A charging pile capable of automatically identifying abnormal states of a new energy vehicle battery and an identification method

Abstract

This invention provides a charging pile and identification method for automatically identifying abnormal states of batteries in new energy vehicles, relating to the field of intelligent charging technology. The method includes: acquiring historical normal charging data of batteries of the same type to form a charging performance management database; acquiring historical charging fault data of batteries of the same type, establishing a pre-charging fault type database and a charging process fault type database respectively, and adjusting the charging performance management database to form an adjusted charging performance management database; acquiring real-time charging access information to form pre-charging fault detection result information; when the pre-charging fault detection result data shows normality, optimizing charging management control; and acquiring real-time charging process information during the optimized charging management control process to form real-time charging process monitoring result information. This method can accurately and promptly identify battery anomalies during vehicle charging to ensure battery safety.

Description

Technical Field

[0001] This invention relates to the field of intelligent charging technology for charging piles, and more specifically, to a charging pile and identification method that can automatically identify abnormal states of batteries in new energy vehicles. Background Technology

[0002] New energy vehicles are gaining increasing popularity and demand. As the market expands, the number of new energy vehicles is also growing. However, the vehicle battery, being the most expensive component in a new energy vehicle, is also the primary source of current problems, such as spontaneous combustion, leakage, and performance issues, which constrain the market prospects of new energy vehicles. To address these problems, more in-depth technical research on vehicle batteries is being conducted. However, technological advancements take a considerable amount of time, necessitating new and efficient processing methods to overcome the current shortcomings and defects in vehicle batteries.

[0003] The most likely ways to monitor abnormal conditions in car batteries are through the vehicle's own monitoring system and external monitoring methods when connected to a charging station. However, the vehicle's own monitoring system cannot independently and efficiently diagnose battery abnormalities accurately. Therefore, monitoring and identifying abnormal states during charging is crucial. Currently, there is no accurate and systematic method for monitoring abnormal states of vehicle batteries using charging stations.

[0004] Therefore, designing a charging pile with orderly charging function and an identification method that can automatically identify abnormal states of new energy vehicle batteries, so as to accurately and timely identify abnormalities of batteries during vehicle charging and ensure the safety of battery use, is an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a method for automatically identifying abnormal states of new energy vehicle batteries. By acquiring normal charging data from batteries of the same type, a charging performance management database based on big data is established. This allows for the rapid determination of the performance state of individual vehicle batteries within the normal range during real-time charging, providing fundamental reference data for subsequent optimized charging management. Simultaneously, for abnormal data occurring during accidents and charging, separate databases are established for pre-charging fault types (analyzed before charging) and charging process fault types (analyzed during charging). This enables comprehensive and accurate identification of abnormal states of vehicle batteries before and during charging, thereby providing control guidance for battery charging. This ensures safety during battery charging and enhances the safety of battery use, improving the reliability and safety of new energy vehicles.

[0006] The present invention also aims to provide a charging pile capable of automatically identifying abnormal states of new energy vehicle batteries. This is achieved by a data acquisition unit that fully collects the basic big data required for anomaly identification and analysis, and a data transmission unit that summarizes and synthesizes the data. Simultaneously, a database unit stores various databases used for anomaly identification. Furthermore, the analysis and processing unit can retrieve the database and real-time data in real time for anomaly identification and analysis, completing the identification and analysis process efficiently and in real time, thus providing a material basis for accurate and efficient identification of abnormal vehicle battery states.

[0007] In a first aspect, the present invention provides a method for automatically identifying abnormal states of batteries in new energy vehicles, comprising: acquiring historical normal charging data of batteries of the same type, performing charging performance analysis based on charging management, and forming a charging performance management database; acquiring historical charging fault data of batteries of the same type, performing fault identification analysis in conjunction with the charging performance management database, establishing a pre-charging fault type database and a charging process fault type database respectively, and adjusting the charging performance management database to form an adjusted charging performance management database; acquiring real-time charging access information, and performing fault detection based on the pre-charging fault type database to form pre-charging fault detection result information; when the pre-charging fault detection result data shows normality, optimizing charging management control; acquiring real-time charging process information during the optimization of charging management control, and performing process monitoring in conjunction with the charging process fault type database to form real-time charging process monitoring result information.

[0008] In this invention, the method acquires normal charging data from batteries of the same type to establish a big data-based charging performance management database. This allows for the rapid determination of the performance state of individual vehicle batteries within the normal range during real-time charging, providing fundamental reference data for subsequent optimized charging management. Simultaneously, for abnormal data occurring during accidents and charging, separate databases are established: a pre-charging fault type database for analysis and comparison before charging, and a charging process fault type database for analysis and comparison during charging. This enables comprehensive and accurate identification of abnormal states of vehicle batteries before and during charging, thereby providing control guidance for vehicle battery charging. This ensures safety during vehicle battery charging and enhances the safety of vehicle battery use, improving the reliability and safety of new energy vehicles.

[0009] One possible implementation involves acquiring historical normal charging data of similar batteries, performing charging performance analysis based on charging management, and forming a charging performance management database. This includes: acquiring change data of different performance management parameters of similar batteries within their effective lifespan, forming lifespan-performance change curves for different performance management parameters, where performance management parameters are performance parameters that change with battery lifespan; acquiring real-time charging data of different performance management parameters during each normal charge of similar batteries, and combining this with the lifespan-performance change curves for different performance management parameters to determine the parameter change range of different performance management parameters under the corresponding real-time lifespan, forming a dataset A of normal performance ranges for different performance management parameters within their effective lifespan. n ,in, n is the number of the different performance management parameters, and t is the time point within the effective service life. This represents the minimum value of the performance management parameter numbered n at time point t. This represents the maximum value of the performance management parameter numbered n at time point t; performance correlation analysis is performed on different performance management parameters within the effective service life, forming a life-performance correlation change curve data; real-time charging data of different performance management parameters are obtained for each normal charge of the same type of battery, and correlation analysis is performed on different real-time charging data in conjunction with the life-performance correlation change curve data to establish a performance correlation change range dataset B within the effective service life, where B = [b min b max ] t Where t is a point in time within the effective service life, and b min b represents the minimum value of the range of performance correlation changes at time point t. max This represents the maximum value of the range of performance correlation changes at time point t; it combines the normal performance range dataset A of all performance management parameters. n The set B, which is related to the range of performance changes, forms a charging performance management database.

