A smart battery early warning system and method based on user-side energy storage devices

Abstract

This invention relates to the field of power technology and discloses an intelligent battery early warning system and method based on user-side energy storage devices. By comprehensively monitoring real-time battery status parameters, environmental parameters, and historical operating data, combined with fault risk assessment, remaining life prediction, and fault trend analysis, the system achieves accurate prediction and early warning of battery performance. Through dynamically adjusting early warning conditions and employing a multi-level early warning mechanism, this invention enables efficient and accurate battery health monitoring and fault prediction, improving the safety and reliability of energy storage systems and extending battery life.

Description

Technical Field

[0001] This invention relates to the field of power technology, and more specifically, to an intelligent battery early warning system and method based on user-side energy storage devices. Background Technology

[0002] With the rapid development of renewable energy technologies, user-side energy storage devices are being used more and more widely in residential and commercial sectors. As a core component of energy storage systems, batteries play a crucial role in the efficiency and safety of these systems. However, batteries are affected by various factors during long-term use, such as the number of charge-discharge cycles, environmental conditions, and load fluctuations. These factors can lead to battery performance degradation or even failure, thereby affecting the stability and safety of the energy storage system. Therefore, developing a technology that can monitor battery status in real time and provide intelligent early warnings is of great significance for improving the reliability of energy storage systems and extending battery life.

[0003] Existing battery management systems mostly employ single-state monitoring methods based on parameters such as battery voltage, current, and temperature, which cannot comprehensively and accurately assess the battery's health status and remaining lifespan. Furthermore, current technologies do not adequately consider environmental factors, resulting in poor accuracy and real-time performance of early warning systems when dealing with complex operating conditions.

[0004] Therefore, there is an urgent need for a more intelligent and comprehensive battery early warning method to achieve accurate battery prediction and management. Summary of the Invention

[0005] In view of this, the present invention proposes an intelligent battery early warning system and method based on user-side energy storage devices, which aims to automatically analyze battery operating data, identify potential fault modes, and issue early warning signals when battery performance deteriorates or is about to fail, thereby effectively avoiding energy storage system shutdowns or safety accidents caused by battery failures.

[0006] This invention proposes a smart battery early warning method based on user-side energy storage devices, comprising:

[0007] The system acquires real-time battery status parameters and calculates corresponding battery status warning values ​​based on these parameters. The real-time battery status parameters include battery voltage, current, temperature, internal resistance, state of charge, and health status.

[0008] Real-time environmental parameters are acquired, and environmental impact predictions are generated based on the relationship between the real-time environmental parameters and battery performance; the real-time environmental parameters include ambient temperature, humidity, vibration, and shock.

[0009] The battery's historical operating parameters are obtained, and the remaining lifespan and failure risk assessment value are analyzed based on the historical operating parameters, battery status warning values, and environmental impact prediction values. The historical operating parameters include the number of charge-discharge cycles, cycle life, and charge-discharge rate.

[0010] The system determines whether an early warning is needed based on the relationship between the remaining lifespan, the failure risk assessment value, and the preset threshold; when the determination result indicates that an early warning is needed, an early warning signal is generated.

[0011] Preferably, the battery status warning value is obtained by nonlinear calculation of real-time battery status parameters, and the calculation formula is as follows:

[0012]

[0013] Where Sb represents the battery status warning value; the value of i ranges from 1 to 6, and P1 to P6 correspond to the battery voltage V, current I, battery temperature Tb, internal resistance Rb, state of charge SOC, and state of health SOH, respectively; αi represents the weighting factor; ηi represents the nonlinear adjustment index; and γ1 represents the correction factor.

[0014] Preferably, the predicted environmental impact value is obtained through multivariate regression calculation, as shown in the following formula:

[0015]

[0016] Where Se represents the predicted environmental impact value; j ranges from 1 to 4; Q1-Q4 represent the ambient temperature Ta, humidity H, vibration intensity Vb, and impact force Im, respectively; βj represents the environmental parameter weights; and γ2 represents the correction factor.

[0017] Preferably, the remaining lifetime calculation is based on an exponential decay model and takes into account environmental impacts, and the calculation formula is as follows:

[0018]

[0019] Where RUL represents the remaining lifetime; R0 represents the initial design lifetime; C represents the number of charge / discharge cycles; L represents the cycle life; D represents the charge / discharge rate; λ1-λ4 represent the influence coefficients; and γ3 represents the correction factor.

[0020] Preferably, the fault risk assessment value is calculated using the entropy weight comprehensive assessment method, and the calculation formula is as follows:

[0021]

[0022] Wherein, FR represents the fault risk assessment value; Xk represents the fault-related parameters; wk represents the entropy weight allocation coefficient; δ represents the smoothing parameter; and γ4 represents the correction factor.

