Battery micro-degradation early warning method based on Kalman filtering and internal resistance correlation analysis

Abstract

The invention discloses a battery micro-degradation early-warning method based on Kalman filtering and internal resistance correlation analysis, and the method comprises the following steps: obtaining the data information of the operation state of a battery based on a time sequence, and carrying out the correction processing of the internal resistance and the self-discharge rate of the battery based on a Kalman battery state model; based on a Kalman battery state model, carrying out degradation characteristic identification quantitative processing on the corrected internal resistance and self-discharge rate of the battery; on the basis of a Kalman battery state model, carrying out degradation trend comparison processing on a degradation characteristic identification quantification processing result and battery operation state data of other time sequences; performing battery degradation degree judgment processing based on the degradation trend comparison processing result, and generating a battery state early warning report; the method achieves the iterative correction of the internal resistance and the self-discharge rate of the battery through the introduction of the Kalman filtering algorithm, captures the micro-threatened change of the performance of the battery, achieves the early precise early warning of the micro-degradation of the battery, and can predict the health state of the battery 15 days in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of battery state of health monitoring, and particularly to a battery micro - deterioration early warning method based on the correlation analysis of Kalman filter and internal resistance. Background Art

[0002] With the wide application of energy storage battery and power battery technologies, battery micro - deterioration early warning technology has become the key to ensuring battery safety and reliability. Existing battery deterioration detection and early warning methods still have significant deficiencies in terms of accuracy, real - time performance, and sensitivity to early micro - deterioration states, and are difficult to meet the requirements of efficient and intelligent early warning. In the prior art, the patent with the publication number CN114047444B provides a method and device for evaluating the health status of a storage battery, which models and predicts the capacity - discharge data through a grey model and outputs the screening results of deteriorated batteries by combining a hidden danger coefficient. However, this technical solution focuses on the macroscopic prediction based on the discharge voltage, lacks in - depth analysis of the correlation between the change of battery internal resistance and the deterioration state, and is difficult to capture the subtle characteristics of early micro - deterioration. At the same time, the grey model has weak adaptability to non - linear dynamic systems, with low prediction accuracy and certain deficiencies in sensitivity to early micro - deterioration states. Especially in the real - time tracking and modeling of the dynamic change of battery internal resistance, the prior art fails to fully explore its deep - level correlation with the battery deterioration state, resulting in a low reliability degree of the early warning system. Summary of the Invention

[0003] Aiming at the above - mentioned prior art, the present invention provides a battery micro - deterioration early warning method based on the correlation analysis of Kalman filter and internal resistance, mainly solving the technical problems existing in the above - mentioned background art.

[0004] To achieve the above object, the technical solution of the embodiment of the present invention is realized as follows: A battery micro - deterioration early warning method based on the correlation analysis of Kalman filter and internal resistance provided by the present invention, the method includes the following steps: S101. Obtain battery operation state data information based on time series, and perform correction processing on the battery internal resistance and self - discharge rate based on the Kalman battery state model; S102. Perform deterioration feature identification and quantification processing on the corrected battery internal resistance and self - discharge rate based on the Kalman battery state model; S103. Based on the Kalman battery state model, perform deterioration trend comparison processing on the results of deterioration feature identification and quantification processing and the battery operation state data of other time series; S104. Construct a battery internal resistance deterioration trend curve based on the results of battery internal resistance deterioration trend comparison processing, and calculate the average value of the change in the battery internal resistance deterioration trend within the unit sliding window length; Construct a self-discharge rate degradation trend curve based on the comparison processing results of the self-discharge rate degradation trends, and calculate the average value of the changes in the self-discharge rate degradation trends within the unit sliding window length; Judge the degree of battery degradation based on the magnitude of the average value of the changes in the battery internal resistance degradation trend and the magnitude of the average value of the changes in the self-discharge rate degradation trend; If there is an average value of the changes in the battery internal resistance degradation trend greater than the set threshold of the battery internal resistance degradation trend, or / and there is an average value of the changes in the self-discharge rate degradation trend greater than the set threshold of the self-discharge rate degradation trend, trigger an alarm and generate a report. Further, the construction of the Kalman battery state model in step S101 includes the following steps: Obtain the historical data information of the battery operating state, and perform initial estimations of the internal resistance and self-discharge rate; Perform quantization processing for identifying degradation characteristics on the initial estimation results; Obtain the battery operating state data information of other time series based on the quantization processing results of the degradation characteristics identification, and perform comparison processing of the degradation trends; Based on the quantization processing results of the degradation characteristics identification and the comparison processing results of the degradation trends, perform model correction processing on the initial Kalman filter model; If the model correction result is consistent with the correction sample result, the model correction is completed; if the model correction result is inconsistent with the correction sample result, re-perform the model correction.

