Mobile power supply battery active fuse protection method and system based on cycle life

Abstract

The application relates to the technical field of battery protection, and discloses a mobile power supply battery active fusing protection method and system based on cycle life, which comprises the following steps: counting the effective charge-discharge cycle times of a battery, coupling state parameters of multi-source data of the battery to obtain electrochemical parameter data; performing time sequence trend analysis on the electrochemical parameter data to obtain resistance change trend, voltage drop speed and temperature rise condition; comprehensively judging the cycle life attenuation state based on the resistance change trend, the voltage drop speed and the temperature rise condition; matching the attenuation level of the protection threshold value of the battery to update the protection threshold value; performing state safety final judgment on the real-time loop current, the real-time terminal voltage and the effective charge-discharge cycle times of the battery to obtain an abnormal state result; performing abnormal response processing on the battery to drive the battery to perform a fusing protection action; and the application can improve the efficiency of the mobile power supply battery active fusing protection based on cycle life.

Description

Technical Field

[0001] This invention relates to the field of battery protection technology, and in particular to a method and system for active fuse protection of mobile power bank batteries based on cycle life. Background Technology

[0002] Existing power bank battery fuse protection technologies mostly use fixed protection thresholds for safety judgment, which do not fully take into account the state evolution characteristics during the battery cycle life decay process. This results in a mismatch between the protection threshold and the actual aging state of the battery, making it impossible to accurately adapt to the safety protection needs of different battery decay stages, and easily leading to over-protection or under-protection.

[0003] Existing technologies lack effective time-series trend analysis and state parameter coupling mechanisms for processing multi-source battery data. This makes it difficult to comprehensively capture the dynamic evolution patterns of resistance changes, voltage fluctuations, and temperature increases, leading to delayed predictions of abnormal battery states and inaccurate triggering of fuse protection actions, thus affecting battery safety and reliability. Therefore, improving the accuracy of protection based on battery cycle life degradation, the timeliness of abnormal state identification, and the effectiveness of fuse protection has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method and system for active fuse protection of mobile power bank batteries based on cycle life, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for active fuse protection of mobile power bank batteries based on cycle life, comprising: S1. Count the effective charge-discharge cycle number of the battery and perform state parameter coupling on the multi-source data of the battery to obtain the electrochemical parameter data of the battery. S2. Perform time-series trend analysis on the electrochemical parameter data to obtain the resistance change trend, voltage drop rate, and temperature rise of the battery. S3. Based on the resistance change trend, the voltage drop rate, and the temperature rise, comprehensively determine the cycle life degradation state of the battery; S4. Based on the cycle life decay state, the protection threshold of the battery is matched with the decay level to update the protection threshold of the battery. S5. Based on the updated protection threshold, perform a final state safety judgment on the real-time loop current, real-time terminal voltage and effective charge-discharge cycle number of the battery to obtain the abnormal state result of the battery. S6. Based on the abnormal state result, perform abnormal response processing on the battery to drive the battery to perform a fuse protection action.

[0006] In a preferred embodiment, the step of counting the effective charge-discharge cycle count of the battery and performing state-parameter coupling on the multi-source data of the battery to obtain the electrochemical parameter data of the battery includes: Record the changes in the state of charge of the battery, and use the cumulative number of such changes in state of charge as the effective charge-discharge cycle count of the battery; The battery's terminal voltage, loop current, battery temperature, and internal equivalent DC resistance are collected to obtain multi-source data of the battery. The multi-source data is timestamped to obtain multi-mode time synchronization data of the multi-source data; The multi-mode time synchronization data is fused to obtain the electrochemical parameter data of the battery.

[0007] In a preferred embodiment, the step of performing time-series trend analysis on the electrochemical parameter data to obtain the resistance change trend, voltage drop rate, and temperature rise of the battery includes: Multidimensional time-series feature decoupling is performed on the electrochemical parameter data to obtain the battery state evolution characteristics of the electrochemical parameter data; The state evolution characteristics of the battery are reconstructed into a state vector to obtain the degradation feature vector of the battery. By performing trend parameter analysis on the degradation feature vector, the resistance change trend, voltage drop rate, and temperature rise of the battery can be obtained.

[0008] In a preferred embodiment, the step of comprehensively determining the cycle life degradation state of the battery based on the resistance change trend, the voltage drop rate, and the temperature rise includes: The degradation sensitivity analysis is performed on the resistance change trend, the voltage drop rate, and the temperature rise to obtain the contribution weight of the battery; Based on the contribution weights, the resistance change trend, the voltage drop rate, and the temperature rise are weighted and fused to obtain the fusion aging factor of the battery. Based on the fusion aging factor, the battery is subjected to life degradation mapping to obtain the preliminary degradation level of the battery; The initial degradation level is compared with the historical state information of the battery to verify the degradation trajectory and confirm the cycle life degradation state of the battery.

[0009] In a preferred embodiment, the formula for calculating the fusion aging factor is as follows: ; In the formula, The fusion aging factor, The trend of the resistance change. The voltage drop rate, The temperature rise situation. The weight is assigned to the resistance change trend. Contribute weight to the rate of voltage drop. Contribute weight to temperature rise. To take the absolute value, This is the square root operation.

[0010] In a preferred embodiment, the step of matching the battery's protection threshold with degradation levels based on the cycle life degradation state to update the battery's protection threshold includes: The degree of degradation is calibrated based on the cycle life degradation state to obtain the degradation level of the battery; The attenuation level is mapped to a preset level protection strategy correspondence table to obtain the protection strategy for the battery; Based on the protection strategy, the protection threshold of the battery is reconstructed to obtain the updated protection threshold of the battery.

[0011] In a preferred embodiment, the step of performing a final state safety assessment on the real-time loop current, real-time terminal voltage, and effective charge-discharge cycle count of the battery based on the updated protection threshold to obtain the abnormal state result of the battery includes: The updated protection threshold and the preset cycle life safety threshold are combined to obtain the safety criterion set of the battery; The real-time loop current, real-time terminal voltage, and effective charge-discharge cycle number of the battery are aligned in the time domain to obtain the real-time state parameters of the battery. Based on the aforementioned safety criterion set, real-time safety assessment is performed on the real-time state parameters to obtain the abnormal state results of the battery.

[0012] In a preferred embodiment, the step of performing real-time safety assessment on the real-time state parameters based on the safety criterion set to obtain the abnormal state result of the battery includes: Based on the safety criterion set, parallel threshold comparison is performed on the real-time state parameters to obtain the battery's electrical parameter comparison result and cycle count comparison result; Diagnostic status labeling is performed on the electrical parameter comparison results and the cycle count comparison results to obtain the battery's electrical parameter abnormality flag and life end abnormality flag; A comprehensive anomaly assessment is performed on the electrical parameter anomaly flag and the end-of-life anomaly flag to obtain the abnormal state result of the battery.

