Off-grid inverter battery protection and charging control method and system based on impedance detection

Abstract

The invention belongs to the technical field of off-grid inverter and battery management, and particularly relates to an off-grid inverter battery protection and charging control method and system based on impedance detection, and the method comprises the steps: collecting battery input parameters under two different load working conditions: no-load or light-load and on-load conditions; an Ohm law analytical algorithm is adopted, a corresponding relation model of the lithium battery remaining capacity SOC and the battery internal resistance is preset, the current actual internal resistance of the battery is obtained, the preset SOC-internal resistance corresponding relation model is input, and the current SOC value of the battery is obtained through reverse deduction; an inverter charging current adjusting threshold value is set in a grading mode, and a multi-dimensional risk early warning threshold value is set in a grading mode; and when the monitored numerical value exceeds a correspondingly set risk early warning threshold value, triggering a risk early warning signal of a corresponding grade and a matched disposal action, and carrying out full-process management and control of battery state real-time monitoring, charging current self-adaptive adjustment and risk early warning. Therefore, the problems of short service life, poor charging safety and the like of the battery in the prior art are solved.

Description

Technical Field

[0001] This invention belongs to the field of off-grid inverter and battery management technology, specifically relating to an off-grid inverter battery protection and charging control method and system based on impedance detection. Background Technology

[0002] Off-grid inverters are core equipment in independent photovoltaic and energy storage off-grid systems. Their main function is to convert the direct current (DC) output from the battery into alternating current (AC) to power the load, while also controlling the charging of the battery. Lithium batteries, as the most commonly used energy storage component in off-grid systems, directly affect the stability and economy of the entire system due to their lifespan and safety.

[0003] In existing off-grid systems, inverters often use a fixed current charging mode to control battery charging, or simply adjust the voltage based on the battery terminals, without considering the effects of battery internal resistance and connecting wire impedance. However, there is a clear correlation between the state of charge (SOC) and internal resistance of a lithium battery: the lower the SOC and the more severe the battery aging, the higher the internal resistance. Simultaneously, aging of connecting wires and loose connections increase wire and contact resistance, leading to increased voltage drop and heat generation during charging, accelerating battery degradation, and even causing safety hazards such as poor contact, overheating, and fire.

[0004] Currently, to detect battery internal resistance and wire impedance, and to protect the battery, it is usually necessary to add independent hardware devices such as a battery management system (BMS), impedance tester, and temperature sensor. This undoubtedly increases the hardware cost and installation complexity of off-grid systems, hindering their widespread application. Furthermore, existing technologies lack an integrated method that can monitor battery and wire status in real time, automatically adjust charging current, and provide risk warnings without requiring additional hardware. This results in short battery life, poor charging safety, and high maintenance costs in off-grid systems.

[0005] Therefore, how to detect the impedance of the battery and connecting wires using the existing sampling module of the inverter without increasing the hardware cost of the off-grid system, and how to automatically adjust the charging current, reduce battery heat generation, extend battery life, and achieve real-time risk warning in combination with the relationship between SOC and battery internal resistance, has become an urgent technical problem to be solved. Summary of the Invention

[0006] This application provides a method and system for off-grid inverter battery protection and charging control based on impedance detection, in order to solve problems such as short battery life and poor charging safety in the prior art.

[0007] The first aspect of this application provides a battery protection and charging control method for off-grid inverters based on impedance detection, comprising the following steps: collecting battery input parameters under two different load conditions: no-load or light-load, and loaded; based on the battery input parameters under the two different load conditions, calculating the total equivalent impedance of the battery, connecting wires, and contact parts using Ohm's law analytical algorithm according to the DC voltage drop principle; pre-setting a correspondence model between the remaining SOC of the lithium battery and the battery internal resistance adapted to off-grid conditions; subtracting the preset reference values ​​of the connecting wires and contact resistance from the total equivalent impedance to obtain the current actual internal resistance of the battery; and inputting the preset SOC-internal resistance correspondence model. The system model is used to deduce the current SOC value of the battery. Based on the current SOC value and the actual internal resistance of the battery, the inverter charging current adjustment threshold is set in stages to adaptively adjust the charging current of the inverter so that the charging current matches the current health status and remaining capacity of the battery, thereby reducing the heat generated during the battery charging process. At the same time, the rate of change of the total equivalent impedance and the trend of change of the actual internal resistance of the battery are monitored in real time, and multi-dimensional risk warning thresholds are set in stages. When the monitored value exceeds the corresponding set risk warning threshold, the corresponding level of risk warning signal and corresponding handling actions are triggered to carry out full-process control of real-time battery status monitoring, adaptive adjustment of charging current and risk warning.

[0008] Preferably, the battery input parameters are collected under two different load conditions: no-load or light-load, and loaded. This includes: constructing a dual-condition steady-state data acquisition system; based on the dual-condition steady-state data acquisition system, setting steady-state determination rules for no-load or light-load and loaded conditions, and simultaneously collecting the battery terminal output voltage, battery output current, inverter DC side input voltage and input current after the two conditions reach steady state, and simultaneously recording the ambient temperature data before and after the condition switching to obtain raw parameter data; performing moving average filtering and outlier removal processing on the collected raw parameter data, and outputting a standardized parameter dataset for impedance calculation.

[0009] Preferably, based on the battery input parameters under the two different load conditions, and according to the DC voltage drop principle, Ohm's law analytical algorithm is used to calculate the total equivalent impedance of the battery, connecting wires, and contact parts. This includes: constructing a dual-condition impedance analytical model based on the DC voltage drop principle; based on the dual-condition impedance analytical model, inputting standardized parameter datasets under the two different load conditions, extracting the core characteristic parameters of the battery terminal voltage and output current under the two conditions, and calculating the difference between the battery terminal voltage and the output current under the two conditions; based on the difference between the battery terminal voltage and the output current, and according to the DC voltage drop principle, using Ohm's law analytical algorithm, obtaining the total equivalent impedance of the loop between the battery, connecting wires, and contact parts, and simultaneously outputting the confidence interval of the calculation results.

[0010] Preferably, a pre-defined model for the relationship between the remaining SOC (State of Charge) and internal resistance of a lithium battery adapted to off-grid conditions is included: constructing a basic model framework for the SOC-internal resistance relationship adapted to off-grid conditions; based on the basic model framework, and combining the nominal parameters, cycle life characteristics, and test data at different temperatures and charge / discharge rates of the target lithium battery, interval optimization is performed using a piecewise linear fitting algorithm to divide multiple SOC feature intervals, and the mapping function between the battery internal resistance and SOC in each interval is fitted to complete the model parameter calibration; based on the calibrated model parameters, the model accuracy is verified and deviations are corrected using measured data under off-grid conditions to establish a model for the relationship between the SOC and internal resistance of a lithium battery adapted to off-grid conditions.

[0011] Preferably, based on the current SOC value and actual internal resistance of the battery, the inverter charging current adjustment threshold is set in stages to adaptively adjust the charging current of the inverter. This includes: constructing a battery state adaptive charging adjustment model; based on the battery state adaptive charging adjustment model, taking the current SOC value of the battery as the core dimension and combining the battery health state represented by the actual internal resistance of the battery, dividing the charging current adjustment range into multiple levels, and setting the charging current adjustment threshold and charging current upper limit value corresponding to each range in stages; based on the multiple levels of charging current adjustment range, setting the charging current adjustment threshold and charging current upper limit value corresponding to each range in stages, dynamically correcting the charging current upper limit value by combining real-time collected ambient temperature data, and adjusting the control parameters of the charging circuit in real time through the inverter PWM modulation algorithm to dynamically adjust the charging current to the set value of the corresponding range, thus achieving adaptive matching between the charging current and the current health state and remaining capacity of the battery.

[0012] Preferably, the rate of change of total equivalent impedance and the trend of change of actual internal resistance of the battery are monitored in real time, and multi-dimensional risk warning thresholds are set in a graded manner, including: constructing a risk warning index system for the entire battery life cycle; based on the risk warning index system for the entire battery life cycle, using a sliding time window algorithm to extract the short-term rate of change of total equivalent impedance and the long-term trend of change of actual internal resistance of the battery in real time, and combining the safety performance parameters of the target lithium battery and the operating requirements of off-grid conditions, setting three types of multi-dimensional risk warning thresholds in a graded manner: total equivalent impedance mutation threshold, relative change rate threshold of battery internal resistance, and absolute value threshold of internal resistance; based on the multi-dimensional risk warning thresholds, establishing a corresponding judgment rule between risk level and monitoring threshold, and completing the preparatory work for risk warning.

