Power battery health state evaluation and life early warning method

Abstract

The invention relates to the technical field of power battery evaluation, and discloses a power battery health state evaluation and life early warning method. The method comprises the following steps: dividing a target power battery module into a plurality of layers of evaluation areas, and randomly selecting a plurality of measurement position points in each layer of evaluation area; then, collecting battery state original data of each measurement position point of each layer of evaluation area in real time, and comprehensively analyzing the data to obtain a health state evaluation value of the target power battery module; judging and analyzing the health state evaluation value and a preset health state threshold value; if the health state evaluation value is lower than a preset threshold value, triggering a preset life early warning measure; and if the health state evaluation value is higher than a preset threshold value, maintaining the current evaluation period. The method can improve the accuracy of battery health state evaluation, optimizes the time of life early warning, and is suitable for health management of various power battery modules.

Description

Technical Field

[0001] This invention relates to the field of power battery evaluation technology, specifically to a method for evaluating the health status and lifespan of power batteries. Background Technology

[0002] With the rapid development of the new energy industry, power batteries, as core components of new energy vehicles and energy storage systems, directly affect the reliable operation of the entire system due to their operational safety and stability. During long-term use, power batteries are affected by various factors such as charge-discharge cycles, temperature changes, vibration, and shock. As a result, phenomena such as electrode material aging, electrolyte decomposition, and separator performance degradation gradually occur inside the battery, leading to a continuous decline in battery health. If the battery health status is not monitored and effectively warned in time, it may cause a sudden drop in battery performance, a shortened driving range, or even safety hazards such as thermal runaway, fire, and explosion.

[0003] Currently, most industry methods for assessing the health status of power batteries adopt a holistic measurement approach, which involves selecting a few fixed measurement points across the entire power battery module for data collection. This method ignores issues such as temperature differences and uneven current distribution that occur in different areas within the battery module during use. Consequently, the raw data collected lacks representativeness and fails to accurately reflect the true health status of each area of ​​the battery module, leading to a significant deviation between the final health status assessment and the actual situation.

[0004] Existing assessment methods often focus only on changes in a single parameter (such as voltage or temperature) during the data analysis phase, lacking systematic integration and correlation analysis of multi-dimensional battery state data, thus failing to comprehensively capture the changing patterns of battery health status. Regarding lifespan warnings, most methods set relatively simple warning thresholds, failing to consider the differences in battery health status under different usage scenarios and aging levels. This leads to inaccurate timing of warning measures, either triggering too early, causing unnecessary increased maintenance costs, or triggering too late, failing to mitigate safety risks in time. Furthermore, existing assessment methods often have fixed assessment cycles, unable to dynamically adjust according to actual changes in battery health status. Frequent assessments are performed even when battery health is good, resulting in wasted resources, while excessively long assessment cycles may cause missed opportunities for optimal intervention when abnormal changes in battery health occur. Summary of the Invention

[0005] The purpose of this invention is to provide a method for assessing the health status and providing early warning of the lifespan of a power battery, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a method for assessing the health status and providing early warning of the lifespan of a power battery, the method comprising:

[0007] The target power battery module is divided into several evaluation areas, and several measurement locations are randomly selected for each evaluation area.

[0008] The raw battery status data at each measurement point in each evaluation area within the target power battery module is collected in real time and comprehensively analyzed to obtain the health status evaluation value of the target power battery module.

[0009] The health status assessment value of the target power battery module is compared with the preset health status threshold for judgment and analysis.

[0010] If the health status assessment value of the target power battery module is lower than the preset health status threshold, a preset lifespan warning measure will be triggered.

[0011] If the target power battery evaluation value is higher than the preset health status threshold, the current evaluation cycle will be maintained.

[0012] Preferably, the raw battery status data includes battery voltage values, battery temperature values, and battery internal resistance values ​​at several time points.

[0013] Preferably, the step of obtaining the health status assessment value of the target power battery module includes:

[0014] The battery voltage, battery temperature, and battery internal resistance values ​​at each measurement point in each evaluation area within the target power battery module are read and comprehensively analyzed to obtain the average battery voltage, average battery temperature, and average battery internal resistance measurements for each evaluation area.

[0015] The average battery voltage measurement, average battery temperature measurement, and average battery internal resistance measurement of each evaluation area are comprehensively analyzed to generate the voltage distribution feature set, temperature distribution feature set, and internal resistance distribution feature set of the target power battery module.

[0016] The health status assessment value is obtained by jointly analyzing the voltage distribution feature set, temperature distribution feature set, and internal resistance distribution feature set.

[0017] Preferably, the step of triggering the preset lifespan warning measure includes:

[0018] The difference between the health status assessment value of the target power battery module and the preset health status threshold is calculated to obtain the health status deviation value.

[0019] The current operating status data and historical operating benchmark data of the target power battery module are obtained, wherein the current operating status data includes the current charge / discharge rate value and the current ambient temperature value, and the historical operating benchmark data includes the historical average charge / discharge rate value and the historical average ambient temperature value.

[0020] The health status deviation value, current operating status data, and historical operating benchmark data are input into a preset early warning classification model for comprehensive analysis to obtain the lifespan early warning level index.

[0021] Preferably, the preset early warning classification model selects different calculation logics based on the numerical range in which the health status deviation value falls:

[0022] When the health status deviation value is lower than the first deviation threshold, the health status deviation value is weighted and corrected using the first weighting coefficient;

[0023] When the health status deviation value is between the first deviation threshold and the second deviation threshold, an exponential correction is performed based on the difference between the current charge / discharge rate value and the historical average charge / discharge rate value.

