, and can better reflect the nonlinear drift characteristics of the current sensor at different temperatures. Finally, the system will use the updated temperature-drift characteristic curve to accurately correct the real-time obtained battery current, thereby obtaining more accurate battery operation data.

[0070] The application further proposes to optimize the above specific operation of forcibly performing a small-amplitude current pulse discharge in response to the battery being in the resting working condition, so as to realize a more intelligent and safer pulse discharge process, thereby improving the accuracy of zero-point drift calibration and the operation reliability of the battery system.

[0071] The above forcibly performing a small-amplitude current pulse discharge in response to the battery being in the resting working condition includes: obtaining the health degree of the current battery and the current battery temperature, adjusting the initial amplitude of the pulse current according to the health degree, and adjusting the pulse duration according to the current battery temperature; Real-time monitoring of the drop rate of the current battery voltage and the actual waveform of the current battery current; Adjusting the amplitude and the pulse duration of the subsequent pulse according to the drop rate of the current battery voltage and the actual waveform of the current battery current; The system monitors the change in the current battery temperature before and after the pulse discharge, and adjusts the interval time between pulses according to the change in the current battery temperature.

[0072] Specifically, before performing the small-amplitude current pulse discharge, the system obtains the health degree of the current battery and the current battery temperature. The battery health degree can be understood as the ratio of the actual available capacity of the battery to the nominal capacity, reflecting the aging degree of the battery; the current battery temperature refers to the real-time temperature of the battery module or the single battery. Based on these parameters, the initial amplitude of the pulse current and the pulse duration can be pre-adjusted. For example, when the battery health degree is low, the initial amplitude of the pulse current is appropriately reduced to avoid excessive impact on the battery; when the current battery temperature is too high or too low, the pulse duration can also be shortened to prevent the battery from further deteriorating at extreme temperatures.

[0073] During the pulse discharge process, the system monitors the current battery voltage drop rate and the actual waveform of the current battery current in real time. The current battery voltage drop rate is an important indicator for evaluating the internal resistance and response characteristics of the battery, and a too fast drop rate may indicate that there is an abnormality inside the battery or that the pulse amplitude is too large. The actual waveform of the current battery current is used to confirm whether the output of the pulse current is stable and meets expectations, such as whether there are distortions or fluctuations.

[0074] Further, according to the real-time monitored current battery voltage drop rate and the actual waveform of the current battery current, the amplitude and pulse duration of subsequent pulses can be dynamically adjusted. For example, if the monitored current battery voltage drop rate is too fast, indicating that the current pulse amplitude may be too large, the amplitude of the subsequent pulse will be appropriately reduced to protect the battery and ensure the effectiveness of the measurement data. If the actual waveform of the current battery current is abnormal, it may also trigger adjustment of the pulse parameters.

[0075] In addition, in order to ensure the safety of the battery during the pulse discharge process and avoid overheating, the system also monitors the change in the current battery temperature before and after the pulse discharge. If it is monitored that the current battery temperature significantly increases after the pulse discharge, the interval time between pulses will be appropriately adjusted according to the change in the current battery temperature, and the interval will be extended to allow the battery to fully dissipate heat and recover, thereby avoiding the adverse effects of heat accumulation on the performance and life of the battery.

[0076] The scheme of the present application effectively solves the above-mentioned limitations that may exist in the forced small-amplitude current pulse discharge process by introducing an adaptive adjustment mechanism for pulse discharge parameters. Specifically, by obtaining the battery health and the current battery temperature before pulse discharge and adjusting the initial amplitude of the pulse current and the pulse duration accordingly, it can be ensured that the initial conditions of the pulse discharge match the actual state of the battery, avoiding the application of inappropriate pulses to aged batteries or batteries at extreme temperatures, thereby reducing the risk of battery damage. During the pulse discharge process, the current battery voltage drop rate and the actual waveform of the current battery current are monitored in real time, and the amplitude and duration of the subsequent pulses are dynamically adjusted according to these real-time feedback, so that the pulse discharge process can be optimized according to the instantaneous response of the battery, ensuring that high-quality measurement data is obtained without damaging the battery. At the same time, by monitoring the change in the current battery temperature before and after pulse discharge and adjusting the interval time between pulses, the thermal effect of the battery is effectively controlled, preventing the battery from overheating, further ensuring the safe operation and life of the battery. It is precisely due to these adaptive adjustments that the calculation of the zero-point drift can be based on more accurate and stable data, thereby improving the accuracy of the calibration.

