CN117507819A - Charging pile capable of automatically identifying abnormal state of new energy vehicle battery and identification method - Google Patents
Charging pile capable of automatically identifying abnormal state of new energy vehicle battery and identification method Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
- B60L3/0046—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/12—Recording operating variables ; Monitoring of operating variables
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/66—Data transfer between charging stations and vehicles
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
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Abstract
The invention provides a charging pile capable of automatically identifying abnormal states of a battery of a new energy vehicle and an identification method, and relates to the technical field of intelligent charging of charging piles. The method comprises the steps of obtaining historical normal charging data of batteries of the same type to form a charging performance management database; acquiring historical charging fault data of the batteries of the same type, respectively establishing a pre-charging fault type database and a charging process fault type database, 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 data of the fault detection result before charging is displayed normally, optimizing charging management control is performed; and acquiring real-time charging process information in the process of optimizing the charging management control to form charging process real-time monitoring result information. The method can accurately and timely identify the abnormality of the battery during the charging of the vehicle so as to ensure the use safety of the battery.
Description
Technical Field
The invention relates to the technical field of intelligent charging of charging piles, in particular to a charging pile capable of automatically identifying abnormal states of a battery of a new energy vehicle and an identification method.
Background
New energy automobiles are increasingly favored and touted by people. With the expansion of new energy automobile markets, the number of new energy automobiles is also increasing. The automobile battery is used as equipment with the largest cost of the new energy automobile, is an object which mainly causes problems in the current new energy automobile, such as nature, electric leakage, running performance and the like, and restricts the market prospect of the new energy automobile. In order to solve these problems, a more intensive technical study on the automobile battery is also being conducted, but the technical alternation also requires a longer time, so that a new efficient processing mode is required to cope with the problem defects existing in the current automobile battery.
The situation that the abnormal condition of the automobile battery is most likely to be monitored is not limited to a monitoring system of the automobile and an external monitoring mode when the automobile is externally connected with a charging pile during charging. The monitoring of the vehicle itself cannot complete the accurate judgment of the battery abnormality completely independently and efficiently. Therefore, monitoring and identification of abnormal conditions during charging is important. At present, no accurate and systematic method for monitoring abnormal states of a vehicle battery by using a charging pile exists.
Therefore, the design of the charging pile with the ordered charging function and the identification method capable of automatically identifying the abnormal state of the battery of the new energy vehicle can accurately and timely identify the abnormal state of the battery when the vehicle is charged so as to ensure the use safety of the battery, and the method is a problem to be solved urgently at present.
Disclosure of Invention
The invention aims to provide an identification method capable of automatically identifying abnormal states of batteries of a new energy vehicle, which is characterized in that normal charging data of the batteries of the same type are obtained, a charging performance management database based on big data is established, and the position of the performance state of an individual vehicle battery in a normal range can be rapidly determined during real-time charging, so that basic reference data is provided for the management of the follow-up optimized charging. Meanwhile, aiming at the occurrence of accidents and abnormal data in the charging process, a pre-charging fault type database for analysis and comparison before charging and a charging process fault type database for analysis and comparison in the charging process are respectively established, so that the identification of abnormal states before and during charging of the vehicle battery can be fully and accurately realized, control and guidance are further provided for charging of the vehicle battery, the safety of the vehicle battery during charging is ensured, the safety of the use of the vehicle battery is improved, and the use reliability and safety of the new energy vehicle are improved.
The invention also aims to provide the charging pile capable of automatically identifying the abnormal state of the battery of the new energy vehicle, basic big data required by abnormality identification analysis are fully acquired through the data acquisition unit, the data are summarized and integrated according to the data transmission unit, and meanwhile, the database unit completes the storage of various databases for abnormality identification. In addition, the analysis processing unit can be used for real-time retrieval of the database and real-time data to perform recognition analysis of abnormal states, real-time and efficient completion of recognition analysis processing is achieved, and a material basis is provided for accurate and efficient recognition of abnormal states of the vehicle battery.