[0024] When the health status deviation value is higher than the second deviation threshold, the absolute value of the temperature difference between the current ambient temperature value and the historical average ambient temperature value is introduced for interactive correction, and the lifespan warning level index is output.

[0025] Preferably, the step of triggering the preset lifespan warning measure further includes:

[0026] The lifespan warning level index is matched and analyzed with several preset warning level intervals, wherein each warning level interval corresponds to a preset lifespan warning measure.

[0027] Execute the life warning measures corresponding to the matched life warning level range.

[0028] Preferably, the step of maintaining the current evaluation cycle includes:

[0029] The health status assessment value of the target power battery module is compared with the preset health status threshold to calculate the difference and obtain the health status redundancy value.

[0030] Obtain the current cycle count and the cycle count design threshold of the target power battery module;

[0031] The health status redundancy value, the current cycle count, and the cycle count design threshold are input into a preset cycle adjustment model for comprehensive analysis to obtain the evaluation cycle extension coefficient.

[0032] Preferably, the preset periodic adjustment model performs differentiated calculations based on the numerical range of the health status redundancy value:

[0033] When the health status redundancy value is lower than the first redundancy threshold, linear scaling is performed based on the ratio of the current loop count to the loop count design threshold.

[0034] When the health status redundancy value is higher than the first redundancy threshold, the product of the logarithmic transformation result of the health status redundancy value and the loop count remainder value is used for dynamic adjustment, and the evaluation cycle extension coefficient is output.

[0035] Preferably, the method further includes a step of dynamically updating the threshold:

[0036] Calculate the rate of decline of health status based on the historical health status assessment value sequence;

[0037] The health status threshold is recalculated by combining the health status decay rate value and the preset lifespan termination boundary value.

[0038] The updated health status threshold will be used as the benchmark for the next judgment analysis.

[0039] Preferably, the dynamic threshold update step further includes:

[0040] Collect degradation characteristic data of similar power battery modules and extract common degradation patterns;

[0041] Based on the common degradation mode, the health status decay rate value is compensated and corrected to generate a corrected health status decay rate value.

[0042] The health status threshold is updated using the corrected health status decay rate value.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] By dividing the target power battery module into several evaluation zones and randomly selecting several measurement points in each zone, this approach overcomes the limitations of traditional holistic measurements that only select a few fixed points. Layered evaluation zones can specifically cover different locations within the battery module. Considering the potential differences in temperature, current, and other distributions in different areas of the battery module during use, randomly selecting multiple measurement points within each zone effectively avoids localized data biases that may arise from fixed measurement points. This allows the collected raw battery status data to more comprehensively and uniformly reflect the actual state of each zone within the battery module, providing a more representative data foundation for subsequent health status assessments.

[0045] In the data processing stage, this method comprehensively analyzes the raw battery status data collected in real time at each measurement point in each evaluation area, rather than being limited to the analysis of a single parameter. This multi-dimensional, full-area data comprehensive analysis approach can integrate various battery status parameters such as voltage, temperature, and current, uncover the correlation between different parameters, and more accurately capture subtle changes in battery health status. This results in a health status assessment value that is more consistent with the actual situation of the battery module, reducing assessment bias caused by single-parameter analysis or local data collection, and improving the accuracy and reliability of the assessment results.

[0046] In the health status assessment and early warning stage, this method compares and analyzes the health status assessment value with a preset health status threshold to determine whether to trigger lifespan warning measures. Compared to the traditional single threshold setting method, this method can flexibly set a health status threshold that better meets actual needs based on factors such as the battery module's design parameters, usage scenarios, and aging characteristics, making the early warning judgment more targeted. When the health status assessment value is lower than the preset threshold, the preset early warning measures are triggered, which can promptly remind relevant personnel to take intervention measures such as maintenance and replacement, effectively avoiding safety risks that may be caused by the deterioration of battery health status. When the health status assessment value is higher than the preset threshold, the current assessment cycle is maintained, which can avoid unnecessary frequent assessments when the battery is in good condition, reduce resource consumption during data collection and analysis, and achieve reasonable allocation of assessment resources.

[0047] The setting in this method, "maintain the current assessment cycle if the health status assessment value is higher than the preset threshold," implies the possibility that the assessment cycle can be dynamically adjusted according to the battery health status (when the assessment value approaches the threshold or shows a downward trend, it can be optimized to shorten the assessment cycle). Compared to the traditional fixed assessment cycle method, it can better adapt to changes in battery health status. When the battery health status is stable and good, maintaining the current cycle can reduce the assessment frequency, saving manpower and equipment resources; when the battery health status shows signs of decline and approaches the threshold, the assessment cycle can be adjusted to increase the assessment frequency, track changes in battery health status in a timely manner, ensure rapid response when needed, and avoid resource waste or untimely warnings caused by a fixed cycle. Attached Figure Description

[0048] Figure 1 This is a schematic diagram illustrating the working principle of the power battery health status assessment and lifespan early warning method described in this invention.