[0023] Preferably, the fault-related parameters include at least three of the following: abnormal battery data, abnormal environmental data, battery gas composition, acoustic emission, impedance spectrum, magnetic field strength, and deformation.

[0024] Preferably, the abnormal battery data includes sudden changes in internal resistance and abnormal temperature; the abnormal environmental data includes vibration and shock.

[0025] Preferably, the fault trend index is calculated based on the time series change rate of the fault risk assessment value, and the calculation formula is as follows:

[0026]

[0027] Where FT represents the failure trend index; τ1 and τ2 represent weighting coefficients; Indicates the rate of change of fault risk; This represents the fault acceleration; T represents the observation time window.

[0028] Preferably, a dynamic correction factor is calculated based on the failure trend index, and the remaining lifetime calculation is optimized:

[0029]

[0030] RUL′=RUL-Δγ;

[0031] Where Δγ represents the dynamic correction factor; ω1, ω2, ω3 represent the adjustment parameters; Tw represents the prediction time window; and RUL′ represents the corrected remaining lifetime.

[0032] Preferably, the early warning determination is calculated based on the remaining lifespan, the failure risk assessment value, and a set threshold, as shown in the following formula:

[0033]

[0034] Where Pw represents the probability of triggering a warning; σ1, σ2, and σ3 represent adjustment coefficients; and θRUL, θFR, and θFT represent the thresholds corresponding to the remaining lifetime, the fault risk assessment value, and the fault trend index, respectively.

[0035] Preferably, when the probability of triggering an early warning exceeds the early warning threshold, a tiered early warning is executed, wherein:

[0036] When θP≤Pw<1.2, it is judged as a mild anomaly, and the graded warning result is a level one warning.

[0037] When 1.2θP≤Pw<1.5, it is judged as a moderate anomaly, and the graded early warning result is a level two early warning.

[0038] When Pw≥1.5θP, it is judged as a severe anomaly, and the graded warning result is a level three warning.

[0039] Where θP represents the warning threshold.

[0040] The present invention also proposes an intelligent battery early warning system based on user-side energy storage devices, which is used to implement the above-mentioned intelligent battery early warning method based on user-side energy storage devices.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0042] The intelligent battery early warning method based on user-side energy storage devices provided by this invention can comprehensively assess the battery's health status, remaining lifespan, and failure risk by real-time monitoring of multi-dimensional parameters (including battery status, environmental conditions, and operational data) and combining them with advanced data processing algorithms. This method has the following beneficial effects:

[0043] Multi-dimensional data fusion: By combining information from multiple dimensions such as battery status parameters, environmental factors, and historical operating data, the health status of the battery can be comprehensively analyzed, avoiding misjudgments caused by a single parameter.

[0044] Dynamic early warning mechanism: By introducing comprehensive evaluation methods such as Fault Trend Index (FT) and Fault Risk Assessment Value (FR), the early warning trigger conditions of the battery can be dynamically adjusted, and the potential battery failures can be predicted in real time, thereby reducing system downtime.

[0045] High-precision remaining life prediction: Combining environmental impact, charging and discharging modes and multi-level correction models, it can accurately predict the remaining life of the battery and generate intelligent early warning signals based on the corrected remaining life, further improving the safety and reliability of the system.

[0046] Multi-level early warning classification: By setting up a multi-level early warning classification mechanism, this invention can flexibly respond to different fault risks, providing three early warning levels: mild, moderate, and severe, to help users take appropriate measures in a timely manner.

[0047] Highly adaptable: This method can be adaptively adjusted according to different battery types and usage scenarios, has broad application prospects, and can meet the needs of various user-side energy storage devices. Attached Figure Description

[0048] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0049] Figure 1 This is a flowchart of the intelligent battery early warning method based on user-side energy storage devices according to the present invention. Detailed Implementation

[0050] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0051] See Figure 1 This embodiment provides a smart battery early warning method based on user-side energy storage devices, including:

[0052] The system acquires real-time battery status parameters and calculates corresponding battery status warning values ​​based on these parameters. The real-time battery status parameters include battery voltage, current, temperature, internal resistance, state of charge, and health status.

[0053] Real-time environmental parameters are acquired, and environmental impact predictions are generated based on the relationship between the real-time environmental parameters and battery performance; the real-time environmental parameters include ambient temperature, humidity, vibration, and shock.

[0054] The battery's historical operating parameters are obtained, and the remaining lifespan and failure risk assessment value are analyzed based on the historical operating parameters, battery status warning values, and environmental impact prediction values. The historical operating parameters include the number of charge-discharge cycles, cycle life, and charge-discharge rate.