[0010] In this invention, the establishment of a charging performance management database is primarily aimed at establishing a reasonable range for the performance parameter variations of batteries of the same type within their lifespan. Considering the different usage conditions of batteries in different vehicles, their performance parameters cannot perfectly align with the time-performance variation curves derived from experimental or theoretical analyses. Therefore, it is necessary to determine a reasonable range based on big data analysis. Within this range, the battery performance is considered to be in a normal state, enabling rapid identification of abnormal charging states. Furthermore, because performance variations exhibit a range, it is possible to distinguish between different states of superior performance within the normal range, thus providing fundamental reference data for charging optimization. It should be noted that performance management parameters, considering the state of battery charging performance changes over its lifespan, should be parameters that exhibit a generally regular change over the lifespan, such as capacity, internal resistance, energy, and power. Other battery rated parameters are not performance management parameters. Of course, for the acquired historical normal charging data, charging data of batteries that experienced accidents before the accidents should be excluded. The length of the excluded time period can be determined according to actual needs, aiming to minimize the inclusion of such abnormal data. It's understandable that battery performance management parameters are not independent of each other; individual performance management parameters might be within the normal range, yet the overall battery performance might exhibit abnormalities. Therefore, when establishing a charging performance management database, in addition to establishing normal range data for each individual performance management parameter, it's also necessary to conduct correlation analysis based on the relationships between these parameters to obtain the normal range for overall performance analysis. For this correlation analysis, big data analytics can be used to identify various correlation factors, such as establishing correlation analysis formulas for different performance management parameters. α t This represents the correlation value at time point t. This represents the correlation factor corresponding to the performance management parameter numbered n at time t. Of course, this factor can also be a constant value that does not change with time. n This indicates the rated value corresponding to the performance management parameter numbered n. This represents the real-time value of the performance management parameter numbered n at time point t. Additionally, it should be noted that this is used to establish a dataset A representing the normal performance range for different performance management parameters. nWhen determining the range of performance-related changes in dataset B, it is necessary not only to consider the large amount of data acquired to define the range, but also to adjust the range by combining the lifetime-performance change curve data. That is, for the range of a single performance management parameter, after determining the range based on the acquired historical data, it is necessary to determine whether the value corresponding to the lifetime-performance change curve data belongs to this range. If it does not belong to this range, the value corresponding to the lifetime-performance change curve data needs to be used as the boundary to expand the range. This can avoid the range deviation caused by the error of the sampling data. The same processing is required for dataset B of performance-related changes to obtain a reasonable and accurate parameter range.

[0011] One possible approach is to acquire historical charging fault data of the same type of battery, combine it with a charging performance management database for fault identification and analysis, establish a pre-charging fault type database and a charging process fault type database, and adjust the charging performance management database to form an adjusted charging performance management database. This includes: acquiring pre-accident charging information data from historical charging fault data, performing charging data analysis based on the charging data before the accident, and establishing a pre-charging fault type database; acquiring abnormal charging information data from historical charging fault data, performing charging data analysis based on different abnormal situations in combination with the charging performance management database, and establishing a charging process fault type database; and adjusting the charging performance management database based on the charging process fault type database to form an adjusted charging performance management database.

[0012] In this invention, identifying abnormal states during charging requires establishing the possible characterization range of performance management parameters under corresponding abnormal states. Considering that different abnormal states can have varying degrees of safety impact on battery use, this invention distinguishes between abnormal state judgment before charging and abnormal state judgment during charging, thereby establishing corresponding pre-charging fault type databases and charging process fault type databases. The pre-charging fault type database primarily focuses on abnormal performance management parameter data obtained before a high-risk accident such as a natural disaster, establishing a corresponding database to confirm these abnormalities before charging and prevent corresponding accidents from occurring during subsequent charging and use. The charging process fault type database analyzes and processes performance management data under possible abnormal conditions, including hardware connection issues, system warnings, and other situations that do not affect safe use, thereby providing data guidance for ensuring normal charging and subsequent battery use and maintenance, as well as timely on-site fault analysis. Of course, it is understandable that, considering the characteristics of big data analysis, for accidental failures, the range of performance management parameters obtained will generally not overlap with the range of data in the charging performance management database. However, for abnormal data obtained during the charging process, there is a situation where the range of data in the charging performance management database overlaps. Therefore, after determining the fault type database for the charging process, it is necessary to adjust the data range of the charging performance management database based on its determined range, so as to more conservatively identify abnormalities in the charging and operating status of the battery and improve the reliability of abnormality identification.

[0013] As one possible implementation, pre-accident charging information data from historical charging fault data is acquired, and charging data analysis based on the pre-accident events is performed to establish a pre-accident fault type database. This includes: acquiring the lifespan and corresponding different performance management parameters from the pre-accident charging information data, forming a parameter accident range set C for different performance management parameters within the effective lifespan. n ,in This represents the minimum value of the performance management parameter numbered n at time point t. This represents the maximum value of the performance management parameter numbered n at time point t; the lifespan and corresponding accident charging information of different performance management parameters in the pre-accident charging information data are analyzed for correlation to form a set D of correlated accident ranges within the effective lifespan, where D = [d min d max ] t d min d represents the minimum value of the range of correlated incidents at time point t. maxThis represents the maximum value of the relevant incident range at time point t; the parameter incident range set C, which combines all performance management parameters. n Together with the relevant accident range set D, a database of pre-charging fault types is formed.

[0014] In this invention, the establishment of the pre-charging fault type database considers both the range of individual performance management parameters and the overall range of correlation analysis. Since the range of performance management data corresponding to an accident generally deviates significantly from the normal performance management data range, it is not necessary to further filter and analyze the acquired accident charging information data to obtain the reasonable range of performance management parameters and the range of correlation analysis corresponding to the accident state.

[0015] One possible implementation involves acquiring process-related abnormal charging information data from historical charging fault data, combining it with a charging performance management database to perform charging data analysis based on different abnormal situations, and forming a charging process fault type database. This includes: acquiring abnormal charging information related to the service life and corresponding different performance management parameters from the process-related abnormal charging information data, forming an initial abnormal information range set for different performance management parameters within their service life cycle; and comparing this initial abnormal information range set with the corresponding normal performance range dataset A in the charging performance management database. n Comparative adjustments are made to form a set of adjustment anomaly information ranges; these sets of adjustment anomaly information ranges for different performance management parameters are then compared with the corresponding parameter accident range sets C in the pre-charging fault type database. n Compare and adjust to form a reasonable range of abnormal information E n ,in, This represents the minimum value of the performance management parameter numbered n at time point t. This represents the maximum value of the performance management parameter numbered n at time point t; abnormal charging information of the service life and corresponding different performance management parameters in the abnormal charging information data is obtained, and correlation analysis is performed to form an initial set of abnormal correlation ranges within the effective service life period; the initial set of abnormal correlation ranges is compared and adjusted with the corresponding performance correlation change range dataset B in the charging performance management database to form an adjusted set of abnormal correlation ranges; the adjusted set of abnormal correlation ranges is compared and adjusted with the set of abnormal correlation accidents D in the pre-charging fault type database to form a reasonable set of abnormal correlation ranges F, where F = [f min f max ] t f min f represents the minimum value of the reasonable correlation anomaly range at time point t. maxThis represents the maximum value of the reasonable correlation anomaly range at time point t; the reasonable anomaly information range set E combining all performance management parameters. n A database of charging process fault types is formed by combining the reasonable correlation anomaly range set F.