[0013] In a preferred embodiment, the step of performing abnormal response processing on the battery based on the abnormal state result to drive the battery to perform a fuse protection action includes: Based on the abnormal state result, a fuse trigger decision is made for the battery to obtain a reversible cut-off command for the battery, so as to control the primary fuse protection action of the battery. After the primary fuse protection is activated, the circuit current of the battery after the interruption is continuously monitored; Based on the continued existence of the circuit current after the interruption, the continuity status of the current path of the battery is verified, and an irreversible disconnection command for the battery is generated according to the verification result to control the secondary fuse protection action of the battery.

[0014] To address the aforementioned problems, this invention also provides a mobile power bank battery active fuse protection system based on cycle life, the system comprising: The data sensing and fusion module is used to count the effective charge-discharge cycle number of the battery and perform state parameter coupling on the multi-source data of the battery to obtain the electrochemical parameter data of the battery. The time-series feature analysis module is used to perform time-series trend analysis on the electrochemical parameter data to obtain the resistance change trend, voltage drop rate, and temperature rise of the battery. The life degradation assessment module is used to comprehensively determine the cycle life degradation status of the battery based on the resistance change trend, the voltage drop rate, and the temperature rise. The dynamic threshold decision module is used to match the protection threshold of the battery with the degradation level according to the cycle life degradation state, so as to update the protection threshold of the battery. The real-time status monitoring module is used to make a final status safety judgment on the real-time loop current, real-time terminal voltage and effective charge-discharge cycle number of the battery based on the updated protection threshold, and obtain the abnormal status result of the battery. An active fuse execution module is used to perform abnormal response processing on the battery based on the abnormal state result, so as to drive the battery to perform fuse protection action.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention statistically analyzes the effective charge-discharge cycles of a battery, performs state parameter coupling and time-series trend analysis on multi-source data, accurately captures the resistance change trend, voltage drop rate, and temperature rise, comprehensively determines the cycle life decay state, realizes dynamic updating of protection thresholds, improves the adaptability of protection thresholds to the actual aging state of the battery, and enhances the accuracy of fuse protection.

[0016] 2. This invention performs real-time safety assessment based on the updated protection threshold, and combines it with a two-level fuse protection mechanism to quickly respond to abnormal states, ensuring that the fuse action is triggered in a timely manner and executed reliably. This not only protects the safety of battery use, but also optimizes the rationality of protection actions, effectively improving the overall efficiency of active fuse protection for mobile power bank batteries. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an embodiment of the active fuse protection method for mobile power bank batteries based on cycle life provided by the present invention. Figure 2 A functional block diagram of a mobile power bank battery active fuse protection system based on cycle life provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a method for active battery fuse protection based on cycle life. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a method for active battery fuse protection based on cycle life according to an embodiment of the present invention. In this embodiment, the method for active battery fuse protection based on cycle life includes: S1. Count the effective charge-discharge cycle number of the battery and perform state parameter coupling on the multi-source data of the battery to obtain the electrochemical parameter data of the battery. In this embodiment of the invention, the step of counting the effective charge-discharge cycles of the battery and performing state-parameter coupling on the multi-source data of the battery to obtain the electrochemical parameter data of the battery includes: Record the changes in the state of charge of the battery, and use the cumulative number of such changes in state of charge as the effective charge-discharge cycle count of the battery; The battery's terminal voltage, loop current, battery temperature, and internal equivalent DC resistance are collected to obtain multi-source data of the battery. The multi-source data is timestamped to obtain multi-mode time synchronization data of the multi-source data; The multi-mode time synchronization data is fused to obtain the electrochemical parameter data of the battery.

[0021] The battery's state of charge (SOC) is continuously monitored throughout its entire lifespan. Once the SOC reaches 100% full charge, the discharge process is continuously tracked until the SOC drops to a preset minimum of 20%. The charging process is then monitored until the SOC returns to 100% full charge, thus completing one full SOC change. The cumulative number of such full SOC changes from the battery's first use to the present moment is directly used as the battery's effective charge-discharge cycle count.

[0022] By deploying voltage sensors at corresponding locations in the battery management system and connecting the two detection terminals of the sensors to the positive and negative terminals of the battery respectively, the potential difference between the positive and negative terminals is collected in real time to accurately obtain terminal voltage data. A current sensor is connected in series in the battery charging and discharging circuit to capture the specific magnitude and direction of the current in the circuit in real time to obtain circuit current data. A temperature sensor is closely attached to the core area of ​​the battery cell to ensure full contact between the sensor and the cell surface, and the actual temperature data of the cell is collected in real time. By integrating a resistance detection module inside the battery, which is connected to the internal circuit of the battery through a dedicated detection circuit, the equivalent DC resistance data inside the battery is directly measured. The four types of data collected—terminal voltage, circuit current, battery temperature, and internal equivalent DC resistance—are organized and archived in chronological order of collection time, and integrated together to form multi-source data of the battery.

[0023] Using the system clock in the battery management system with millisecond-level accuracy as a unified benchmark, a unique time stamp containing the year, month, day, hour, minute, second, and millisecond is added to each piece of collected terminal voltage data, loop current data, battery temperature data, and internal equivalent DC resistance data. Then, a time stamp index table is established, and the data is searched sequentially according to the order of the time stamps. Terminal voltage data, loop current data, battery temperature data, and internal equivalent DC resistance data with the same time stamp are associated one-to-one, ensuring that all multi-source data are completely synchronized in the time dimension without any time difference, thus obtaining multi-mode time synchronization data of multi-source data.

[0024] The time-synchronized terminal voltage, loop current, battery temperature, and internal equivalent DC resistance data are correlated and integrated. Data screening criteria are set according to the battery's operating characteristics. Terminal voltage data must be within the reasonable fluctuation range of the battery's nominal voltage, loop current data must conform to the normal variation range under charge and discharge conditions, temperature data must be within the battery's safe operating temperature range, and the deviation of equivalent DC resistance data from the battery's nominal resistance must be controlled within the allowable range. Valid information in each type of data is screened according to these criteria, removing duplicate records and abnormal data such as jumps caused by sensor momentary failures or external interference. Key information reflecting the battery's electrochemical characteristics is extracted from various valid data, such as the stable value range of terminal voltage, the average rate of change of loop current, the temperature variation range, and the stable reading of equivalent DC resistance. This key information is integrated into a structured dataset according to a unified data format, ultimately yielding the battery's electrochemical parameter data.