[0055] The system determines whether an early warning is needed based on the relationship between the remaining lifespan, the failure risk assessment value, and the preset threshold; when the determination result indicates that an early warning is needed, an early warning signal is generated.

[0056] As can be seen, this embodiment proposes an innovative intelligent battery early warning method, which is based on user-side energy storage devices, and its main steps include:

[0057] First, the battery status parameters are acquired in real time, and then the corresponding battery status warning values ​​are calculated based on these real-time battery status parameters. These real-time battery status parameters specifically include key indicators such as battery voltage, current, temperature, internal resistance, state of charge, and health status.

[0058] Secondly, environmental parameters are acquired in real time, and based on the relationship between these real-time environmental parameters and battery performance, environmental impact predictions are generated. These real-time environmental parameters cover factors such as ambient temperature, humidity, vibration, and shock.

[0059] Next, the historical operating parameters of the battery are obtained. These historical operating parameters, battery status warning values, and environmental impact prediction values ​​are used for comprehensive analysis to assess the remaining lifespan and failure risk of the battery. The historical operating parameters mainly include the number of charge-discharge cycles, cycle life, and charge-discharge rate.

[0060] Finally, the system determines whether it is necessary to issue a warning signal based on the relationship between the battery's remaining lifespan, the failure risk assessment value, and the preset threshold; if the determination result indicates that a warning is necessary, a corresponding warning signal will be generated.

[0061] It is understood that the intelligent battery early warning method in this embodiment not only considers the battery's own real-time status parameters, but also fully integrates real-time environmental parameters and the battery's historical operating parameters, thereby achieving a comprehensive assessment of the battery's remaining lifespan and failure risk. The application of this method can greatly improve the safety and reliability of user-side energy storage devices, effectively avoiding various problems caused by battery failures. Simultaneously, through real-time early warnings, users can promptly understand the battery's status and take corresponding measures to ensure the normal operation of the equipment. Therefore, the intelligent battery early warning method in this embodiment has broad application prospects and significant practical value.

[0062] In some embodiments of this application, the battery status warning value is obtained by nonlinear calculation of real-time battery status parameters, and the calculation formula is as follows:

[0063]

[0064] Where Sb represents the battery status warning value; the value of i ranges from 1 to 6, and P1 to P6 correspond to the battery voltage V, current I, battery temperature Tb, internal resistance Rb, state of charge SOC, and state of health SOH, respectively; αi represents the weighting factor; ηi represents the nonlinear adjustment index; and γ1 represents the correction factor.

[0065] As can be seen, the battery status warning value in this embodiment is obtained through a nonlinear calculation method of real-time battery status parameters, and the specific calculation formula is as follows:

[0066]

[0067] In this formula, Sb represents the battery state warning value; the variable i ranges from 1 to 6, where P1 to P6 represent the six key parameters of the battery, including battery voltage V, current I, battery temperature Tb, internal resistance Rb, state of charge SOC, and state of health SOH; αi is the corresponding weighting factor; ηi is the exponent used to adjust the nonlinear relationship; and γ1 is a correction factor.

[0068] It is understandable that this embodiment, by introducing a nonlinear calculation method, can more accurately reflect the complex relationships between battery state parameters, thereby improving the accuracy and reliability of early warnings. Furthermore, the introduction of the weighting factor αi quantifies the contribution of different parameters in the early warning calculation, further enhancing the flexibility and adaptability of the early warning system. The nonlinear adjustment index ηi helps to capture minute changes in battery state parameters, improving the sensitivity of the early warning system. The addition of the correction factor γ1 allows for necessary adjustments to the calculation results, ensuring the accuracy and reliability of the early warning results. This early warning method, which comprehensively considers multiple factors and performs nonlinear calculations, provides strong protection for the safe operation of user-side energy storage devices.

[0069] In some embodiments of this application, the predicted environmental impact value is obtained by multivariate regression calculation, as shown in the following formula:

[0070]

[0071] Where Se represents the predicted environmental impact value; j ranges from 1 to 4; Q1-Q4 represent the ambient temperature Ta, humidity H, vibration intensity Vb, and impact force Im, respectively; βj represents the environmental parameter weights; and γ2 represents the correction factor.

[0072] As can be seen, this embodiment uses multivariate regression analysis to calculate the predicted environmental impact. This method, through a series of mathematical calculations, can effectively predict the potential impact of environmental factors on specific situations or equipment.