In a first aspect, the present invention provides an identification method capable of automatically identifying an abnormal state of a battery of a new energy vehicle, including obtaining historical normal charging data of a battery 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 the batteries of the same type, carrying out fault identification analysis by combining a charging performance management database, respectively establishing a pre-charging fault type database and a charging process fault type database, 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 according to a pre-charging fault type database to form pre-charging fault detection result information; when the data of the fault detection result before charging is displayed normally, optimizing charging management control is performed; and acquiring real-time charging process information in the process of optimizing the charging management control, and performing process monitoring by combining a charging process fault type database to form charging process real-time monitoring result information.
According to the method, the normal charging data of the batteries of the same type are obtained, the charging performance management database based on big data is built, and the position of the performance state of the individual vehicle battery in the normal range can be rapidly determined during real-time charging, so that basic reference data is provided for the subsequent management of optimized charging. Meanwhile, aiming at the occurrence of accidents and abnormal data in the charging process, a pre-charging fault type database for analysis and comparison before charging and a charging process fault type database for analysis and comparison in the charging process are respectively established, so that the identification of abnormal states before and during charging of the vehicle battery can be fully and accurately realized, control and guidance are further provided for charging of the vehicle battery, the safety of the vehicle battery during charging is ensured, the safety of the use of the vehicle battery is improved, and the use reliability and safety of the new energy vehicle are improved.
As one possible implementation manner, obtaining historical normal charging data of the batteries of the same type, performing charging performance analysis based on charging management, and forming a charging performance management database, including: acquiring the change data of different performance management parameters of the same type of battery in the effective service life period to form life-performance change curve data of the different performance management parameters, wherein the performance management parameters are performance parameters which change along with the service life of the battery; acquiring real-time charging data of different performance management parameters of the same type of battery during normal charging each time, determining parameter variation ranges of the different performance management parameters under corresponding real-time service lives by combining life-performance variation curve data of the different performance management parameters, and forming a performance normal range data set A of the different performance management parameters in an effective service life period n Wherein, the method comprises the steps of, wherein,n is the number of different performance management parameters, t is the time point in the effective life cycle, < >>Minimum value representing the range of performance management parameters numbered n at time point t, +.>A maximum value representing a range in which the performance management parameter numbered n is located at the time point t; performing performance correlation analysis in the effective service life period by combining different performance management parameters to form life-performance correlation change curve data; acquiring real-time charging data of different performance management parameters of the same type of battery during normal charging each time, performing correlation analysis on the different real-time charging data by combining life-performance correlation change curve data, and establishing a performance correlation change range data set B in an effective service life period, wherein B= [ B ] min ，b max ] t Wherein t is the time point in the effective life cycle, b min A minimum value, b, representing the corresponding range of performance-related changes at time t max A maximum value representing a corresponding performance-related variation range at a time point t; performance normal range dataset A incorporating all performance management parameters n And a performance correlation variation range set B, forming a charging performance management database.
In the invention, the establishment of the charging performance management database is mainly to establish the reasonable range of the performance parameter variation of the same type of battery in the service life period, and the performance parameter of the battery cannot be completely consistent with the time performance variation curve formed by experiment or theoretical analysis in consideration of different service conditions of different vehicle batteries, so that the reasonable range needs to be determined based on the analysis of big data, the performance of the battery in the range is in a normal state, the battery can be rapidly judged in the process of identifying the abnormal state of charging, and meanwhile, the battery is characterized in thatThe change of the performance shows range, so that the excellent performance of different states in a normal range can be distinguished, and the performance can be used as basic reference data for charge optimization. Here, for the performance management parameter, since the state of the battery charging performance under the change of the service life period is considered, the performance management parameter should be a parameter that shows regular change with the change of the service life period, such as capacity, internal resistance, energy, power, etc., and the rated parameters of other batteries are not performance management parameters. Of course, for the obtained historical normal charging data, the charging data of the battery before the accident should be eliminated, and the length of the eliminated time period can be determined according to the actual needs, so as to avoid the abnormal data from being included in the battery as much as possible. It will be appreciated that the performance management parameters of the battery are not independent of each other, and that there may be a single performance management parameter that is within normal limits, but that the performance of the battery as a whole may exhibit anomalies. Thus, when establishing the charging performance management database, it is necessary to acquire a normal range under the overall performance analysis based on analysis of correlation establishment correlation of the charging performance management parameters, taking into consideration establishment of normal range data for each individual performance management parameter. For correlation analysis, various correlation factors can be determined based on big data analysis, such as establishing correlation analysis formulas for different performance management parameters α t Represents the correlation value at time t, < ->The correlation factor corresponding to the performance management parameter numbered n at time t is represented, but may be a constant value which does not change with time, a n Represents the nominal value corresponding to the performance management parameter numbered n, < ->Indicating that the performance management parameter numbered n is at the point in timeAnd (5) acquiring a real-time value at t. In addition, it should be noted that, in the normal performance range data set A for establishing different performance management parameters n When the range of the obtained large data is considered for the performance correlation change range data set B, the range adjustment is required to be performed in combination with the life-performance change curve data, namely, after the range is determined based on the obtained history data for the range of a single performance management parameter, whether the value corresponding to the life-performance change curve data belongs to the range is required to be judged, and if the value corresponding to the life-performance change curve data does not belong to the range, the range expansion is required to be performed by taking the value corresponding to the life-performance change curve data as the boundary, so that the range deviation caused by the error of the sampled data can be avoided, and the reasonable and accurate parameter range is required to be obtained based on the same processing for the performance correlation change range data set B.