[0005] Further, the process of obtaining the battery operating state data information based on the time series and dynamically tracking and correcting the battery internal resistance and self-discharge rate based on the Kalman battery state model in step S101 includes the following steps: Perform noise smoothing processing on the battery operating state data based on the Kalman battery state model; Perform abnormal fluctuation removal processing on the battery operating state data after noise smoothing processing; Partition the battery operating state data after abnormal fluctuation removal processing according to each unit of time; Perform identification and marking processing of the battery internal resistance characteristics and self-discharge rate characteristics on the battery operating state data after partitioning processing.

[0006] Further, the quantization processing for identifying degradation characteristics on the corrected internal resistance and self-discharge rate based on the Kalman battery state model in step S102 includes the following steps: Generate a battery internal resistance change curve and a self-discharge rate change curve according to the battery internal resistance characteristics and self-discharge rate characteristics of each sub-interval; Perform continuous change trend characteristic identification and number marking processing on the battery internal resistance change curve and the self-discharge rate change curve; Identify and extract the micro-incremental features, mutation features, periodic fluctuation features, and non-linear drift features of the continuously changing trend features marked by numbers. Quantify the feature intensities of the micro-incremental features, mutation features, periodic fluctuation features, and non-linear drift features identified and extracted through feature recognition.

[0007] Furthermore, the specific process of comparing the deterioration trend of the quantification result of deterioration feature recognition with the battery operation status data of other time series based on the Kalman battery state model in step S103 is as follows: The Kalman battery state model selects the corresponding deterioration trend curve from the battery operation status data of other time periods based on the quantification result of deterioration feature recognition in the current time period. Perform feature recognition and feature intensity quantification on the deterioration trend curves of other time periods. Compare the feature intensities of the quantification result of deterioration feature recognition in the current time period with the quantification results of feature intensities of other time periods. If the feature intensity comparison value is greater than or equal to the set threshold of feature intensity, it is determined that there is a battery state deterioration trend in the quantification result of deterioration feature recognition in the current time period; otherwise, it is determined that there is no battery state deterioration trend in the quantification result of deterioration feature recognition.

[0008] Furthermore, in step S104, construct a battery internal resistance deterioration trend curve based on the battery internal resistance deterioration trend comparison result, and calculate the average value of the battery internal resistance deterioration trend change within the unit sliding window length. Its expression is as follows: Where represents the difference in battery internal resistance change between the th node and the th node, is the number of days of the sliding window length, is the average value of the battery internal resistance deterioration trend change.

[0009] Furthermore, in step S104, construct a self-discharge rate deterioration trend curve based on the self-discharge rate deterioration trend comparison result, and calculate the average value of the self-discharge rate deterioration trend change within the unit sliding window length. Its expression is as follows: Where represents the difference in battery self-discharge rate change between the th node and the th node, is the number of days of the sliding window length, is the average value of the self-discharge rate deterioration trend change.

[0010] Further, the determination of the battery degradation degree based on the magnitudes of the mean values of the battery internal resistance degradation trend change and the self-discharge rate degradation trend change in step S104 includes the following steps: If the mean value of the battery internal resistance degradation trend change is greater than the first-level set threshold of the battery internal resistance degradation trend, or / and the mean value of the self-discharge rate degradation trend change is greater than the first-level set threshold of the self-discharge rate degradation trend, it is determined that the battery state degradation degree is a mild degradation trend; If the mean value of the battery internal resistance degradation trend change is greater than the second-level set threshold of the battery internal resistance degradation trend, or / and the mean value of the self-discharge rate degradation trend change is greater than the second-level set threshold of the self-discharge rate degradation trend, it is determined that the battery state degradation degree is a moderate degradation trend; If the mean value of the battery internal resistance degradation trend change is greater than the third-level set threshold of the battery internal resistance degradation trend, or / and the mean value of the self-discharge rate degradation trend change is greater than the third-level set threshold of the self-discharge rate degradation trend, it is determined that the battery state degradation degree is a severe degradation trend. The beneficial effects of the present invention are as follows: The Kalman battery state model is used to perform quantification processing on the degradation characteristics of the corrected battery internal resistance and self-discharge rate, and perform comparison processing on the degradation trends to determine the battery degradation degree, generating a battery state warning report. The Kalman filter algorithm is used to correct the battery internal resistance and self-discharge rate, capturing the micro-precursor changes in the battery performance, so as to achieve early and accurate warning of battery micro-degradation. By setting the number of days of the sliding window length, the battery health state can be predicted 15 days in advance. Description of the Drawings

