Storage battery operation state visualization system and method based on polarization internal resistance detection

Abstract

The invention provides a storage battery operation state visualization system and method based on polarization internal resistance detection, relates to the technical field of storage battery condition testing, and determines corresponding retardation failure values and steady-state excitation characteristics of a storage battery in discharging before and after seamless bridging. Determining an on-load switching instruction of the storage battery in the on-load operation test according to the retardation failure value and the steady-state excitation characteristic; performing echelon judgment on the capacity health portrait to obtain a capacity check deviation value of the charge capacity of the storage battery when the polarization internal resistance changes, and determining a variable load attenuation inflection point of the operation state of the storage battery when the host computer capacity check is skipped according to the capacity check deviation value; alternative testing based on fault prediction is carried out on the balance response rules of the storage battery in different operation conditions, and a test result is visually displayed. According to the invention, alternate testing and visualization based on fault prediction can be carried out on the balance response rule under the dynamic discharge and variable load capacity checking working conditions, so that the accuracy of the operation state evaluation of the storage battery is improved.

Description

Technical Field

[0001] This application relates to the field of battery condition testing technology, and more specifically, to a battery operating status visualization system and method based on polarization internal resistance detection. Background Technology

[0002] Battery condition testing is a core prerequisite for ensuring the safe and stable operation of batteries. Its core purpose is to assess the actual performance, health status, and potential failure risks of batteries by detecting key parameters, providing a basis for operation and maintenance decisions. It is widely used in scenarios such as new energy vehicles, energy storage systems, and data center UPS. Traditional testing methods mainly rely on offline capacity testing and static internal resistance testing: offline capacity testing requires disconnecting the battery from the system and calculating the actual capacity through full charge and discharge; static internal resistance testing only measures the ohmic internal resistance when the battery is at rest, which cannot reflect the changes in polarization internal resistance under dynamic operating conditions such as discharge and load. Since polarization internal resistance is directly related to the battery's dynamic load capacity, static data can easily lead to misjudgments of the battery's true condition. In addition, traditional test data is often presented in a scattered tabular format, lacking integration and visualization. Operation and maintenance personnel need to spend a lot of time analyzing the data, making it difficult to grasp the overall operating status of the battery in a timely and accurate manner. These shortcomings have created a demand for battery operating status visualization based on polarization internal resistance detection.

[0003] However, existing methods for visualizing battery operating status based on polarization resistance detection largely rely on polarization resistance data collected under static, unloaded conditions. They fail to consider seamless bridging states and switching host capacity scenarios, resulting in a significant disconnect between test data and actual battery discharge under load and variable load conditions. This leads to a lack of adaptability for load switching commands, inaccurate location of load degradation inflection points, and failure to cover differences in balancing response rules under various operating conditions. Consequently, the visualization results fail to accurately reflect the battery's dynamic health status and operational risks. Therefore, how to conduct alternating testing and visualization of balancing response rules based on fault prediction under dynamic discharge and variable load capacity conditions to improve the accuracy of battery operating status assessment remains a current technical challenge. Summary of the Invention

[0004] This application provides a battery operating status visualization system and method based on polarization internal resistance detection, which can perform alternating testing and visualization of equalization response rules based on fault prediction under dynamic discharge and variable load capacity conditions, so as to improve the accuracy of battery operating status assessment.

[0005] In a first aspect, this application provides a method for visualizing the operating status of a battery based on polarization internal resistance detection, the method comprising the following steps: During the battery discharge process, the polarization internal resistance of the battery is tested based on fault prediction, and the operating status data of the battery during operation is collected. The operational status data is used for utility identification to obtain the hysteresis failure value and steady-state excitation characteristics of the battery before and after seamless bridging during discharge. Then, the load switching command of the battery during load operation test is determined by the hysteresis failure value and the steady-state excitation characteristics. Obtain the capacity health profile of the battery when it is remotely connected to the actual load, perform a tiered judgment on the capacity health profile, obtain the core capacity deviation value of the battery's charge capacity when the polarization internal resistance changes, and then determine the variable load attenuation inflection point of the battery's operating state when switching the host core capacity based on the core capacity deviation value. Based on the load switching command and the variable load attenuation inflection point, the equalization response rule of the battery under different operating conditions is subjected to alternating tests based on fault prediction, and the test results are visualized.

[0006] In this embodiment, the polarization internal resistance test refers to the test that measures the additional internal resistance caused by electrochemical and concentration polarization during battery discharge using the AC injection method, in order to reflect its dynamic load capacity.

[0007] In this embodiment, the utility identification of the operating state data to obtain the hysteresis failure value and steady-state excitation characteristics of the battery before and after seamless bridging during discharge specifically includes: Identify the effectiveness jump constraints of battery current and voltage during discharge based on the aforementioned operating status data; By performing bridging segmentation on the utility jump constraint, the bridging response sequence of the discharging battery under seamless bridging is obtained; Extract the hysteresis failure value and steady-state excitation characteristics of the battery before and after seamless bridging from the bridging response sequence.

[0008] In this embodiment, the steady-state excitation characteristics refer to the set of regular response characteristic parameters of voltage and current when the battery enters a stable operating state after a brief transient process following a seamless jump-connection operation.

[0009] In this embodiment, obtaining the capacity health profile of the battery when it is remotely connected to an actual load includes: Acquire multi-source timing data of the battery when it is remotely connected to an actual load; The capacity degradation level of the battery is determined by the multi-source time-series operating data; Based on the capacity degradation levels, a capacity health profile of the battery is constructed when it is remotely connected to an actual load.