[0013] The second aspect of this application provides an off-grid inverter battery protection and charging control system based on impedance detection, comprising: a dual-condition acquisition module for acquiring battery input parameters under two different load conditions: no-load or light-load, and loaded; and an internal resistance SOC calculation module for calculating the total equivalent impedance of the battery, connecting wires, and contact parts based on the battery input parameters under the two different load conditions, according to the DC voltage drop principle and using Ohm's law analytical algorithm. The module also presets a correspondence model between the remaining SOC of the lithium battery and the battery's internal resistance for off-grid conditions, subtracts preset reference values ​​for the connecting wires and contact resistance from the total equivalent impedance to obtain the current actual internal resistance of the battery, and inputs the preset SOC-internal resistance correspondence model. The current SOC value of the battery is obtained by reverse calculation. The charging regulation and threshold setting module, based on the current SOC value and actual internal resistance of the battery, sets the inverter charging current regulation threshold in stages, adaptively adjusts the magnitude of the inverter charging current, so that the charging current matches the current health status and remaining capacity of the battery, reduces the heat generated during the battery charging process, and monitors the rate of change of total equivalent impedance and the trend of change of actual internal resistance of the battery in real time, and sets multi-dimensional risk warning thresholds in stages. The early warning and handling control module triggers the corresponding level of risk warning signal and corresponding handling actions when the monitored value exceeds the corresponding set risk warning threshold, and performs full-process control of real-time battery status monitoring, adaptive adjustment of charging current and risk warning.

[0014] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement an off-grid inverter battery protection and charging control method based on impedance detection as described in the above embodiments.

[0015] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement an off-grid inverter battery protection and charging control method based on impedance detection as described in the above embodiments.

[0016] The fifth aspect of this application provides a computer program product, including a computer program or instructions, for implementing an off-grid inverter battery protection and charging control method based on impedance detection as described in the above embodiments.

[0017] Therefore, this application offers the following beneficial effects: By collecting battery input parameters under different load conditions, analyzing and calculating the total equivalent impedance, and inversely calculating the current SOC value of the battery, the method combines SOC with actual internal resistance to achieve adaptive adjustment of charging current and multi-dimensional risk warning. The benefits are significant: This method reuses the existing sampling and control unit of the inverter, achieving all functions through software algorithm optimization alone, without requiring additional hardware, reducing system cost and installation complexity, and facilitating widespread adoption; By detecting the actual internal resistance of the battery and adaptively adjusting the charging current to match the current state of the battery, it reduces charging heat generation, avoids battery damage, and significantly extends battery life; Real-time monitoring of impedance change trends enables multi-dimensional risk warning, allowing for early detection of potential hazards and improving the safety and reliability of off-grid systems; No hardware modifications are required, only software upgrades are needed to adapt to various types of inverters and lithium batteries, and the warning threshold and charging level can be flexibly adjusted, making it highly versatile and practical, and widely applicable to various off-grid energy storage systems. Thus, it solves the problems of short battery life and poor charging safety in existing technologies.

[0018] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0019] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of an off-grid inverter battery protection and charging control method based on impedance detection, according to an embodiment of this application. Figure 2 This is a graph showing the SOC-internal resistance relationship of a lithium iron phosphate battery according to an embodiment of this application. Figure 3 This is a schematic diagram of a battery protection and charging control system for an off-grid inverter based on impedance detection, according to an embodiment of this application. Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0020] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0021] The following description, with reference to the accompanying drawings, illustrates an off-grid inverter battery protection and charging control method and system based on impedance detection, according to an embodiment of this application. To address the poor charging safety issues mentioned in the background, this application provides a battery protection and charging control method for off-grid inverters based on impedance detection. This method collects battery input parameters under different load conditions, analyzes and calculates the total equivalent impedance, and inversely calculates the current state of charge (SOC) of the battery. Combining the SOC with the actual internal resistance, it achieves adaptive adjustment of the charging current and multi-dimensional risk warning. The benefits are significant: this method reuses the existing sampling and control unit of the inverter, achieving all functions through software algorithm optimization without adding extra hardware, reducing system cost and installation complexity, and facilitating widespread adoption; by detecting the actual internal resistance of the battery and adaptively adjusting the charging current to match the current battery state, it reduces charging heat generation, avoids battery damage, and significantly extends battery life; real-time monitoring of impedance change trends enables multi-dimensional risk warning, early detection of potential problems, and improved safety and reliability of the off-grid system; no hardware modification is required, only software upgrades are needed to adapt to various types of inverters and lithium batteries, and the warning threshold and charging level can be flexibly adjusted, making it highly versatile and practical, and widely applicable to various off-grid energy storage systems. Thus, it solves the problems of short battery life and poor charging safety in existing technologies.

[0022] Specifically, Figure 1 This application provides an embodiment of an off-grid inverter battery protection and charging control method based on impedance detection.

[0023] like Figure 1 As shown, the off-grid inverter battery protection and charging control method based on impedance detection includes the following steps: In step S101, battery input parameters are collected under two different load conditions: no load, light load, and load.

[0024] It is understood that the embodiments of this application collect battery input parameters under two load conditions: no-load, light-load, and loaded. This allows for the acquisition of voltage and current data under different battery operating states, providing necessary basis for calculating the total equivalent impedance. It eliminates calculation errors caused by single-condition acquisition, accurately removes interference from wires and contact resistance, and obtains the true battery internal resistance. This provides accurate data support for subsequent SOC back-calculation, adaptive adjustment of charging current, and battery risk warning, ensuring the accuracy and reliability of battery protection and charging control in off-grid inverters.

[0025] In this embodiment, battery input parameters are collected under two different load conditions: no-load or light-load, and loaded. This includes: constructing a dual-condition steady-state data acquisition system; based on the dual-condition steady-state data acquisition system, setting steady-state determination rules for no-load or light-load and loaded conditions, and simultaneously collecting the battery terminal output voltage, battery output current, inverter DC side input voltage and input current after the two conditions reach steady state, and simultaneously recording the ambient temperature data before and after the condition switching to obtain raw parameter data; performing moving average filtering and outlier removal processing on the collected raw parameter data, and outputting a standardized parameter dataset for impedance calculation.

[0026] Among them, the dual-condition steady-state data acquisition system is a systematic acquisition method for measuring parameters and recording data when the equipment or system reaches a stable operating state under two different operating conditions.

[0027] It is understood that, by constructing a standardized acquisition framework and setting clear steady-state judgment rules, the embodiments of this application can simultaneously and accurately acquire core input parameters such as battery output voltage and output current after steady-state operation under two typical operating conditions: no-load or light-load, and under load. They also simultaneously record ambient temperature data before and after operating condition switching. After processing with moving average filtering and outlier removal, a standardized and highly reliable parameter dataset is output. This provides accurate data support for subsequent calculations of total equivalent impedance based on the DC voltage drop principle, extraction of actual battery internal resistance, and SOC back-calculation. Simultaneously, it comprehensively covers typical operating scenarios for off-grid inverter batteries, ensuring the accuracy and adaptability of subsequent adaptive charging current adjustment, multi-dimensional risk warning, and full-process control, providing a fundamental guarantee for the scientific nature of battery protection and charging control.

[0028] For example, taking a 100Ah lithium iron phosphate battery pack paired with an off-grid inverter as the data acquisition target, a dual-condition steady-state data acquisition system was constructed under an initial ambient temperature of 25℃. First, the inverter was started and placed in an unloaded steady state, running continuously for 30 minutes until the parameters showed no fluctuation. Simultaneously, the battery terminal output voltage was 51.20V, the output current was 0.05A, the inverter DC-side input voltage was 51.18V, and the input current was 0.03A. The ambient temperature was recorded as stable at 25.1℃ before and after the condition switch. Then, the system was switched to a light-load condition, with a 100W resistive load connected. After the system stabilized for 25 minutes, the battery terminal output voltage was 51.15V, the output current was 2.05A, the DC-side input voltage was 51.12V, and the input current was 2.01A, with the ambient temperature at 25.3℃. Then… Switching to the load condition and connecting a 1500W resistive load, after the system stabilized for 30 minutes, the battery output voltage was 50.80V, the output current was 30.10A, the DC input voltage was 50.75V, and the input current was 29.85A, with the ambient temperature rising to 26.0℃. Finally, the above raw data were processed by moving average filtering and outlier removal, and the standardized parameter dataset under no-load / light-load conditions was obtained as follows: battery voltage 51.18V, output current 1.05A, DC input voltage 51.15V, and input current 1.02A. The standardized parameter dataset under the load condition was obtained as follows: battery voltage 50.80V, output current 30.10A, DC input voltage 50.75V, and input current 29.85A. The steady-state data acquisition and standardization processing under both operating conditions were completed.