As a possible implementation manner, historical charging fault data of the same type of battery is obtained, fault identification analysis is performed in combination with a charging performance management database, a pre-charging fault type database and a charging process fault type database are respectively established, and the charging performance management database is adjusted to form an adjusted charging performance management database, including: acquiring pre-accident charging information data in historical charging fault data, analyzing the charging data before accident occurrence, and establishing a pre-charging fault type database; acquiring process abnormal charging information data in historical charging fault data, and carrying out charging data analysis based on different abnormal conditions by combining a charging performance management database to form a charging process fault type database; and adjusting the charging performance management database according to the fault type database in the charging process to form an adjusted charging performance management database.
In the invention, the identification of the abnormal state during charging needs to establish the corresponding possible characterization range of the performance management parameter under the abnormal state, and the different abnormal states are considered to cause different degrees of safety influence on the use of the battery, so the invention divides the abnormal state into the abnormal state judgment before charging and the abnormal state judgment during charging, and further correspondingly establishes the pre-charging fault type database and the charging process fault type database. For the fault type database before charging, the performance management parameter abnormal data obtained before accidents with higher danger levels such as nature occur are mainly aimed at, a corresponding database is established, and further, the previous confirmation is carried out before charging, so that the situation of safety accidents caused by corresponding accidents in the subsequent charging and using processes is avoided. For the fault type database in the charging process, the possible analysis and processing of performance management data in abnormal conditions including hardware connection, system warning and no influence on safe use are performed, so that data guidance is provided for ensuring normal charging or subsequent battery use maintenance and timely presence fault combing. Of course, it can be understood that, considering the characteristics of big data analysis, for an accident fault, the obtained range of the performance management parameter does not substantially coincide with the range data in the charging performance management database, but for a situation that the range data in the charging performance management database coincides with the range of the abnormal data obtained in the charging process, the data range of the charging performance management database needs to be adjusted based on the determined range after the charging process fault type database is determined, so as to more conservatively identify the charging and running states of the battery, and improve the reliability of the abnormal identification.
As one possible implementation manner, acquiring pre-accident charging information data in historical charging fault data, performing charging data analysis based on the pre-accident charging information data, and establishing a pre-charging fault type database, including: acquiring service life and accident charging information of corresponding different performance management parameters in pre-accident charging information data to form a parameter accident range set C of the different performance management parameters in the effective service life period n Wherein Minimum value representing the range of performance management parameters numbered n at time point t, +.>A maximum value representing a range in which the performance management parameter numbered n is located at the time point t; carrying out correlation analysis on service life and accident charging information of corresponding different performance management parameters in the pre-accident charging information data to form a correlation accident range set D in the effective service life period, wherein D= [ D ] min ，d max ] t ，d min A minimum value d representing a corresponding correlation event range at time t max A maximum value representing a corresponding correlation event range at a time point t; parameter incident scope set C combining all performance management parameters n And a correlation accident range set D, forming a pre-charging fault type database.