[0010] In this embodiment, the capacity health profile refers to a visualized health status model that integrates multi-dimensional operating parameters with capacity degradation levels as the core.

[0011] In this embodiment, the capacity deviation value refers to a parameter that quantifies the difference between the actual capacity result and the theoretical capacity value of the battery.

[0012] In this embodiment, determining the inflection point of load degradation of the battery's operating state when switching the host capacity based on the capacity deviation value specifically includes: The dynamic capacity decay gradient of the battery during the capacity jump process is determined based on the aforementioned capacity deviation value; The degradation adjacency trajectory of the battery during variable load operation is determined based on the dynamic capacity degradation gradient. The degradation adjacency trajectory determines the inflection point of the variable load attenuation when the battery's operating state changes during the main unit capacity jump.

[0013] In this embodiment, the switching host capacity calculation refers to switching the host responsible for capacity calculation when performing battery capacity calculation, ensuring continuous capacity calculation, adapting to variable load scenarios, and supporting capacity calculation operations that accurately determine the operating status.

[0014] Secondly, this application provides a battery operating status visualization system based on polarization internal resistance detection, used to execute a battery operating status visualization method based on polarization internal resistance detection. The battery operating status visualization system includes: The data acquisition module is used to perform polarization internal resistance testing on the battery based on fault prediction during battery discharge, and to collect the battery's operating status data during operation. The utility identification module is used to perform utility identification on the operating status data to obtain the hysteresis failure value and steady-state excitation characteristics of the battery before and after seamless bridging during discharge. Then, the load switching command of the battery during load operation test is determined by the hysteresis failure value and the steady-state excitation characteristics. The tiered determination module is used to obtain the capacity health profile of the battery when it is remotely connected to the actual load, perform tiered determination on the capacity health profile, obtain the core capacity deviation value of the battery's charge capacity when the polarization internal resistance changes, and then determine the variable load attenuation inflection point of the battery's operating state when switching the host core capacity based on the core capacity deviation value. The visualization module is used to perform alternating tests based on fault prediction on the equalization response rules of the battery under different operating conditions according to the load switching command and the variable load attenuation inflection point, and to visualize the test results.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: During battery discharge, a polarization resistance test based on fault prediction is performed on the battery, and operating status data of the battery during operation is collected. Utility identification is performed on the operating status data to obtain the corresponding hysteresis failure value and steady-state excitation characteristics of the battery before and after seamless bridging during discharge. Then, the load switching command of the battery during load operation test is determined by the hysteresis failure value and the steady-state excitation characteristics. The capacity health profile of the battery when it is remotely connected to an actual load is obtained, and the capacity health profile is judged in stages to obtain the core capacity deviation value of the battery's charge capacity when the polarization resistance changes. Then, the core capacity deviation value is used to determine the variable load attenuation inflection point of the battery's operating status when switching the host core capacity. Based on the load switching command and the variable load attenuation inflection point, the equalization response rule of the battery under different operating conditions is subjected to alternating test based on fault prediction, and the test results are visualized.

[0016] Therefore, this application can improve the quality of basic data for battery operating status assessment, addressing the shortcomings of existing battery operating status visualization technologies based on polarization internal resistance detection, such as static condition testing, data disconnection from actual dynamic discharge under load, and incomplete basic data. Specifically, by collecting polarization internal resistance and multi-dimensional operating data under dynamic discharge conditions, it overcomes the limitations of traditional offline static testing, avoids static data bias, and compensates for the incompleteness of single-parameter collection, laying a reliable foundation for assessment. Furthermore, determining the load switching command can solve the problems of insufficient adaptability of load frequency switching amplitude and difficulty in predicting bridging failures in existing technologies, avoiding battery failures caused by experience-based settings and improving the safety and adaptability of load testing. Furthermore, determining the core capacity deviation value and the inflection point of load attenuation can eliminate the distortion problem of traditional core capacity ignoring polarization internal resistance, realize remote health assessment and attenuation critical point location, and avoid the downtime loss of offline core capacity. Finally, based on the load switching command and the inflection point of load attenuation, the equalization response rules are subjected to alternating tests based on fault prediction and visualized, which can solve the problems of insufficient coverage of existing equalization test conditions and low operation and maintenance efficiency, and improve the reliability and decision-making efficiency of equalization control.

[0017] In summary, the technical solution adopted in this application can perform alternating testing and visualization of the equalization response rule based on fault prediction under dynamic discharge and variable load capacity conditions, thereby improving the accuracy of battery operating status assessment. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is an exemplary flowchart of a method for visualizing the operating status of a battery based on polarization internal resistance detection, provided in this application. Figure 2 This is a flowchart illustrating the process of determining the load switching command according to the present application; Figure 3 This is a flowchart illustrating the process for determining the capacity deviation value provided in this application; Figure 4 This is a module structure diagram of a battery operating status visualization system based on polarization internal resistance detection, provided in this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0021] This application provides a battery operation status visualization system and method based on polarization internal resistance detection. The core of the system involves performing a polarization internal resistance test on the battery during discharge based on fault prediction, and collecting the battery's operation status data. The system performs utility identification on the operation status data to obtain the corresponding hysteresis failure value and steady-state excitation characteristics of the battery before and after seamless bridging during discharge. Then, the load switching command for the battery during load operation testing is determined based on the hysteresis failure value and the steady-state excitation characteristics. The system obtains a capacity health profile of the battery when it is remotely connected to an actual load, performs tiered judgment on the capacity health profile to obtain the core capacity deviation value of the battery's charge capacity when the polarization internal resistance changes, and then determines the variable load attenuation inflection point of the battery's operation status when switching to the host core capacity based on the core capacity deviation value. Based on the load switching command and the variable load attenuation inflection point, the system performs alternating tests on the battery's equalization response rules under different operating conditions based on fault prediction, and visualizes the test results.