[0029] In step S102, based on the battery input parameters under two different load conditions, and according to the DC voltage drop principle, Ohm's law analytical algorithm is used to calculate the total equivalent impedance of the battery, connecting wires, and contact parts. A preset model of the correspondence between the remaining SOC of the lithium battery and the battery internal resistance is used to adapt to the off-grid condition. The preset reference values ​​of the connecting wires and contact resistance are subtracted from the total equivalent impedance to obtain the current actual internal resistance of the battery. The preset SOC-internal resistance correspondence model is input to deduce the current SOC value of the battery.

[0030] Among them, the Ohm's Law analytical algorithm refers to the calculation method based on Ohm's Law, which uses two known electrical quantities to calculate a third unknown quantity.

[0031] It is understood that this application embodiment, based on the DC voltage drop principle, accurately analyzes the correlation between the battery terminal voltage difference and the output current difference under two different load conditions, efficiently calculates the total equivalent impedance of the circuit between the battery and the connecting wires and contact parts, and outputs the confidence interval of the calculation results to ensure the accuracy and reliability of the impedance calculation. This provides a scientific and accurate calculation basis for subsequent extraction of the actual internal resistance of the battery, deduction of the preset wire and contact resistance benchmark value, back-calculation of the SOC value, and input of the SOC internal resistance correspondence model. In turn, it supports the adaptive adjustment of the inverter charging current and the setting of multi-dimensional risk warning thresholds, ensuring that the battery charging process is accurately matched with its own health status and remaining power, reducing charging heat generation, and providing core algorithm support for the scientific and reliable nature of the entire process of off-grid inverter battery protection and charging control.

[0032] For example, taking a 12V lithium battery pack paired with an off-grid inverter as an example, the dual-condition acquisition module collects the battery input voltage of 12.80V and input current of 0.5A under no-load and light-load conditions, and the battery input voltage of 12.40V and input current of 10A under load conditions. Based on the DC voltage drop principle and Ohm's law, the total equivalent impedance of the battery, connecting wires, and contact parts is calculated using the voltage difference ΔU = 12.80V - 12.40V = 0.40V and the current difference ΔI = 10A - 0.5A = 9.5A under the two conditions, through the analytical algorithm of Ohm's law. After deducting the system's preset reference value of 0.01Ω for the connecting wires and contact resistance, the actual internal resistance of the battery is 0.0321Ω. Substituting this internal resistance into the preset SOC-internal resistance correspondence model, the current remaining SOC value of the battery can be deduced to be approximately 35%.

[0033] In this embodiment, based on battery input parameters under two different load conditions, and according to the DC voltage drop principle, Ohm's law analytical algorithm is used to calculate the total equivalent impedance of the battery, connecting wires, and contact parts. This includes: constructing a dual-condition impedance analytical model based on the DC voltage drop principle; inputting standardized parameter datasets under two different load conditions based on the dual-condition impedance analytical model, extracting the core characteristic parameters of battery terminal voltage and output current under the two conditions, and calculating the difference between battery terminal voltage and output current under the two conditions; based on the difference between battery terminal voltage and output current, and according to the DC voltage drop principle, using Ohm's law analytical algorithm, obtaining the total equivalent impedance of the loop between the battery, connecting wires, and contact parts, and simultaneously outputting the confidence interval of the calculation results.

[0034] The dual-condition impedance analytical model is a mathematical model used to describe and analyze the equivalent impedance characteristics and their variation laws of a system under two different operating conditions.

[0035] It is understood that the embodiments of this application rely on the DC voltage drop principle to construct an analytical framework adapted to off-grid conditions. It receives standardized parameter datasets under two different load conditions, accurately extracts the core characteristic parameters of battery terminal voltage and output current under the two conditions, and calculates the difference between battery terminal voltage and output current under the two conditions. This provides standardized model support for calculating the total equivalent impedance of the circuit using Ohm's law analytical algorithm, ensuring the accuracy and stability of the total equivalent impedance calculation. At the same time, it lays a solid foundation for subsequent deduction of preset connection wire and contact resistance reference values, obtaining the actual internal resistance of the battery, and back-calculating the current SOC value of the battery. This supports the adaptive adjustment of the inverter charging current and the construction of a multi-dimensional risk warning system, ensuring the scientificity and reliability of the entire process of off-grid inverter battery protection and charging control, and achieving precise control of battery status.

[0036] For example, using a 48V 100Ah lithium iron phosphate battery, a dual-condition impedance analytical model based on the DC voltage drop principle is first constructed. The model input is defined as the dual-condition steady-state standardized parameters, and the output is the total equivalent impedance of the battery, connecting wires, and contact parts, along with their corresponding confidence intervals. The dual-condition parameters, after Kalman filtering, are input into the model. Under no-load conditions, the battery terminal voltage is 49.2V and the output current is 0.02A. Under load conditions, the inverter DC-side input voltage is 48.7V and the input current is 104A. The battery terminal voltage and output current are then extracted from these two sets of parameters. After determining the core characteristic parameters, the battery terminal voltage difference under dual operating conditions is calculated to be 0.5V and the output current difference is 103.98A. Based on these voltage and current differences, the model's built-in Ohm's law analytical algorithm is used to calculate the total equivalent impedance of the circuit to be approximately 0.0048Ω. Simultaneously, considering the accuracy of the inverter's sampling unit and the filtering effect, the 95% confidence interval of the calculated result is output as 0.0047Ω-0.0049Ω. The entire model analysis process only reuses the data collected by the inverter's existing sampling module, without requiring additional hardware.

[0037] In this embodiment, a pre-defined model of the relationship between the remaining SOC (State of Charge) and internal resistance of a lithium battery adapted to off-grid conditions is included: constructing a basic model framework for the SOC-internal resistance relationship adapted to off-grid conditions; based on the basic model framework, combining the nominal parameters, cycle life characteristics, and test data at different temperatures and charge / discharge rates of the target lithium battery, interval optimization is performed using a piecewise linear fitting algorithm to divide multiple SOC feature intervals, and the mapping function between the battery internal resistance and SOC in each interval is fitted to complete the model parameter calibration; based on the calibrated model parameters, the model accuracy is verified and the deviation is corrected using measured data under off-grid conditions, thus establishing a model of the relationship between the SOC and internal resistance of a lithium battery adapted to off-grid conditions.

[0038] Among them, the piecewise linear fitting algorithm is a method that divides data into multiple continuous intervals and uses linear functions to fit within each interval to approximate a complex curve as a whole or process non-stationary data.

[0039] It can be understood that the embodiments of this application can accurately adapt to the non-linear characteristics of the internal resistance change of lithium batteries in different remaining power intervals. By dividing multiple remaining power characteristic intervals and respectively fitting the mapping relationship between the internal resistance and the remaining power, it effectively avoids the calculation deviation caused by single global fitting, greatly improves the calibration accuracy and adaptation ability of the model, combines the nominal parameters and measured data of the target lithium battery to complete the optimization of model parameters, enables the constructed model of the corresponding relationship between the remaining power and the internal resistance to cover the full working temperature, full cycle life cycle and full remaining power interval under off-grid conditions, provides a reliable algorithm support for accurately inferring the remaining power through the actual internal resistance of the battery, and further ensures the accuracy of battery state judgment, laying a precise data foundation for subsequent adaptive adjustment of charging current and multi-dimensional risk warning.

[0040] It should be noted that multiple SOC characteristic intervals are divided, and the mapping functions between the battery internal resistance and SOC within each interval are respectively fitted. A three-segment piecewise polynomial model is adopted, and the full SOC interval is divided into three segments: a low SOC segment (0 ≤ SOC ≤ 0.3), a medium SOC segment (0.3 < SOC ≤ 0.8), and a high SOC segment (0.8 < SOC ≤ 1.0) for fitting respectively.

[0041] Formula of the three-segment piecewise polynomial model: <**********>; ; ; Among them, is the ohmic internal resistance of the battery; is the ohmic internal resistance of the lithium battery strongly related to the remaining power SOC of the battery; is the quadratic term fitting coefficient in the low SOC segment, such as ; is the remaining power of the battery; is the linear term fitting coefficient in the low SOC segment, such as ; is the constant term fitting coefficient in the low SOC segment, such as ; is the linear term fitting coefficient in the medium SOC segment, such as ; is the constant term fitting coefficient in the medium SOC segment, such as ; is the quadratic term fitting coefficient in the high SOC segment, such as ; For example, the fitting coefficients of the first-order term in the high SOC segment. ; For the constant term fitting coefficients in the high SOC segment, for example .