[0016] In this invention, for abnormal charging information, since the range of performance management parameters obtained under abnormal conditions lies between the range of normal charging performance management parameters and the range of performance management parameters corresponding to accident information, there is overlap between the ranges of the two performance management parameters. To improve the reliability of anomaly identification and avoid missing abnormal states, when establishing the charging process fault type database, after establishing the initial range based on the original abnormal data, it is also necessary to compare and adjust it with the corresponding normal performance management parameter range. Areas overlapping with the normal performance management parameter range are also included in the abnormal data range. Furthermore, it is compared and adjusted with the corresponding data range in the pre-charging fault type database, removing any overlapping portions and incorporating them into the pre-charging fault type database. This improves the reliability of identifying accidental faults.

[0017] As one possible implementation, the charging performance management database is adjusted based on the charging process fault type database to form an adjusted charging performance management database, including: setting a reasonable range of abnormal information for different performance management parameters, E. n Performance normal range dataset A, which differs from the performance management parameters in the charging performance management database. n The following comparisons and adjustments were made to create a dataset G representing the normal performance range for different performance management parameters. n If the reasonable range of abnormal information is set E n and the corresponding normal performance range dataset A n If there is an intersection, then G n =A n -(E n ∩A n ), and vice versa. n =A n The reasonable correlation anomaly range set F is compared and adjusted with the performance correlation change range dataset B in the charging performance management database to form the adjusted performance correlation change range dataset H: If the reasonable correlation anomaly range set F and the performance correlation change range dataset B have an intersection, then H = B - (F ∩ B), otherwise H = B; Combine this with the adjusted normal performance range dataset G of all performance management parameters. n Together with the dataset H representing the range of changes in performance correlation, a database for adjusting charging performance management is formed.

[0018] In this invention, since the adjustment of overlapping data ranges was considered when establishing the charging process fault type database, it is necessary to adjust the range data in the affected charging performance management database to ensure the uniqueness of the correspondence between data range and state during anomaly identification. This improves the reliability and efficiency of anomaly state identification.

[0019] One possible implementation involves acquiring real-time charging access information and performing fault detection based on a pre-charging fault type database to generate pre-charging fault detection result information. This includes: acquiring the battery lifespan and corresponding access values ​​of different performance management parameters from the real-time charging access information, performing correlation analysis on the access values ​​of different performance management parameters to generate usage correlation values; and comparing the access values ​​of different performance management parameters with the corresponding parameter accident range set C in the pre-charging fault type database. n A one-to-one comparison will be performed, comparing the correlation values ​​with the correlation accident range set D in the pre-charging fault type database, and the following analysis and judgment will be made: If there is an access value for a performance management parameter that belongs to the corresponding parameter accident range set C... n If the value used for the performance management parameter does not belong to the corresponding parameter accident range set C, then a fault detection anomaly information is generated before charging, and the detection result is judged to be abnormal; if ... access value of the performance management parameter does not belong to the corresponding parameter accident range set C, then a fault detection anomaly information is generated before charging, and the detection result is judged to be abnormal. n If the correlation value used does not belong to the correlation accident range set D, then normal information for fault detection before charging is formed, and the detection result is judged to be normal.

[0020] In this invention, after establishing a database of pre-charging fault types, the performance management parameters obtained in practice can be directly compared and judged before charging. Considering the significant impact of accidental abnormal states on battery and personnel safety, the identification of accidental abnormal states is extremely rigorous; the result is considered abnormal simply because any one of the evaluation criteria is not met.

[0021] As one possible implementation, if the fault detection result data before charging is normal, then optimized charging management control is performed, including: when the fault detection result data before charging is normal, charging management control based on extending battery life is performed during battery charging.

[0022] In this invention, after a fault detection test shows normal results before charging, the battery can be charged normally. Considering the reliability of battery use and increasing the quality of battery products, charging management can be implemented based on the current battery data to extend battery life, such as adjusting charging strategies and charging capacity. Furthermore, adaptive charging control adjustments can be made based on the position of the battery's performance parameters within the normal charging performance management parameter range to achieve the optimal state value within the data range.

[0023] One possible implementation involves acquiring real-time charging process information during the optimization of charging management and control, and combining this information with a charging process fault type database for process monitoring. This includes: acquiring the battery lifespan and corresponding process values ​​of different performance management parameters from real-time charging process information; performing correlation analysis on the process values ​​of different performance management parameters to form process correlation values; setting a duration threshold T; and comparing the process values ​​of different performance management parameters with the reasonable abnormal information range set E corresponding to the charging process fault type database. n A one-to-one comparison is performed, comparing the process correlation values ​​with the reasonable correlation anomaly range set F in the charging process fault type database, and the following analysis and judgment are made: If there is a process value of a performance management parameter that belongs to the corresponding reasonable anomaly information range set E... n If the process correlation value falls within the reasonable correlation anomaly range set F and the duration exceeds the duration threshold T, then an abnormal charging process information is generated; if the process values ​​of the performance management parameters do not fall within the corresponding reasonable anomaly information range set E, then an abnormal charging process information is generated. n If the process correlation value does not belong to the reasonable correlation anomaly range set F, then normal charging process information is generated; if the process value of a performance management parameter belongs to the corresponding reasonable anomaly information range set E, then normal charging process information is generated. n If the duration of the process does not exceed the duration threshold T, then normal charging information is generated; if the process correlation value belongs to the reasonable correlation abnormal range set F, and the duration does not exceed the duration threshold T, then normal charging information is generated.

[0024] In this invention, the establishment of a charging process fault type database is for identifying and monitoring abnormal states that occur during the charging process. Considering that the data on abnormal states during charging deviates relatively little from the normal performance management parameter range, a time threshold is set as a crucial criterion for judgment. Only when the data falls within the abnormal data range and reaches the time threshold is an abnormal state of the battery determined. It is understood that identifying an abnormal state provides a data foundation for subsequent fault handling, enabling better and more efficient battery maintenance and technical processing.