[0077] Through the above technical scheme, the present application can realize more accurate and safer calibration of the current sensor zero-point drift. Specifically, by dynamically adjusting the pulse discharge parameters according to the health, temperature, and real-time response (voltage drop rate, current waveform) of the battery, the potential damage to the battery caused by pulse discharge can be significantly reduced, prolonging the service life of the battery. At the same time, this adaptive pulse discharge strategy can ensure that high-quality measurement data is obtained under different battery states, thereby improving the accuracy and reliability of the reverse calculation of the current sensor zero-point drift. As a result, the operation data processing method of the household energy storage system can obtain more accurate battery current data, thereby improving the determination accuracy of the battery equivalent resistance and the real-time capacity parameters of the battery, and ultimately optimizing the discharge control strategy to improve the operation efficiency and safety of the entire energy storage system.

[0078] In some preferred embodiments, the following is illustrated by a specific example. Suppose a battery module in a home energy storage system, the health of its single battery is 80%, and the current battery temperature is 20°C. When performing current sensor zero drift calibration, the system first sets the initial amplitude of the pulse current to 0.5C (C is the nominal capacity of the battery) according to the health, and sets the pulse duration to 100 milliseconds according to the current battery temperature. After performing the first pulse discharge, the system monitors that the current battery voltage drop rate is slightly higher than expected, and the actual waveform of the current battery current appears slightly distorted. Based on this feedback, the system automatically adjusts the parameters of the subsequent pulse, such as adjusting the amplitude of the subsequent pulse to 0.4C, and adjusting the pulse duration to 80 milliseconds. At the same time, if it is monitored that the current battery temperature rises by 1°C after the first pulse discharge, the system will accordingly extend the interval time between pulses from the default 5 seconds to 8 seconds to ensure that the battery has enough time to dissipate heat. Through this dynamic adjustment, even if the battery state changes, it can ensure that the pulse discharge process can effectively obtain calibration data and maximize the protection of the battery.

[0079] The application further proposes to adjust the amplitude and pulse duration of the subsequent pulse according to the current battery voltage drop rate and the actual waveform of the current battery current, including: when the current battery voltage drop rate exceeds a preset threshold, stopping the current pulse discharge and isolating the battery; according to the average voltage drop rate of the remaining healthy single battery in the battery module, readjusting the amplitude and duration of the subsequent pulse.

[0080] Specifically, the "preset threshold" refers to a pre-set upper limit value of the voltage drop rate, which is usually determined according to the type, health, temperature and safety operation specification of the battery. When it is monitored that the voltage drop rate of a single battery exceeds this preset threshold, it indicates that the single battery may have internal failure, serious capacity decay or excessive internal resistance, etc. Abnormal conditions, and continuing to pulse discharge may cause over-discharge or further damage. Therefore, the current pulse discharge of the abnormal single battery is immediately stopped at this time, and it is isolated from the normal operation loop of the battery module to prevent the spread of faults and protect other healthy batteries.

[0081] Wherein, after isolating the abnormal single battery, in order to ensure the accuracy and safety of the subsequent pulse discharge, the "re-adjusting the amplitude and duration of the subsequent pulse according to the average voltage drop rate of the remaining healthy single battery in the battery module" means that the system will recalculate the average voltage drop rate of all the single batteries in the current module which are not isolated and are judged to be healthy. Based on this more representative average value, the system will dynamically adjust the current amplitude and duration of the subsequent pulse to adapt to the actual health status and response characteristics of the current module, so as to realize the accurate testing and management of the remaining healthy batteries.

[0082] The scheme of the present application can identify and respond to abnormal single batteries in the module in a timely manner by introducing a real-time monitoring and threshold judgment mechanism for the voltage drop rate of single batteries. When the voltage drop rate of a single battery is abnormal, the pulse discharge to it is immediately stopped and it is isolated, which directly avoids further damage to the faulty battery and prevents the overall module performance from being degraded or potential safety hazards caused by the faulty battery. It is this immediate isolation mechanism that enables the subsequent pulse discharge operation to be performed on a more healthy and stable battery group. On this basis, by adjusting the subsequent pulse parameters only using the average voltage drop rate of the remaining healthy single batteries, the accuracy and effectiveness of the adjustment are ensured, and the interference of abnormal battery data on the overall parameter adjustment is avoided, thereby improving the reliability of the battery module characteristic evaluation.