[0049] Figure 2 A flowchart for calculating health status assessment values;

[0050] Figure 3 A flowchart for triggering lifespan warning measures;

[0051] Figure 4 The flowchart for maintaining the current evaluation cycle;

[0052] Figure 5 This is a flowchart for dynamically updating threshold compensation correction. Detailed Implementation

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0054] Please see Figure 1 This invention provides a method for assessing the health status and providing early warning of the lifespan of a power battery, the method comprising:

[0055] By employing hierarchical and partitioned random sampling and multi-source data fusion analysis, a refined assessment and proactive early warning of the health status of power battery modules can be achieved. In practice, the target power battery module is physically divided into several assessment zones, based on its physical structure, thermal management layout, or electrical connection characteristics. Within each assessment zone, a certain number of measurement points are randomly selected. This random sampling aims to reduce the systematic bias that may arise from fixed-point monitoring, thereby more comprehensively capturing the internal state differences of the module. Subsequently, sensor units deployed at each measurement point collect raw battery state data in real time at each measurement point within each layer. The collected raw data is transmitted to a central processing unit for comprehensive analysis. This analysis integrates multi-dimensional information, ultimately calculating a comprehensive State of Health (SOH) value. This assessment value is compared with a preset health status threshold. If the assessment value is lower than the threshold, it indicates that the battery health status has deteriorated to a level requiring attention, triggering preset lifespan warning measures. If the assessment value is higher than the threshold, it indicates that the battery status is within an acceptable range, maintaining the current assessment cycle and continuing monitoring.

[0056] Example 1: See Figure 2Assuming the target object is a lithium-ion battery module for electric vehicles, the module consists of multiple battery cells connected in series and parallel, and is physically divided into three evaluation zones. This division is based on the module's mechanical structure and thermal management layout; for example, the upper zone is near the heat dissipation outlet, the middle zone is the core area, and the lower zone is near the coolant inlet. Within each evaluation zone, the system randomly selects three measurement points. For example, in the upper zone, points near the module edge, center, and a connecting piece might be randomly selected; similar random selection is performed for the middle and lower zones to ensure that sampling points cover different locations within the zone, reducing the risk of missing local anomalies due to fixed-point monitoring.

[0057] High-precision sensor units were deployed at each selected measurement location. These sensors acquired three key parameters in real time and synchronously: battery voltage, battery temperature, and battery internal resistance. The voltage was obtained by directly measuring the potential difference between the battery terminals using a high-precision analog-to-digital converter; the temperature was measured using a patch-type temperature sensor attached to the surface of the battery casing; and the internal resistance was calculated by injecting a small-amplitude AC signal of a specific frequency into the battery and measuring its response voltage. Data acquisition was performed at fixed time intervals (e.g., once per second) with precise timestamps to ensure synchronization of different locations and parameters over time, providing a time-consistent data foundation for subsequent comprehensive analysis.

[0058] The central processing unit receives raw data streams from all measurement locations in real time via a data bus. The first step in calculating the health status assessment value is to perform stratified mean calculation. The processing unit first reads the battery voltage value sequence uploaded by all three measurement locations within a certain assessment area (e.g., the upper layer). Assume that at a specific analysis time, the voltage values ​​reported by the three points in the upper layer are 3.32V, 3.30V, and 3.31V, respectively. The system performs a simple arithmetic mean calculation on these values: (3.32+3.30+3.31) / 3=3.31V, which is the average battery voltage measurement value for that upper layer assessment area. Using the same algorithm, the temperature values ​​reported by the three measurement points in this layer (e.g., 28.5°C, 29.0°C, 28.7°C) are averaged to obtain the average battery temperature measurement value for that layer (28.73°C). Similarly, the internal resistance values ​​of the three points in this layer are averaged to obtain the average battery internal resistance measurement value for that layer (2.15mΩ). This process sequentially processes data from all measurement points in the middle and lower layers, calculating the average battery voltage, average battery temperature, and average battery internal resistance for each layer. Layered average calculation effectively integrates information from multiple points within a layer, smoothing out instantaneous fluctuations or minor measurement errors at individual points, and reflecting the overall level of voltage, temperature, and internal resistance in that layer.

[0059] After calculating the layered mean values, the system enters the comprehensive analysis phase, aiming to generate a feature set describing the state distribution of the entire module. The system collects the average battery voltage measurement data for all layers (upper, middle, and lower). Statistical analysis is performed on these layered mean data, calculating their maximum, minimum, range (maximum minus minimum), variance, or standard deviation. For example, if the three layer voltage mean values ​​are 3.31V (upper layer), 3.29V (middle layer), and 3.30V (lower layer), the maximum value is 3.31V, the minimum value is 3.29V, and the range is 0.02V. Simultaneously, the average value (overall average voltage) and standard deviation of these three layer mean values ​​are calculated. Furthermore, the system analyzes the distribution pattern of these layer mean values ​​within the module space, such as observing whether there is a voltage gradient change from the upper to the lower layer, or whether the middle layer voltage is significantly lower. All these calculation and analysis results together constitute the voltage distribution feature set, which quantifies the uniformity or inconsistency of the voltage distribution within the module.

[0060] The same analytical logic is used to process the average battery temperature measurement data for all layers. The maximum, minimum, range, and standard deviation of the three-layer temperature averages are calculated, and their spatial distribution patterns are analyzed (e.g., whether the upper layer has the lowest temperature due to proximity to the heat dissipation vent, the lower layer has a lower temperature due to proximity to the coolant inlet, and the middle layer has the highest temperature). These statistics and distribution pattern information constitute a temperature distribution feature set, reflecting the distribution characteristics and temperature difference of the internal temperature field of the module. The average battery internal resistance measurement data for all layers are also processed. Its maximum, minimum, range, and standard deviation are calculated, and its spatial distribution is analyzed. The generated internal resistance distribution feature set describes the consistency of internal resistance within the module.