[0022] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1 As shown in the figure, this is an exemplary flowchart of a battery operating status visualization method based on polarization internal resistance detection according to this embodiment of the present application. The battery operating status visualization method includes the following steps: In step S1, the battery is subjected to a polarization internal resistance test based on fault prediction during the battery discharge process, and the battery's operating status data during operation is collected.

[0023] In practical implementation, firstly, prepare the testing equipment: select a high-precision AC internal resistance tester with a range of 0-100mΩ and an accuracy of ±0.1mΩ; install voltage sensors with a range of 0-5V and an accuracy of ±0.001V on the positive and negative terminals of each individual battery; connect a current sensor with a range of 0-100A and an accuracy of ±0.1A in series in the main discharge circuit; attach three temperature sensors with a range of -20℃ to 80℃ and an accuracy of ±0.5℃ to the battery casing; install a power sensor at the load end; and simultaneously link the battery management system (BMS) and import historical fault data for this battery model. Then, after wiring, set the parameters: tester injection frequency 1kHz, injection current 50mA; BMS acquisition frequency: electrical parameters (individual cell voltage, discharge current) 100ms / time, temperature 1s / time, load power 500ms / time; and simultaneously set fault warning thresholds based on historical fault data (e.g., triggering a warning when a single sudden change in polarization internal resistance >3mΩ or a continuous 3-second individual cell voltage difference >80mV). Finally, the battery discharge system is started. After the discharge current stabilizes to the set value, the tester (collecting polarization internal resistance once per second) and BMS are turned on simultaneously. The data is transmitted to the cloud in real time, linked and integrated according to timestamps, and after filtering out abnormal values, it is stored as a time series dataset of "time - polarization internal resistance - cell voltage - discharge current - temperature - load power". This time series dataset is used as the operating status data of the battery during operation, which will not be elaborated here.

[0024] It should be noted that, in this application, polarization internal resistance test refers to the test of measuring the additional internal resistance caused by electrochemical and concentration polarization during battery discharge using the AC injection method to reflect its dynamic load capacity; operating status data refers to the data collected by sensors during battery discharge that reflects the real-time operating status of the battery.

[0025] In step S2, the operating status data is used for utility identification to obtain the hysteresis failure value and steady-state excitation characteristics of the battery before and after seamless bridging during discharge. Then, the load switching command of the battery during load operation test is determined by the hysteresis failure value and the steady-state excitation characteristics.

[0026] In this embodiment, the utility identification of the operating state data to obtain the hysteresis failure value and steady-state excitation characteristics of the battery before and after seamless bridging during discharge can be achieved through the following steps: Identify the effectiveness jump constraints of battery current and voltage during discharge based on the aforementioned operating status data; By performing bridging segmentation on the utility jump constraint, the bridging response sequence of the discharging battery under seamless bridging is obtained; Extract the hysteresis failure value and steady-state excitation characteristics of the battery before and after seamless bridging from the bridging response sequence.

[0027] In practice, firstly, the seamless jump-connection time point is extracted from the operational status data, and current and voltage timing data segments of preset durations before and after this time point are extracted. A sliding window method is used to divide the data window, with the window duration set based on the battery discharge cycle and jump-connection response delay characteristics. The difference in current and voltage between adjacent windows is calculated as the jump amplitude. Combining the battery's rated electrical parameters and industry jump-connection performance standards, a constraint threshold is set based on whether the jump amplitude affects the jump-connection stability (the threshold range is determined through verification using historical successful and failed jump data). This clarifies that jump amplitudes within this range are valid jumps, while those exceeding it are invalid jumps, thus forming a utility jump constraint for battery current and voltage during discharge. Then, the precise time point (denoted as T0) of the seamless jump-connection occurrence is extracted from the Battery Management System (BMS) logs. Using T0 as the dividing point, the data conforming to the utility jump constraint is split into two segments: the pre-jump sequence (T0-5 seconds to T0) and the post-jump sequence (T0 to T0+5 seconds). The two data segments are sorted in ascending order by timestamp, and the "distance bridging time" (e.g., T0-3 seconds, T0+2 seconds) corresponding to each data group is added. The sorted data is used as the bridging response sequence of the discharging battery during seamless bridging. Each data point in the bridging response sequence is associated with current jump value, voltage jump value, and time information. Finally, from the data segment before bridging in the bridging response sequence, the maximum jump amplitude of current and voltage and the preset standard jump amplitude are extracted. The hysteresis failure value is calculated by the ratio of the difference between the two (the calculation model can be determined based on bridging response theory and fitting with a large amount of experimental data). From the data segment after bridging, a stable operating data interval where the current and voltage fluctuation amplitude are stable within the preset range is selected. Parameters such as the average current, average voltage, fluctuation amplitude, and response delay time within this interval are extracted. These parameters together constitute the steady-state excitation characteristics, that is, the hysteresis failure value and steady-state excitation characteristics of the discharging battery before and after seamless bridging.