[0025] The beneficial effects include accurately obtaining the effective charge-discharge cycle count of the battery, precisely reflecting the actual wear and tear of the battery, comprehensively collecting four core data types—terminal voltage, loop current, battery temperature, and internal equivalent DC resistance—to form multi-source data. Millisecond-level timestamp alignment ensures that all data are completely synchronized in the time dimension, avoiding subsequent analysis deviations caused by data asynchrony. Furthermore, rigorous data screening and key information extraction complete data fusion, effectively eliminating invalid and abnormal data, ensuring the accuracy, completeness, and reliability of electrochemical parameter data. This provides high-quality, highly reliable data support for subsequent time-series trend analysis of electrochemical parameter data, guaranteeing the accuracy and effectiveness of subsequent analysis steps.

[0026] S2. Perform time-series trend analysis on the electrochemical parameter data to obtain the resistance change trend, voltage drop rate, and temperature rise of the battery. In this embodiment of the invention, the step of performing time-series trend analysis on the electrochemical parameter data to obtain the resistance change trend, voltage drop rate, and temperature rise of the battery includes: Multidimensional time-series feature decoupling is performed on the electrochemical parameter data to obtain the battery state evolution characteristics of the electrochemical parameter data; The state evolution characteristics of the battery are reconstructed into a state vector to obtain the degradation feature vector of the battery. By performing trend parameter analysis on the degradation feature vector, the resistance change trend, voltage drop rate, and temperature rise of the battery can be obtained.

[0027] The acquired electrochemical parameter data was comprehensively categorized and organized. This data includes four core types of data collected at different time points throughout the monitoring cycle: terminal voltage, loop current, battery temperature, and internal equivalent DC resistance. Independent data groups were established for each data type, and all data were precisely broken down into their corresponding dimensions. Data was arranged chronologically according to the monitoring cycle for each dimension, and time distribution curves were plotted to clearly show the data's distribution pattern over time. By comparing the correlation of changes in data from different dimensions at the same time point, the degree of mutual influence between data dimensions was analyzed. Superimposed interference information caused by inter-dimensional interactions was removed, and the changing characteristics of each dimension over time were extracted individually, including key information such as peak values, trough values, stable intervals, amplitude of change, and duration. This yielded a complete picture of the battery's state evolution characteristics, reflecting the gradual evolution of the battery's operating state from its initial state to the current moment.

[0028] Using a uniformly distributed timeline as the core framework, the extracted battery state evolution characteristics across various dimensions—terminal voltage, loop current, battery temperature, and internal equivalent DC resistance—are systematically integrated. Based on the total monitoring period, key time nodes are divided at fixed time intervals to ensure even coverage of the entire monitoring process without missing any critical periods. For each key time node, the state evolution characteristics of each dimension are precisely quantified, clearly recording the specific numerical performance of each characteristic at that node. Following a pre-defined dimensional order of terminal voltage, loop current, battery temperature, and internal equivalent DC resistance, the quantified characteristic data corresponding to each dimension of each key time node are sequentially entered, forming a matrix-style structured vector. Each vector element corresponds to a specific dimension of battery state characteristics at a particular time node, ultimately yielding a degradation feature vector that comprehensively reflects the entire process of battery performance degradation from normal to gradual decline.

[0029] For the specific dimensions of resistance, voltage, and temperature in the degradation feature vector, the numerical changes of each dimension are analyzed sequentially according to key time nodes. The time intervals for all key time nodes are kept consistent. For resistance-related data, the values ​​of two adjacent key time nodes are extracted, and the difference between the values ​​of the latter and former nodes is calculated. The positive and negative proportions of all adjacent node differences are statistically analyzed. If the proportion of positive values ​​exceeds half, the overall trend is considered to be increasing; if the proportion of negative values ​​exceeds half, the overall trend is considered to be decreasing. The trend of battery resistance change is summarized by combining all the difference patterns. For voltage-related data, the voltage values ​​of adjacent key time nodes are extracted. The voltage value of the former node is subtracted from the voltage value of the latter node to obtain the voltage decrease amplitude between adjacent nodes. This amplitude is divided by a fixed time interval to calculate the voltage decrease rate per unit time, which is the battery voltage drop rate. For temperature-related data, extract the temperature values ​​of adjacent key time nodes, subtract the temperature value of the previous node from the temperature value of the next node, and statistically obtain the temperature rise between adjacent nodes. Divide this rise by a fixed time interval to calculate the temperature rise rate per unit time, thus obtaining the temperature rise of the battery.

[0030] The beneficial effects include a step-by-step analytical approach—classification, vector reconstruction, and trend analysis—to process electrochemical parameter data in depth, accurately separating time-series features across various dimensions, avoiding feature distortion caused by inter-dimensional interference, and clearly constructing feature vectors that comprehensively reflect the battery performance degradation process, making the battery degradation state more intuitive and identifiable. Simultaneously, it accurately extracts three core trend data categories: resistance change trend, voltage drop rate, and temperature rise, covering the dynamic changes of key battery performance indicators. This provides comprehensive, accurate, and highly reliable trend data support for subsequent comprehensive assessment of battery cycle life degradation, ensuring the accuracy and reliability of subsequent life degradation determination results.

[0031] S3. Based on the resistance change trend, the voltage drop rate, and the temperature rise, comprehensively determine the cycle life degradation state of the battery; In this embodiment of the invention, the step of comprehensively determining the cycle life degradation state of the battery based on the resistance change trend, the voltage drop rate, and the temperature rise includes: The degradation sensitivity analysis is performed on the resistance change trend, the voltage drop rate, and the temperature rise to obtain the contribution weight of the battery; Based on the contribution weights, the resistance change trend, the voltage drop rate, and the temperature rise are weighted and fused to obtain the fusion aging factor of the battery. Based on the fusion aging factor, the battery is subjected to life degradation mapping to obtain the preliminary degradation level of the battery; The initial degradation level is compared with the historical state information of the battery to verify the degradation trajectory and confirm the cycle life degradation state of the battery.

[0032] The formula for calculating the fusion aging factor is as follows: ; In the formula, The fusion aging factor, The trend of the resistance change. The voltage drop rate, The temperature rise situation. The weight is assigned to the resistance change trend. Contribute weight to the rate of voltage drop. Contribute weight to temperature rise. To take the absolute value, This is the square root operation.