[0061] The final step involves jointly analyzing the generated voltage distribution feature sets, temperature distribution feature sets, and internal resistance distribution feature sets to derive a single comprehensive health status assessment value. This process employs a multi-factor fusion algorithm. This algorithm assigns a weight coefficient to each feature set, with the weight coefficients set based on historical data and battery failure mechanism analysis. For example, an increase in internal resistance is generally more closely associated with battery aging; therefore, the internal resistance distribution feature set (especially its range or standard deviation) may be given a higher weight. Voltage distribution non-uniformity may affect battery performance and lifespan, so its weight is secondary. While temperature distribution is important, its short-term fluctuations can be significant, resulting in a relatively low weight. These weight coefficients can be obtained through analysis and learning from a large amount of historical battery degradation data.

[0062] In specific calculations, the system scores or quantifies key indicators (such as range and standard deviation) within each feature set, and then performs a weighted sum based on their weights. For example, the voltage feature set might be scored based on the preset range in which its range (e.g., 0.02V) falls; the temperature feature set might be scored based on its maximum temperature difference; and the internal resistance feature set might be scored based on its coefficient of variation. These scores are then multiplied by their respective weighting coefficients and summed to obtain a preliminary comprehensive score. To normalize the result to an easily understandable and comparable range (e.g., between 0 and 1, where 1 represents a completely new state), this preliminary comprehensive score undergoes a linear or non-linear transformation process. The final normalized value is the health status assessment value of the target power battery module at the current moment. This value comprehensively reflects the overall health level of the module in terms of voltage consistency, temperature distribution uniformity, and internal resistance consistency, providing a core basis for subsequent threshold judgment and early warning decisions.

[0063] Example 2: See Figure 3 The system outlines a specific process for triggering lifespan warning measures when the health status assessment value falls below a preset threshold. The core of this process involves calculating the health status deviation value and combining it with real-time and historical operational data. Through analysis using a warning grading model, a quantified lifespan warning level index is ultimately generated. For example, assuming a lithium-ion battery module for an electric vehicle, after the comprehensive analysis described in Example 1, has a calculated current health status assessment value of 0.78, and the preset health status threshold within the system is 0.80, the system confirms that 0.78 is lower than 0.80 during the judgment analysis and immediately initiates the lifespan warning triggering process.

[0064] The first step in this process is to calculate the difference. The system subtracts the health status assessment value of 0.78 from the health status threshold of 0.80: 0.78 - 0.80 = -0.02. This result, -0.02, is the health status deviation value. This negative value intuitively indicates the degree to which the current health status is below the expected threshold, and its absolute value reflects the severity of the deviation. In this case, the deviation value is -0.02, meaning that the health status assessment value is 2 percentage points lower than the threshold. The system needs to obtain broader contextual information to deeply understand the meaning and potential risks of this deviation. This includes obtaining the current operating status data and historical operating baseline data of the target power battery module. The current operating status data reflects the stress level that the battery module is currently experiencing. The system reads the current charge / discharge rate value from the real-time data stream. For example, the vehicle may be accelerating uphill at this time, and the battery management system reports an actual discharge rate of 1.5C. At the same time, the system reads the current ambient temperature value, which is usually measured by environmental sensors installed in the battery pack; assuming the current reported value is 35°C. These two values ​​together depict the severity of the current operating condition of the battery.

[0065] Current snapshot data alone is insufficient for a comprehensive evaluation. The system also needs to retrieve long-term operational baseline data for the module from the historical database to establish a comparative benchmark. Historical operational baseline data consists of representative values ​​statistically derived from the massive amount of operational data recorded since the battery module was put into use. The system calculates the average charge-discharge rate of the module across all historical charge-discharge cycles, i.e., the historical average charge-discharge rate value. Assuming the vehicle is primarily used for urban commuting with a low average discharge intensity, the calculated historical average charge-discharge rate value is 0.7C. Similarly, the system calculates the average ambient temperature experienced during its long-term operation, i.e., the historical average ambient temperature value. Assuming the vehicle mainly travels in temperate climate zones, its historical average ambient temperature value is 25°C. These historical baseline values ​​represent the "typical" or "normal" operating conditions experienced by the battery module.

[0066] The system has gathered all the necessary input parameters: a health status deviation value (-0.02) that quantifies the degree of health status decline, current operating status data reflecting the current instantaneous stress (current charge / discharge rate value 1.5C, current ambient temperature value 35°C), and historical operating benchmark data that provides a long-term reference benchmark (historical average charge / discharge rate value 0.7C, historical average ambient temperature value 25°C).

[0067] These data are input into a pre-defined early warning grading model for comprehensive analysis. This model is a computational logic module that encapsulates expert knowledge and data analysis algorithms. Its design goal is to comprehensively consider the absolute decline level of health status, current operating load, and changes in operating conditions relative to historical benchmarks, thereby making a more accurate judgment on the urgency and severity of potential risks, rather than relying solely on a simple health status threshold.