[0025] Secondly, this invention provides a charging pile capable of automatically identifying abnormal states of new energy vehicle batteries, employing the identification method for automatically identifying abnormal states of new energy vehicle batteries provided in the first aspect, comprising: a data acquisition unit for acquiring real-time charging access information and real-time charging process information of the vehicle battery; a database unit for storing and updating a charging management database, a pre-charging fault type database, and a charging process fault type database; a data transmission unit for acquiring and uploading the real-time charging access information and real-time charging process information acquired by the data acquisition unit, and downloading and updating historical normal charging data and historical charging fault data; an analysis and processing unit for acquiring historical normal charging data from the data transmission unit to perform charging performance analysis based on charging management, forming a charging performance management database; acquiring historical charging fault data from the data transmission unit and combining it with the charging performance management database to perform fault identification analysis, establishing a pre-charging fault type database and a charging process fault type database respectively, and adjusting the charging performance management database to form an adjusted charging performance management database; acquiring real-time charging access information from the data transmission unit to perform fault detection, forming pre-charging fault detection result information; and acquiring real-time charging process information from the data transmission unit to perform process monitoring, forming real-time charging process monitoring result information.

[0026] In this invention, the charging pile fully collects the basic big data required for anomaly identification and analysis through a data acquisition unit, and summarizes and integrates the data through a data transmission unit. Simultaneously, a database unit stores various databases used for anomaly identification. Furthermore, the analysis and processing unit can retrieve the database and real-time data in real time for anomaly state identification and analysis, completing the identification and analysis process efficiently and in real time, providing a material basis for accurate and efficient identification of vehicle battery anomalies.

[0027] The beneficial effects of the charging pile and identification method for automatically identifying abnormal battery states of new energy vehicles provided by this invention are as follows:

[0028] This method acquires normal charging data from similar batteries to establish a big data-based charging performance management database. This allows for the rapid determination of the individual vehicle battery's performance status within the normal range during real-time charging, providing fundamental reference data for subsequent optimized charging management. Simultaneously, for abnormal data occurring during accidents and charging, separate databases are established: a pre-charging fault type database for analysis and comparison before charging, and a charging process fault type database for analysis and comparison during charging. This enables comprehensive and accurate identification of abnormal states of the vehicle battery before and during charging, thereby providing control guidance for vehicle battery charging. This ensures safety during vehicle battery charging and enhances the safety of vehicle battery use, improving the reliability and safety of new energy vehicles.

[0029] This charging station uses a data acquisition unit to collect the basic big data required for anomaly identification and analysis. The data is then aggregated and synthesized by the data transmission unit, while the database unit stores various databases used for anomaly identification. Furthermore, the analysis and processing unit can retrieve databases and real-time data in real time for anomaly identification and analysis, completing the identification and analysis process efficiently and in real time. This provides a material basis for accurately and efficiently identifying abnormal states of vehicle batteries. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 The diagram illustrates the steps of an identification method for automatically identifying abnormal states of batteries in new energy vehicles, as provided in an embodiment of the present invention. Detailed Implementation

[0032] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention.

[0033] New energy vehicles are gaining increasing popularity and demand. As the market expands, the number of new energy vehicles is also growing. However, the vehicle battery, being the most expensive component in a new energy vehicle, is also the primary source of current problems, such as spontaneous combustion, leakage, and performance issues, which constrain the market prospects of new energy vehicles. To address these problems, more in-depth technical research on vehicle batteries is being conducted. However, technological advancements take a considerable amount of time, necessitating new and efficient processing methods to overcome the current shortcomings and defects in vehicle batteries.

[0034] The most likely ways to monitor abnormal conditions in car batteries are through the vehicle's own monitoring system and external monitoring methods when connected to a charging station. However, the vehicle's own monitoring system cannot independently and efficiently diagnose battery abnormalities accurately. Therefore, monitoring and identifying abnormal states during charging is crucial. Currently, there is no accurate and systematic method for monitoring abnormal states of vehicle batteries using charging stations.

[0035] refer to Figure 1 This invention provides a method for automatically identifying abnormal states of new energy vehicle batteries. This method acquires normal charging data from batteries of the same type and establishes a big data-based charging performance management database. This allows for the rapid determination of the individual battery's performance state within the normal range during real-time charging, providing fundamental reference data for subsequent optimized charging management. Simultaneously, for abnormal data occurring during accidents and charging, separate databases are established: a pre-charging fault type database for analysis and comparison before charging and a charging process fault type database for analysis and comparison during charging. This enables comprehensive and accurate identification of abnormal states of vehicle batteries before and during charging, thereby providing control guidance for battery charging. This ensures safety during battery charging and enhances the safety of battery use, improving the reliability and safety of new energy vehicles.

[0036] The identification method for automatically recognizing abnormal states of batteries in new energy vehicles specifically includes the following steps:

[0037] S1: Obtain historical normal charging data of the same type of battery, perform charging performance analysis based on charging management, and form a charging performance management database.

[0038] To acquire historical normal charging data for similar batteries, perform charging performance analysis based on charging management, and establish a charging performance management database. This includes: acquiring change data of different performance management parameters for similar batteries within their effective lifespan, forming lifespan-performance change curves for different performance management parameters, where performance management parameters are those that change with battery lifespan; acquiring real-time charging data of different performance management parameters during each normal charge of similar batteries, and combining this with the lifespan-performance change curves for different performance management parameters to determine the parameter change range of different performance management parameters under the corresponding real-time lifespan, forming a dataset A of normal performance ranges for different performance management parameters within their effective lifespan. n ,in, n is the number of the different performance management parameters, and t is the time point within the effective service life. This represents the minimum value of the performance management parameter numbered n at time point t. This represents the maximum value of the performance management parameter numbered n at time point t; performance correlation analysis is performed on different performance management parameters within the effective service life, forming a life-performance correlation change curve data; real-time charging data of different performance management parameters are obtained for each normal charge of the same type of battery, and correlation analysis is performed on different real-time charging data in conjunction with the life-performance correlation change curve data to establish a performance correlation change range dataset B within the effective service life, where B = [b min b max ] t Where t is a point in time within the effective service life, and b min b represents the minimum value of the range of performance correlation changes at time point t. max This represents the maximum value of the range of performance correlation changes at time point t; it also represents the normal performance range dataset A, which combines all performance management parameters. n The set B, which is related to the range of performance changes, forms a charging performance management database.