[0033] Degradation sensitivity analysis was conducted on resistance change trends, voltage drop rates, and temperature rise. A large number of battery samples of the same model as the target battery were collected, covering different usage stages including brand new, lightly used, moderately used, heavily used, and near-end-of-life states. For each stage, data on resistance change trends, voltage drop rates, and temperature rises were continuously collected, along with the corresponding cycle life degradation data, ensuring that each set of trend data was supported by clear lifespan degradation data. All collected data were categorized and organized according to usage stage, establishing a correlation database between resistance change trends, voltage drop rates, temperature rises, and cycle life degradation. The database clearly labeled each data point with auxiliary information such as battery usage time, charge / discharge cycle count, and environmental usage conditions. By analyzing the data variation patterns in the database, a fixed standard for the magnitude of change was set, determined based on the battery's nominal performance parameters. The specific change in cycle life degradation was calculated for each trend data point increasing by this fixed magnitude. If for each increase of the resistance change trend by this fixed amount, the corresponding change in the degree of cycle life decay is significantly greater than the change in the degree of decay when the voltage drop rate and temperature rise increase by the same amount, then the resistance change trend is determined to be more sensitive to the life decay, and is given a higher contribution weight. Similarly, the changes in the degree of life decay corresponding to the voltage drop rate and temperature rise are calculated sequentially, sorted according to the magnitude of the changes, and assigned corresponding contribution weights respectively, finally obtaining three contribution weights that match each trend data.

[0034] Based on the three contribution weights, a weighted fusion of resistance change trend, voltage drop rate, and temperature rise is performed. First, the resistance change trend data is preprocessed by adding one to its original value to avoid calculation anomalies caused by zero or negative values. Then, the natural logarithm of this addition is calculated to smooth the numerical fluctuations of the resistance change trend, making the data more reflective of the true aging process. The logarithmic result is then multiplied by the contribution weight corresponding to the resistance change trend to obtain the first part of the value. Next, the absolute value of the voltage drop rate is taken to eliminate the influence of the voltage drop direction on the calculation result, retaining only the core information of the drop magnitude. This absolute value is multiplied by the contribution weight corresponding to the voltage drop rate to obtain the second part of the value. Finally, the original value of the temperature rise is squared to amplify the weight of the temperature rise's impact on battery aging, as abnormal temperature increases usually have a more significant destructive effect on battery life. The squared result is then multiplied by the contribution weight corresponding to the temperature rise to obtain the third part of the value. The values ​​in the first, second, and third parts are added together to obtain the sum of the three data items. At the same time, the three contribution weights are squared separately, and the results of the three squares are added together to obtain the weight sum of squares. The square root of the weight sum of squares is calculated, and the weights are normalized by the square root operation. Finally, the sum of the three values ​​obtained above is divided by this square root to obtain the fusion aging factor that can comprehensively reflect the overall aging degree of the battery.

[0035] A pre-defined standard for classifying battery life degradation levels is established based on extensive aging test data of similar batteries. This standard clearly defines the numerical range of the fusion aging factor corresponding to different degradation levels. The degradation levels are divided into five tiers from slight to severe: slight degradation, mild degradation, moderate degradation, severe degradation, and critical degradation. Each tier corresponds to a fixed and non-overlapping numerical range. The boundaries of the numerical ranges are determined through statistical analysis of key nodes in battery life degradation, ensuring clear division of adjacent tiers without any ambiguous areas. The fusion aging factor, calculated through weighted fusion, is compared one by one with the pre-defined numerical range. First, it is determined which range the specific value of the fusion aging factor falls into. If the fusion aging factor is in the smallest range, the battery is classified as slightly degraded; if it is in a larger range, it is classified as more severely degraded, and so on. This direct correspondence method completes the life degradation mapping, obtaining the initial degradation level of the battery. During the mapping process, the deviation between the specific value of the fusion aging factor and the corresponding range is recorded, providing a reference for subsequent verification.

[0036] Historical battery status information is retrieved and stored in a chronologically archived database. This includes the effective charge-discharge cycle count, electrochemical parameter data, resistance trends, voltage drop rates, temperature rise, and corresponding degradation levels from the battery's initial use. This ensures the completeness and continuity of the historical data. The preliminary degradation level obtained this time is compared stage by stage with the degradation levels in the historical status information. The comparison is divided into multiple stages based on the interval of charge-discharge cycles to analyze the overall trajectory of battery degradation and check whether the preliminary degradation level is consistent with the historical degradation trend. If the battery has historically been in a state of slow degradation, and the degree of degradation in the initial stage conforms to this slow evolution pattern and is consistent with the change in degradation level in adjacent stages, then the initial degradation level is confirmed to be reasonable. If the initial degradation level deviates from the historical degradation trajectory, such as a sudden and significant increase or decrease in the degree of degradation that exceeds the reasonable range of historical changes, then the calculation process of the fusion aging factor should be re-examined to check for errors in each step of the calculation. Then, it should be checked whether the life degradation mapping standard is suitable for the actual use scenario of the current battery. If there is a mismatch, the mapping standard should be adjusted until the initial degradation level is consistent with the historical degradation trajectory, and finally the cycle life degradation status of the battery is confirmed.

[0037] The beneficial effects include: by collecting sample data covering multiple usage stages and analyzing the system's degradation sensitivity, accurately determining the contribution weights of resistance change trends, voltage drop rates, and temperature rises to battery life degradation, ensuring that the effects of each influencing factor are reasonably quantified, and avoiding judgment bias caused by a single factor dominating. The weighted fusion process, through multi-step numerical preprocessing and computation, effectively integrates trend data from three different dimensions into a unified aging factor, comprehensively and objectively reflecting the overall aging state of the battery, eliminating the impact of data fluctuations and dimensional differences. Life degradation mapping is based on a clearly defined numerical range established from experimental data, achieving a precise correspondence between the fused aging factor and the initial degradation level, ensuring the consistency and accuracy of level determination. Degradation trajectory verification, combined with complete historical battery state information, performs layer-by-layer verification and correction of the initial degradation level, avoiding judgment errors caused by single data or accidental factors. The entire process is progressive and logically rigorous, ensuring that the final battery cycle life degradation state has extremely high accuracy and reliability, providing a precise and comprehensive decision-making basis for subsequent updates to protection thresholds based on degradation status, and ensuring a high degree of fit between the protection threshold and the actual battery state.

[0038] S4. Based on the cycle life decay state, the protection threshold of the battery is matched with the decay level to update the protection threshold of the battery. In this embodiment of the invention, the step of matching the battery's protection threshold with the degradation level based on the cycle life degradation state to update the battery's protection threshold includes: The degree of degradation is calibrated based on the cycle life degradation state to obtain the degradation level of the battery; The attenuation level is mapped to a preset level protection strategy correspondence table to obtain the protection strategy for the battery; Based on the protection strategy, the protection threshold of the battery is reconstructed to obtain the updated protection threshold of the battery.