[0083] In some preferred embodiments, the following is described by a specific example. Assume that a battery module in a household energy storage system contains multiple single batteries. When performing a small-amplitude current pulse discharge to reverse calculate the zero-point drift of the current sensor, the system will monitor the voltage drop rate of each single battery in real time. For example, a preset threshold is set to 5 mV per millisecond. If during a certain pulse discharge process, the voltage drop rate of single battery A suddenly reaches 8 mV per millisecond, exceeding the preset threshold, the system will immediately stop the pulse discharge to single battery A and electrically isolate it through the corresponding switch or relay. At this time, the system will record that single battery A is in an abnormal state. Subsequently, for the remaining healthy single batteries (such as single batteries B, C, D, etc.) in the module, the system will calculate their average voltage drop rate. Assuming that the average voltage drop rate of the remaining healthy single batteries is 3 mV per millisecond, the system will adjust the amplitude and duration of the subsequent pulse according to this average value, for example, slightly reduce the pulse current amplitude or appropriately extend the pulse duration, to ensure that the characteristic evaluation of these healthy batteries is performed within a safe and effective range, while avoiding the interference of abnormal batteries on the overall evaluation result.

[0084] To this end, the detailed description of the present application further discloses a household energy storage system operation data processing system, the household energy storage system comprising a battery module, such as Figure 2 As shown in the figure, the system comprises: An acquisition and determination module 201, which acquires operation parameters of the battery module in real time; the operation parameters comprise battery voltage, battery current, battery temperature, residual capacity, and total capacity of each single battery; and the battery equivalent resistance is determined according to the battery voltage and the battery current; A prediction module 202, which is configured to predict a minimum voltage value reached by the battery voltage under different discharge powers according to the residual capacity and the battery equivalent resistance, and determine the minimum voltage value as a first voltage; A processing module 203, which is configured to determine a real-time capability parameter of the battery according to the operation parameters, with a constraint that the first voltage is higher than a sum of a low-voltage protection threshold and a safety margin; the real-time capability parameter of the battery comprises a maximum discharge power and available energy; A control module 204, which is configured to perform discharge control according to a grid discharge request and the real-time capability parameter of the battery.

[0085] The above merely describes the embodiments of the present application and is not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for processing operation data of a home energy storage system, the home energy storage system comprising a battery module, characterized by, The method comprises: acquiring running parameters of the battery module in real time; the running parameters comprise battery voltage, battery current, battery temperature, residual capacity, and total capacity of each single battery; determining battery equivalent resistance according to the battery voltage and the battery current; determining a first voltage by predicting the lowest voltage value reached by the battery voltage under different discharge powers according to the residual capacity and the battery equivalent resistance; determining battery real-time capability parameters according to the running parameters, with the constraint that the first voltage is higher than the sum of a low-voltage protection threshold and a safety margin; the battery real-time capability parameters comprise maximum discharge power and available energy; performing discharge control according to a power grid discharge request and the battery real-time capability parameters.

2. A method of processing data from a home energy storage system according to claim 1, wherein, The method comprises: determining the battery equivalent resistance by measuring the ratio of the instantaneous change of the battery terminal voltage to the instantaneous change of the current; 3. A method of processing data from a domestic energy storage system according to claim 2, wherein, before determining the first voltage by predicting the lowest voltage value reached by the battery voltage under different discharge powers according to the residual capacity and the battery equivalent resistance, the method further comprises correcting the battery voltage according to the determined measurement deviation of the battery voltage. The method comprises:

4. A method of processing data from a domestic energy storage system according to claim 3, wherein, correcting the battery voltage according to the determined measurement deviation of the battery voltage, which comprises: comparing each single battery voltage in the battery module with the average voltage of the module under the static working condition of the battery, and determining the persistent deviation of the single battery voltage within a certain time length; and correcting the corresponding battery voltage according to the persistent deviation to obtain the preliminarily corrected battery voltage. The power grid discharge request comprises a requested discharge power and a requested discharge energy; the method of performing discharge control according to the power grid discharge request and the battery real-time capability parameters comprises: comparing the power grid discharge request and the battery real-time capability parameters, and performing discharge control according to the minimum value of the requested discharge power and the maximum discharge power, and the minimum value of the requested discharge energy and the available energy. The method further comprises: determining a first equivalent resistance by using the preliminarily corrected battery voltage and the battery current under the condition of small-amplitude charging and discharging of the battery; comparing the first equivalent resistance with a reference equivalent resistance corresponding to the current battery temperature and the residual capacity of the battery to determine a residual deviation; 5. The method of claim 3, wherein, determining and quantifying the zero-point drift value of the current sensor in response to the nonlinear correlation between the residual deviation and the battery temperature; correcting the battery current according to the zero-point drift value to obtain the corrected battery current. The method comprises: acquiring voltage data of each single battery under the static working condition, and determining the first persistent deviation of the voltage data from the historical average voltage within a certain time length; acquiring the overall average value of the voltage of all single batteries under the static working condition, and determining the second persistent deviation of the overall average value from the reference average static voltage within a certain time length; The method comprises: correcting the corresponding battery voltage according to the persistent deviation to obtain the preliminarily corrected battery voltage. According to the second persistent deviation, the battery voltage corresponding to each single battery is preliminarily corrected; and according to the first persistent deviation, the corresponding battery voltage is secondarily corrected.

6. A method of processing data from a home energy storage system according to claim 4, wherein, The first equivalent resistance is determined by using the preliminarily corrected battery voltage and battery current under the small-amplitude charging and discharging condition of the battery, and the method comprises the following steps: The battery voltage and battery current under the small-amplitude charging and discharging condition of the battery are acquired, and the battery current change rate is monitored; When the battery current change rate reaches a preset threshold, the sampling parameters of the battery voltage and battery current are adjusted; the sampling parameters comprise a sampling frequency and a sampling duration; According to the sampled battery voltage and battery current data, the steady-state voltage change amount and the steady-state current change amount are determined; and the first equivalent resistance is determined by using the ratio of the steady-state voltage change amount to the steady-state current change amount.

7. A method of processing data from a home energy storage system according to claim 4, wherein, When the residual deviation and the battery temperature have a nonlinear correlation, the zero-point drift value of the current sensor is determined and quantified, and the method comprises the following steps: According to the interval of the battery temperature, the corresponding temperature-drift characteristic curve is determined; When the battery is in a static condition, a small-amplitude current pulse discharge is forced; the zero-point drift of the current sensor is reversely calculated according to the preliminarily corrected battery voltage; The reversely calculated zero-point drift is associated with a predetermined battery temperature point, and the temperature-drift characteristic curve is updated.

8. A method of processing data from a domestic energy storage system according to claim 7, wherein, When the battery is in a static condition, a small-amplitude current pulse discharge is forced, and the method comprises the following steps: The health degree of the current battery and the current battery temperature are acquired; the initial amplitude of the pulse current is adjusted according to the health degree; and the pulse duration is adjusted according to the current battery temperature; The current battery voltage drop rate and the actual waveform of the current battery current are monitored in real time; The amplitude and duration of the subsequent pulse are adjusted according to the current battery voltage drop rate and the actual waveform of the current battery current; The interval between the pulses is adjusted according to the change of the current battery temperature before and after the pulse discharge.

9. A method for processing operational data of a home energy storage system according to claim 8, characterized in that, The amplitude and duration of the subsequent pulse are adjusted according to the current battery voltage drop rate and the actual waveform of the current battery current, and the method comprises the following steps: When the current battery voltage drop rate exceeds a preset threshold, the current pulse discharge is stopped, and the current battery is isolated; The amplitude and duration of the subsequent pulse are re-adjusted according to the average voltage drop rate of the healthy single batteries remaining in the battery module.

10. A home energy storage system operation data processing system, the home energy storage system comprising a battery module, characterized by, The system comprises: An acquisition and determination module, which acquires the operating parameters of a battery module in real time; the operating parameters comprise the battery voltage, battery current, battery temperature, residual capacity and total capacity of each single battery; and the battery equivalent resistance is determined according to the battery voltage and battery current; A prediction module, which is configured to predict the minimum voltage value of the battery voltage under different discharging powers according to the residual capacity and battery equivalent resistance, and determine the first voltage; A processing module, which is configured to determine the real-time capability parameters of the battery according to the operating parameters, with the constraint that the first voltage is higher than the sum of a low-voltage protection threshold and a safety margin; the real-time capability parameters of the battery comprise the maximum discharging power and available energy. A control module is configured to perform discharge control according to a grid discharge request and the real-time capability parameter of the battery.

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