[0073] In the above formula, Se represents the predicted environmental impact value, which is the core indicator we focus on. The variable j ranges from 1 to 4, covering the four main environmental parameters we are concerned with. Specifically, Q1 to Q4 represent the four environmental factors: ambient temperature Ta, humidity H, vibration intensity Vb, and impact force Im, respectively. Each environmental factor corresponds to a weight βj, which reflects the degree of contribution of that environmental factor to the overall predicted environmental impact value. γ2 is a correction factor used to adjust and optimize the final prediction results, ensuring the accuracy and reliability of the predicted values.

[0074] It is understandable that this embodiment uses multivariate regression analysis to calculate the predicted environmental impact value. This not only considers the combined impact of multiple environmental factors on battery performance but also quantifies the contribution of different environmental factors in the prediction by introducing weight βj. This method makes the environmental impact prediction more accurate, helping to identify and address potential environmental risks in advance, thereby further ensuring the safe operation of user-side energy storage equipment. Furthermore, the addition of the correction factor γ2 allows for necessary adjustments and optimizations to the prediction results, ensuring their accuracy and reliability. In summary, the intelligent battery early warning method of this embodiment, based on a comprehensive consideration of real-time battery status parameters, real-time environmental parameters, and historical operating parameters, achieves a comprehensive and accurate assessment of battery remaining life and failure risk by introducing nonlinear calculation methods and multivariate regression analysis. It has broad application prospects and significant practical value.

[0075] In some embodiments of this application, the remaining lifetime calculation is based on an exponential decay model and takes into account environmental impacts, as shown in the following formula:

[0076]

[0077] Where RUL represents the remaining lifetime; R0 represents the initial design lifetime; C represents the number of charge / discharge cycles; L represents the cycle life; D represents the charge / discharge rate; λ1-λ4 represent the influence coefficients; and γ3 represents the correction factor.

[0078] As can be seen, this embodiment employs a remaining life calculation method based on an exponential decay model, and fully considers the influence of environmental factors during the calculation process. In the above formula, RUL represents the remaining life of the device or battery, R0 represents its initial design life, C represents the number of charge-discharge cycles, L represents the cycle life, D represents the charge-discharge rate, and λ1 to λ4 represent different influence coefficients, which reflect the degree of influence of different environmental factors on the device's lifespan. Furthermore, γ3 is a correction factor used to fine-tune the calculation results to ensure accuracy.

[0079] It is understood that this embodiment achieves accurate prediction of battery remaining life by employing a remaining life calculation method based on an exponential decay model and incorporating the influence of environmental factors. This method not only considers the battery's own physical characteristics, such as the number of charge-discharge cycles, cycle life, and charge-discharge rate, but also fully integrates environmental factors, such as temperature, humidity, vibration, and shock, and their potential impact on battery life. The introduction of influence coefficients λ1 to λ4 quantifies the specific impact of different environmental factors on battery life, thereby improving the accuracy of the prediction. The addition of correction factor γ3 allows for necessary adjustments and optimizations to the prediction results, ensuring their reliability and practicality. This remaining life prediction method, which comprehensively considers multiple factors and performs accurate calculations, provides strong support for the safe operation of user-side energy storage devices. It helps users understand the remaining battery life in a timely manner, enabling them to take appropriate maintenance or replacement measures and avoid various problems caused by battery depletion. Therefore, the intelligent battery early warning method and its remaining life prediction method in this embodiment have broad application prospects and significant practical value, and are of great importance for improving the safety and reliability of user-side energy storage devices.

[0080] In some embodiments of this application, the fault risk assessment value is calculated using the entropy weight comprehensive assessment method, and the calculation formula is as follows:

[0081]

[0082] Wherein, FR represents the fault risk assessment value; Xk represents the fault-related parameters; wk represents the entropy weight allocation coefficient; δ represents the smoothing parameter; and γ4 represents the correction factor.

[0083] As can be seen, this embodiment uses the entropy weight comprehensive evaluation method to calculate the fault risk assessment value. This method can effectively consider various fault-related parameters to obtain a comprehensive evaluation result. In this formula, FR represents the fault risk assessment value, which is a key indicator used to measure the degree of fault risk that the system or equipment may encounter during operation. Xk represents various fault-related parameters, which can be diverse, including but not limited to temperature, pressure, vibration, etc., all of which are key factors affecting the stability of equipment operation. wk represents the entropy weight allocation coefficient, which is used to represent the importance weight of each fault-related parameter in the overall evaluation. The entropy weight method can scientifically allocate these weights to ensure the accuracy of the evaluation results. δ represents the smoothing parameter, which is used to handle possible data anomalies or extreme values ​​to ensure the smooth progress of the evaluation process. Finally, γ4 represents the correction factor, which is used to fine-tune the evaluation results to adapt to specific application scenarios or conditions, ensuring the applicability and accuracy of the evaluation results.