In the invention, the establishment of the fault type database before charging also considers two aspects of a single performance management parameter range and an overall correlation analysis range. Because the range of the performance management data corresponding to the accident generally deviates from the normal performance management data range, the range of the reasonable performance management parameters and the range of the correlation analysis corresponding to the accident state can be obtained without further screening and analyzing the acquired accident charging information data.
As one possible implementation manner, acquiring process abnormal charging information data in historical charging fault data, and performing charging data analysis based on different abnormal conditions in combination with a charging performance management database to form a charging process fault type database, including: acquiring service life and abnormal charging information of corresponding different performance management parameters in process abnormal charging information data, and forming an initial abnormal information range set of the different performance management parameters in a service life period; the initial abnormal information range set of different performance management parameters is matched with the corresponding performance normal range data set A in the charging performance management database n Performing contrast adjustment to form an adjustment abnormal information range set; adjusting abnormal information range set and charging of different performance management parametersCorresponding parameter incident range set C in pre-electricity fault type database n Performing contrast adjustment to form a reasonable abnormal information range set E n Wherein, the method comprises the steps of, wherein, minimum value representing the range of performance management parameters numbered n at time point t, +.>A maximum value representing a range in which the performance management parameter numbered n is located at the time point t; acquiring service life and abnormal charging information of corresponding different performance management parameters in abnormal charging information data in the process, and performing correlation analysis to form an initial correlation abnormal range set in an effective service life period; comparing and adjusting the initial correlation abnormal range set with a corresponding performance correlation variation range data set B in a charging performance management database to form an adjustment correlation abnormal range set; comparing and adjusting the adjustment correlation abnormal range set with the correlation accident range set D in the pre-charging fault type database to form a reasonable correlation abnormal range set F, wherein F= [ F ] min ，f max ] t ，f min Minimum value representing reasonable correlation anomaly range corresponding to time point t, f max A maximum value representing a corresponding reasonable correlation anomaly range at a time point t; reasonable anomaly information Range set E incorporating all Performance management parameters n And a reasonable correlation abnormal range set F is used for forming a fault type database in the charging process.
In the present invention, the range of the acquired performance management parameter in the abnormal state is between the range of the normal charging performance management parameter and the range of the performance management parameter corresponding to the accident information with respect to the abnormal charging information, and thus there is a case where the ranges of the performance management parameters overlap with each other. In order to improve the reliability of anomaly identification and avoid missing an anomaly state, when the establishment of the fault type database in the charging process is carried out, after an initial range is established based on original anomaly data, the initial range is also compared and adjusted with the range of corresponding normal performance management parameters, the position where the intersection exists with the range of the normal performance management parameters is also classified into the range of the anomaly data, the comparison and adjustment are carried out with the corresponding data range in the fault type database before charging, and the part where the intersection exists is removed and is integrated into the fault type database before charging, so that the reliability of the identification of the accident fault can be improved.
As one possible implementation manner, according to the charging process fault type database, the charging performance management database is adjusted to form an adjusted charging performance management database, which includes: reasonable anomaly information Range set E for different performance management parameters n And a performance normal range data set A of different performance management parameters in a charging performance management database n The following comparative adjustment is performed to form an adjustment performance normal range data set G of different performance management parameters n : if the information range is reasonable E n And corresponding performance normal range dataset A n If there is intersection, G n ＝A n -(E n ∩A n ) On the contrary, G n ＝A n The method comprises the steps of carrying out a first treatment on the surface of the The reasonable correlation abnormal range set F and the performance correlation variation range data set B in the charging performance management database are subjected to the following comparative adjustment to form an adjustment performance correlation variation range data set H: if the reasonable correlation abnormal range set F and the performance correlation variation range data set B are intersected, H=B- (F n B), otherwise H=B; adjusting Performance Normal Range data set G in combination with all Performance management parameters n And adjusting the performance correlation variation range data set H to form an adjusted charging performance management database.
In the invention, of course, when the adjustment of the range of the coincident data is considered in the establishment of the fault type database of the charging process, the range adjustment of the range data in the affected charging performance management database is needed to ensure that the data range and the corresponding state have uniqueness in the abnormal identification. The reliability and the high efficiency of the abnormal state identification are improved.