[0042] For example, to adapt to off-grid conditions, a SOC-internal resistance correspondence model for a 48V 100Ah lithium iron phosphate battery was constructed. A piecewise linear fitting algorithm was used to divide the entire SOC range into three characteristic intervals: 0%-30%, 30%-80%, and 80%-100%, for interval optimization. Based on measured battery DC internal resistance (DCR) data, 0% SOC corresponds to 33.4 mΩ, 20% SOC to 20.35 mΩ, 30% SOC to 16.98 mΩ, 50% SOC to 12.0 mΩ, 80% SOC to 10.48 mΩ, and 100% SOC to 10.1 mΩ. For the 0%-30% low SOC interval, with 0% SOC corresponding to 33.4 mΩ and 30% SOC corresponding to 16.98 mΩ as feature points, a linear mapping function r = -0.547 was obtained through fitting. The linear mapping function r = -0.13mΩ / %×SOC+33.4mΩ is fitted to the SOC range of 30%-80%, with 16.98mΩ corresponding to 30% SOC and 10.48mΩ corresponding to 80% SOC as feature points. The linear mapping function r = -0.019mΩ / %×SOC+12.0mΩ is fitted to the high SOC range of 80%-100%, with 10.48mΩ corresponding to 80% SOC and 10.1mΩ corresponding to 10 ... SOC range of 30%-80% SOC. The linear mapping function r = -0.13mΩ / %×SOC+20.88mΩ is fitted to the SOC range of 80%-100% SOC, with 10.48mΩ corresponding to 80% SOC and 10.1mΩ corresponding to 100% SOC as feature points. The linear mapping function r = -0.019mΩ / %×SOC+12.0mΩ is fitted to the SOC range of 80%-100% SOC and 10.48mΩ corresponding to 100% SOC as feature points. The linear mapping function r = -0.019mΩ / %×SOC+12.0mΩ is fitted to the SOC range of 80%-100% SOC and 10.48mΩ corresponding to 100% SOC. The linear mapping function r = -0

[0043] In step S103, based on the current SOC value and actual internal resistance of the battery, the inverter charging current adjustment threshold is set in stages to adaptively adjust the charging current of the inverter so that the charging current matches the current health status and remaining capacity of the battery, thereby reducing the heat generated during the battery charging process. At the same time, the rate of change of the total equivalent impedance and the trend of change of the actual internal resistance of the battery are monitored in real time, and multi-dimensional risk warning thresholds are set in stages.

[0044] The total equivalent impedance refers to the equivalent parameter in a complex circuit network that, when viewed from a specified port, maintains the exact voltage and current relationship at that port after replacing the entire network with a single impedance element.

[0045] It is understood that the total equivalent impedance in this application embodiment can intuitively reflect the overall electrical impedance characteristics of the battery, connecting wires, and contact parts, and is a core indicator characterizing the overall state of the battery circuit. After being obtained through dual-condition calculation, the actual internal resistance of the battery can be obtained by subtracting the preset reference value of the connecting wires and contact resistance, and then the remaining battery capacity SOC can be calculated. This provides a key basis for setting the charging current adjustment threshold in stages and adaptively matching the charging current. At the same time, real-time monitoring of its change rate can also set multi-dimensional risk warning thresholds, enabling real-time monitoring of battery status, charging control, and risk warning throughout the entire process, ensuring the safety and stability of the off-grid inverter battery operation.

[0046] For example, in an application scenario where a 48V 100Ah lithium iron phosphate battery is paired with a 10kW off-grid photovoltaic inverter, the total equivalent impedance of the battery, connecting wires, and contact parts is calculated based on the DC voltage drop principle. First, the no-load voltage U0 of the battery terminal after Kalman filtering under no-load conditions is collected by the existing sampling unit on the DC side of the inverter, which is 51.2V. Then, the DC input voltage U_in of the inverter under load conditions after filtering with the same algorithm is collected, which is 48.62V and the DC input current I_in is 104A. Subsequently, the data is substituted into the core formula R_total=(U0-U_in) / I_in to calculate the total equivalent impedance, which is approximately 0.0248Ω. This value is an overall impedance value that combines the actual internal resistance of the battery, the resistance of the connecting wires, and the contact resistance. Based on the reference value of 0.0048Ω for the connecting wires and contact resistance calibrated during the system installation and commissioning phase, subtracting this reference value from the total equivalent impedance directly yields the current actual internal resistance of the battery as 0.02Ω, which is completely consistent with the battery's factory standard internal resistance. This clearly reflects the overall impedance state of the battery and connecting lines at this time. Furthermore, the entire calculation process only reuses the data collected by the existing sampling module of the inverter, without the need for additional hardware.

[0047] In this embodiment, based on the current SOC value and actual internal resistance of the battery, the inverter charging current adjustment threshold is set in stages to adaptively adjust the charging current of the inverter. This includes: constructing a battery state adaptive charging adjustment model; based on the battery state adaptive charging adjustment model, taking the current SOC value of the battery as the core dimension and combining the battery health state represented by the actual internal resistance of the battery, dividing the charging current adjustment range into multiple levels, and setting the charging current adjustment threshold and charging current upper limit value corresponding to each range in stages; based on the multiple levels of charging current adjustment range, setting the charging current adjustment threshold and charging current upper limit value corresponding to each range in stages, dynamically correcting the charging current upper limit value by combining real-time collected ambient temperature data, and adjusting the control parameters of the charging circuit in real time through the inverter PWM modulation algorithm to dynamically adjust the charging current to the set value of the corresponding range, thereby achieving adaptive matching between the charging current and the current health state and remaining capacity of the battery.

[0048] Among them, the battery state adaptive charging regulation model is an intelligent control strategy that dynamically optimizes charging parameters based on the real-time state of the battery to improve charging efficiency, extend battery life, and ensure safety.

[0049] Understandably, this application implements a multi-level charging current adjustment range based on the battery's current remaining charge (SOC) as the core dimension and combined with the battery's health status characterized by actual internal resistance. Corresponding adjustment thresholds and current upper limits are set, and the upper limit of the charging current is dynamically corrected according to the real-time ambient temperature. The charging control parameters are adjusted in real time through the inverter's PWM modulation algorithm to achieve precise matching between the charging current and the battery's current health status and remaining charge. This effectively reduces heat generation during battery charging, improves charging efficiency and battery operation safety, and ensures the stability and reliability of off-grid inverter battery charging control.

[0050] For example, an adaptive charging adjustment model for a 48V 100Ah lithium iron phosphate battery paired with a 10kW off-grid photovoltaic inverter was constructed. The model uses battery SOC as the core dimension and the actual internal resistance as a secondary dimension, dynamically adjusting the upper limit of the charging current based on ambient temperature data. The battery's factory standard internal resistance is initially set to 0.02Ω, and the rated charging current of the matched inverter is set to 20A. Then, the charging current adjustment range and corresponding thresholds are defined in stages. When the model detects that the battery's current SOC is 65% and the actual internal resistance is 0.02Ω, which is less than 1.2 times the standard internal resistance, it is determined to be in a medium-voltage state. If the battery is in a healthy state, the charging current is adjusted to 70% of the rated charging current, i.e., 14A, according to the rules. A constant current charging mode is used to balance charging efficiency and heat generation. If the actual internal resistance of the battery is subsequently detected to rise to 0.025Ω, which is more than 1.2 times the standard internal resistance, the model determines that the battery has slightly degraded. Regardless of the current SOC range, the charging current is reduced to below 30% of the rated charging current, i.e., ≤6A, and a battery degradation warning is triggered. The entire adjustment process uses the inverter PWM modulation algorithm to adjust the control parameters of the charging circuit in real time to achieve precise matching between the charging current and the current state of the battery.

[0051] In this embodiment, the rate of change of total equivalent impedance and the trend of change of actual internal resistance of the battery are monitored in real time, and multi-dimensional risk warning thresholds are set in a graded manner. This includes: constructing a risk warning index system for the entire battery life cycle; based on the risk warning index system for the entire battery life cycle, using a sliding time window algorithm to extract the short-term rate of change of total equivalent impedance and the long-term trend of change of actual internal resistance of the battery in real time, and combining the safety performance parameters of the target lithium battery and the operating requirements of off-grid conditions, setting three types of multi-dimensional risk warning thresholds in a graded manner: total equivalent impedance mutation threshold, relative change rate threshold of battery internal resistance, and absolute value threshold of internal resistance; based on the multi-dimensional risk warning thresholds, establishing a corresponding judgment rule between risk level and monitoring threshold, and completing the preparatory work for risk warning.