[0084] It is understandable that this embodiment uses the entropy weight comprehensive evaluation method to calculate the fault risk assessment value, achieving a comprehensive and scientific assessment of the fault risk of the system or equipment. This method not only considers multiple fault-related parameters but also rationally allocates the importance weight of each parameter through the entropy weight method, making the assessment results more accurate and reliable. Simultaneously, the introduction of the smoothing parameter δ effectively handles the problem of data anomalies or extreme values, ensuring the stability and reliability of the assessment process. The addition of the correction factor γ4 allows for necessary adjustments and optimizations to the assessment results to adapt to different application scenarios or conditions, further improving the practicality and accuracy of the assessment results. This fault risk assessment method, which comprehensively considers multiple factors and conducts precise evaluation, provides strong protection for the safe operation of user-side energy storage equipment, helping users to promptly identify and address potential fault risks, thereby ensuring the stable operation of the system and extending the service life of the equipment. Therefore, the intelligent battery early warning method and its fault risk assessment method in this embodiment also have broad application prospects and significant practical value.

[0085] In some embodiments of this application, the fault-related parameters include at least three of the following: abnormal battery data, abnormal environmental data, battery gas composition, acoustic emission, impedance spectrum, magnetic field strength, and deformation.

[0086] It is understood that the fault-related parameters involved in this embodiment are not limited to any one or more of the following: abnormal battery data, abnormal environmental data, battery gas composition, acoustic emission, impedance spectrum, magnetic field strength, and deformation, but rather cover at least three of these parameters. A comprehensive consideration of these parameters allows for a more complete assessment of the battery system's health status, thereby identifying potential fault risks at an early stage and ensuring the safe and stable operation of the battery system.

[0087] In some embodiments of this application, the abnormal battery data includes sudden changes in internal resistance and abnormal temperature; the abnormal environmental data includes vibration and shock.

[0088] It is understood that the battery anomaly data and environmental anomaly data mentioned in this embodiment are important components of fault risk assessment. Internal resistance mutations and temperature anomalies, as two key indicators of battery anomaly data, directly reflect changes in the battery's internal state. Internal resistance mutations may indicate damage to the battery's internal structure or electrolyte deterioration, while temperature anomalies may be caused by overheating or overcooling, both of which may foreshadow impending battery failure. Vibration and shock in environmental anomaly data are equally significant, as they can damage the battery's physical structure, accelerate the aging process, and increase the risk of failure. By comprehensively considering these fault-related parameters, the intelligent battery early warning system of this embodiment can more accurately assess the health status of the battery system, provide users with timely warning information, and ensure the safe and stable operation of the battery system.

[0089] In some embodiments of this application, a fault trend index is calculated based on the time series change rate of the fault risk assessment value, and the calculation formula is as follows:

[0090]

[0091] Where FT represents the failure trend index; τ1 and τ2 represent weighting coefficients; Indicates the rate of change of fault risk; This represents the fault acceleration; T represents the observation time window.

[0092] As can be seen, this embodiment uses a method to calculate the fault trend index based on the time series change rate of the fault risk assessment value. In this formula, FT represents the fault trend index, a key indicator used to predict and assess the potential fault risk of equipment or systems. τ1 and τ2 represent weighting coefficients, which adjust the relative importance of the fault risk change rate and fault acceleration according to different application scenarios and requirements. This represents the rate of change of fault risk, reflecting the rate at which fault risk changes over time within the observation time window T. This represents the fault acceleration, which describes the acceleration of the rate of change of fault risk over time, i.e., the trend of the rate of change. T represents the observation time window, the length of which can be adjusted according to actual conditions to adapt to different monitoring needs and accuracy requirements.

[0093] Understandably, this embodiment achieves dynamic prediction and assessment of potential failure risks of equipment or systems by introducing the Failure Trend Index (FT). The FT considers not only the rate of change of failure risk (the rate at which failure risk changes over time) but also the concept of failure acceleration, describing the acceleration of the rate of change of failure risk over time. This comprehensive consideration of both indicators makes the prediction of failure risk more comprehensive and accurate. Simultaneously, the introduction of weighting coefficients τ1 and τ2 allows for adjustment of the relative importance of the rate of change of failure risk and failure acceleration according to different application scenarios and needs, thereby improving the flexibility and adaptability of the prediction results. The setting of the observation time window T provides users with the possibility of adjusting the monitoring accuracy and frequency according to actual needs, further enhancing the practicality and flexibility of the method. Through this failure trend assessment method that comprehensively considers multiple factors and performs dynamic prediction, the safe operation of user-side energy storage equipment is more effectively guaranteed. Users can understand the potential failure risks of equipment or systems more promptly, thereby taking corresponding preventive measures to avoid failures, ensuring stable system operation, and extending the service life of equipment. Therefore, the intelligent battery early warning method and its fault trend assessment method in this embodiment also demonstrate broad application prospects and important practical value.