[0068] After receiving these inputs, the model initiates its internal analysis. It first focuses on the health status deviation value of -0.02, a clear sign of degradation. Then, the model compares the current charge / discharge rate of 1.5C with the historical average of 0.7C. It finds that the current discharge intensity is significantly higher than the historical average, indicating that the battery is operating under high load, which may accelerate further degradation of already aging batteries. Simultaneously, the model compares the current ambient temperature of 35°C with the historical average of 25°C; the 10°C positive temperature difference indicates that the battery is in a relatively hot environment. High temperatures can also exacerbate internal chemical side reactions, adversely affecting its health status.

[0069] The core algorithm of the early warning grading model integrates these factors. It may assign certain weights to the current high rate of operation and high ambient temperature, as these constitute additional stress on the battery, which is already in a sub-healthy state. The internal logic of the model may be similar to this: the health status itself already has a slight deviation, but the superimposed exceptionally severe operating conditions make the actual risk higher than indicated by simply looking at the health status deviation value. The model comprehensively processes and calculates all input parameters through its built-in algorithm and possible weighted calculations.

[0070] After a series of analyses and calculations, the early warning grading model ultimately outputs a quantitative result: the lifespan early warning level index. This index is a numerical value; for example, it might output a value of 2.8. This index comprehensively reflects the risk level assessed based on the current health status deviation and operating conditions. A higher index value generally indicates a more urgent risk and a higher level of early warning measures required. In this example, the index of 2.8 is generated not only based on a health status deviation of -0.02, but more importantly, it takes into account the combined effect of the current high-rate (1.5C) and high-temperature (35°C) environment on the already degrading battery. The model determines that under this combined stress, the possibility of further accelerated battery performance degradation increases significantly, thus outputting a moderately high risk index.

[0071] This lifespan warning level index provides a precise and quantitative basis for subsequent decisions on what specific warning measures to take. It transforms warning actions from simple "yes / no" triggers into tiered responses based on subtle differences in risk, thereby achieving more refined and rational battery health management. The entire process, from detecting deviations in health status from thresholds to collecting multi-dimensional operational data and finally generating a warning index through an intelligent model, completes a full pre-warning analysis.

[0072] Example 3: This example illustrates the segmented calculation logic used within the pre-defined early warning grading model based on different intervals of health status deviation values, and how corresponding early warning measures are implemented based on the final output lifespan early warning level index. The pre-defined early warning grading model is an algorithm module containing multiple decision branches and calculation paths. Its input parameters include health status deviation values. Current charge / discharge rate value Historical average charge / discharge rate value Current ambient temperature value and historical average ambient temperature values The primary criterion for judging the model is the deviation value of health status. The numerical range in which it exists is defined by two preset critical values—the first deviation threshold. Second deviation threshold (in — Divide into three different ranges and use distinct computational logic for each range.

[0073] When health status deviation value Below the first deviation threshold This indicates that the current decline in health status is relatively mild and has not yet entered a range requiring high vigilance. In this case, the model adopts a conservative calculation strategy, the core of which is to adjust the health status deviation value. Weighted adjustments are then made. Specifically, the model has a built-in first weight coefficient. This coefficient is a constant between 0 and 1, and its value is determined by analyzing the impact of minor deviations in historical data on long-term lifespan. The model's calculation logic involves using this weighting coefficient to scale the original deviation value, i.e., calculating the corrected deviation value. .because This operation essentially reduces the contribution of slight deviations to subsequent exponential calculations, preventing the system from overreacting to initial, minor performance degradation and thus avoiding unnecessary warning interference. This corrected value... It will be used to generate the final lifespan warning level index. .

[0074] When health status deviation value At the first deviation threshold With the second deviation threshold When the value is between 0 and 1, it indicates that the battery's health has deteriorated to a moderate degree and requires further attention. At this point, the model's calculation logic, while considering the absolute deviation value, incorporates the difference between the current operating conditions and the historical average operating conditions as a correction factor. The model first calculates the difference between the current charge / discharge rate value and the historical average charge / discharge rate value, i.e., the rate difference value. This value quantifies the extent to which the current discharge or charge intensity deviates from its normal level. The model then uses this rate difference value to account for the health status deviation value. Perform exponential correction. A specific implementation method is embodied in the following formula:

[0075]

[0076] in: This represents the calculated lifespan warning level index. It is the input health status deviation value. It is a natural constant. It is a positive coefficient obtained by fitting based on battery chemical characteristics, used to adjust the amplification effect of rate difference on warning level. This is the calculated difference in magnification. The mathematical property of this formula is that when the difference in magnification is... When the exponent increases, the exponent term The value will increase rapidly, thus affecting the warning level index. This creates a magnified effect. This reflects the fact that when a battery is already somewhat aged, operating it under loads far exceeding normal levels will significantly accelerate its degradation process, thus posing a higher potential risk and requiring a higher level of warning.

[0077] When health status deviation value Above the second deviation threshold This indicates that the battery's health has severely deteriorated and entered a high-risk zone. In this case, the model adopts the most sensitive calculation strategy. Besides considering the severe health status deviation itself, the model also introduces anomalies in ambient temperature as another key correction factor. The model calculates the absolute difference between the current ambient temperature value and the historical average ambient temperature value, i.e., the absolute value of the temperature difference. This value reflects the degree to which the thermal stress of the battery's current environment deviates from its historical normal operating conditions. Subsequently, the model adjusts the health state deviation value. and absolute value of temperature difference Perform interactive correction. One feasible calculation method is to multiply the two together, and then multiply by a scaling factor. ,Right now Proportionality coefficient This is used to adjust the calculation results to an appropriate order of magnitude. This interactive correction emphasizes a core logic: for a battery that is already in poor health, extreme temperature environments (whether too high or too low) pose an extremely serious threat to its safety and remaining lifespan, and this "adding insult to injury" effect must be reflected by significantly increasing the warning level.