As one possible implementation manner, acquiring real-time charging access information, and performing fault detection according to a pre-charging fault type database to form pre-charging fault detection result information, including: acquiring the service life of the battery of the real-time charging access information and the corresponding access values of different performance management parameters, and performing correlation analysis on the access values of the different performance management parameters to form a use correlation value; the access value of different performance management parameters is combined with the corresponding parameter accident range set C in the fault type database before charging n One-to-one comparison is carried out, the using correlation value is compared with a correlation accident range set D in a fault type database before charging, and the following analysis and judgment are carried out: if the access value of the performance management parameter belongs to the corresponding parameter accident range set C n Forming fault detection abnormal information before charging and judging that the detection result is abnormal; if the using correlation value belongs to the correlation accident range set D, forming fault detection abnormal information before charging, and judging that the detection result is abnormal; if the access values of the performance management parameters do not belong to the corresponding parameter accident range set C n And if the using correlation value does not belong to the correlation accident range set D, forming the fault detection normal information before charging, and judging that the detection result is normal.
In the invention, after the pre-charging fault type database is established, the comparison judgment before charging can be directly carried out according to the data of the performance management parameters acquired by facts. Here, since the occurrence of the accident abnormal state has a large influence on the safety of the battery and the personnel, the accident abnormal state is strictly recognized, and the detection result is considered to be abnormal only when any one of the items does not reach the criterion.
As a possible implementation manner, when the data of the fault detection result before charging is displayed normally, performing optimal charging management control, including: when the data of the fault detection result before charging shows normal, charging management control based on prolonging the service life of the battery is performed when the battery is charged.
In the invention, the battery can be normally charged after the fault detection is performed before charging to show a normal result, the reliability of the battery is considered, the use quality of a battery product is increased, and the charging management which can consider prolonging the service life of the battery, such as the adjustment of a charging strategy, the adjustment of the charging capacity and the like, can be performed based on the current data of the battery during charging. In addition, the adaptive charge control adjustment can be performed according to the position of the performance parameter of the secondary battery in the normal charge performance management parameter range data so as to achieve the optimal state value in the data range.
As one possible implementation manner, acquiring real-time charging process information in the process of optimizing charging management control, and performing process monitoring in combination with a charging process fault type database, including: acquiring the service life of the battery of the real-time charging process information and corresponding process values of different performance management parameters, and performing correlation analysis on the process values of the different performance management parameters to form a process correlation value; setting a duration threshold T, and setting a process value of different performance management parameters and a corresponding reasonable abnormal information range set E in a charging process fault type database n One-to-one comparison is carried out, the process correlation value is compared with a reasonable correlation abnormal range set F in a charging process fault type database, and the following analysis and judgment are carried out: if the process value of the performance management parameter belongs to the corresponding reasonable abnormal information range set E n And if the duration exceeds the duration threshold T, forming abnormal information of the charging process; if the process correlation value belongs to a reasonable correlation abnormal range set F and the duration exceeds a duration threshold T, forming abnormal information of the charging process; if the process values of the performance management parameters do not belong to the corresponding reasonable abnormal information range set E n And the process correlation value does not belong to the reasonable correlation abnormal range set F, so that normal information of the charging process is formed; if the process value of the performance management parameter belongs to the corresponding reasonable abnormal information range set E n If the duration time does not exceed the duration time threshold T, normal information of the charging process is formed; if the process correlation value belongs to the reasonable correlation abnormal range set F and the duration time does not exceed the duration time threshold T, normal information of the charging process is formed.
In the invention, the establishment of the fault type database in the charging process is to identify and monitor the abnormal state in the charging process, wherein the degree of deviation of the data of the abnormal state in the charging process from the data range of the normal performance management parameter is considered to be small, so that the time threshold is set as an important investigation item for judgment, and the abnormal state of the battery is determined under the condition that the abnormal data range is met and the time threshold is reached. Of course, it can be appreciated that the determination of the abnormal state provides a data basis for subsequent fault handling to better and more efficiently implement maintenance and technical handling of the battery.