[0028] It should be noted that, in this application, utility identification refers to the process of filtering effective information and eliminating useless data from battery operating status data to clarify the data utility; seamless bridging refers to the connection operation that ensures continuous power supply by switching loads during battery discharge without operational interruption or significant performance fluctuations; utility jump constraint refers to the rule that limits whether the jump in battery current and voltage during discharge reflects the stability of seamless bridging; bridging response sequence refers to the effective current and voltage data sets before and after bridging arranged in chronological order after bridging segmentation; hysteresis failure value is a parameter that quantifies the degree of hysteresis in the current and voltage response of the battery before and after seamless bridging during discharge; steady-state excitation characteristics refer to the set of regular response characteristic parameters of voltage and current when the battery enters a stable operating state after a brief transient process following the seamless bridging operation; bridging segmentation refers to the operation of dividing data that meets the utility jump constraint into two segments, "before bridging" and "after bridging," with the seamless bridging occurrence time as the boundary.

[0029] Preferably, in this embodiment, the load switching command of the battery during load operation testing is determined by the hysteresis failure value and the steady-state excitation characteristics, with reference to... Figure 2 As shown in the figure, this is a flowchart illustrating the process of determining the load switching instruction in some embodiments of this application. In this embodiment, the determination of the load switching instruction can be achieved through the following steps: In step S21, the stagnation failure value and steady-state excitation characteristics are analyzed to extract the dynamic load-bearing range and steady-state injection capacity of the battery during the seamless jump-connection process; In step S22, the frequency disturbance attribute during load frequency switching is determined based on the dynamic load range and the steady-state injection capability; In step S23, a frequency point compensation strategy for suppressing sluggishness and maintaining steady-state connection is generated based on the frequency point disturbance attributes. In step S24, the load switching command for the battery during load operation test is determined according to the frequency compensation strategy.

[0030] In practical implementation, firstly, the hysteresis failure value and steady-state excitation characteristics are retrieved, and a calculation model is established in conjunction with the rated electrical parameters of the battery. If the hysteresis failure value is at a low level, the extreme values ​​of current and voltage before and after bridging are extracted from the steady-state excitation characteristics, and the dynamic load-bearing range is calculated according to "safety redundancy coefficient × extreme value range". The redundancy coefficient is determined based on bridging response theory and more than 50 sets of experimental data. If the hysteresis failure value is too high, the load-bearing range is narrowed to improve safety. From the stable operation data segment of the steady-state excitation characteristics, the average current value within a continuous preset time period is extracted, and after verification by multiple bridging experiments, it is determined as the steady-state current injection capability. Next, the corresponding rules between the dynamic load-bearing range and the frequency disturbance attribute are set: if the current range of the dynamic load-bearing range is >5A and the voltage range is >0.3V, the battery is judged to have strong anti-interference capability during frequency switching, and the frequency disturbance attribute is "low sensitivity"; if the current range is 2~5A and the voltage range is 0.1~0.3V, it is judged to be "medium sensitivity"; if the current range is <2A and the voltage range is <0.1V, it is judged to be "high sensitivity". Then, considering the steady-state current injection capability: if the steady-state current injection capability is greater than 80% of the rated current, the current disturbance attribute is maintained; if it is less than 60%, the disturbance attribute level is increased by 1 level (e.g., from low sensitivity to medium sensitivity). Finally, information including "sensitivity level and impulse tolerance threshold" is compiled, and this information is used as the frequency disturbance attribute during load frequency switching. Then, if the frequency disturbance attribute is "low sensitivity", a "high-frequency smooth switching" strategy is generated: the frequency switching interval is set to 0.5 seconds, the frequency change in each switching does not exceed 2Hz, and the current fluctuation during switching is kept within the dynamic carrying range; if it is "medium sensitivity", a "medium-frequency buffer switching" strategy is generated: the switching interval is extended to 1 second, the frequency change is controlled within 1Hz, and the current is adjusted to 90% of the steady-state injection capacity before switching; if it is "high sensitivity", a "low-frequency progressive switching" strategy is generated: the switching interval is set to 2 seconds, the frequency change does not exceed 0.5Hz, the current compensation module is enabled during switching, and the current difference is supplemented in real time to suppress the jamming, that is, a frequency compensation strategy for suppressing jamming and maintaining steady-state connection is generated. Finally, the frequency-related parameters in the frequency compensation strategy are converted into specific values: If the compensation strategy is "high-frequency smooth switching," referring to industry high-frequency load testing standards and considering the battery's rated capacity, the load frequency is set to 10Hz, with the maximum frequency fluctuation during switching not exceeding 2Hz; if it is "medium-frequency buffer switching," considering the steady-state injection current capability, the load frequency is set to 5Hz, with a 1-second switching interval; if it is "low-frequency gradual switching," the load frequency is set to 3Hz, with the current pre-adjusted to 90% of the steady-state injection current capability before switching. Finally, the frequency values ​​and switching requirements are compiled into a standardized instruction format, which is then used as the load switching instruction for the battery during load operation testing.

[0031] It should be noted that, in this application, the load operation test refers to the test that simulates the actual load conditions of the battery and detects the operating parameters to verify its load capacity and operational stability; the dynamic load range refers to the range of current and voltage changes that the battery can stably withstand during seamless bridging; the steady-state current injection capability refers to the battery's ability to maintain a stable current output after seamless bridging; the frequency disturbance attribute refers to the degree and characteristics of interference to the battery current and voltage during load frequency switching; the frequency compensation strategy refers to the operation used to reduce the impact of frequency switching on the battery, suppress the jamming phenomenon, and maintain the steady state after bridging; and the load switching command is a standardized command for the load switching frequency, interval, and amplitude during the battery load operation test.