[0039] The establishment of a charging performance management database primarily aims to define a reasonable range for the performance parameter variations of batteries of the same type throughout their lifespan. Considering the different usage conditions of batteries in different vehicles, their performance parameters cannot perfectly align with the time-performance variation curves derived from experimental or theoretical analyses. Therefore, it is necessary to determine a reasonable range based on big data analysis. Within this range, battery performance is considered to be in a normal state, enabling rapid identification of abnormal charging conditions. Furthermore, because performance variations exhibit a range, it is possible to distinguish between different states of superior performance within the normal range, thus providing fundamental reference data for charging optimization. It is important to note that performance management parameters, considering the state of battery charging performance changes over its lifespan, should be parameters that exhibit a generally regular variation over the lifespan, such as capacity, internal resistance, energy, and power. Other battery rated parameters are not performance management parameters. Of course, for the acquired historical normal charging data, charging data of batteries that experienced accidents before the accidents should be excluded. The length of the excluded time period can be determined based on actual needs, aiming to minimize the inclusion of such abnormal data. It's understandable that battery performance management parameters are not independent of each other; individual performance management parameters might be within the normal range, yet the overall battery performance might exhibit abnormalities. Therefore, when establishing a charging performance management database, in addition to establishing normal range data for each individual performance management parameter, it's also necessary to conduct correlation analysis based on the relationships between these parameters to obtain the normal range for overall performance analysis. For this correlation analysis, big data analytics can be used to identify various correlation factors, such as establishing correlation analysis formulas for different performance management parameters. α t This represents the correlation value at time point t. This represents the correlation factor corresponding to the performance management parameter numbered n at time t. Of course, this factor can also be a constant value that does not change with time. n This indicates the rated value corresponding to the performance management parameter numbered n. This represents the real-time value of the performance management parameter numbered n at time point t. Additionally, it should be noted that this is used to establish a dataset A representing the normal performance range for different performance management parameters. nWhen determining the range of performance-related changes in dataset B, it is necessary not only to consider the large amount of data acquired to define the range, but also to adjust the range by combining the lifetime-performance change curve data. That is, for the range of a single performance management parameter, after determining the range based on the acquired historical data, it is necessary to determine whether the value corresponding to the lifetime-performance change curve data belongs to this range. If it does not belong to this range, the value corresponding to the lifetime-performance change curve data needs to be used as the boundary to expand the range. This can avoid the range deviation caused by the error of the sampling data. The same processing is required for dataset B of performance-related changes to obtain a reasonable and accurate parameter range.

[0040] S2: Obtain historical charging fault data of the same type of battery, combine it with the charging performance management database to perform fault identification and analysis, establish a fault type database before charging and a fault type database during charging, and adjust the charging performance management database to form an adjusted charging performance management database.

[0041] Historical charging fault data of the same type of battery is acquired, and fault identification and analysis are performed in conjunction with the charging performance management database. A pre-charging fault type database and a charging process fault type database are established, and the charging performance management database is adjusted to form an adjusted charging performance management database. This includes: acquiring pre-accident charging information data from historical charging fault data, performing charging data analysis based on the charging data before the accident, and establishing a pre-charging fault type database; acquiring abnormal charging information data from historical charging fault data, performing charging data analysis based on different abnormal situations in conjunction with the charging performance management database, and forming a charging process fault type database; and adjusting the charging performance management database based on the charging process fault type database to form an adjusted charging performance management database.

[0042] Identifying abnormal states during charging requires establishing the possible representation range of performance management parameters under corresponding abnormal states. Considering that different abnormal states can have varying degrees of safety impact on battery use, this invention distinguishes between abnormal state judgment before charging and abnormal state judgment during charging, and accordingly establishes a pre-charging fault type database and a charging process fault type database. The pre-charging fault type database mainly focuses on abnormal performance management parameter data obtained before a high-risk accident such as a natural disaster, and establishes a corresponding database to confirm these abnormalities before charging, thus preventing corresponding accidents from occurring during subsequent charging and use. The charging process fault type database analyzes and processes performance management data under possible abnormal conditions, including hardware connection issues, system warnings, and other situations that do not affect safe use, thereby providing data guidance for ensuring normal charging and subsequent battery use and maintenance, as well as timely on-site fault identification. Of course, it is understandable that, considering the characteristics of big data analysis, for accidental failures, the range of performance management parameters obtained will generally not overlap with the range of data in the charging performance management database. However, for abnormal data obtained during the charging process, there is a situation where the range of data in the charging performance management database overlaps. Therefore, after determining the fault type database for the charging process, it is necessary to adjust the data range of the charging performance management database based on its determined range, so as to more conservatively identify abnormalities in the charging and operating status of the battery and improve the reliability of abnormality identification.

[0043] This involves acquiring pre-accident charging information from historical charging fault data, performing charging data analysis based on the pre-accident events, and establishing a pre-accident fault type database. This includes: acquiring accident charging information based on the lifespan and corresponding performance management parameters from the pre-accident charging information data, forming a parameter accident range set C for different performance management parameters within the effective lifespan. n ,in This represents the minimum value of the performance management parameter numbered n at time point t. This represents the maximum value of the performance management parameter numbered n at time point t; the lifespan and corresponding accident charging information of different performance management parameters in the pre-accident charging information data are analyzed for correlation to form a set D of correlated accident ranges within the effective lifespan, where D = [d min d max ] t d min d represents the minimum value of the range of correlated incidents at time point t. maxThis represents the maximum value of the relevant incident range at time point t; the parameter incident range set C, which combines all performance management parameters. n Together with the relevant accident range set D, a database of pre-charging fault types is formed.

[0044] The establishment of the pre-charging fault type database also considers both the range of individual performance management parameters and the overall scope of correlation analysis. Since the range of performance management data corresponding to an accident generally deviates significantly from the normal performance management data range, further filtering and analysis of the acquired accident charging information data is unnecessary to obtain the reasonable range of performance management parameters and the scope of correlation analysis under accident conditions.

[0045] This process involves acquiring abnormal charging information data from historical charging fault data and combining it with a charging performance management database to perform charging data analysis based on different abnormal situations. This results in a charging process fault type database, including: acquiring abnormal charging information related to the lifespan and corresponding performance management parameters from the abnormal charging information data; forming an initial abnormal information range set for different performance management parameters within their lifespan; and comparing this initial abnormal information range set with the corresponding normal performance range dataset A in the charging performance management database. n Comparative adjustments are made to form a set of adjustment anomaly information ranges; these sets of adjustment anomaly information ranges for different performance management parameters are then compared with the corresponding parameter accident range sets C in the pre-charging fault type database. n Compare and adjust to form a reasonable range of abnormal information E n ,in, This represents the minimum value of the performance management parameter numbered n at time point t. This represents the maximum value of the performance management parameter numbered n at time point t; abnormal charging information of the service life and corresponding different performance management parameters in the abnormal charging information data is obtained, and correlation analysis is performed to form an initial set of abnormal correlation ranges within the effective service life period; the initial set of abnormal correlation ranges is compared and adjusted with the corresponding performance correlation change range dataset B in the charging performance management database to form an adjusted set of abnormal correlation ranges; the adjusted set of abnormal correlation ranges is compared and adjusted with the set of abnormal correlation accidents D in the pre-charging fault type database to form a reasonable set of abnormal correlation ranges F, where F = [f min f max ] t f min f represents the minimum value of the reasonable correlation anomaly range at time point t. maxThis represents the maximum value of the reasonable correlation anomaly range at time point t; the reasonable anomaly information range set E combining all performance management parameters. n A database of charging process fault types is formed by combining the reasonable correlation anomaly range set F.