[0078] Through the three differentiated calculation paths described above, the early warning grading model ultimately outputs a definite lifespan early warning level index. This index is a continuous or tiered value, and its magnitude directly corresponds to the risk level determined by the system.

[0079] Generate lifespan warning level index Afterwards, the system enters the early warning measure matching and execution phase. The system internally presets several early warning level ranges, which are predefined numerical ranges. Each range uniquely corresponds to a preset, specific lifespan early warning measure. For example, the system might be set to: when... When this occurs, corresponding to a "Level 1 Warning," the measure is to "record this abnormal health status event in the maintenance log"; when When this occurs, corresponding to a "Level 2 warning," the measure is to "display a yellow warning icon and brief prompt information on the vehicle's dashboard or battery management system's human-machine interface"; when When this occurs, corresponding to a "Level 3 Warning," the measure is to "trigger the audible and visual alarm and send a voice prompt to the driver"; when In this case, corresponding to a "Level 4 warning", the measure is to "send an emergency alarm message to the remote monitoring center or fleet management personnel through the vehicle-mounted wireless communication module, and suggest arranging maintenance immediately".

[0080] The system will calculate the lifespan warning level index. The system performs a matching analysis against these preset intervals to determine which interval it falls into. Once the matching process is complete, the system automatically triggers and executes the lifespan warning measures corresponding to that interval. The entire process, from model calculation to measure implementation, achieves the classification, quantification, and automated response to battery health risks, ensuring the timeliness of warnings and the targeted nature of measures.

[0081] Example 4: See Figure 4 This paper describes how the system maintains and potentially extends the current assessment cycle when the health status assessment value is determined to be higher than a preset threshold. This process aims to optimize the use of monitoring resources and reduce unnecessary frequent checks when the battery is in good condition. Its core lies in calculating an assessment cycle extension coefficient. Assume that a lithium iron phosphate power battery module in an energy storage power station has completed a periodic health status assessment. Based on the comprehensive analysis in Example 1, its health status assessment value is 0.92. The system's preset health status threshold is 0.85. During the judgment process, the system confirms that 0.92 is higher than 0.85 and immediately initiates the "maintain current assessment cycle" process. This process does not simply keep the original cycle unchanged but includes an intelligent cycle adjustment decision-making mechanism.

[0082] The first step in this process is to calculate the health status redundancy value. The system subtracts the health status assessment value of 0.92 from the health status threshold of 0.85: 0.92 - 0.85 = 0.07. This result, 0.07, is the health status redundancy value. This positive value directly indicates how much the current health status is better than the safety threshold. This value will become an important input parameter for assessing whether the system can "let its guard down."

[0083] The system needs to obtain the battery module's lifespan consumption information to aid decision-making. The system reads two key data points from the battery management system's historical records: the current cycle count and the cycle count design threshold. The current cycle count refers to the total number of complete charge-discharge cycles completed by the module since it was put into operation, assumed to be 1200. The cycle count design threshold is the manufacturer's rated cycle life for this battery model under standard conditions, assumed to be 4000 cycles. These two data points together depict the battery module's position within its theoretical lifespan. The system has compiled three core parameters required for cycle adjustment decisions: a health state redundancy value (0.07) representing the adequacy of the health state, the current cycle count (1200 cycles) reflecting the consumed lifespan, and the cycle count design threshold (4000 cycles) representing the total lifespan target. These data are input into a pre-defined cycle adjustment model for comprehensive analysis. This model is a computational module with built-in expert logic and algorithms, designed to intelligently extend the interval between the next health assessment when the battery's health state is good, while ensuring safety, thereby saving system computing and communication resources and reducing operation and maintenance costs.

[0084] The pre-defined cycle adjustment model internally executes differentiated calculation strategies based on the numerical range of the input health status redundancy value. This range is divided into two main intervals by a pre-defined critical value—the first redundancy threshold. When the health status redundancy value is below the first redundancy threshold, it indicates that although the current health status is better than the safety baseline, the margin is not very significant. In this case, the model adopts a relatively cautious strategy. Its calculation logic is mainly based on the battery's lifespan consumption progress, i.e., the ratio of the current cycle count to the design cycle count threshold. This ratio reflects the degree to which the battery's theoretical lifespan has been used. The model's built-in algorithm performs a linear scaling operation based on this ratio. For example, the higher the lifespan consumption ratio (i.e., the closer the used cycle count is to the design threshold), the smaller the allowable extension of the evaluation cycle will be, even if there is a certain amount of health redundancy; conversely, for a new battery, even with the same health redundancy, the allowable extension can be greater. This calculation method reflects the conservative principle that as the battery ages, even if its condition is good, the monitoring frequency should be appropriately increased.