[0039] Based on the confirmed battery cycle life degradation state, this state integrates core information such as resistance change trend, voltage drop rate, temperature rise, and fusion aging factor, and pre-defined degradation degree calibration standards. Degradation is divided into five levels from low to high: slight degradation, mild degradation, moderate degradation, severe degradation, and critical degradation. Each level corresponds to specific judgment criteria. Slight degradation corresponds to the fusion aging factor being in the minimum range and resistance, voltage, and temperature changes conforming to the initial degradation characteristics; mild degradation corresponds to the fusion aging factor being in the second minimum range and the trends slightly accelerating; moderate degradation corresponds to the fusion aging factor being in the middle range and the change trend being obvious; severe degradation corresponds to the fusion aging factor being in the larger range and the change trend being drastic; and critical degradation corresponds to the fusion aging factor being in the maximum range and approaching the end-of-life threshold. Each characteristic of the current cycle life degradation state is compared with the pre-defined calibration standards one by one. The level that is fully met is directly calibrated as that level, ultimately yielding the battery's degradation level.

[0040] A pre-defined protection strategy correspondence table is provided. This table was developed based on extensive data from degradation simulation experiments, safety performance tests, and actual use of similar batteries. The table clearly defines the one-to-one correspondence between each degradation level and its corresponding protection strategy. Slight degradation corresponds to a standard protection strategy, maintaining basic battery safety standards; mild degradation corresponds to a moderately enhanced protection strategy, slightly tightening key protection indicators; moderate degradation corresponds to a strengthened protection strategy, comprehensively tightening core protection indicators; severe degradation corresponds to a strict protection strategy, significantly lowering protection thresholds and increasing monitoring frequency; and critical degradation corresponds to an emergency protection strategy, activating the highest level of safety protection and limiting charging and discharging power. Using the calibrated degradation level as the search criterion, a matching entry is found in the pre-defined protection strategy correspondence table to directly obtain the corresponding protection strategy for that degradation level.

[0041] The initial protection thresholds of the battery are extracted, including core safety indicators such as the real-time loop current protection threshold and the real-time terminal voltage protection threshold. Based on the obtained protection strategy, the adjustment rules for each indicator are defined. For minor degradation levels, only a small correction is made to the initial protection threshold to ensure the effectiveness of basic protection. For mild degradation levels, the real-time loop current protection threshold is slightly reduced by a preset ratio, and the allowable fluctuation range of the real-time terminal voltage protection threshold is narrowed. For moderate degradation levels, the real-time loop current protection threshold is further reduced, the real-time terminal voltage fluctuation range is tightened, and the threshold monitoring cycle is shortened. For severe degradation levels, the real-time loop current protection threshold is significantly reduced, the safe range of the real-time terminal voltage is strictly limited, and the threshold verification frequency is increased. For critical degradation levels, the real-time loop current protection threshold is reduced to the lowest safe value, the real-time terminal voltage protection threshold is limited to the narrowest safe range, and the threshold effectiveness is continuously verified. The initial protection thresholds are adjusted and reconstructed item by item according to the above adjustment rules to ensure that each protection indicator accurately matches the requirements of the current protection strategy, ultimately resulting in the updated protection thresholds for the battery.

[0042] The beneficial effects are as follows: the battery degradation level is accurately determined by scientific degradation degree calibration standards; the degradation level and protection strategy are accurately matched by a preset level protection strategy correspondence table; the initial protection threshold is reconstructed in a targeted manner according to the protection strategy, so that the updated protection threshold is highly adapted to the actual cycle life degradation state of the battery. This avoids the safety hazards of insufficient protection when the degradation is severe, and prevents over-protection from affecting the user experience when the degradation is slight. It significantly improves the rationality and accuracy of the protection threshold, and provides a reliable benchmark for the final state safety judgment based on the updated protection threshold, ensuring the safety protection effect of the battery throughout its entire life cycle.

[0043] S5. Based on the updated protection threshold, perform a final state safety judgment on the real-time loop current, real-time terminal voltage and effective charge-discharge cycle number of the battery to obtain the abnormal state result of the battery. In this embodiment of the invention, the step of performing a final state safety assessment on the real-time loop current, real-time terminal voltage, and effective charge-discharge cycle count of the battery based on the updated protection threshold to obtain the abnormal state result of the battery includes: The updated protection threshold and the preset cycle life safety threshold are combined to obtain the safety criterion set of the battery; The real-time loop current, real-time terminal voltage, and effective charge-discharge cycle number of the battery are aligned in the time domain to obtain the real-time state parameters of the battery. Based on the aforementioned safety criterion set, real-time safety assessment is performed on the real-time state parameters to obtain the abnormal state results of the battery.

[0044] The step of performing real-time safety assessment on the real-time state parameters based on the safety criterion set to obtain the abnormal state result of the battery includes: Based on the safety criterion set, parallel threshold comparison is performed on the real-time state parameters to obtain the battery's electrical parameter comparison result and cycle count comparison result; Diagnostic status labeling is performed on the electrical parameter comparison results and the cycle count comparison results to obtain the battery's electrical parameter abnormality flag and life end abnormality flag; A comprehensive anomaly assessment is performed on the electrical parameter anomaly flag and the end-of-life anomaly flag to obtain the abnormal state result of the battery.

[0045] The updated protection threshold is obtained. This threshold is precisely adjusted based on the battery's cycle life degradation state. The real-time loop current protection threshold and real-time terminal voltage protection threshold corresponding to each degradation level have been verified through multiple sets of experiments with the same type of battery to ensure a high degree of compatibility with the actual aging state of the battery. At the same time, the preset cycle life safety threshold is retrieved. This threshold is obtained by conducting full life cycle charge and discharge cycle tests on thousands of the same type of battery, recording the effective charge and discharge cycle count when a safety hazard occurs in each battery, and taking the minimum value among all test data as a fixed value, which represents the upper limit of the maximum effective charge and discharge cycle count that the battery can safely operate, and can cover the most stringent safety scenarios. The updated protection thresholds for the two types of electrical parameters are categorized and integrated with the preset cycle life safety thresholds, arranged in the order of real-time loop current protection threshold, real-time terminal voltage protection threshold, and cycle life safety threshold. A unique identifier and corresponding safety judgment logic description are established for each threshold. The real-time loop current protection threshold clearly indicates the maximum allowable current value, the real-time terminal voltage protection threshold indicates the upper and lower limits, and the cycle life safety threshold indicates the specific number of cycles. The safety judgment criteria for each threshold are described in detail in text form, namely, the real-time loop current must not exceed the indicated maximum current value, the real-time terminal voltage must be within the indicated upper and lower limits, and the effective charge and discharge cycle count must not exceed the indicated maximum number of cycles to avoid ambiguity. The values, identifiers, judgment criteria, and applicable scenarios of these thresholds are integrated into a structured set of safety criteria for easy retrieval and application during subsequent real-time analysis.