[0046] For abnormal charging information, the range of performance management parameters obtained under abnormal conditions falls between the range of normal charging performance management parameters and the range of performance management parameters corresponding to accident information, resulting in overlap between the two ranges. To improve the reliability of anomaly identification and avoid missing abnormal states, when establishing the charging process fault type database, after establishing the initial range based on the original abnormal data, it is necessary to compare and adjust it with the corresponding normal performance management parameter range. Areas overlapping with the normal performance management parameter range are also included in the abnormal data range. Then, it is compared and adjusted with the corresponding data range in the pre-charging fault type database, removing any overlapping portions and incorporating them into the pre-charging fault type database. This improves the reliability of identifying accidental faults.

[0047] Based on the charging process fault type database, the charging performance management database is adjusted to form an adjusted charging performance management database, including: setting the reasonable abnormal information range E for different performance management parameters. n Performance normal range dataset A, which differs from the performance management parameters in the charging performance management database. n The following comparisons and adjustments were made to create a dataset G representing the normal performance range for different performance management parameters. n If the reasonable range of abnormal information is set E n and the corresponding normal performance range dataset A n If there is an intersection, then G n =A n -(E n ∩A n ), and vice versa. n =A n The reasonable correlation anomaly range set F is compared and adjusted with the performance correlation change range dataset B in the charging performance management database to form the adjusted performance correlation change range dataset H: If the reasonable correlation anomaly range set F and the performance correlation change range dataset B have an intersection, then H = B - (F ∩ B), otherwise H = B; Combine this with the adjusted normal performance range dataset G of all performance management parameters. n Together with the dataset H representing the range of changes in performance correlation, a database for adjusting charging performance management is formed.

[0048] Of course, since the adjustment of overlapping data ranges was considered when establishing the charging process fault type database, it is necessary to adjust the range data in the affected charging performance management database to ensure the uniqueness of the correspondence between data range and status during anomaly identification. This improves the reliability and efficiency of anomaly status identification.

[0049] S3: Obtain real-time charging access information and perform fault detection based on the pre-charging fault type database to generate pre-charging fault detection result information.

[0050] The system acquires real-time charging access information and performs fault detection based on a pre-charging fault type database, generating pre-charging fault detection results. This includes: acquiring the battery lifespan and corresponding access values ​​for different performance management parameters from the real-time charging access information; performing correlation analysis on the access values ​​of different performance management parameters to generate usage correlation values; and comparing the access values ​​of different performance management parameters with the corresponding parameter accident range set C in the pre-charging fault type database. n A one-to-one comparison will be performed, comparing the correlation values ​​with the correlation accident range set D in the pre-charging fault type database, and the following analysis and judgment will be made: If there is an access value for a performance management parameter that belongs to the corresponding parameter accident range set C... n If the value used for the performance management parameter does not belong to the corresponding parameter accident range set C, then a fault detection anomaly information is generated before charging, and the detection result is judged to be abnormal; if ... access value of the performance management parameter does not belong to the corresponding parameter accident range set C, then a fault detection anomaly information is generated before charging, and the detection result is judged to be abnormal. n If the correlation value used does not belong to the correlation accident range set D, then normal information for fault detection before charging is formed, and the detection result is judged to be normal.

[0051] After establishing a database of pre-charging fault types, performance management parameters obtained from actual data can be directly compared and judged before charging. Considering the significant impact of accidental abnormal states on battery and personnel safety, the identification of accidental abnormal states is extremely rigorous; a test result is considered abnormal if any one of the evaluation criteria is not met.

[0052] S4: If the fault detection results before charging are normal, then optimize the charging management control.

[0053] If the pre-charging fault detection result data is normal, then optimized charging management control is implemented, including: when the pre-charging fault detection result data is normal, charging management control based on extending battery life is implemented during battery charging.

[0054] After a fault detection test shows normal results before charging, the battery can be charged normally. Considering battery reliability and improving product quality, charging management can be implemented based on current battery data to extend battery life, such as adjusting charging strategies and charging capacity. Additionally, adaptive charging control adjustments can be made based on the battery's performance parameters falling within the normal charging performance management parameter range to achieve optimal performance within that range.

[0055] S5: During the optimization of charging management and control, real-time charging process information is obtained, and the process is monitored in conjunction with the charging process fault type database to form real-time monitoring results information of the charging process.

[0056] In optimizing charging management and control, real-time charging process information is acquired and monitored in conjunction with a charging process fault type database. This includes: acquiring the battery lifespan and corresponding process values ​​of different performance management parameters from real-time charging process information; performing correlation analysis on the process values ​​of different performance management parameters to form process correlation values; setting a duration threshold T; and comparing the process values ​​of different performance management parameters with the reasonable abnormal information range set E corresponding to the charging process fault type database. n A one-to-one comparison is performed, comparing the process correlation values ​​with the reasonable correlation anomaly range set F in the charging process fault type database, and the following analysis and judgment are made: If there is a process value of a performance management parameter that belongs to the corresponding reasonable anomaly information range set E... n If the process correlation value falls within the reasonable correlation anomaly range set F and the duration exceeds the duration threshold T, then an abnormal charging process information is generated; if the process values ​​of the performance management parameters do not fall within the corresponding reasonable anomaly information range set E, then an abnormal charging process information is generated. n If the process correlation value does not belong to the reasonable correlation anomaly range set F, then normal charging process information is generated; if the process value of a performance management parameter belongs to the corresponding reasonable anomaly information range set E, then normal charging process information is generated. n If the duration of the process does not exceed the duration threshold T, then normal charging information is generated; if the process correlation value belongs to the reasonable correlation abnormal range set F, and the duration does not exceed the duration threshold T, then normal charging information is generated.

[0057] The establishment of a charging process fault type database aims to identify and monitor abnormal states that occur during the charging process. Considering that the deviation of abnormal state data from the normal performance management parameter range is relatively small, a time threshold is set as a crucial criterion for judgment. An abnormal state is only determined when the data falls within the abnormal data range and reaches the time threshold simultaneously. It is understandable that identifying abnormal states provides a data foundation for subsequent fault handling, enabling better and more efficient battery maintenance and technical processing.