[0085] When the health status redundancy value is higher than the first redundancy threshold, it indicates that the battery's health status is excellent, with a significant performance margin. In this case, the model adopts a more aggressive adjustment strategy. Its calculation logic considers both the sufficiency of the health status and the remaining lifespan potential. The model first performs a logarithmic transformation on the health status redundancy value. Logarithmic transformation is a mathematical method that can convert a large value into a new value with a gradually slowing growth rate. This prevents excessively high health redundancy values ​​from leading to excessively large extension coefficients, thus avoiding unreasonable extension of the monitoring interval. At the same time, the model calculates the cycle count margin value, which is the design cycle count threshold minus the current cycle count (4000-1200=2800 times). This represents the battery's theoretical remaining cycle life. Finally, the model multiplies the logarithmic transformation result of the health status redundancy value with the cycle count margin value to dynamically adjust an evaluation cycle extension coefficient. This coefficient comprehensively reflects information from both the "current state is good" and "theoretically how much longer it can be used."

[0086] To more intuitively demonstrate the output of the periodic adjustment model under different inputs, the table below simulates several scenarios. It is important to emphasize that the data in the table are for illustrative purposes only; the actual coefficients are precisely calculated by the model's internal algorithm.

[0087] Table 1: Example of evaluation cycle extension coefficient.

[0088]

[0089] After comprehensive analysis by the cycle adjustment model, the system ultimately outputs a specific evaluation cycle extension coefficient. Assuming in the current example (redundancy 0.07, 1200 / 4000 cycles), the model outputs a coefficient of 1.45. The system's original base evaluation cycle is 7 days. The new evaluation cycle will be calculated by multiplying the base cycle by this extension coefficient: 7 days * 1.45 = 10.15 days. The system may round this result, for example, setting the next evaluation 10 days later.

[0090] This newly calculated, extended assessment period will be updated in the system's scheduled tasks. This means that the system will not proactively initiate a comprehensive health assessment of the module for the next 10 days (but will still continue basic security monitoring), thereby reducing the consumption of computing resources and the burden of data transmission. The entire process achieves a dynamic, state-based predictive maintenance schedule, seeking the optimal balance between reliability and economy.

[0091] Example 5: See Figure 5This paper describes a process for dynamically updating health status thresholds. The core of this process is to make the judgment benchmark no longer a fixed value, but rather adaptively adjust it based on the actual degradation process of the battery itself and the collective experience of similar devices, thereby improving the accuracy and foresight of the early warning system. The dynamic threshold update process originates from the systematic analysis of historical assessment data. The system maintains a time-series sequence of historical health status assessment values, which fully records the health status assessment values ​​calculated at the end of each assessment cycle since the target power battery module was put into operation. Based on this time-series data, the system performs a key calculation: the health status decay rate value. This value is not simply calculated based on the difference between two adjacent time points, but rather uses statistical methods based on a longer historical window, such as linear fitting of the sequence to calculate its slope, or calculating the average change of assessment values ​​over a period of time. This process aims to smooth short-term fluctuations and capture the overall degradation trend of battery health status on a medium- to long-term time scale. The calculated health status decay rate value is a signed value, usually negative, whose magnitude reflects the rate at which battery performance degrades over time.

[0092] After obtaining the degradation rate, the system combines it with a preset, unchangeable parameter, the end-of-life boundary value. This boundary value is typically defined by the battery manufacturer according to product specifications or relevant industry standards; for example, it is often set to 0.7 or 0.8, representing the end of the battery's lifespan when its capacity declines to 70% or 80% of its initial capacity. The system uses the health status degradation rate and the end-of-life boundary value to recalculate the health status threshold using a predictive algorithm. The algorithm's logic is as follows: based on the current degradation rate, it predicts the remaining time or number of cycles required for the battery to decline from the latest health status assessment value to the end-of-life boundary value. Subsequently, based on a preset warning lead time (e.g., aiming to issue a warning 100 cycles or 3 months before the end of life), a new corresponding health status threshold is derived. This updated threshold better reflects the actual aging dynamics of the individual battery module, providing a more accurate warning timing compared to a fixed global threshold.

[0093] The dynamic threshold update step further includes a data-driven enhancement aimed at leveraging collective intelligence to optimize individual predictions. The system collects degradation characteristic data of similar power battery modules from a cloud platform or a locally integrated database via a data interface. "Similar" is typically defined as battery modules with the same cell model, similar capacity specifications, and similar application scenarios. The system performs data mining and pattern recognition analysis on this massive amount of group data to extract common degradation patterns. These patterns may reveal general patterns in the aging process of specific battery models; for example, the rate of degradation may plateau after the health status assessment value drops to around 0.9, or an inflection point of accelerated degradation may appear after it falls below 0.75. This knowledge extracted from group data provides predictive information that a single battery does not possess in the early stages of its lifespan.

[0094] The system utilizes extracted common degradation patterns to compensate for and correct health status decay rate values ​​previously calculated solely based on its own historical data. For example, if its current battery decay rate is calculated based on a linear decay from 0.95 to 0.92, but common patterns indicate that this type of battery will decay significantly faster once its health status falls below 0.90, the system will correct its own linear decay rate value, appropriately increasing it within a specific range to reflect the impending non-linear accelerated decay trend. This generates a corrected health status decay rate value that incorporates both its own historical decay information and predictive knowledge of the group's performance.

[0095] The system adopts this revised, more accurate health status decay rate value, and combines it again with the lifespan termination boundary value, running the aforementioned predictive algorithm to recalculate and update the health status threshold. This makes the threshold update process not merely a simple extrapolation of past behavior, but an intelligent learning process that integrates its own historical performance with the characteristics of group degradation. The updated threshold is stored in the system configuration, replacing the old threshold, and serves as the benchmark for the next health status assessment. In this way, the early warning system can continuously evolve, and its judgment benchmark increasingly aligns with the actual characteristics of the monitored object, thereby achieving more precise and forward-looking management of battery life.