[0094] In some embodiments of this application, a dynamic correction factor is calculated based on a failure trend index, and the remaining lifetime calculation is optimized:

[0095]

[0096] RUL′=RUL-Δγ;

[0097] Where Δγ represents the dynamic correction factor; ω1, ω2, ω3 represent the adjustment parameters; Tw represents the prediction time window; and RUL′ represents the corrected remaining lifetime.

[0098] As can be seen, this embodiment adopts an innovative method: calculating a dynamic correction factor based on a failure trend index, and further optimizing the remaining lifetime calculation process. First, a series of calculation steps are used to determine the failure trend index; this step is crucial because it directly affects the accuracy of the dynamic correction factor. Then, these failure trend indices are used to dynamically calculate a correction factor, which will be used to adjust and optimize the remaining lifetime calculation. In this process, Δγ represents the calculated dynamic correction factor; ω1, ω2, and ω3 represent the parameters used for adjustment, which can be adjusted according to different application scenarios and requirements; Tw represents the prediction time window, which defines the time range for predicting the remaining lifetime; and RUL′ represents the remaining lifetime adjusted by the dynamic correction factor, which is more accurate and reliable than the original remaining lifetime calculation result.

[0099] Understandably, this embodiment achieves a more accurate calculation and prediction of battery remaining life by introducing a dynamic correction factor Δγ. Traditional remaining life calculation methods often ignore the development of fault trends and rely solely on the current operating state for prediction, which may lead to inaccurate results. This embodiment, however, calculates a dynamic correction factor by comprehensively considering the fault trend index and applies it to the remaining life calculation, thus more accurately reflecting the actual battery remaining life. The introduction of adjustment parameters ω1, ω2, and ω3 allows for adjustments based on different battery types, operating environments, and user needs, improving the flexibility and adaptability of the calculation results. The setting of the prediction time window Tw provides users with the possibility of adjusting the prediction range according to actual needs, further enhancing the practicality and flexibility of the method. Through this remaining life calculation method that comprehensively considers multiple factors and performs dynamic corrections, users can more accurately understand the battery's remaining life, thereby taking corresponding measures in advance to avoid system downtime or damage due to battery failure, ensuring stable system operation and extending battery life. Therefore, the intelligent battery early warning method and its remaining life calculation method in this embodiment not only improve the accuracy of prediction, but also show broad application prospects and important practical value.

[0100] In some embodiments of this application, the warning determination is calculated based on the remaining lifespan, the failure risk assessment value, and a set threshold, as shown in the following formula:

[0101]

[0102] Where Pw represents the probability of triggering a warning; σ1, σ2, and σ3 represent adjustment coefficients; and θRUL, θFR, and θFT represent the thresholds corresponding to the remaining lifetime, the fault risk assessment value, and the fault trend index, respectively.

[0103] As can be seen, the early warning determination in this embodiment is calculated based on the remaining lifetime, the fault risk assessment value, and a set threshold. Here, Pw represents the probability of triggering an early warning; σ1, σ2, and σ3 represent adjustment coefficients; and θRUL, θFR, and θFT represent the thresholds corresponding to the remaining lifetime, the fault risk assessment value, and the fault trend index, respectively. In some specific embodiments, the calculation and setting of these parameters are based on a comprehensive analysis of historical data and real-time monitoring information to ensure the accuracy and timeliness of the early warning system.

[0104] It is understandable that this embodiment achieves a more comprehensive and accurate early warning judgment of battery failure risk by comprehensively considering multiple factors such as remaining lifespan, failure risk assessment value, and failure trend index. The early warning trigger probability Pw is a key output indicator, reflecting the likelihood of battery failure. This indicator is calculated not only by considering the remaining lifespan θRUL (the estimated length of time the battery can continue to operate normally), but also by introducing two important parameters: the failure risk assessment value θFR and the failure trend index θFT. The failure risk assessment value θFR reflects the current magnitude of battery failure risk, while the failure trend index θFT describes the trend of failure risk over time. The comprehensive consideration of these three indicators makes the early warning judgment more comprehensive and accurate. Simultaneously, the introduction of adjustment coefficients σ1, σ2, and σ3 allows for adjusting the relative importance of these three parameters according to different application scenarios and needs, thereby improving the flexibility and adaptability of the early warning results. Through this method of comprehensively considering multiple factors and making dynamic early warning judgments, users can more accurately understand the battery failure risk situation, thereby taking corresponding preventive measures in advance to avoid system downtime or damage caused by battery failure, ensuring stable system operation and extending battery life. Therefore, the intelligent battery early warning method and its early warning judgment criteria in this embodiment not only improve the accuracy of early warning, but also demonstrate broad application prospects and important practical value.