[0032] In step S3, a capacity health profile of the battery is obtained when it is remotely connected to an actual load. The capacity health profile is then subjected to a tiered judgment to obtain the core capacity deviation value of the battery's charge capacity when the polarization internal resistance changes. The core capacity deviation value is then used to determine the variable load attenuation inflection point of the battery's operating state when switching the host core capacity.

[0033] In this embodiment, obtaining the capacity health profile of the battery when it is remotely connected to an actual load can be achieved through the following steps: Acquire multi-source timing data of the battery when it is remotely connected to an actual load; The capacity degradation level of the battery is determined by the multi-source time-series operating data; Based on the capacity degradation levels, a capacity health profile of the battery is constructed when it is remotely connected to an actual load.

[0034] In practice, firstly, voltage sensors with a range of 0-5V and an accuracy of ±0.001V are installed on the positive and negative terminals of the battery cells. A current sensor with a range of 0-100A and an accuracy of ±0.1A is connected in series in the main discharge circuit. Three temperature sensors with a range of -20℃ to 80℃ and an accuracy of ±0.5℃ are attached to the casing. A power sensor is installed at the load end. All sensors are linked to the Battery Management System (BMS). The BMS data is transmitted to the cloud platform in real time via an Internet of Things (IoT) module. The data acquisition frequency is set as follows: voltage and current 100ms / time, temperature 1s / time, and power 500ms / time. Data is integrated according to timestamps, and outliers outside the normal range are filtered out, ultimately forming a multi-source time-series dataset of "time – cell voltage – discharge current – ​​temperature – load power – cumulative cycle count". Then, key parameters are extracted from the multi-source time-series operating data: the current actual capacity is calculated based on current and time (actual capacity = discharge current × discharge time, discharge stops at the cutoff voltage), and the capacity retention rate is calculated in conjunction with the battery's rated capacity (capacity retention rate = current actual capacity / rated capacity × 100%); the current polarization internal resistance and initial polarization internal resistance are extracted from the data, and the internal resistance growth rate is calculated (internal resistance growth rate = (current polarization internal resistance - initial polarization internal resistance) / initial polarization internal resistance × 100%). Referring to industry standards, the following classification levels are set: capacity retention rate ≥ 80% and internal resistance growth rate ≤ 20% is "Level 1 (mild degradation)"; capacity retention rate 60%–79% and internal resistance growth rate 21%–40% is "Level 2 (moderate degradation)"; capacity retention rate 40%–59% and internal resistance growth rate 41%–60% is "Level 3 (severe degradation)"; and capacity retention rate < 40% is "Level 4 (failure degradation)". This is used to determine the battery's capacity degradation level. Finally, a data visualization tool (such as Matplotlib) is used to construct a profile: the horizontal axis represents the cumulative number of charge-discharge cycles, and the vertical axis represents the capacity retention rate. A curve is plotted showing the change in capacity retention rate with the number of cycles, and different colors are used to label the curve segments according to the capacity degradation level (green for level 1, yellow for level 2, orange for level 3, and red for level 4). The internal resistance growth rate and SOC error rate for the corresponding number of cycles are labeled next to the curve (the SOC error rate is extracted and calculated from multi-source time-series data). The current capacity degradation level, current actual capacity, and rated capacity are labeled at the top of the profile, and the data acquisition time range and sensor accuracy information are added at the bottom. Finally, a capacity health profile of the battery, including curves, numerical labels, and classification indicators, is formed when the battery is remotely connected to an actual load.

[0035] It should be noted that, in this application, multi-source time-series operating data refers to a set of operating information collected from different monitoring dimensions of remote online load operation of the battery and arranged in chronological order; capacity degradation level refers to the level of degradation of battery capacity performance; and capacity health profile refers to a visualized health status model that integrates multi-dimensional operating parameters with capacity degradation level as the core.

[0036] Preferably, in this embodiment, the capacity health profile is subjected to tiered judgment to obtain the core capacity deviation value of the battery's charge capacity when the polarization internal resistance changes, with reference to... Figure 3 As shown in the figure, this is a flowchart illustrating the determination of the capacity deviation value in some embodiments of this application. In this embodiment, the determination of the capacity deviation value can be achieved by the following steps: In step S31, the capacity health profile is analyzed, and the polarization internal resistance coordination sequence of the battery is extracted; In step S32, the elastic offset rule between the internal resistance change and the charging capacity is determined based on the internal resistance coordination sequence; In step S33, a guiding correction strategy for the battery's charge capacity when the polarization internal resistance changes is determined according to the elastic offset rule. In step S34, the core capacity deviation value of the battery under varying polarization resistance is determined according to the guidance correction strategy.