[0011] Figure 1 It is a flowchart of the steps of the battery micro-degradation warning method based on Kalman filtering and internal resistance correlation analysis. Detailed Embodiments

[0012] The technical solution of the present invention will be further elaborated in detail below in conjunction with the drawings in the specification and specific embodiments. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. In the following description, the expression "some embodiments" is used, which describes a subset of all possible embodiments. However, it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0013] In the following description, a large number of specific details are given to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some well-known technical features in the art are not described.

[0014] It should be understood that the present invention can be implemented in different forms and should not be construed as limited to the embodiments presented herein. On the contrary, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the present invention to those skilled in the art. And the purpose of the terms used herein is only to describe specific embodiments and is not a limitation of the present invention. As used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the terms "comprising" and / or "including", when used in this specification, determine the presence of the described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups. As used herein, the term "and / or" includes any and all combinations of the related listed items.

[0015] It should be further noted that when an element is referred to as "fixed to" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "inner", "outer", "left", "right" and similar expressions used herein are for illustrative purposes only and do not represent the only implementation.

[0016] To fully understand the present invention, detailed structures will be presented in the following description to explain the technical solutions proposed by the present invention. The optional embodiments of the present invention are described in detail below. However, in addition to these detailed descriptions, the present invention can also have other implementations.

[0017] Please refer to the attached Figure 1 A battery micro-deterioration warning method based on Kalman filtering and internal resistance correlation analysis provided by the present invention, the method comprising the following steps: S101, obtaining battery operation state data information based on a time series, and performing correction processing on the battery internal resistance and self-discharge rate based on a Kalman battery state model; Specifically, obtaining battery operation state data information based on a time series includes battery voltage, battery current, charge and discharge cycle times, temperature, internal resistance and self-discharge rate. By constructing a Kalman battery state model to correct the battery internal resistance and self-discharge rate, the corrected battery internal resistance and self-discharge rate are obtained.

[0018] S102, performing deterioration feature identification and quantification processing on the corrected battery internal resistance and self-discharge rate based on the Kalman battery state model; S103, performing deterioration trend comparison processing on the result of the deterioration feature identification and quantification processing and the battery operation state data of other time series based on the Kalman battery state model; S104. Construct a battery internal resistance degradation trend curve based on the comparison processing results of the battery internal resistance degradation trend, and calculate the average value of the battery internal resistance degradation trend change within the unit sliding window length; Construct a self-discharge rate degradation trend curve based on the comparison processing results of the self-discharge rate degradation trend, and calculate the average value of the self-discharge rate degradation trend change within the unit sliding window length; Judge the degree of battery degradation based on the magnitude of the average value of the battery internal resistance degradation trend change and the magnitude of the average value of the self-discharge rate degradation trend change; If there is an average value of the battery internal resistance degradation trend change greater than the set threshold of the battery internal resistance degradation trend, or / and there is an average value of the self-discharge rate degradation trend change greater than the set threshold of the self-discharge rate degradation trend, trigger an alarm and generate a report. Further, the construction of the Kalman battery state model in the step S101 includes the following steps: Obtain the historical data information of the battery operating state, and perform initial estimation of the internal resistance and self-discharge rate; Perform quantification processing on the degradation characteristics identification of the initial estimation result; Obtain the battery operating state data information of other time series based on the quantification processing result of the degradation characteristics identification, and perform comparison processing on the degradation trend; Based on the quantification processing result of the degradation characteristics identification and the comparison processing result of the degradation trend, perform model correction processing on the initial Kalman filter model; If the model correction result is consistent with the correction sample result, the model correction is completed; if the model correction result is inconsistent with the correction sample result, re-perform model correction.