[0058] This invention also provides a charging pile capable of automatically identifying abnormal states of new energy vehicle batteries. The identification method provided by this invention includes: a data acquisition unit for acquiring real-time charging access information and real-time charging process information of the vehicle battery; a database unit for storing and updating a charging management database, a pre-charging fault type database, and a charging process fault type database; a data transmission unit for acquiring and uploading the real-time charging access information and real-time charging process information acquired by the data acquisition unit, and downloading and updating historical normal charging data and historical charging fault data; an analysis and processing unit for acquiring historical normal charging data from the data transmission unit to perform charging performance analysis based on charging management, forming a charging performance management database; acquiring historical charging fault data from the data transmission unit and combining it with the charging performance management database for fault identification analysis, establishing a pre-charging fault type database and a charging process fault type database respectively, and adjusting the charging performance management database to form an adjusted charging performance management database; acquiring real-time charging access information from the data transmission unit for fault detection, forming pre-charging fault detection result information; and acquiring real-time charging process information from the data transmission unit for process monitoring, forming real-time charging process monitoring result information.

[0059] This charging station uses a data acquisition unit to collect the basic big data required for anomaly identification and analysis. The data is then aggregated and synthesized by the data transmission unit, while the database unit stores various databases used for anomaly identification. Furthermore, the analysis and processing unit can retrieve databases and real-time data in real time for anomaly identification and analysis, completing the identification and analysis process efficiently and in real time. This provides a material basis for accurately and efficiently identifying abnormal states of vehicle batteries.

[0060] In summary, the beneficial effects of the charging pile with orderly charging function and the identification method that can automatically identify abnormal battery states of new energy vehicles provided in the embodiments of the present invention are as follows:

[0061] This method acquires normal charging data from similar batteries to establish a big data-based charging performance management database. This allows for the rapid determination of the individual vehicle battery's performance status within the normal range during real-time charging, providing fundamental reference data for subsequent optimized charging management. Simultaneously, for abnormal data occurring during accidents and charging, separate databases are established: a pre-charging fault type database for analysis and comparison before charging, and a charging process fault type database for analysis and comparison during charging. This enables comprehensive and accurate identification of abnormal states of the vehicle battery before and during charging, thereby providing control guidance for vehicle battery charging. This ensures safety during vehicle battery charging and enhances the safety of vehicle battery use, improving the reliability and safety of new energy vehicles.

[0062] This charging station uses a data acquisition unit to collect the basic big data required for anomaly identification and analysis. The data is then aggregated and synthesized by the data transmission unit, while the database unit stores various databases used for anomaly identification. Furthermore, the analysis and processing unit can retrieve databases and real-time data in real time for anomaly identification and analysis, completing the identification and analysis process efficiently and in real time. This provides a material basis for accurately and efficiently identifying abnormal states of vehicle batteries.

[0063] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0064] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0065] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0066] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0067] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0068] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0069] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0070] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0071] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0072] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for automatically identifying abnormal states of batteries in new energy vehicles, characterized in that, include: Obtain historical normal charging data of the same type of battery, perform charging performance analysis based on charging management, and form a charging performance management database; Historical charging fault data of the same type of battery is obtained, and fault identification and analysis are performed in combination with the charging performance management database. A fault type database before charging and a fault type database during charging are established respectively, and the charging performance management database is adjusted to form an adjusted charging performance management database. Obtain real-time charging access information and perform fault detection based on the pre-charging fault type database to generate pre-charging fault detection result information; If the pre-charging fault detection results show normal data, then optimized charging management control will be implemented. During the optimization of charging management and control, real-time charging process information is obtained, and the process is monitored in conjunction with the charging process fault type database to form real-time monitoring result information of the charging process. This includes acquiring historical normal charging data for batteries of the same type, performing charging performance analysis based on charging management, and forming a charging performance management database, including: Acquire the change data of different performance management parameters of the same type of battery within the effective service life period, and form the life-performance change curve data of different performance management parameters, wherein the performance management parameters are performance parameters that change with the battery service life. Real-time charging data of different performance management parameters during each normal charge of the same type of battery is obtained, and combined with the life-performance change curve data of different performance management parameters, the parameter change range of different performance management parameters under the corresponding real-time lifespan is determined, forming a dataset of normal performance range of different performance management parameters within the effective lifespan period. ,in, = Where n is the number of the different performance management parameters, and t is a time point within the effective service life period. This represents the minimum value of the range of the performance management parameter numbered n at time point t. This represents the maximum value of the range of the performance management parameter numbered n at time point t; By combining different performance management parameters, a performance correlation analysis is performed within the effective service life period to generate life-performance correlation change curve data. Real-time charging data of different performance management parameters for each normal charge of the same type of battery is obtained, and correlation analysis is performed on the different real-time charging data in combination with the lifespan-performance correlation curve data to establish a performance correlation variation range dataset B within the effective lifespan period, where B = Where t is a point in time within the effective service life period. This represents the minimum value of the range of performance correlation changes at time point t. This represents the maximum value of the range of performance correlation changes at time point t; The dataset combining the performance normal range of all the aforementioned performance management parameters The charging performance management database is formed by combining the performance-related variation range set B. Historical charging fault data of the same type of battery is obtained, and fault identification and analysis are performed in conjunction with the charging performance management database. A fault type database before charging and a fault type database during charging are established respectively. The charging performance management database is then adjusted to form an adjusted charging performance management database, including: Obtain the pre-accident charging information data from the historical charging fault data, perform charging data analysis based on the charging data before the accident, and establish the pre-accident fault type database. Obtain process abnormal charging information data from the historical charging fault data, and combine it with the charging performance management database to perform charging data analysis based on different abnormal situations, thereby forming the charging process fault type database. Based on the charging process fault type database, the charging performance management database is adjusted to form the adjusted charging performance management database.

2. The method for automatically identifying abnormal states of batteries in new energy vehicles according to claim 1, characterized in that, The step of acquiring pre-accident charging information data from the historical charging fault data, performing charging data analysis based on the pre-accident charging data, and establishing the pre-charging fault type database includes: Obtain the lifespan and corresponding accident charging information for different performance management parameters from the pre-accident charging information data, and form a parameter accident range set for different performance management parameters within the effective lifespan period. ,in = , This represents the minimum value of the range of the performance management parameter numbered n at time point t. This represents the maximum value of the range of the performance management parameter numbered n at time point t; The lifespan and corresponding accident charging information with different performance management parameters from the pre-accident charging information data are analyzed for correlation to form a set D of correlated accident ranges within the effective lifespan, where D = , This represents the minimum value of the range of correlated incidents at time point t. This represents the maximum value of the range of related incidents at time point t; The parameter incident range set combining all the aforementioned performance management parameters Together with the aforementioned relevant accident range set D, a pre-charging fault type database is formed.