[0037] In practice, firstly, all labeled polarization resistance values ​​are extracted from the capacity health profile. Simultaneously, the cumulative charge-discharge cycle count and current charge capacity (charge capacity = current actual capacity / rated capacity × 100%) corresponding to each resistance value are extracted. These data are sorted in ascending order by cumulative charge-discharge cycle count, and the three sets of data—"cycle count - polarization resistance - charge capacity"—are matched one-to-one, forming a table sequence where each row contains three parameters. For any missing data in the sequence, linear interpolation between adjacent valid data is used to complete the sequence, ultimately yielding a complete polarization resistance coordination sequence. Next, 10 evenly spaced data sets are selected from the polarization resistance coordination sequence, and the difference between each set of data and the initial data (the first recorded polarization resistance and charge capacity) is calculated to obtain the "polarization resistance change" and the "charge capacity offset." The average offset of charge capacity corresponding to different internal resistance variation ranges is statistically analyzed: when the polarization internal resistance variation is 0-10mΩ, the average offset of charge capacity is calculated; the same applies when the variation is 11-20mΩ and 21-30mΩ. Based on the statistical results, rules are set, such as "for every 10mΩ increase in polarization internal resistance, the corresponding offset of charge capacity decreases by 5%" and "for every 10mΩ increase in polarization internal resistance, the reduction in charge capacity offset is expanded to 8%", forming an elastic offset rule between internal resistance variation and charge capacity. Then, based on the elastic offset rule, the polarization internal resistance variation range is divided, and different correction strategies are formulated accordingly: when the polarization internal resistance variation is 0-10mΩ, a "basic correction strategy" is adopted, calculated as "corrected charge capacity = current charge capacity + (initial internal resistance - current internal resistance) / 10 × 5%"; when the variation is 11-20mΩ, a "moderate correction strategy" is adopted, increasing the correction coefficient from 5% to 6%, and the formula is adjusted to "corrected charge capacity = current charge capacity + (initial internal resistance - current internal resistance) / 10 × 6%"; when the variation is >20mΩ, a "deep correction strategy" is adopted, with the correction coefficient set at 8%, and an "upper limit of correction value" is added to ensure that the corrected charge capacity does not exceed 95% of the initial charge capacity, avoiding over-correction, thus forming a guiding correction strategy for the battery's charge capacity when the polarization internal resistance changes. Finally, the original battery capacity data (uncorrected charge capacity variation data with cycle count) is retrieved, grouped by cumulative charge-discharge cycle count, with each group corresponding to a polarization internal resistance variation. Based on the guided correction strategy, the original charge capacity data for each set is corrected: a basic correction strategy is used for cycles 1-50 (internal resistance change 0-10mΩ), a medium correction strategy is used for cycles 51-100 (internal resistance change 11-20mΩ), and a deep correction strategy is used for cycles 101 and above (internal resistance change >20mΩ), resulting in the corrected charge capacity. The capacity deviation value for each set of data can be obtained through the calculation model "capacity deviation value = traditional capacity charge capacity - corrected charge capacity", which gives the capacity deviation value of the battery charge capacity when the polarization internal resistance changes.

[0038] It should be noted that, in this application, "tiered determination" refers to the process of classifying the state levels of a battery according to its relevant characteristics; "polarization internal resistance coordination sequence" refers to an ordered set formed by associating polarization internal resistance data in the capacity health profile with corresponding time / cycle count and charge capacity data; "elastic offset rule" refers to the criteria for clarifying the correspondence between the change in polarization internal resistance and the offset in charge capacity; "guided correction strategy" refers to a specific scheme that guides how to adjust the charge capacity assessment results according to the change in polarization internal resistance; and "capacity deviation value" refers to a parameter that quantifies the difference between the actual capacity result and the theoretical capacity value of the battery.

[0039] In this embodiment, determining the inflection point of variable load attenuation of the battery's operating state when switching the host capacity based on the capacity deviation value can be achieved through the following steps: The dynamic capacity decay gradient of the battery during the capacity jump process is determined based on the aforementioned capacity deviation value; The degradation adjacency trajectory of the battery during variable load operation is determined based on the dynamic capacity degradation gradient. The degradation adjacency trajectory determines the inflection point of the variable load attenuation when the battery's operating state changes during the main unit capacity jump.

[0040] In practice, firstly, all capacity deviation values ​​are sorted to obtain a capacity deviation value sequence. Complete data segments before and after capacity jumps (including the standby main unit capacity jump stage and the primary main unit capacity jump stage) are extracted from this sequence. The data is then divided into equally spaced data groups based on the cumulative charge-discharge cycle count. The ratio of the change in capacity deviation value in each group to the corresponding cycle count interval is calculated to obtain the capacity decay rate for each group. The difference in decay rates between adjacent groups is then calculated to form a dynamic capacity decay gradient table of "cycle count interval - decay rate difference," for example, "50-55 cycles: 0.2% / cycle, 101-106 cycles: 0.8% / cycle," which gives the dynamic capacity decay gradient of the battery during the capacity jump process. Then, using the cumulative cycle count interval in the dynamic capacity decay gradient table as the horizontal axis and the corresponding decay rate as the vertical axis, each "cycle count interval - decay rate" data point is marked on the coordinate system. The data points are arranged in ascending order of cumulative cycle count and connected by a smooth curve, ensuring that the curve passes through all marked points. For obvious data fluctuations in the curve (such as a sudden increase and subsequent decrease in the single decay rate), the original data of the core capacity deviation is used for verification. If it is an outlier, the mean of two adjacent points is used to correct the curve. This ultimately forms a continuous decay adjacency trajectory from before the jump to after the jump. The cycle number nodes corresponding to "jump start" and "jump end" are marked on this continuous decay adjacency trajectory, thus obtaining the decay adjacency trajectory of the battery under variable load operation. Finally, 20 consecutive data points are selected before and after the jump on the decay adjacency trajectory, and the slope of the line connecting each data point to the previous data point is calculated (slope = (later decay rate - previous decay rate) / (later cycle number interval - previous cycle number interval)). A slope threshold is set: when the slope of three consecutive data points exceeds three times the average slope of the previous 10 data points, it is determined to be a sudden increase in the decay rate. The starting data point that first meets this condition is found, and its corresponding cumulative cycle number and polarization internal resistance (internal resistance data matched to that cycle number from the core capacity deviation value) are extracted. For example, if the slope exceeds the threshold for the first time in the 102nd cycle and the polarization resistance is 20mΩ, then "cumulative cycle count 102 times, polarization resistance 20mΩ" is the inflection point of the battery's operating state when switching the host core capacity.