[0052] Among them, the sliding time window algorithm is an algorithm that divides time into multiple continuous small intervals and dynamically updates the count within the window as time progresses, thereby achieving smooth statistics and flow limiting of traffic or events.

[0053] Understandably, this application implements a method that extracts the short-term rate of change of total equivalent impedance and the long-term trend of change of actual internal resistance of battery in real time through a sliding time window. This enables dynamic and continuous monitoring of battery state changes, timely captures the changing characteristics of impedance and internal resistance, and provides accurate and real-time data support for setting multi-dimensional risk warning thresholds and triggering corresponding warning signals. This ensures the timeliness and accuracy of battery state monitoring and improves the intelligence level and operational reliability of off-grid inverter battery protection and charging control.

[0054] For example, in the application scenario of a 48V 100Ah lithium iron phosphate battery paired with a 10kW off-grid photovoltaic inverter, a sliding time window algorithm is used for monitoring in the battery life cycle risk warning index system. A short sliding time window of 1 hour and a sliding step of 10 minutes are set to extract the rate of change of total equivalent impedance. A long sliding time window of 24 hours and a sliding step of 1 hour are used to capture the trend of change of the battery's actual internal resistance. The battery's factory standard internal resistance is 0.02Ω, the initial value of the total equivalent impedance is 0.0048Ω, the threshold for the rate of change of impedance for abnormal wires and contacts is 0.01Ω / h, and the threshold for the internal resistance for battery degradation warning is 1.2 times the standard internal resistance, i.e., 0.024Ω. Within a short window of one hour, the algorithm sequentially collected the total equivalent impedance values ​​at 10-minute intervals: 0.0048Ω, 0.0060Ω, 0.0082Ω, 0.0105Ω, 0.0128Ω, and 0.0150Ω. The calculated short-time change rate of the total equivalent impedance reached 0.0102Ω / h, exceeding the preset threshold. Simultaneously, within a long window of 24 hours, the algorithm continuously collected the actual internal resistance of the battery at 1-hour intervals. The value gradually increased from an initial 0.02Ω, reaching 0.0245Ω at the end of the window, exceeding the internal resistance threshold for battery degradation warning. Through continuous data extraction and calculation within a sliding window, the algorithm accurately captured the abrupt change trend of the total equivalent impedance and the continuous upward trend of the actual internal resistance of the battery, providing accurate real-time data support for subsequent risk warning judgment.

[0055] In step S104, when the monitored value exceeds the corresponding set risk warning threshold, the corresponding level of risk warning signal and corresponding handling actions are triggered to carry out full-process control of real-time battery status monitoring, adaptive adjustment of charging current and risk warning.

[0056] It is understood that the embodiments of this application trigger corresponding level warning signals and execute corresponding handling actions when the monitored value exceeds the preset risk warning threshold, thereby realizing full-process control of real-time battery status monitoring, adaptive adjustment of charging current and risk warning. It can predict and respond to abnormal battery operation in a timely manner, accurately match the charging current with the current battery status, reduce energy consumption and heat generation during the charging process, ensure the safe and stable operation of the battery, improve the intelligence level of charging control, and effectively extend the service life of lithium batteries and the operational reliability of off-grid inverters.

[0057] For example, in an application scenario involving a 48V 100Ah lithium iron phosphate battery paired with a 10kW off-grid photovoltaic inverter, the preset thresholds for the total equivalent impedance change rate for abnormal conductors and contacts are: 0.01Ω / h; battery degradation warning threshold: actual internal resistance exceeding the standard internal resistance by 1.2 times (0.024Ω) for 24 hours; and emergency alarm threshold: actual internal resistance exceeding the standard internal resistance by 1.5 times (0.03Ω) or total equivalent impedance exceeding 0.0348Ω. During real-time monitoring, if a short-term change rate of 0.012Ω / h is detected, exceeding the abnormal conductor and contact warning threshold, this type of warning signal is immediately triggered, and a maintenance reminder is pushed to the backend. Simultaneously, the charging current is slightly reduced to the rated value. The initial charge current is 60% of the rated 20A, i.e., 12A. Subsequently, if the battery's actual internal resistance rises to 0.025Ω and remains above this value for 24 hours, a battery degradation warning is triggered. The charging current is immediately reduced to below 30% of the rated current, i.e., 6A, while continuously tracking changes in battery internal resistance in real time. If the internal resistance further climbs to 0.031Ω, exceeding the emergency alarm threshold, an emergency alarm is immediately triggered. The inverter stops all charging operations, simultaneously issuing audible and visual alarm signals and pushing fault information to the backend monitoring system. Throughout the entire process, the battery's SOC, actual internal resistance, and total equivalent impedance are constantly monitored in real time. The charging current is adaptively adjusted according to the warning level, completing the entire process of battery status monitoring, charging regulation, and risk warning control.

[0058] The following will illustrate a specific embodiment of an off-grid inverter battery protection and charging control method based on impedance detection. Figure 2 As shown, it includes: Using a 15kW off-grid photovoltaic energy storage system to power a communication base station in a mountainous area of ​​Northwest my country without grid coverage as an example, this system is equipped with a lithium iron phosphate battery pack with a nominal voltage of 48V and a rated capacity of 200Ah. The standard internal resistance r_batt_std of the battery is 10mΩ under standard operating conditions at 25℃ and a SOC of 50%. The DC connection uses 35mm² copper core wires with a total wire length of 8m. The system is installed in an outdoor protective cabinet with an average annual temperature of 10℃-30℃. It mainly powers the base station communication equipment and supporting maintenance and living facilities, and is completely dependent on the off-grid photovoltaic system for operation. Before the upgrade, the system used a fixed rated current charging... The off-grid inverter, operating in electric mode for three years, has consistently experienced issues such as severe overheating during battery charging, rapid degradation of individual battery life, and the need to replace 1-2 faulty batteries annually. Furthermore, there have been safety hazards related to loose and overheating wiring terminals. Therefore, this upgrade utilizes an impedance-based off-grid inverter battery protection and charging control method. The upgrade process completely reuses the original inverter hardware architecture, only optimizing the algorithm of the built-in MCU control software. No additional hardware devices such as a BMS, impedance tester, or extra temperature sensor are required. The complete system operation and full-process management process after the upgrade is as follows: First, the sampling data acquisition step is performed, utilizing the off-grid... The inverter's DC side originally had a voltage sampling unit with an accuracy of ±0.4%FS and a current sampling unit with an accuracy of ±0.7%FS. Battery input parameters were collected strictly according to the method requirements under two different load conditions: no-load, light-load, and loaded. The no-load / light-load condition was set to a state where the inverter load rate is ≤5%, and the inverter only supplies power to its own control circuit with no additional AC load connected. The single sampling duration was set to 200ms, and the sampling frequency was set to 50Hz. After continuously collecting 30 sets of data, a Kalman filter algorithm was used to subtract ripple voltage interference within 100mV on the inverter's DC side, finally obtaining a stable no-load voltage at the battery terminal. U0=57.8V; then, the load switching was manually triggered by the inverter's built-in control unit to connect a resistive load with a rated power of 7.5kW, so that the inverter load rate reached 50%, which met the triggering condition of load rate ≥30% under load conditions. Using the same sampling frequency of 50Hz and single sampling duration of 200ms, 30 sets of inverter DC input voltage U_in and DC input current I_in under load conditions were continuously collected. After Kalman filtering and smoothing, a stable DC input voltage U_in=55.7V and DC input current I_in=150A were obtained, completing the accurate acquisition of core input parameters under two operating conditions.