[0105] In some embodiments of this application, when the probability of triggering an early warning exceeds an early warning threshold, a tiered early warning is executed, wherein:

[0106] When θP≤Pw<1.2, it is judged as a mild anomaly, and the graded warning result is a level one warning.

[0107] When 1.2θP≤Pw<1.5, it is judged as a moderate anomaly, and the graded early warning result is a level two early warning.

[0108] When Pw≥1.5θP, it is judged as a severe anomaly, and the graded warning result is a level three warning.

[0109] Where θP represents the warning threshold.

[0110] It is understood that in this embodiment, when the probability of triggering an early warning exceeds the set early warning threshold, the system will execute a tiered early warning mechanism. Specifically:

[0111] If the warning probability Pw is less than or equal to the warning threshold θP multiplied by 1.2, i.e. θP≤Pw<1.2, then the system will determine the current situation as a mild anomaly and set the result of the graded warning as a level one warning.

[0112] If the warning probability Pw is between the warning threshold θP multiplied by 1.2 and the warning threshold θP multiplied by 1.5, that is, 1.2θP≤Pw<1.5, then the system will determine the current situation as moderately abnormal and set the result of the graded warning as a level two warning.

[0113] If the warning probability Pw is greater than or equal to the warning threshold θP multiplied by 1.5, i.e. Pw≥1.5θP, then the system will determine the current situation as a severe anomaly and set the result of the graded warning as a level three warning.

[0114] Here, θP represents the warning threshold, which is a key parameter used by the system to determine the degree of abnormality.

[0115] It is understandable that this embodiment achieves graded management and response to battery failure risks by setting different levels of warning thresholds. This graded warning mechanism not only improves the pertinence and effectiveness of the warning system but also provides users with clearer guidance on coping measures. In the case of a Level 1 warning for minor anomalies, users can take some routine monitoring and maintenance measures to ensure the normal operation of the battery; in the case of a Level 2 warning for moderate anomalies, users need to strengthen the monitoring and maintenance of the battery and may need to perform some additional checks and tests to prevent the failure from worsening; in the case of a Level 3 warning for severe anomalies, users need to take corresponding emergency measures immediately, such as shutting down for repair or replacing the battery, to avoid serious system damage or safety accidents caused by battery failure. Through this graded warning mechanism, users can take corresponding coping measures according to different levels of warning information, thereby more effectively managing and controlling battery failure risks, ensuring the stable operation of the system and extending the battery's lifespan. Therefore, the intelligent battery warning method and its graded warning mechanism in this embodiment not only improve the accuracy and pertinence of the warnings but also provide users with clearer and more effective guidance on coping measures, demonstrating broad application prospects and important practical value.

[0116] This embodiment also proposes an intelligent battery early warning system based on user-side energy storage devices to implement the above-mentioned intelligent battery early warning method based on user-side energy storage devices.

[0117] It is understood that the intelligent battery warning system of this embodiment is designed to execute the above-mentioned warning method efficiently and accurately.

[0118] Preferably, the system integrates an advanced data acquisition module capable of real-time monitoring of key battery parameters, such as voltage, current, and temperature, and inputting this data into the early warning algorithm for processing. Furthermore, the system is equipped with a powerful computing and analysis unit responsible for executing complex early warning judgment logic, including predicting remaining battery life, calculating fault risk assessment values, and analyzing fault trend indices. To ensure timely transmission of early warning information, the system also possesses high-speed communication capabilities, enabling it to push early warning results to users or relevant management systems in real time.

[0119] In practice, the intelligent battery early warning system first collects real-time battery monitoring data and analyzes and processes this data using a built-in algorithm model. Based on the processing results, the system calculates the early warning trigger probability Pw and determines the current early warning level according to a preset tiered early warning mechanism. Once the early warning trigger probability exceeds the early warning threshold, the system immediately initiates the corresponding early warning response process, promptly conveying the early warning information to the user through various means such as audible and visual alarms, SMS notifications, and email alerts. Based on the received early warning information, the user can quickly take appropriate countermeasures, thereby effectively avoiding potential risks and losses caused by battery failure.

[0120] In summary, the intelligent battery early warning system of this embodiment provides users with a powerful battery fault early warning solution through its comprehensive early warning judgment criteria, precise hierarchical early warning mechanism, and efficient data processing capabilities. The application of this system will greatly improve the safety and reliability of user-side energy storage devices, providing strong guarantees for the stable operation of the power system and the long-term use of batteries.