[0041] It should be noted that in this application, "switching host capacity calculation" refers to switching the host responsible for capacity calculation during battery capacity calculation, ensuring continuous capacity calculation, adapting to variable load scenarios, and supporting accurate determination of operating status during capacity calculation; "dynamic capacity decay gradient" refers to parameters that quantify the change in the capacity decay rate of the battery during capacity switching as it operates; "degradation adjacency trajectory" refers to the continuous change in the decay state during capacity switching, facilitating the location of transition nodes with abrupt decay changes; and "variable load decay inflection point" refers to the key node that marks the performance degradation of the battery during capacity switching.

[0042] In step S4, the equalization response rule of the battery under different operating conditions is subjected to alternating tests based on fault prediction according to the load switching command and the variable load attenuation inflection point, and the test results are visualized.

[0043] In specific implementation, the equalization response rules of the battery under different operating conditions are subjected to alternating tests based on fault prediction according to the load switching command and the variable load attenuation inflection point, and the test results are visualized. This can be achieved in the following way: First, based on the load switching command (including switching interval and amplitude) and the variable load attenuation inflection point (divided into before and after the inflection point with a polarization internal resistance of 20mΩ as the boundary), three typical operating conditions are combined: high-frequency load + before attenuation inflection point, low-frequency load + before attenuation inflection point, and low-frequency load + after attenuation inflection point. Before the test, the battery is fully charged, and the ambient temperature is fixed at 25℃ to eliminate interference. Additional fault prediction scenarios such as "internal resistance close to the fault threshold" and "critical deviation of single cell voltage" are added to form a multi-dimensional test condition library. The test is started in the order of the operating conditions in the multi-dimensional test condition library. The load corresponding to the load frequency point is loaded first. After the operation is stable, the single cell voltage is monitored. When the battery management system (BMS) detects that the maximum difference in single cell voltage is >50mV, the equalization response rule is automatically triggered. Record the individual cell voltage difference before equalization, the time to equalize until the voltage difference ≤30mV, and the capacity loss during the equalization process for each test. Repeat the test 5 times for each operating condition and take the average value. Finally, use data visualization tools (such as Matplotlib) to draw a line chart of "operating condition - equalization time" and a bar chart of "operating condition - capacity loss", and label the mean of each group of data to form a visualization report containing charts and key conclusions. It will not be elaborated here.

[0044] It should be noted that in this application, the equalization response rule refers to the specific control logic of the battery management system in order to maintain the consistency of the battery cell voltage and regulate the charging and discharging current; the alternating test refers to the process of testing the effect of the equalization response rule in turn according to the combination of load switching command and variable load attenuation inflection point.

[0045] Therefore, this application can improve the quality of basic data for battery operating status assessment, addressing the shortcomings of existing battery operating status visualization technologies based on polarization internal resistance detection, such as static condition testing, data disconnection from actual dynamic discharge under load, and incomplete basic data. Specifically, by collecting polarization internal resistance and multi-dimensional operating data under dynamic discharge conditions, it overcomes the limitations of traditional offline static testing, avoids static data bias, and compensates for the incompleteness of single-parameter collection, laying a reliable foundation for assessment. Furthermore, determining the load switching command can solve the problems of insufficient adaptability of load frequency switching amplitude and difficulty in predicting bridging failures in existing technologies, avoiding battery failures caused by experience-based settings and improving the safety and adaptability of load testing. Furthermore, determining the core capacity deviation value and the inflection point of load attenuation can eliminate the distortion problem of traditional core capacity ignoring polarization internal resistance, realize remote health assessment and attenuation critical point location, and avoid the downtime loss of offline core capacity. Finally, based on the load switching command and the inflection point of load attenuation, the equalization response rules are subjected to alternating tests based on fault prediction and visualized, which can solve the problems of insufficient coverage of existing equalization test conditions and low operation and maintenance efficiency, and improve the reliability and decision-making efficiency of equalization control.

[0046] In summary, the technical solution adopted in this application can perform alternating testing and visualization of the equalization response rule based on fault prediction under dynamic discharge and variable load capacity conditions, thereby improving the accuracy of battery operating status assessment.

[0047] Example 2: This application provides a battery operating status visualization system based on polarization internal resistance detection, referring to... Figure 4 As shown in the figure, this is a block structure diagram of a battery operating status visualization system based on polarization internal resistance detection according to this embodiment of the present application. The battery operating status visualization system includes: The data acquisition module 100 is used to perform polarization internal resistance testing on the battery based on fault prediction during the battery discharge process, and to collect the operating status data of the battery during operation. The utility identification module 200 is used to perform utility identification on the operating state data to obtain the corresponding hysteresis failure value and steady-state excitation characteristics of the battery before and after seamless bridging during discharge, and then determine the load switching command of the battery during load operation test based on the hysteresis failure value and the steady-state excitation characteristics. The tiered determination module 300 is used to obtain the capacity health profile of the battery when it is running under actual load remotely online, to perform tiered determination on the capacity health profile, to obtain the core capacity deviation value of the battery's charge capacity when the polarization internal resistance changes, and then to determine the variable load attenuation inflection point of the battery's operating state when switching the host core capacity based on the core capacity deviation value. The visualization module 400 is used to perform alternating tests based on fault prediction on the equalization response rules of the battery under different operating conditions according to the load switching command and the variable load attenuation inflection point, and to visualize the test results.