[0046] The battery management system (BMS) uses high-precision Hall effect current sensors and differential voltage sensors to collect real-time loop current and terminal voltage data from the battery. The current sensor, installed in the main charging / discharging circuit, captures the instantaneous loop current value through electromagnetic induction. The voltage sensor is directly connected to the battery's positive and negative terminals and uses a differential measurement method to eliminate interference. Both sensors collect data every millisecond and convert it into a digital signal, while simultaneously recording millisecond-level timestamps generated by the BMS to ensure data accuracy. The system also retrieves the count of effective charge / discharge cycles, stored in the BMS's non-volatile memory. This count is automatically incremented after each complete charge / discharge cycle, and the latest value is read directly during retrieval. Using the system clock of the battery management system as a unified benchmark, a time-stamped index database is established. Real-time loop current data and real-time terminal voltage data collected every millisecond are recorded into the database according to the time stamp. The effective charge-discharge cycle count corresponding to the same time stamp is then associated with that entry. Each time stamp is checked to see if the three types of data exist. If any type of data is missing for a certain time stamp, all data corresponding to that time stamp is discarded, ensuring that the three types of data are completely synchronized in the time dimension without any time difference. The synchronized three types of data are organized and archived in a structured format of "time stamp - real-time loop current - real-time terminal voltage - effective charge-discharge cycle count", and updated once every millisecond to obtain the real-time status parameters of the battery, ensuring the real-time performance and integrity of the data.

[0047] Based on the thresholds in the safety criterion set, three independent comparison threads are launched to simultaneously perform parallel threshold comparisons on three types of data in the real-time status parameters, significantly improving the judgment efficiency. One thread is specifically responsible for comparing the real-time loop current with the real-time loop current protection threshold. It compares the instantaneous value of the real-time loop current with the maximum allowable current value in the safety criterion set every millisecond. If the instantaneous value is greater than the maximum allowable current value, the electrical parameter comparison result at that time point is determined to be abnormal. Another thread is responsible for comparing the real-time terminal voltage with the real-time terminal voltage protection threshold. It compares the instantaneous value of the real-time terminal voltage with the upper and lower limits in the safety criterion set. If the instantaneous value is higher than the upper limit or lower than the lower limit, the electrical parameter comparison result at that time point is determined to be abnormal. The third thread is responsible for comparing the effective charge-discharge cycle count with the cycle life safety threshold. It compares the current effective charge-discharge cycle count with the maximum allowable count in the safety criterion set. If the current count is greater than the maximum allowable count, the cycle count comparison result is determined to be abnormal. Each comparison thread outputs a comparison result every millisecond. If any electrical parameter comparison thread outputs an abnormal result, the overall electrical parameter comparison result is abnormal. Only when both electrical parameter comparison threads output normal results is the electrical parameter comparison result normal. The output of the loop count comparison thread is directly used as the loop count comparison result, and finally, a clear electrical parameter comparison result and loop count comparison result are obtained.

[0048] Based on the results of parallel threshold comparisons, diagnostic status labels are assigned, establishing an anomaly label database containing label type, label code, anomaly description, associated time stamp, and specific value. If the electrical parameter comparison result is abnormal, the system automatically generates an electrical parameter anomaly label record in the anomaly label database, with the label code corresponding to "electrical parameter anomaly," and the anomaly description clearly indicating whether the real-time loop current exceeds the limit, the real-time terminal voltage exceeds the limit, or both exceed the limit, while also associating the corresponding time stamp and the specific value exceeding the limit. If the cycle count comparison result is abnormal, a lifespan end anomaly label record is generated, with the label code corresponding to "lifespan end anomaly," and the anomaly description indicating the current effective charge / discharge cycle count and the specific value of the cycle life safety threshold, also associating the corresponding time stamp. If both types of comparison results are normal, no record is generated in the anomaly label database; if both types of comparison results are abnormal, two records, one for electrical parameter anomaly and one for lifespan end anomaly, are generated simultaneously to ensure complete recording of the anomaly type and provide a clear basis for subsequent comprehensive evaluation.

[0049] A comprehensive anomaly assessment is performed on the obtained electrical parameter anomaly flags and end-of-life anomaly flags. Records in the anomaly flag database are read periodically, and anomaly information is sorted out in chronological order. If the anomaly flag database contains only electrical parameter anomaly flag records and no end-of-life anomaly flag records, the anomaly descriptions of the electrical parameter anomaly flags are traversed to determine whether a single electrical parameter exceeds the standard or both electrical parameters exceed the standard. The final anomaly state of the battery is determined to be an electrical parameter anomaly, with the specific type of exceedance. If only end-of-life anomaly flag records exist and no electrical parameter anomaly flag records exist, the anomaly state is directly determined to be an end-of-life anomaly, and the difference between the current cycle number and the safety threshold is marked. If both electrical parameter anomaly flag records and end-of-life anomaly flag records exist, the anomaly state is determined to be a dual anomaly of electrical parameters and end-of-life, and the specific information of the two types of anomalies is integrated. If there are no anomaly flag records in the anomaly flag database, the anomaly state is determined to be normal. After each assessment, an assessment report containing the anomaly state result, the time of anomaly occurrence, and the specific anomaly parameters is generated, ultimately forming a clear and detailed battery anomaly state result.

[0050] The beneficial effects are that by integrating the updated protection thresholds with the preset cycle life safety thresholds, it incorporates both dynamic electrical parameter thresholds adapted to the current battery degradation state and fixed cycle life safety thresholds based on extensive experimental data, forming a comprehensive safety criterion set covering both electrical parameter safety and lifespan safety. This ensures that the judgment criteria are both targeted and without judgment loopholes. Time-domain parameter alignment employs high-precision sensors, millisecond-level acquisition frequencies, and a rigorous data synchronization verification mechanism to guarantee the time synchronization, data integrity, and acquisition accuracy of real-time status parameters from the source, avoiding misjudgments caused by data issues. Parallel threshold comparison improves judgment efficiency through multi-threaded synchronous processing, meeting real-time assessment needs. Diagnostic status labeling clearly locates anomaly details through structured records, and comprehensive anomaly assessment integrates various information to form a comprehensive result, avoiding bias. The entire process is logically rigorous and the steps are precise. Each step has undergone scientific design and rigorous verification to ensure that the obtained anomaly status results are accurate, comprehensive, real-time, and clearly directional. This provides a highly accurate and reliable decision-making basis for subsequent anomaly response processing and fuse protection actions, ensuring that fuse protection actions can accurately adapt to the anomaly type and improve the effectiveness of battery safety protection.