[0019] Further, the process of obtaining the battery operating state data information based on the time series in the step S101 and dynamically tracking and correcting the battery internal resistance and self-discharge rate based on the Kalman battery state model includes the following steps: Perform noise smoothing processing on the battery operating state data based on the Kalman battery state model; Perform abnormal fluctuation removal processing on the battery operating state data after noise smoothing processing; Partition the battery operating state data after abnormal fluctuation removal processing according to each unit of time; Perform identification and marking processing on the battery internal resistance characteristics and self-discharge rate characteristics of the partitioned battery operating status data. Specifically, perform noise smoothing processing on the battery operating status data through the Kalman battery state model to eliminate noise interference. Perform abnormal fluctuation removal processing on the battery operating data after noise smoothing. For example, within a certain time period, if a drastic jump in the battery internal resistance or self-discharge rate is detected, mark it as an abnormal fluctuation and perform elimination processing, and fill it with the average value according to the values adjacent to the abnormal value. Divide the battery operating status data after abnormal fluctuation removal processing into multiple sub-intervals per unit time (such as every 60 minutes) to obtain the operating status data of several sub-interval pools, and perform identification and marking processing on the battery internal resistance characteristics and self-discharge rate characteristics of the battery operating status data in each sub-interval to identify the battery internal resistance characteristics and self-discharge rate characteristics.

[0020] Further, the identification and quantification processing of the degradation characteristics of the corrected internal resistance and self-discharge rate based on the Kalman battery state model in step S102 includes the following steps: Generate a battery internal resistance change curve and a self-discharge rate change curve according to the battery internal resistance characteristics and self-discharge rate characteristics of each sub-interval; Perform continuous change trend feature identification and number marking processing on the battery internal resistance change curve and the self-discharge rate change curve; Perform micro-increment feature, mutation feature, periodic fluctuation feature, and non-linear drift feature identification and extraction processing on the continuously changing trend features with number marking; Perform feature intensity quantification processing on the micro-increment feature, mutation feature, periodic fluctuation feature, and non-linear drift feature after feature identification and extraction processing.

[0021] Specifically, generate a first battery internal resistance change curve based on the battery internal resistance characteristics of the first time period, perform continuous change trend feature identification and number marking processing on the first battery internal resistance change curve, and identify that the continuous change trend features of the first battery internal resistance change curve have micro-increment features, mutation features, periodic fluctuation features, and non-linear drift features. Denote the micro-increment feature in the first battery internal resistance change curve as 、denote the mutation feature as 、denote the periodic fluctuation feature as 、denote the non-linear drift feature as . And perform feature intensity quantification processing to obtain the feature intensity quantification processing result of the first battery internal resistance change curve. The feature intensity quantification processing result of the first battery internal resistance change curve includes the quantified micro-increment feature 、the quantified mutation feature 、the quantified periodic fluctuation feature 、the quantified non-linear drift feature .

[0022] Specifically, a first self-discharge rate change curve is generated based on the self-discharge rate characteristics in the first time period. Continuous change trend feature recognition and number marking processing are performed on the first self-discharge rate change curve, and it is recognized that the continuous change trend features of the first self-discharge rate change curve include micro-increment features, mutation features, periodic fluctuation features, and non-linear drift features. The micro-increment features in the first self-discharge rate change curve are denoted as , the mutation features are denoted as , the periodic fluctuation features are denoted as , and the non-linear drift features are denoted as . Feature intensity quantization processing is carried out to obtain the feature intensity quantization processing result of the first self-discharge rate change curve. The feature intensity quantization processing result of the first self-discharge rate change curve includes the quantized micro-increment features , the quantized mutation features , the quantized periodic fluctuation features , and the quantized non-linear drift features .

[0023] By performing continuous change trend feature recognition and number marking processing on the battery internal resistance change curve and the self-discharge rate change curve, when comparing the deterioration trend with the battery operation state data of other time series based on the Kalman battery state model for the quantification processing result of the deterioration feature recognition, the speed of the deterioration trend comparison processing is greatly improved.

[0024] Specifically, the battery internal resistance and self-discharge rate are corrected based on the Kalman battery state model. Deterioration feature recognition and quantification processing are performed on the corrected battery internal resistance and self-discharge rate through the Kalman battery state model to obtain the quantification processing result of the deterioration feature recognition. The deterioration feature recognition and quantification processing include micro-increment features, mutation features, periodic fluctuation features, and non-linear drift features. By performing feature intensity quantization processing on the micro-increment features, mutation features, periodic fluctuation features, and non-linear drift features, the operation state of the battery can be intuitively reflected, thereby improving the accuracy of the battery health state assessment.