[0059] Next, we proceed to the calculation of total equivalent impedance and the reverse estimation of battery internal resistance and SOC. Based on the battery input parameters collected under two different load conditions, and according to the principle of DC voltage drop, Ohm's law analytical algorithm is used to calculate the total equivalent impedance of the battery, connecting wires, and contact parts using the formula R_total=(U0-U_in) / I_in. Substituting the filtered parameters collected above into the formula, we get R_total=(57.8V-55.7V) / 150A=2.1V / 150A=0.014Ω=14mΩ. This total equivalent impedance... The impedance is the sum of the battery's current actual internal resistance r_batt, the connecting wire resistance R_wire, and the contact resistance R_contact, i.e., R_total = r_batt + R_wire + R_contact. This completes the accurate calculation of the total equivalent impedance. Next, the actual internal resistance of the battery is extracted. First, the reference value R_ref for the connecting wire and contact resistance is determined. This reference value is calibrated during the system installation and commissioning phase. The calibration process is as follows: after the initial system installation, when the wires are not aged, the connections are not loose, and the ambient temperature is 25℃, a standard working condition is maintained. Under these circumstances, the total equivalent impedance baseline value R_total_ref = 14mΩ was obtained. Combined with the battery's factory-calibrated SOC 50% standard internal resistance r_batt_std = 10mΩ, the baseline value R_ref = 14mΩ - 10mΩ = 4mΩ was calculated using the formula R_ref = R_total_ref - r_batt_std. This value also conforms to the calculated physical resistance of copper wire (copper resistivity 1.75 × 10^-8 Ω・m, theoretical resistance of 8m long 35mm² copper wire is 4mΩ). Considering that the temperature coefficient of resistance of copper wire is 0.00393 / ℃, R_ref will be dynamically corrected according to the ambient temperature collected by the inverter's built-in temperature sensor during system operation. The ambient temperature during this operation was 22℃, so the correction factor for R_ref is 1+(22℃-25℃)×0.00393 / ℃≈0.988. The corrected R_ref=4mΩ×0.988≈3.95mΩ. This temperature correction has a negligible impact on the calculation results. The correction effect is significant under extreme high and low temperature conditions.

[0060] Based on this, by subtracting the corrected connecting wire and contact resistance reference value R_ref from the calculated total equivalent impedance R_total, we can obtain the current actual internal resistance of the battery, r_batt_real = R_total - R_ref = 14mΩ - 3.95mΩ ≈ 10.05mΩ. This completes the extraction of the actual internal resistance of the battery. Next, we proceed to the battery SOC deduction step. Here, we need to use a pre-set model that adapts to off-grid conditions, showing the relationship between the remaining capacity SOC of the lithium iron phosphate battery and its internal resistance. This model was calibrated before the system modification for the 200Ah / 48V lithium iron phosphate battery pack used in this project, under a standard ambient temperature of 25℃, through a complete charge-discharge cycle test. During the test, the battery's ohmic internal resistance R0 and polarization internal resistance R were recorded at SOC values ​​of 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%. p The DC internal resistance (DCR) is the sum of the ohmic internal resistance and the polarization internal resistance, which is the battery internal resistance r_batt used for SOC inverse calculation in this method. The final calibration results in the following relationships: SOC=0%, DCR=35mΩ; SOC=10%, DCR=26mΩ; SOC=20%, DCR=18mΩ; SOC=30%, DCR=12mΩ; SOC=40%, DCR=11mΩ; SOC=50%, DCR=10mΩ; SOC=60%, DCR=10.2mΩ; SOC=70%, DCR=10.1mΩ; SOC=80%, DCR=10.3mΩ; SOC=90%, DCR=10.2mΩ; SOC=70%, DCR=10.1mΩ; SOC=80%, DCR=10.3mΩ; SOC=90%, DCR=10.2mΩ; SOC=70%, DCR=10.2mΩ; SOC=70%, DCR=10.2mΩ; SOC=80%, DCR=10.3mΩ; SOC=90%, DCR=10.2mΩ; SOC=70%, DCR=10.2mΩ; SOC=70%, DCR=10.2mΩ; SOC=70%, DCR=10.2mΩ; SOC=80%, DCR=10.2mΩ; SOC=90%, DCR=10.2mΩ; SOC=7 ... =10.4mΩ; when SOC=100%, DCR=10.5mΩ. At the same time, the functional relationship between SOC and battery internal resistance is obtained by fitting a quadratic function as r_batt=0.00032×SOC²-0.031×SOC+10.75, where the fitting coefficients a=0.00032, b=-0.031, and c=10.75. This function opens upward, and the peak (the lowest point of internal resistance) corresponds to SOC≈48%, which completely coincides with the lowest stable internal resistance region marked by orange shading. The fitting deviation in the core working range (30%-100%) is ≤3%. In the 0%-30% low SOC range, due to the steep change in internal resistance, the linear interpolation method of the calibrated two-dimensional data table is used to back-calculate SOC to ensure the accuracy of back-calculation across the entire range.

[0061] Substituting SOC=50% into the function yields r_batt=10mΩ, which is completely consistent with the calibration standard value. Substituting SOC=100% yields r_batt=10.85mΩ, with a deviation of only 3.3% from the calibration value of 10.5mΩ. Valid real solutions are available across the entire SOC range. The changing pattern of this relationship perfectly matches the SOC-internal resistance curve of the lithium iron phosphate battery. The solid blue line represents the battery's ohmic internal resistance R0, and the dashed green line represents the battery's polarization internal resistance R. p The solid red line represents the battery's DC internal resistance, DCR = R0 + R. p The orange shaded area represents the lowest and most stable internal resistance region, corresponding to the SOC range of 30% to 80%. The curve clearly shows that the battery's DC internal resistance exhibits a standard U-shaped distribution. The DC internal resistance reaches its maximum value of 35 mΩ at 0% SOC. As the SOC increases from 0% to 30%, the DC internal resistance decreases rapidly. The decrease in polarization resistance is much greater than that of ohmic resistance, making it the dominant factor in this range. When the SOC is within the 30% to 80% range, the battery's ohmic internal resistance, polarization resistance, and DC internal resistance are all at their lowest and most stable levels. In a stable state, the internal resistance fluctuation range within this interval does not exceed 2mΩ, representing the optimal range for highest battery charging and discharging efficiency and lowest heat generation. When the State of Charge (SOC) exceeds 80%, the battery internal resistance will slowly and slightly increase. The SOC-internal resistance correspondence model calibrated by this system provides accurate numerical basis for SOC back-calculation. The previously calculated actual battery internal resistance r_batt_real = 10.05mΩ, combined with the fully charged characteristic of an open-circuit voltage of 57.8V, is linearly interpolated using a calibrated two-dimensional data table, and further aided by a fitting function. After verification, excluding solutions with the same internal resistance in the low SOC range, the current SOC value of the battery is calculated to be approximately 98%, thus accurately identifying the remaining battery capacity. Next, the adaptive charging current adjustment stage begins. Based on the calculated current SOC of 98% and the actual internal resistance r_batt_real = 10.05mΩ, the inverter charging current adjustment threshold is set in stages. The rated charging current of the battery adapted to this inverter is 0.2C (40A), and the standard internal resistance of the battery is r_batt_std = 10mΩ. The first step is... Battery status assessment: The current SOC is 98% ≥ 80%, and r_batt_real = 10.05mΩ ≤ r_batt_std × 1.2 = 12mΩ, which meets the criteria for high battery capacity and healthy battery status. Therefore, according to the method's tiered adjustment logic, the inverter charging current is adjusted to 30%-50% of the rated charging current, and in this case, it is set to 40% of the rated current, i.e., 16A. At the same time, it is switched to constant voltage float charging mode, with the float charging voltage set at 54.6V to avoid overcharging of the battery and resulting heat generation and irreversible damage.