[0048] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0049] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compactdisc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0050] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A method for visualizing the operating state of a storage battery based on polarization internal resistance detection, characterized by, The battery operating state visualization method comprises the following steps: During the discharge of the battery, a polarization internal resistance test based on fault prediction is performed on the battery, and operating state data of the battery during operation is collected; The operating state data is subjected to utility recognition to obtain corresponding blocking failure values and steady-state excitation characteristics of the battery before and after seamless cross connection during discharge, and then the blocking failure values and the steady-state excitation characteristics are used to determine a load switching instruction of the battery during a load test; A capacity health portrait of the battery during remote online access to actual load operation is obtained, the capacity health portrait is subjected to step judgment to obtain a core capacity deviation value of the charge capacity of the battery when the polarization internal resistance changes, and then the core capacity deviation value is used to determine a variable load attenuation inflection point of the operating state of the battery when the host core capacity is switched; According to the load switching instruction and the variable load attenuation inflection point, an alternating test based on fault prediction is performed on the equalization response rules of the battery in different operating conditions, and the test results are visualized and displayed.

2. The method of visualizing the state of operation of a battery based on the detection of the polarization resistance according to claim 1, characterized in that, The polarization internal resistance test refers to a test for additional internal resistance caused by electrochemistry and concentration polarization during discharge of the battery by using an alternating current injection method to reflect the dynamic load capacity.

3. The method of visualizing the state of operation of a battery based on the detection of the polarization resistance as claimed in claim 1, characterized in that, The utility recognition of the operating state data to obtain corresponding blocking failure values and steady-state excitation characteristics of the battery before and after seamless cross connection during discharge specifically comprises: The utility jump constraint of the battery current and voltage during discharge is recognized according to the operating state data; The utility jump constraint is segmented to obtain a cross connection response sequence of the battery during seamless cross connection during discharge; The corresponding blocking failure values and steady-state excitation characteristics of the battery before and after seamless cross connection during discharge are extracted from the cross connection response sequence.

4. The method of visualizing the state of operation of a battery based on the detection of the polarization resistance as claimed in claim 1, characterized in that, The steady-state excitation characteristics refer to a set of regular response characteristic parameters of voltage and current when the battery enters a stable operating state after a short transient process after seamless cross connection operation.

5. The method of visualizing the state of operation of a battery based on the detection of the polarization resistance as claimed in claim 1, characterized in that, The capacity health portrait of the battery during remote online access to actual load operation specifically comprises: Multi-source time sequence operating data generated by the battery during remote online access to actual load operation is obtained; The capacity degradation level of the battery is determined through the multi-source time sequence operating data; The capacity health portrait of the battery during remote online access to actual load operation is constructed according to the capacity degradation level.

6. The method of visualizing the state of operation of a battery based on the detection of the polarization resistance as claimed in claim 1, characterized in that, The capacity health portrait refers to a visual health state model integrating multi-dimensional operating parameters with the capacity degradation level as the core.

7. The method of visualizing the state of operation of a battery based on the detection of the polarization resistance as claimed in claim 1, characterized in that, The core capacity deviation value refers to a parameter quantifying the difference between the actual core capacity result of the battery and the theoretical core capacity value.

8. The method of visualizing the state of operation of a battery based on the detection of polarization resistance according to claim 1, characterized in that, Determining the variable load attenuation inflection point of the operating state of the battery when the host core capacity is switched according to the core capacity deviation value specifically comprises: A dynamic capacity decay gradient of the battery during the core capacity switching process is determined according to the core capacity deviation value; A decay adjacent track of the battery during variable load operation is determined according to the dynamic capacity decay gradient; The variable load attenuation inflection point of the operating state of the battery when the host core capacity is switched is determined from the decay adjacent track.

9. The method of visualizing the state of operation of a battery based on the detection of polarization resistance according to claim 1, characterized in that, The jump host core capacity refers to a host responsible for core capacity calculation when the battery core capacity is switched, guarantees continuous core capacity, adapts to variable load scenarios, and supports accurate determination of the running state of the core capacity operation.

10. A battery operating state visualization system based on polarization resistance detection for performing a battery operating state visualization method based on polarization resistance detection according to any one of claims 1 to 9, characterized by The battery running state visualization system comprises: A data acquisition module for performing polarization resistance testing based on fault prediction on the battery during discharge and acquiring running state data of the battery during operation; An effectiveness recognition module for performing effectiveness recognition on the running state data to obtain corresponding resistance failure values and steady-state excitation characteristics of the battery before and after seamless crossover during discharge, and then determining a load switching instruction of the battery during load running testing from the resistance failure values and the steady-state excitation characteristics; A ladder determination module for obtaining a capacity health portrait of the battery when remotely connected to an actual load for online operation, performing ladder determination on the capacity health portrait, obtaining a core capacity deviation value of the charge capacity of the battery when the polarization resistance changes, and then determining a variable load decay inflection point of the running state of the battery when the host core capacity jumps from the core capacity deviation value; A visualization module for performing alternating testing based on fault prediction on the equalization response rules of the battery in different operating conditions according to the load switching instruction and the variable load decay inflection point, and visually displaying the test results.

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