[0025] Furthermore, by constructing the battery internal resistance deterioration trend curve and the self-discharge rate deterioration trend curve, the battery internal resistance deterioration trend and the self-discharge rate deterioration trend can be very intuitively observed. And by the magnitudes of the change means of the battery internal resistance deterioration trend and the change means of the self-discharge rate deterioration trend, the battery deterioration degree is judged to generate a battery state warning report, thereby significantly improving the reliability of the battery deterioration monitoring.

[0026] Further, the specific process of comparing the deterioration trend between the quantification result of the deterioration feature recognition based on the Kalman battery state model and the battery operation state data of other time series in step S103 is as follows: Based on the quantification result of the deterioration feature recognition in the current time period, the Kalman battery state model selects the corresponding deterioration trend curve from the battery operation state data of other time periods. Perform feature recognition and feature intensity quantification on the deterioration trend curves of other time periods. Compare the feature intensity between the quantification result of the deterioration feature recognition in the current time period and the quantification result of the feature intensity of other time periods. If the feature intensity comparison value is greater than or equal to the set threshold of the feature intensity, it is determined that there is a battery state deterioration trend in the quantification result of the deterioration feature recognition in the current time period; otherwise, it is determined that there is no battery state deterioration trend in the quantification result of the deterioration feature recognition.

[0027] Specifically, based on the quantification result of the battery internal resistance deterioration feature recognition in the current time period, the Kalman battery state model selects the battery internal resistance deterioration trend curve from the battery operation state data of the previous time period. Perform feature recognition and feature intensity quantification on the battery internal resistance deterioration trend curve of the previous time period to obtain the quantification result of the battery internal resistance deterioration feature recognition in the previous time period. Compare the feature intensity of the battery internal resistance between the quantification result of the battery internal resistance deterioration feature recognition in the current time period and the quantification result of the battery internal resistance deterioration feature recognition in the previous time period to obtain the battery internal resistance feature intensity comparison result. If the battery internal resistance feature intensity comparison value is greater than or equal to the set threshold of the battery internal resistance feature intensity, it is determined that there is a battery state deterioration trend in the quantification result of the battery internal resistance deterioration feature recognition in the current time period; otherwise, it is determined that there is no battery state deterioration trend in the quantification result of the battery internal resistance deterioration feature recognition in the current time period.

[0028] Based on the quantification result of the self-discharge rate deterioration feature recognition in the current time period, the Kalman battery state model selects the self-discharge rate deterioration trend curve from the battery operation state data of the previous time period. Perform feature recognition and feature intensity quantification on the self-discharge rate deterioration trend curve of the previous time period to obtain the quantification result of the self-discharge rate deterioration feature recognition in the previous time period. Compare the feature intensity of the self-discharge rate between the quantification result of the self-discharge rate deterioration feature recognition in the current time period and the quantification result of the self-discharge rate deterioration feature recognition in the previous time period to obtain the self-discharge rate feature intensity comparison result. If the self-discharge rate feature intensity comparison value is greater than or equal to the set threshold of the self-discharge rate feature intensity, it is determined that there is a battery state deterioration trend in the quantification result of the self-discharge rate deterioration feature recognition in the current time period; otherwise, it is determined that there is no battery state deterioration trend in the quantification result of the self-discharge rate deterioration feature recognition in the current time period.

[0029] Further, in step S104, a battery internal resistance degradation trend curve is constructed based on the comparison processing result of the battery internal resistance degradation trend, and the average value of the battery internal resistance degradation trend change within the unit sliding window length is calculated. Its expression is as follows: Wherein, represents the difference in battery internal resistance change between the th node and the th node, is the number of days of the sliding window length, is the average value of the battery internal resistance degradation trend change.

[0030] Specifically, through the Kalman battery state model, the quantification processing result of the degradation characteristics is compared with the battery operation state data of other time series to perform a degradation trend comparison process, and a degradation trend comparison processing result is obtained. The degradation trend comparison processing result includes the battery internal resistance degradation trend comparison processing result and the self-discharge rate degradation trend comparison processing result. Based on the degradation trend comparison processing result, a battery degradation degree determination process is performed, and a battery state warning report is generated. By setting the unit sliding window length to 15 days, the battery health state in the next 15 days can be predicted.