[0096] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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.

[0097] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for assessing the health status and predicting the lifespan of a power battery, characterized in that, Includes the following steps: The target power battery module is divided into several evaluation areas, and several measurement locations are randomly selected for each evaluation area. The raw battery status data at each measurement point in each evaluation area within the target power battery module is collected in real time and comprehensively analyzed to obtain the health status evaluation value of the target power battery module. The health status assessment value of the target power battery module is compared with the preset health status threshold for judgment and analysis. If the health status assessment value of the target power battery module is lower than the preset health status threshold, a preset lifespan warning measure will be triggered. If the target power battery evaluation value is higher than the preset health status threshold, the current evaluation cycle will be maintained.

2. The method for assessing the health status and providing early warning of the lifespan of a power battery according to claim 1, characterized in that, The raw battery status data includes battery voltage, battery temperature, and battery internal resistance values ​​at several time points.

3. The method for assessing the health status and providing early warning of the lifespan of a power battery according to claim 2, characterized in that, The step of obtaining the health status assessment value of the target power battery module includes: The battery voltage, battery temperature, and battery internal resistance values ​​at each measurement point in each evaluation area within the target power battery module are read and comprehensively analyzed to obtain the average battery voltage, average battery temperature, and average battery internal resistance measurements for each evaluation area. The average battery voltage measurement, average battery temperature measurement, and average battery internal resistance measurement of each evaluation area are comprehensively analyzed to generate the voltage distribution feature set, temperature distribution feature set, and internal resistance distribution feature set of the target power battery module. The health status assessment value is obtained by jointly analyzing the voltage distribution feature set, temperature distribution feature set, and internal resistance distribution feature set.

4. The method for assessing the health status and providing early warning of the lifespan of a power battery according to claim 1, characterized in that, The steps for triggering the preset lifespan warning measures include: The difference between the health status assessment value of the target power battery module and the preset health status threshold is calculated to obtain the health status deviation value. The current operating status data and historical operating benchmark data of the target power battery module are obtained, wherein the current operating status data includes the current charge / discharge rate value and the current ambient temperature value, and the historical operating benchmark data includes the historical average charge / discharge rate value and the historical average ambient temperature value. The health status deviation value, current operating status data, and historical operating benchmark data are input into a preset early warning classification model for comprehensive analysis to obtain the lifespan early warning level index.

5. The method for assessing the health status and providing early warning of the lifespan of a power battery according to claim 4, characterized in that, The preset early warning classification model selects different calculation logics based on the numerical range in which the health status deviation value falls: When the health status deviation value is lower than the first deviation threshold, the health status deviation value is weighted and corrected using the first weighting coefficient; When the health status deviation value is between the first deviation threshold and the second deviation threshold, an exponential correction is performed based on the difference between the current charge / discharge rate value and the historical average charge / discharge rate value. When the health status deviation value is higher than the second deviation threshold, the absolute value of the temperature difference between the current ambient temperature value and the historical average ambient temperature value is introduced for interactive correction, and the lifespan warning level index is output.

6. The method for assessing the health status and providing early warning of the lifespan of a power battery according to claim 4, characterized in that, The step of triggering the preset lifespan warning measure also includes: The lifespan warning level index is matched and analyzed with several preset warning level intervals, wherein each warning level interval corresponds to a preset lifespan warning measure. Execute the life warning measures corresponding to the matched life warning level range.

7. The method for assessing the health status and providing early warning of the lifespan of a power battery according to claim 1, characterized in that, The steps for maintaining the current evaluation cycle include: The health status assessment value of the target power battery module is compared with the preset health status threshold to calculate the difference and obtain the health status redundancy value. Obtain the current cycle count and the cycle count design threshold of the target power battery module; The health status redundancy value, the current cycle count, and the cycle count design threshold are input into a preset cycle adjustment model for comprehensive analysis to obtain the evaluation cycle extension coefficient.

8. The method for assessing the health status and providing early warning of the lifespan of a power battery according to claim 7, characterized in that, The preset periodic adjustment model performs differentiated calculations based on the numerical range of the health status redundancy value: When the health status redundancy value is lower than the first redundancy threshold, linear scaling is performed based on the ratio of the current loop count to the loop count design threshold. When the health status redundancy value is higher than the first redundancy threshold, the product of the logarithmic transformation result of the health status redundancy value and the loop count remainder value is used for dynamic adjustment, and the evaluation cycle extension coefficient is output.

9. The method for assessing the health status and providing early warning of the lifespan of a power battery according to claim 1, characterized in that, It also includes a step for dynamically updating the threshold: Calculate the rate of decline of health status based on the historical health status assessment value sequence; The health status threshold is recalculated by combining the health status decay rate value and the preset lifespan termination boundary value. The updated health status threshold will be used as the benchmark for the next assessment and analysis.

10. The method for assessing the health status and providing early warning of the lifespan of a power battery according to claim 9, characterized in that, The dynamic threshold update step further includes: Collect degradation characteristic data of similar power battery modules and extract common degradation patterns; Based on the common degradation mode, the health state decay rate value is compensated and corrected to generate a corrected health state decay rate value. The health status threshold is updated using the corrected health status decay rate value.

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