[0121] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0122] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0123] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0124] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A smart battery early warning method based on user-side energy storage devices, characterized in that, include: Obtain real-time battery status parameters and calculate corresponding battery status warning values ​​based on the real-time battery status parameters; The real-time battery status parameters include battery voltage, current, temperature, internal resistance, state of charge, and health status. Real-time environmental parameters are acquired, and environmental impact predictions are generated based on the relationship between the real-time environmental parameters and battery performance; the real-time environmental parameters include ambient temperature, humidity, vibration, and shock. Obtain historical operating parameters of the battery, and analyze the remaining lifespan and failure risk assessment value based on the historical operating parameters, battery status warning values, and environmental impact prediction values. The historical operating parameters include charge / discharge cycles, cycle life, and charge / discharge rate. Whether an early warning is needed is determined based on the relationship between the remaining lifespan, the failure risk assessment value, and the preset threshold. When the judgment result indicates that an early warning is required, an early warning signal is generated; The early warning determination is based on the remaining lifespan, the failure risk assessment value, the failure trend index, and a preset threshold, calculated as follows: Where Pw represents the probability of triggering a warning; RUL′ represents the corrected remaining lifetime; FR represents the fault risk assessment value; FT represents the fault trend index; σ1, σ2, and σ3 represent adjustment coefficients; and θRUL, θFR, and θFT represent the preset thresholds corresponding to the remaining lifetime, fault risk assessment value, and fault trend index, respectively.

2. The intelligent battery early warning method based on user-side energy storage devices according to claim 1, characterized in that, The battery status warning value is obtained by nonlinear calculation of real-time battery status parameters, and the calculation formula is as follows: Where Sb represents the battery status warning value; the value of i ranges from 1 to 6, and P1 to P6 correspond to the battery voltage V, current I, battery temperature Tb, internal resistance Rb, state of charge SOC, and state of health SOH, respectively; αi represents the weighting factor; ηi represents the nonlinear adjustment index; and γ1 represents the correction factor.

3. The intelligent battery early warning method based on user-side energy storage devices according to claim 2, characterized in that, The predicted environmental impact values ​​were obtained through multivariate regression calculation, as shown in the following formula: Where Se represents the predicted environmental impact value; j ranges from 1 to 4; Q1-Q4 represent the ambient temperature Ta, humidity H, vibration intensity Vb, and impact force Im, respectively; βj represents the environmental parameter weights; and γ2 represents the correction factor.

4. The intelligent battery early warning method based on user-side energy storage devices according to claim 3, characterized in that, The remaining lifetime calculation is based on an exponential decay model and takes into account environmental impacts. The calculation formula is as follows: Where RUL represents the remaining lifetime; R0 represents the initial design lifetime; C represents the number of charge / discharge cycles; L represents the cycle life; D represents the charge / discharge rate; λ1-λ4 represent the influence coefficients; and γ3 represents the correction factor.

5. The intelligent battery early warning method based on user-side energy storage devices according to claim 4, characterized in that, The fault risk assessment value is calculated using the entropy weight comprehensive assessment method, and the calculation formula is as follows: Wherein, FR represents the fault risk assessment value; Xk represents the fault-related parameters; wk represents the entropy weight allocation coefficient; δ represents the smoothing parameter; and γ4 represents the correction factor.

6. The intelligent battery early warning method based on user-side energy storage devices according to claim 5, characterized in that, The failure trend index is calculated based on the time series change rate of the failure risk assessment value, and the calculation formula is as follows: Where FT represents the failure trend index; τ1 and τ2 represent weighting coefficients; Indicates the rate of change of fault risk; This represents the fault acceleration; T represents the observation time window.

7. The intelligent battery early warning method based on user-side energy storage devices according to claim 6, characterized in that, A dynamic correction factor is calculated based on the failure trend index, and the remaining lifetime calculation is optimized: RUL′=RUL-Δγ; Where Δγ represents the dynamic correction factor; ω1, ω2, ω3 represent the adjustment parameters; Tw represents the prediction time window; and RUL′ represents the corrected remaining lifetime.

8. The intelligent battery early warning method based on user-side energy storage devices according to claim 7, characterized in that, When the probability of triggering an alert exceeds the alert threshold, a tiered alert is executed, wherein: When θP≤Pw<1.2, it is judged as a mild anomaly, and the graded warning result is a level one warning. When 1.2θP≤Pw<1.5, it is judged as a moderate anomaly, and the graded early warning result is a level two early warning. When Pw≥1.5θP, it is judged as a severe anomaly, and the graded warning result is a level three warning. Where θP represents the warning threshold.

9. A smart battery early warning system based on user-side energy storage devices, characterized in that, This method is used to implement the intelligent battery early warning method based on user-side energy storage devices as described in any one of claims 1-8.

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