[0031] Further, in step S104, a self-discharge rate degradation trend curve is constructed based on the comparison processing result of the self-discharge rate degradation trend, and the average value of the self-discharge rate degradation trend change within the unit sliding window length is calculated. Its expression is as follows: Wherein, represents the difference in battery self-discharge rate change between the th node and the th node, is the number of days of the sliding window length, is the average value of the self-discharge rate degradation trend change.

[0032] Further, judging the battery degradation degree based on the magnitude of the average value of the battery internal resistance degradation trend change and the magnitude of the average value of the self-discharge rate degradation trend change includes the following steps: If the average value of the battery internal resistance degradation trend change is greater than the first-level set threshold of the battery internal resistance degradation trend, or / and the average value of the self-discharge rate degradation trend change is greater than the first-level set threshold of the self-discharge rate degradation trend, it is determined that the battery state degradation degree is a mild degradation trend; If the average value of the battery internal resistance degradation trend change is greater than the second-level set threshold of the battery internal resistance degradation trend, or / and the average value of the self-discharge rate degradation trend change is greater than the second-level set threshold of the self-discharge rate degradation trend, it is determined that the battery state degradation degree is a moderate degradation trend; If the average value of the change in the battery internal resistance deterioration trend is greater than the third-level set threshold of the battery internal resistance deterioration trend, and / or the average value of the change in the self-discharge rate deterioration trend is greater than the third-level set threshold of the self-discharge rate deterioration trend, then it is determined that the degree of battery state deterioration is a severe deterioration trend.

[0033] Specifically, by constructing the battery internal resistance deterioration trend curve and the self-discharge rate deterioration trend curve, the battery internal resistance deterioration trend and the self-discharge rate deterioration trend can be observed very intuitively, and the degree of battery deterioration can be judged by the magnitude of the average value of the change in the battery internal resistance deterioration trend and the magnitude of the average value of the change in the self-discharge rate deterioration trend to generate a battery state warning report, thereby significantly improving the reliability of battery deterioration monitoring.

[0034] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. The protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A battery micro-deterioration warning method based on Kalman filtering and internal resistance correlation analysis, characterized in that The method includes the following steps: S101. Obtain battery operation status data information based on a time series, and perform correction processing on the battery internal resistance and self-discharge rate based on a Kalman battery state model; S102. Perform deterioration feature identification and quantification processing on the corrected battery internal resistance and self-discharge rate based on the Kalman battery state model; S103. Based on the Kalman battery state model, compare the deterioration trend of the result of the deterioration feature identification and quantification processing with the battery operation status data of other time series; S104. Construct a deterioration trend curve of the battery internal resistance based on the result of the comparison of the deterioration trend of the battery internal resistance, and calculate the average value of the change in the deterioration trend of the battery internal resistance within the unit sliding window length; Construct a deterioration trend curve of the self-discharge rate based on the result of the comparison of the deterioration trend of the self-discharge rate, and calculate the average value of the change in the deterioration trend of the self-discharge rate within the unit sliding window length; Judge the degree of battery deterioration based on the magnitude of the average value of the change in the deterioration trend of the battery internal resistance and the magnitude of the average value of the change in the deterioration trend of the self-discharge rate; If there is an average value of the change in the deterioration trend of the battery internal resistance greater than the set threshold of the deterioration trend of the battery internal resistance, or / and there is an average value of the change in the deterioration trend of the self-discharge rate greater than the set threshold of the deterioration trend of the self-discharge rate, trigger an alarm and generate a report.

2. The battery micro-deterioration warning method based on Kalman filtering and internal resistance correlation analysis according to claim 1, characterized in that The construction of the Kalman battery state model in step S101 includes the following steps: Obtain the historical data information of the battery operation status, and perform initial estimation of the internal resistance and self-discharge rate; Perform deterioration feature identification and quantification processing on the initial estimation result; Obtain the battery operation status data information of other time series based on the result of the deterioration feature identification and quantification processing, and perform deterioration trend comparison processing; Based on the result of the deterioration feature identification and quantification processing and the result of the deterioration trend comparison processing, perform model correction processing on the initial Kalman filter model; If the model correction result is consistent with the correction sample result, the model correction is completed; if the model correction result is inconsistent with the correction sample result, re-perform model correction.

3. The battery micro-deterioration warning method based on Kalman filtering and internal resistance correlation analysis according to claim 2, characterized in that The process of obtaining the battery operation status data information based on the time series and dynamically tracking and correcting the battery internal resistance and self-discharge rate based on the Kalman battery state model in step S101 includes the following steps: Perform noise smoothing processing on the battery operation status data based on the Kalman battery state model; Perform abnormal fluctuation removal processing on the battery operation status data after noise smoothing processing; Partition the battery operation status data after abnormal fluctuation removal processing according to each unit of time; Perform identification and marking processing on the battery internal resistance characteristics and self-discharge rate characteristics of the partitioned battery operation status data.