[0062] During the subsequent continuous operation of the system, when the battery SOC decreased to 60% due to continuous discharge of the load, the system re-executed the sampling and calculation process. It collected the battery terminal no-load voltage U0 = 52.2V under no-load conditions, the inverter DC input voltage U_in = 50.07V under load conditions, and the DC input current I_in = 150A. The total equivalent impedance R_total = (52.2 - 50.07) / 150 = 2.13 / 150 = 0.0142Ω = 14.2mΩ. Subtracting the corrected R_ref = 3.95mΩ, the actual battery internal resistance r_batt_real = 14.2mΩ - 3.95mΩ ≈ 10.25mΩ. Substituting these values ​​into the SOC... The C-internal resistance model yields a SOC of 60%. At this point, 30% < SOC < 80%, and r_batt_real ≤ 12mΩ, meeting the criteria for a medium charge level and healthy state. Therefore, the charging current is adjusted to 50%-80% of the rated charging current, set to 70% (28A) in this case. A constant current charging mode is used to balance charging efficiency and heat generation control, achieving a balance between fast charging and battery protection. When the battery continues to discharge to SOC = 25%, the system performs a full-process calculation again, collecting the battery terminal open-circuit voltage U0 = 48.8V under no-load conditions (this voltage is the stable open-circuit voltage after the battery has been idle for 2 hours, conforming to the SOC-OCV correspondence of lithium iron phosphate batteries). According to industry testing standards, under load conditions, the inverter's DC input voltage U_in = 45.95V and DC input current I_in = 150A. The calculated total equivalent impedance R_total = (48.8 - 45.95) / 150 = 2.85 / 150 = 0.019Ω = 19mΩ. Subtracting the corrected R_ref = 3.95mΩ, the actual battery internal resistance r_batt_real = 19mΩ - 3.95mΩ ≈ 15.05mΩ. Substituting this into the SOC-internal resistance model, we get SOC = 25% ≤ 30%, and r_batt_real > r_batt_std × 1.5 = 15mΩ, which meets the emergency alarm criteria in this application. The inverter immediately stops charging and triggers an emergency alarm, pushing the alarm information to the operation and maintenance backend to avoid battery thermal runaway and irreversible damage caused by high current charging at low SOC. After 3 years of battery use, obvious cycle aging phenomena appear. At this time, the system collected parameters and calculated that the actual internal resistance of the battery was r_batt_real = 13mΩ, which has exceeded r_batt_std × 1.2 = 12mΩ and has lasted for more than 24 hours. At this time, regardless of the battery SOC range, the charging current is reduced to below 30% of the rated charging current, i.e., below 12A, and a battery degradation warning is triggered. When battery aging further intensifies, the actual internal resistance rises to 16mΩ, exceeding r_batt_std × 1.2.When R_total = 15mΩ, the inverter immediately stops charging and triggers an emergency alarm to prevent further battery damage or even thermal runaway. While the charging current is adaptively adjusted, the system simultaneously performs real-time risk monitoring and early warning. It monitors the rate of change of the total equivalent impedance R_total (ΔR / Δt) and the actual internal resistance r_batt_real of the battery in real time, setting multi-dimensional risk warning thresholds. The first level is for abnormal wire and contact warnings. The threshold is set so that when R_total - R_ref < 0.05Ω and ΔR / Δt > 0.01Ω / h, it is determined that the wire is aging or the connection is loose, triggering an early warning signal. The second level is a battery degradation warning, with a threshold set to trigger when r_batt_real > r_batt_std × 1.2 and the duration is ≥ 24 hours. The third level is an emergency alarm, with a threshold set to trigger when r_batt_real > r_batt_std × 1.5 = 15mΩ, or R_total > R_ref + r_batt_std × 1.5 = 19mΩ (i.e., 4mΩ + 10mΩ × 1.5 = 19mΩ). Simultaneously, the following actions are taken: the inverter stops charging, audible and visual alarms are activated, and the alarm information is pushed to the background monitoring system via the 4G communication module.

[0063] In the eighth month of system operation, oxidation and loosening of the wiring terminals occurred due to outdoor wind, sand, and cyclical changes in temperature and humidity. At this time, the system monitored in real time that the total equivalent impedance R_total rapidly increased from 14mΩ to 25mΩ, with an impedance change rate of ΔR / Δt = 0.011Ω / h within one hour, exceeding the preset threshold of 0.01Ω / h. At the same time, R_total - R_ref = 25mΩ - 4mΩ = 21mΩ < 0.05Ω, which met the triggering conditions for the wire and contact abnormality warning. The system immediately triggered the warning signal and pushed the alarm information through the base station operation and maintenance platform. The system alerted maintenance personnel to promptly inspect the wiring terminals. Upon arrival, the personnel found the terminals to be loose. After tightening and applying rust prevention treatment, the total equivalent impedance returned to the normal level of 14mΩ, avoiding the safety hazard of excessive contact resistance, overheating, and fire caused by loose terminals. However, in the fourth year of system operation, the system detected that the battery's actual internal resistance r_batt_real remained stable at 13mΩ for 15 consecutive days, exceeding r_batt_std × 1.2 = 12mΩ, and the duration far exceeded the 24-hour threshold. Therefore, a battery degradation warning was triggered, reminding maintenance personnel to replace the degraded battery to prevent further damage. To prevent base station communication interruptions due to battery failure, the system repeats the entire process of sampling, impedance calculation, internal resistance extraction, SOC deduction, current adjustment, and risk monitoring every 10 minutes throughout its entire lifecycle. This achieves closed-loop, end-to-end control of real-time battery status monitoring, adaptive charging current adjustment, and risk warning. After the system was modified using this method, statistical data from 12 consecutive months of operation showed that the average temperature rise during battery charging decreased from 12℃ to 8.4℃, and the charging heat generation decreased by 30%, which is completely consistent with the measured data range of this application. The battery cycle life is expected to be [missing information]. The charging cycle time was increased from 3,500 to 4,375, a 25% improvement. Simultaneously, three terminal loosening warnings and two battery degradation warnings were triggered, all of which were addressed in advance without any safety incidents or base station outages. This completely resolved the core issues of the original system, such as high charging heat generation, short battery life, and the inability to provide early warnings of safety hazards. Furthermore, the entire upgrade process did not involve any new hardware; it was completed solely through software algorithm upgrades. The upgrade cost was only 5% of adding a separate BMS system, combining economy, practicality, and safety, and is fully adaptable to the complex operating conditions of various off-grid inverters.

[0064] In summary, this invention optimizes and upgrades the system through built-in MCU control software algorithms, eliminating the need for additional hardware such as a BMS. The upgrade cost is only 5% of that of adding a separate BMS system, demonstrating outstanding economic efficiency and practicality. Simultaneously, it addresses the core pain points and safety hazards of the original system under fixed charging mode, such as severe battery overheating, rapid lifespan degradation, frequent failures, and loose wiring terminals causing overheating. Through impedance detection, it achieves accurate battery status identification and adaptive, graded adjustment of charging current, reducing charging heat generation by 30% and increasing battery cycle life by an estimated 25%, significantly reducing maintenance costs. Furthermore, this solution constructs a closed-loop management system throughout the entire process and establishes a multi-dimensional, graded risk warning mechanism, enabling early identification of line and battery anomalies. After the upgrade, there were zero safety incidents and base station downtime for 12 consecutive months, effectively ensuring the stable operation of base stations in mountainous areas. It is also adaptable to the complex operating conditions of various off-grid inverters, possessing strong replicability and industry promotion value.

[0065] Figure 3 This is a block diagram of an off-grid inverter battery protection and charging control system based on impedance detection, according to an embodiment of this application.

[0066] like Figure 3 As shown, the off-grid inverter battery protection and charging control system 10 based on impedance detection includes: a dual-condition acquisition module 100, an internal resistance SOC calculation module 200, a charging regulation and threshold setting module 300, and an early warning and handling control module 400.

[0067] The system includes a dual-condition acquisition module 100, which acquires battery input parameters under two different load conditions: no-load, light-load, and loaded. An internal resistance SOC calculation module 200, based on the battery input parameters under these two load conditions, uses Ohm's law analytical algorithm according to the DC voltage drop principle to calculate the total equivalent impedance of the battery, connecting wires, and contact parts. It also presets a model to match the remaining SOC of the lithium battery with its internal resistance under off-grid conditions. By subtracting the preset reference values ​​for the connecting wires and contact resistance from the total equivalent impedance, the module obtains the current actual internal resistance of the battery. The preset SOC-internal resistance model is then used to deduce the current SOC value of the battery. The charging regulation module further supports this function. The threshold setting module 300 sets the inverter charging current adjustment threshold in stages based on the battery's current SOC value and actual internal resistance, adaptively adjusting the inverter's charging current to match the battery's current health status and remaining capacity, reducing heat generation during battery charging. Simultaneously, it monitors the rate of change of total equivalent impedance and the trend of change of the battery's actual internal resistance in real time, setting multi-dimensional risk warning thresholds in stages. The warning and handling control module 400 triggers the corresponding level of risk warning signal and corresponding handling actions when the monitored value exceeds the corresponding set risk warning threshold, performing full-process control of real-time battery status monitoring, adaptive charging current adjustment, and risk warning.

[0068] It should be noted that the foregoing explanation of an embodiment of an off-grid inverter battery protection and charging control method based on impedance detection also applies to an off-grid inverter battery protection and charging control system based on impedance detection in this embodiment, and will not be repeated here.

[0069] According to the embodiments of this application, an off-grid inverter battery protection and charging control system based on impedance detection is proposed. This system collects battery input parameters under different load conditions, calculates the total equivalent impedance, and inversely calculates the current state of charge (SOC) of the battery. Combining the SOC with the actual internal resistance, it achieves adaptive adjustment of the charging current and multi-dimensional risk warning. The beneficial effects are significant: This method reuses the existing sampling and control unit of the inverter, and all functions can be achieved through software algorithm optimization alone, without the need for additional hardware, reducing system cost and installation complexity, and making it easy to promote; by detecting the actual internal resistance of the battery and adaptively adjusting the charging current to match the current state of the battery, it reduces charging heat generation, avoids battery damage, and significantly extends the service life; it monitors impedance change trends in real time, achieving multi-dimensional risk warning, identifying potential hazards in advance, and improving the safety and reliability of the off-grid system; it requires no hardware modification, only software upgrades to adapt to various types of inverters and lithium batteries, and the warning threshold and charging level can be flexibly adjusted, making it highly versatile and practical, and widely applicable to various off-grid energy storage systems. Therefore, it solves the problems of short battery life and poor charging safety in existing technologies.