[0051] S6. Based on the abnormal state result, perform abnormal response processing on the battery to drive the battery to perform a fuse protection action.

[0052] In this embodiment of the invention, the step of performing abnormal response processing on the battery based on the abnormal state result to drive the battery to perform a fuse protection action includes: Based on the abnormal state result, a fuse trigger decision is made for the battery to obtain a reversible cut-off command for the battery, so as to control the primary fuse protection action of the battery. After the primary fuse protection is activated, the circuit current of the battery after the interruption is continuously monitored; Based on the continued existence of the circuit current after the interruption, the continuity status of the current path of the battery is verified, and an irreversible disconnection command for the battery is generated according to the verification result to control the secondary fuse protection action of the battery.

[0053] Based on the obtained abnormal battery status results, the abnormality type is clearly marked as electrical parameter abnormality, end-of-life abnormality, or dual abnormality. A preset fuse triggering decision rule is established: whenever the abnormal status result is abnormal, the fuse triggering process is initiated without additional judgment conditions. The decision system directly generates a reversible disconnection command for the battery based on the abnormal status result. This command includes key information such as trigger time, actuator, and action duration. The core function of the reversible disconnection command is to control the primary fuse protection action while preserving the possibility of subsequent recovery. The reversible disconnection command is sent to the electronic switching device in the battery main circuit via the battery management system's control bus. Upon receiving the command, the electronic switching device disconnects its internal contacts within 1 millisecond, cutting off the battery's charging and discharging main circuit, completing the primary fuse protection action. At this time, the battery only temporarily interrupts power output or input, without causing permanent structural damage.

[0054] The moment the primary fuse protection action is completed, a high-precision shunt resistor current sensor in the battery main circuit is immediately activated. This sensor, connected in series with the main circuit, can capture minute current changes and continuously collects circuit current data after the interruption at a frequency of once per millisecond. The collected current signal is converted into a standard voltage signal, which is then transmitted to the data processing unit of the battery management system after analog-to-digital conversion. Simultaneously, the time stamp of each acquisition is recorded, forming continuous circuit current timing data after the interruption. This ensures uninterrupted monitoring, comprehensively capturing the dynamic changes in the circuit current after the primary fuse trips, and leaving no detail of current fluctuations unnoticed.

[0055] Based on continuously collected circuit current timing data after interruption, a continuity verification standard is set. If the circuit current value collected for 10 consecutive milliseconds is zero, the battery current path is considered successfully disconnected. If any current value collected within 10 milliseconds is greater than zero, the current path is considered not completely disconnected or has reconnected. After verifying the continuity of the current path, if the verification result is successful disconnection and the abnormal state result is a slight electrical parameter abnormality, an irreversible cutoff command can be temporarily withheld, and continuous monitoring is sufficient. If the verification result is not disconnected or reconnected, or the abnormal state result is a life-end abnormality or a double abnormality, an irreversible cutoff command for the battery is immediately generated. This command is sent to the one-time thermal fuse inside the battery. Upon receiving the command, the device uses its internal heating wire to heat and melt the core conductive component, completely cutting off the current path. Once melted, it cannot be restored, completing the battery's secondary fuse protection action and ensuring the complete elimination of safety hazards.

[0056] The beneficial effects include precise triggering of fuse decisions based on clear abnormal state results; primary fuse protection action achieves rapid response and retains the possibility of recovery through reversible cut-off commands, avoiding irreversible losses caused by misjudgment; continuous monitoring of the circuit current after interruption ensures timely understanding of the fuse effect; on / off state verification provides a scientific basis for triggering secondary fuses; secondary fuses completely cut off dangerous paths through irreversible cut-off commands; the graded fuse mechanism takes into account both response speed and protection reliability; the entire process is logically coherent and executed accurately, effectively improving the safety and effectiveness of battery fuse protection, comprehensively responding to different types of abnormal states, and minimizing battery safety risks.

[0057] like Figure 2 The diagram shown is a functional block diagram of a mobile power bank battery active fuse protection system based on cycle life provided in an embodiment of the present invention.

[0058] The cycle life-based mobile power bank battery active fuse protection system 100 of the present invention can be installed in an electronic device. Depending on the functions implemented, the cycle life-based mobile power bank battery active fuse protection system 100 may include a data sensing and fusion module 101, a time-series feature analysis module 102, a lifespan degradation assessment module 103, a dynamic threshold decision module 104, a real-time status monitoring module 105, and an active fuse execution module 106. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0059] In this embodiment, the functions of each module / unit are as follows: The data sensing and fusion module 101 is used to count the effective charge-discharge cycle number of the battery and perform state parameter coupling on the multi-source data of the battery to obtain the electrochemical parameter data of the battery. The time-series feature analysis module 102 is used to perform time-series trend analysis on the electrochemical parameter data to obtain the resistance change trend, voltage drop rate and temperature rise of the battery. The life degradation assessment module 103 is used to comprehensively determine the cycle life degradation state of the battery based on the resistance change trend, the voltage drop rate and the temperature rise. The dynamic threshold decision module 104 is used to match the protection threshold of the battery with the degradation level according to the cycle life degradation state, so as to update the protection threshold of the battery. The real-time status monitoring module 105 is used to make a final status safety judgment on the real-time loop current, real-time terminal voltage and effective charge-discharge cycle number of the battery based on the updated protection threshold, and obtain the abnormal status result of the battery. The active fuse execution module 106 is used to perform abnormal response processing on the battery based on the abnormal state result, so as to drive the battery to perform fuse protection action.

[0060] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0061] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0062] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0063] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0064] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for active fuse protection of mobile power bank batteries based on cycle life, characterized in that, The method includes: S1. Count the effective charge-discharge cycle number of the battery and perform state parameter coupling on the multi-source data of the battery to obtain the electrochemical parameter data of the battery. S2. Perform time-series trend analysis on the electrochemical parameter data to obtain the resistance change trend, voltage drop rate, and temperature rise of the battery. S3. Based on the resistance change trend, the voltage drop rate, and the temperature rise, comprehensively determine the cycle life degradation state of the battery; S4. Based on the cycle life decay state, the protection threshold of the battery is matched with the decay level to update the protection threshold of the battery. S5. Based on the updated protection threshold, perform a final state safety judgment on the real-time loop current, real-time terminal voltage and effective charge-discharge cycle number of the battery to obtain the abnormal state result of the battery. S6. Based on the abnormal state result, perform abnormal response processing on the battery to drive the battery to perform a fuse protection action.