4. The battery micro-deterioration warning method based on Kalman filtering and internal resistance correlation analysis according to claim 3, wherein The process of performing deterioration feature identification and quantification processing on the corrected internal resistance and self-discharge rate based on the Kalman battery state model in step S102 includes the following steps: Generate a battery internal resistance change curve and a self-discharge rate change curve according to the battery internal resistance characteristics and self-discharge rate characteristics of each sub-interval; Perform continuous change trend feature identification and number marking processing on the battery internal resistance change curve and the self-discharge rate change curve; Identify and extract the micro-incremental features, mutation features, periodic fluctuation features, and non-linear drift features of the continuously changing trend features marked by numbers. Quantify the feature intensities of the micro-incremental features, mutation features, periodic fluctuation features, and non-linear drift features that have undergone feature identification and extraction processing.

5. The battery micro-deterioration early warning method based on Kalman filtering and internal resistance correlation analysis according to claim 4, characterized in that, The specific process of comparing the deterioration trend between the result of deterioration feature identification and quantification processing and the battery operation state data of other time series based on the Kalman battery state model in step S103 is as follows: The Kalman battery state model selects the corresponding deterioration trend curve from the battery operation state data of other time periods based on the result of deterioration feature identification and quantification processing in the current time period. Conduct feature identification and feature intensity quantification processing on the deterioration trend curves of other time periods. Compare the feature intensities between the result of deterioration feature identification and quantification processing in the current time period and the result of feature intensity quantification processing in other time periods. If the feature intensity comparison value is greater than or equal to the set feature intensity threshold, it is determined that there is a battery state deterioration trend in the result of deterioration feature identification and quantification processing in the current time period. Otherwise, it is determined that there is no battery state deterioration trend in the result of deterioration feature identification and quantification processing.

6. The battery micro-deterioration warning method based on Kalman filtering and internal resistance correlation analysis according to claim 5, characterized in that, In step S104, a battery internal resistance deterioration trend curve is constructed based on the comparison processing result of the battery internal resistance deterioration trend, and the average value of the battery internal resistance deterioration trend change within the unit sliding window length is calculated. The expression is as follows: Among them, represents the difference in battery internal resistance change between the th node and the th node, is the number of days of the sliding window length, is the average value of the battery internal resistance deterioration trend change.

7. The battery micro-deterioration warning method based on Kalman filtering and internal resistance correlation analysis according to claim 6, characterized in that, In step S104, a self-discharge rate deterioration trend curve is constructed based on the comparison processing result of the self-discharge rate deterioration trend, and the average value of the change in the self-discharge rate deterioration trend within the unit sliding window length is calculated. The expression is as follows: Wherein, represents the difference in the battery self-discharge rate change between the th node and the th node, is the number of days of the sliding window length, is the average value of the change in the self-discharge rate deterioration trend.

8. The battery micro-deterioration warning method based on Kalman filtering and internal resistance correlation analysis according to claim 7, wherein The steps for judging the degree of battery deterioration based on the magnitude of the mean change in the battery internal resistance deterioration trend and the magnitude of the mean change in the self-discharge rate deterioration trend in step S104 are as follows: If the mean change in the battery internal resistance deterioration trend is greater than the first-level set threshold of the battery internal resistance deterioration trend, or / and the mean change in the self-discharge rate deterioration trend is greater than the first-level set threshold of the self-discharge rate deterioration trend, it is determined that the degree of battery state deterioration is a mild deterioration trend. If the mean change in the battery internal resistance deterioration trend is greater than the second-level set threshold of the battery internal resistance deterioration trend, or / and the mean change in the self-discharge rate deterioration trend is greater than the second-level set threshold of the self-discharge rate deterioration trend, it is determined that the degree of battery state deterioration is a moderate deterioration trend. If the mean change in the battery internal resistance deterioration trend is greater than the third-level set threshold of the battery internal resistance deterioration trend, or / and the mean change in the self-discharge rate deterioration trend is greater than the third-level set threshold of the self-discharge rate deterioration trend, it is determined that the degree of battery state deterioration is a severe deterioration trend.

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