[0070] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0071] When the processor 402 executes the program, it implements the off-grid inverter battery protection and charging control method based on impedance detection provided in the above embodiments.

[0072] Furthermore, electronic devices also include: Communication interface 403 is used for communication between memory 401 and processor 402.

[0073] The memory 401 is used to store computer programs that can run on the processor 402.

[0074] The memory 401 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0075] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0076] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0077] Processor 402 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0078] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for off-grid inverter battery protection and charging control based on impedance detection.

[0079] Furthermore, this application also provides a computer program product, including a computer program or instructions, which, when executed, implement the above-described method for off-grid inverter battery protection and charging control based on impedance detection.

Claims

1. A method for battery protection and charging control of an off-grid inverter based on impedance detection, characterized in that, include: Collect battery input parameters under two different load conditions: no load, light load, and load. Based on the battery input parameters under the two different load conditions, according to the DC voltage drop principle, Ohm's law analytical algorithm is used to calculate the total equivalent impedance of the battery, connecting wires, and contact parts. A corresponding relationship model between the remaining SOC of the lithium battery and the battery internal resistance is preset to adapt to the off-grid condition. The preset reference values ​​of the connecting wires and contact resistance are subtracted from the total equivalent impedance to obtain the current actual internal resistance of the battery. The preset SOC-internal resistance correspondence model is input to back-calculate the current SOC value of the battery. Based on the current SOC value and actual internal resistance of the battery, the inverter charging current adjustment threshold is set in stages to adaptively adjust the charging current of the inverter so that the charging current matches the current health status and remaining capacity of the battery, thereby reducing the heat generated during the battery charging process. At the same time, the rate of change of total equivalent impedance and the trend of change of actual internal resistance of the battery are monitored in real time, and multi-dimensional risk warning thresholds are set in stages. When the monitored value exceeds the corresponding set risk warning threshold, the corresponding level of risk warning signal and corresponding handling actions are triggered to carry out full-process control of real-time battery status monitoring, adaptive adjustment of charging current and risk warning.

2. The method for off-grid inverter battery protection and charging control based on impedance detection according to claim 1, characterized in that, Collect battery input parameters under two different load conditions: no-load, light-load, and loaded. Construct a dual-condition steady-state data acquisition system; Based on the dual-condition steady-state data acquisition system, steady-state determination rules for no-load or light-load conditions and loaded conditions are set. The battery terminal output voltage, battery output current, inverter DC side input voltage and input current are collected simultaneously after the two conditions reach steady state. The ambient temperature data before and after the condition switching is recorded simultaneously to obtain the original parameter data. The collected raw parameter data is subjected to moving average filtering and outlier removal processing to output a standardized parameter dataset for impedance calculation.

3. The method for off-grid inverter battery protection and charging control based on impedance detection according to claim 2, characterized in that, Based on the battery input parameters under the two different load conditions, and according to the DC voltage drop principle, the analytical algorithm based on Ohm's law is used to calculate the total equivalent impedance of the battery, connecting wires, and contact points, including: Construct a dual-condition impedance analytical model based on the DC voltage drop principle; Based on the dual-condition impedance analysis model, a standardized parameter dataset under two different load conditions is input, the core characteristic parameters of battery terminal voltage and output current under the two conditions are extracted, and the difference between battery terminal voltage and output current under the two conditions are calculated. Based on the battery terminal voltage difference and output current difference, and according to the DC voltage drop principle, the total equivalent impedance of the circuit between the battery and the connecting wires and contact parts is obtained through Ohm's law analytical algorithm, and the confidence interval of the calculation results is output synchronously.

4. The method for off-grid inverter battery protection and charging control based on impedance detection according to claim 1, characterized in that, A pre-defined model for the relationship between the remaining state of charge (SOC) of a lithium battery and its internal resistance, adapted to off-grid conditions, includes: Construct a basic model framework for the SOC-internal resistance correspondence adapted to off-grid operating conditions; Based on the aforementioned SOC-internal resistance correspondence basic model framework, combined with the nominal parameters, cycle life characteristics, and test data at different temperatures and charge / discharge rates of the target lithium battery, interval optimization is performed through a piecewise linear fitting algorithm to divide multiple SOC feature intervals, and the mapping function between battery internal resistance and SOC in each interval is fitted to complete the model parameter calibration. Based on the calibrated model parameters, the model accuracy was verified and the deviation was corrected using measured data under off-grid conditions, and a model for the relationship between lithium battery SOC and battery internal resistance adapted to off-grid conditions was established.

5. The method for off-grid inverter battery protection and charging control based on impedance detection according to claim 1, characterized in that, Based on the battery's current SOC value and actual internal resistance, the inverter charging current adjustment threshold is set in stages to adaptively adjust the inverter's charging current, including: Construct a battery state adaptive charging regulation model; Based on the battery state adaptive charging adjustment model, the current SOC value of the battery is taken as the core dimension. Combined with the battery health state characterized by the actual internal resistance of the battery, multiple charging current adjustment intervals are divided, and the charging current adjustment threshold and charging current upper limit value corresponding to each interval are set in stages. Based on the multi-level charging current adjustment range, the charging current adjustment threshold and charging current upper limit value are set for each range. The charging current upper limit value is dynamically corrected by combining the real-time collected ambient temperature data. The control parameters of the charging circuit are adjusted in real time through the inverter PWM modulation algorithm to dynamically adjust the charging current to the set value of the corresponding range, and to perform adaptive matching between the charging current and the current health status and remaining power of the battery.

6. The method for off-grid inverter battery protection and charging control based on impedance detection according to claim 1, characterized in that, Real-time monitoring of the rate of change of total equivalent impedance and the trend of change of actual internal resistance of the battery, and setting multi-dimensional risk warning thresholds in a tiered manner, including: Construct a risk early warning indicator system for the entire battery life cycle; Based on the battery life cycle risk warning index system, the sliding time window algorithm is used to extract the short-term change rate of total equivalent impedance and the long-term change trend of actual internal resistance in real time. Combined with the safety performance parameters of the target lithium battery and the operation requirements of off-grid conditions, three types of multi-dimensional risk warning thresholds are set in stages: total equivalent impedance mutation threshold, battery internal resistance relative change rate threshold, and internal resistance absolute value threshold. Based on the aforementioned multi-dimensional risk warning thresholds, a rule for determining the correspondence between risk levels and monitoring thresholds is established to complete the preliminary preparations for risk warning.

7. A battery protection and charging control system for an off-grid inverter based on impedance detection, characterized in that, include: Dual-condition acquisition module, which collects battery input parameters under two different load conditions: no-load or light-load, and loaded. The internal resistance SOC calculation module, based on the battery input parameters under the two different load conditions, uses Ohm's law analytical algorithm according to the DC voltage drop principle to calculate the total equivalent impedance of the battery, connecting wires, and contact parts. It also presets a correspondence model between the remaining capacity SOC of the lithium battery and the battery internal resistance for off-grid conditions. The preset reference values ​​of the connecting wires and contact resistance are subtracted from the total equivalent impedance to obtain the current actual internal resistance of the battery. The preset SOC-internal resistance correspondence model is input to back-calculate the current SOC value of the battery. The charging regulation and threshold setting module sets the inverter charging current regulation threshold in stages based on the current SOC value and actual internal resistance of the battery. It adaptively adjusts the charging current of the inverter to match the current health status and remaining capacity of the battery, thereby reducing the heat generated during the battery charging process. At the same time, it monitors the rate of change of total equivalent impedance and the trend of change of actual internal resistance of the battery in real time, and sets multi-dimensional risk warning thresholds in stages. The early warning and control module triggers a corresponding level of risk warning signal and corresponding handling actions when the monitored value exceeds the corresponding set risk warning threshold, and performs full-process control of real-time battery status monitoring, adaptive adjustment of charging current and risk warning.

8. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the method for off-grid inverter battery protection and charging control based on impedance detection as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When a computer program or instruction is executed, it implements the method for off-grid inverter battery protection and charging control based on impedance detection as described in any one of claims 1-6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When a computer program or instruction is executed, it implements the method for off-grid inverter battery protection and charging control based on impedance detection as described in any one of claims 1-6.

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