2. The active fuse protection method for mobile power bank batteries based on cycle life as described in claim 1, characterized in that, The effective charge-discharge cycle count of the battery is statistically analyzed, and state-parameter coupling is performed on the multi-source data of the battery to obtain the electrochemical parameter data of the battery, including: Record the changes in the state of charge of the battery, and use the cumulative number of such changes in state of charge as the effective charge-discharge cycle count of the battery; The battery's terminal voltage, loop current, battery temperature, and internal equivalent DC resistance are collected to obtain multi-source data of the battery. The multi-source data is timestamped to obtain multi-mode time synchronization data of the multi-source data; The multi-mode time synchronization data is fused to obtain the electrochemical parameter data of the battery.

3. The active fuse protection method for mobile power bank batteries based on cycle life as described in claim 1, characterized in that, The step of performing time-series trend analysis on the electrochemical parameter data to obtain the resistance change trend, voltage drop rate, and temperature rise of the battery includes: Multidimensional time-series feature decoupling is performed on the electrochemical parameter data to obtain the battery state evolution characteristics of the electrochemical parameter data; The state evolution characteristics of the battery are reconstructed into a state vector to obtain the degradation feature vector of the battery. By performing trend parameter analysis on the degradation feature vector, the resistance change trend, voltage drop rate, and temperature rise of the battery can be obtained.

4. The active fuse protection method for mobile power bank batteries based on cycle life as described in claim 1, characterized in that, The method of comprehensively determining the cycle life degradation state of the battery based on the resistance change trend, the voltage drop rate, and the temperature rise includes: The degradation sensitivity analysis is performed on the resistance change trend, the voltage drop rate, and the temperature rise to obtain the contribution weight of the battery; Based on the contribution weights, the resistance change trend, the voltage drop rate, and the temperature rise are weighted and fused to obtain the fusion aging factor of the battery. Based on the fusion aging factor, the battery is subjected to life degradation mapping to obtain the preliminary degradation level of the battery; The initial degradation level is compared with the historical state information of the battery to verify the degradation trajectory and confirm the cycle life degradation state of the battery.

5. The active fuse protection method for mobile power bank batteries based on cycle life as described in claim 4, characterized in that, The formula for calculating the fusion aging factor is as follows: ； In the formula, The fusion aging factor, The trend of the resistance change. The voltage drop rate, The temperature rise situation. The weight is assigned to the resistance change trend. Contribute weight to the rate of voltage drop. Contribute weight to temperature rise. To take the absolute value, This is the square root operation.

6. The active fuse protection method for mobile power bank batteries based on cycle life as described in claim 1, characterized in that, The step of matching the battery's protection threshold with the degradation level based on the cycle life degradation state to update the battery's protection threshold includes: The degree of degradation is calibrated based on the cycle life degradation state to obtain the degradation level of the battery; The attenuation level is mapped to a preset level protection strategy correspondence table to obtain the protection strategy for the battery; Based on the protection strategy, the protection threshold of the battery is reconstructed to obtain the updated protection threshold of the battery.

7. The active fuse protection method for mobile power bank batteries based on cycle life as described in claim 1, characterized in that, Based on the updated protection threshold, a final state safety assessment is performed on the real-time loop current, real-time terminal voltage, and effective charge-discharge cycle count of the battery to obtain the abnormal state result of the battery, including: The updated protection threshold and the preset cycle life safety threshold are combined to obtain the safety criterion set of the battery; The real-time loop current, real-time terminal voltage, and effective charge-discharge cycle number of the battery are aligned in the time domain to obtain the real-time state parameters of the battery. Based on the aforementioned safety criterion set, real-time safety assessment is performed on the real-time state parameters to obtain the abnormal state results of the battery.

8. The active fuse protection method for mobile power bank batteries based on cycle life as described in claim 7, characterized in that, The step of performing real-time safety assessment on the real-time state parameters based on the safety criterion set to obtain the abnormal state result of the battery includes: Based on the safety criterion set, parallel threshold comparison is performed on the real-time state parameters to obtain the battery's electrical parameter comparison result and cycle count comparison result; Diagnostic status labeling is performed on the electrical parameter comparison results and the cycle count comparison results to obtain the battery's electrical parameter abnormality flag and life end abnormality flag; A comprehensive anomaly assessment is performed on the electrical parameter anomaly flag and the end-of-life anomaly flag to obtain the abnormal state result of the battery.

9. The active fuse protection method for mobile power bank batteries based on cycle life as described in claim 1, characterized in that, The step of performing anomaly response processing on the battery based on the abnormal state result to drive the battery to perform a fuse protection action includes: Based on the abnormal state result, a fuse trigger decision is made for the battery to obtain a reversible cut-off command for the battery, so as to control the primary fuse protection action of the battery. After the primary fuse protection is activated, the circuit current of the battery after the interruption is continuously monitored; Based on the continued existence of the circuit current after the interruption, the continuity status of the current path of the battery is verified, and an irreversible disconnection command for the battery is generated according to the verification result to control the secondary fuse protection action of the battery.

10. A mobile power bank battery active fuse protection system based on cycle life, characterized in that, For implementing the active fuse protection method for mobile power bank batteries based on cycle life as described in claim 1, the system comprises: The data sensing and fusion module is used to count the effective charge-discharge cycle number of the battery and perform state parameter coupling on the multi-source data of the battery to obtain the electrochemical parameter data of the battery. The time-series feature analysis module is used to perform time-series trend analysis on the electrochemical parameter data to obtain the resistance change trend, voltage drop rate, and temperature rise of the battery. The life degradation assessment module is used to comprehensively determine the cycle life degradation status of the battery based on the resistance change trend, the voltage drop rate, and the temperature rise. The dynamic threshold decision module is used to match the protection threshold of the battery with the degradation level according to the cycle life degradation state, so as to update the protection threshold of the battery. The real-time status monitoring module is used to make a final status safety judgment on the real-time loop current, real-time terminal voltage and effective charge-discharge cycle number of the battery based on the updated protection threshold, and obtain the abnormal status result of the battery. An active fuse execution module is used to perform abnormal response processing on the battery based on the abnormal state result, so as to drive the battery to perform fuse protection action.

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