3. The method for automatically identifying abnormal states of batteries in new energy vehicles according to claim 2, characterized in that, The process of acquiring abnormal charging information data from the historical charging fault data, and combining it with the charging performance management database to perform charging data analysis based on different abnormal situations, forms the charging process fault type database, including: The abnormal charging information data of the process abnormal charging information includes the service life and the corresponding abnormal charging information of different performance management parameters, forming an initial abnormal information range set of different performance management parameters within the service life period; The initial abnormal information range set with different performance management parameters is compared with the corresponding normal performance range dataset in the charging performance management database. Compare and adjust accordingly to form a set of adjustment anomaly information ranges; The set of abnormal adjustment information for different performance management parameters is compared with the corresponding set of parameter accident ranges in the pre-charging fault type database. Compare and adjust to form a reasonable range of abnormal information. ,in, = , This represents the minimum value of the range of the performance management parameter numbered n at time point t. This represents the maximum value of the range of the performance management parameter numbered n at time point t; The abnormal charging information data of the process abnormal charging information includes the service life and the corresponding abnormal charging information of different performance management parameters. Correlation analysis is performed to form an initial set of correlation abnormal ranges within the effective service life period. The initial correlation anomaly range set is compared and adjusted with the corresponding performance correlation change range dataset B in the charging performance management database to form an adjusted correlation anomaly range set; The adjusted abnormal correlation range set is compared and adjusted with the abnormal correlation accident range set D in the pre-charging fault type database to form a reasonable abnormal correlation range set F, where F = , This represents the minimum value of the reasonable range of abnormal correlations at time point t. This represents the maximum value of the reasonable correlation anomaly range at time point t; The reasonable range of anomaly information combining all the aforementioned performance management parameters The reasonable correlation anomaly range set F forms the charging process fault type database.

4. The method for automatically identifying abnormal states of batteries in new energy vehicles according to claim 3, characterized in that, The step of adjusting the charging performance management database based on the charging process fault type database to form the adjusted charging performance management database includes: The set of reasonable anomaly information ranges for different performance management parameters. The normal performance range dataset of the performance management parameters that differs from the charging performance management database. The following comparisons and adjustments were made to create a dataset showing the normal performance range for different performance management parameters. : If the reasonable abnormal information range set and the corresponding normal performance range dataset If there is an intersection, then = -( ∩ ), and vice versa = ; The reasonable correlation anomaly range set F is compared and adjusted with the performance correlation change range dataset B in the charging performance management database to form the adjusted performance correlation change range dataset H: If the set of reasonable correlation anomalies F intersects with the set of performance correlation change ranges B, then H = B - (F ∩ B); otherwise, H = B. Combine all the performance management parameters to adjust the normal performance range dataset The adjusted charging performance management database is formed by combining the adjusted performance correlation change range dataset H with the adjusted performance correlation change range dataset H.

5. The method for automatically identifying abnormal states of batteries in new energy vehicles according to claim 4, characterized in that, The process of acquiring real-time charging access information and performing fault detection based on the pre-charging fault type database to generate pre-charging fault detection result information includes: The battery lifespan and the corresponding access values ​​of different performance management parameters are obtained from the real-time charging access information, and a correlation analysis is performed on the access values ​​of different performance management parameters to form a usage correlation value. The access values ​​of different performance management parameters are compared with the corresponding parameter accident range set in the pre-charging fault type database. A one-to-one comparison is performed, comparing the usage correlation values ​​with the correlation accident range set D in the pre-charging fault type database, and the following analysis and judgment are made: If the access value of the performance management parameter belongs to the corresponding parameter incident range set. If this occurs, abnormal fault detection information will be generated before charging, and the detection result will be judged to be abnormal. If the correlation value used belongs to the correlation accident range set D, then abnormal information for pre-charging fault detection is formed, and the detection result is judged to be abnormal. If none of the access values ​​of the performance management parameters belong to the corresponding parameter incident range set If the correlation value used does not belong to the correlation accident range set D, then normal information for pre-charging fault detection is formed, and the detection result is judged to be normal.

6. The method for automatically identifying abnormal states of batteries in new energy vehicles according to claim 5, characterized in that, When the pre-charging fault detection result data shows normal, optimized charging management control is performed, including: When the pre-charging fault detection results show that the data is normal, charging management control based on extending battery life is implemented when charging the battery.

7. The method for automatically identifying abnormal states of batteries in new energy vehicles according to claim 6, characterized in that, The process of acquiring real-time charging process information during optimized charging management and control, and combining this information with the charging process fault type database for process monitoring, includes: The battery lifespan and corresponding process values ​​of different performance management parameters are obtained from the real-time charging process information, and a correlation analysis is performed on the process values ​​of different performance management parameters to form a process correlation value. Set a duration threshold T, and compare the process values ​​of different performance management parameters with the reasonable abnormal information range set corresponding to the charging process fault type database. A one-to-one comparison is performed, comparing the process correlation values ​​with the reasonable correlation anomaly range set F in the charging process fault type database, and the following analysis and judgment are made: If the process value of the performance management parameter belongs to the corresponding reasonable anomaly information range set. If the duration exceeds the duration threshold T, then abnormal charging information is generated. If the process correlation value belongs to the reasonable correlation anomaly range set F, and the duration exceeds the duration threshold T, then charging process anomaly information is formed. If none of the process values ​​of the performance management parameters belong to the corresponding set of reasonable abnormal information ranges If the process correlation value does not belong to the reasonable correlation abnormal range set F, then normal charging process information is formed. If the process value of the performance management parameter belongs to the corresponding reasonable anomaly information range set. If the duration does not exceed the duration threshold T, then normal charging information is generated. If the process correlation value belongs to the reasonable correlation anomaly range set F, and the duration does not exceed the duration threshold T, then normal charging process information is formed.

8. A charging pile for automatically identifying abnormal states of batteries in new energy vehicles, employing the identification method for automatically identifying abnormal states of batteries in new energy vehicles as described in any one of claims 1-7, characterized in that... include: The data acquisition unit is used to acquire real-time charging access information and real-time charging process information of the vehicle battery; The database unit is used to store, update, and adjust the charging management database, the pre-charging fault type database, and the charging process fault type database. The data transmission unit is used to acquire and upload the real-time charging access information and real-time charging process information collected by the data acquisition unit, and to download and update historical normal charging data and historical charging fault data. The analysis and processing unit is used to acquire historical normal charging data of the data transmission unit and perform charging performance analysis based on charging management to form a charging performance management database; to acquire historical charging fault data of the data transmission unit and combine it with the charging performance management database to perform fault identification analysis; to establish a pre-charging fault type database and a charging process fault type database respectively; and to adjust the charging performance management database to form an adjusted charging performance management database; to acquire real-time charging access information of the data transmission unit for fault detection and form pre-charging fault detection result information; and to acquire real-time charging process information of the data transmission unit for process monitoring and form real-time charging process monitoring result information.

Images