In a second aspect, the present invention provides a charging pile capable of automatically identifying an abnormal state of a battery of a new energy vehicle, and the identifying method capable of automatically identifying an abnormal state of a battery of a new energy vehicle provided in the first aspect includes: the data acquisition unit is used for acquiring real-time charging access information and real-time charging process information of the vehicle battery; the database unit is used for storing, updating and adjusting a charging management database, a pre-charging fault type database and a charging process fault type database; the data transmission unit is used for acquiring the real-time charging access information and the real-time charging process information acquired by the data acquisition unit, uploading the real-time charging access information and the real-time charging process information, and downloading and updating the historical normal charging data and the historical charging fault data; the analysis processing unit is used for acquiring historical normal charging data of the data transmission unit, performing charging performance analysis based on charging management to form a charging performance management database, acquiring historical charging fault data of the data transmission unit, performing fault identification analysis by combining the charging performance management database, respectively establishing a pre-charging fault type database and a charging process fault type database, adjusting the charging performance management database to form an adjusted charging performance management database, performing fault detection on real-time charging access information of the data transmission unit to form pre-charging fault detection result information, performing process monitoring on the real-time charging process information of the data transmission unit to form charging process real-time monitoring result information.
According to the invention, the charging pile fully collects basic big data required by abnormality identification analysis through the data collection unit, gathers and synthesizes the data according to the data transmission unit, and meanwhile, the database unit completes the storage of various databases for abnormality identification. In addition, the analysis processing unit can be used for real-time retrieval of the database and real-time data to perform recognition analysis of abnormal states, real-time and efficient completion of recognition analysis processing is achieved, and a material basis is provided for accurate and efficient recognition of abnormal states of the vehicle battery.
The charging pile capable of automatically identifying the abnormal state of the battery of the new energy vehicle and the identification method have the beneficial effects that:
according to the method, the normal charging data of the batteries of the same type are obtained, the charging performance management database based on big data is built, and the position of the performance state of the individual vehicle battery in a normal range can be rapidly determined during real-time charging, so that basic reference data is provided for the subsequent management of optimal charging. Meanwhile, aiming at the occurrence of accidents and abnormal data in the charging process, a pre-charging fault type database for analysis and comparison before charging and a charging process fault type database for analysis and comparison in the charging process are respectively established, so that the identification of abnormal states before and during charging of the vehicle battery can be fully and accurately realized, control and guidance are further provided for charging of the vehicle battery, the safety of the vehicle battery during charging is ensured, the safety of the use of the vehicle battery is improved, and the use reliability and safety of the new energy vehicle are improved.
The charging pile fully collects basic big data required by abnormality identification analysis through the data collection unit, gathers and synthesizes the data according to the data transmission unit, and meanwhile, the database unit completes the storage of various databases for abnormality identification. In addition, the analysis processing unit can be used for real-time retrieval of the database and real-time data to perform recognition analysis of abnormal states, real-time and efficient completion of recognition analysis processing is achieved, and a material basis is provided for accurate and efficient recognition of abnormal states of the vehicle battery.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments of the present invention will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a step diagram of an identification method capable of automatically identifying abnormal states of a battery of a new energy vehicle according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention.
New energy automobiles are increasingly favored and touted by people. With the expansion of new energy automobile markets, the number of new energy automobiles is also increasing. The automobile battery is used as equipment with the largest cost of the new energy automobile, is an object which mainly causes problems in the current new energy automobile, such as nature, electric leakage, running performance and the like, and restricts the market prospect of the new energy automobile. In order to solve these problems, a more intensive technical study on the automobile battery is also being conducted, but the technical alternation also requires a longer time, so that a new efficient processing mode is required to cope with the problem defects existing in the current automobile battery.
The situation that the abnormal condition of the automobile battery is most likely to be monitored is not limited to a monitoring system of the automobile and an external monitoring mode when the automobile is externally connected with a charging pile during charging. The monitoring of the vehicle itself cannot complete the accurate judgment of the battery abnormality completely independently and efficiently. Therefore, monitoring and identification of abnormal conditions during charging is important. At present, no accurate and systematic method for monitoring abnormal states of a vehicle battery by using a charging pile exists.
Referring to fig. 1, an embodiment of the present invention provides an identification method capable of automatically identifying an abnormal state of a battery of a new energy vehicle. According to the method, the normal charging data of the batteries of the same type are obtained, the charging performance management database based on big data is built, and the position of the performance state of the individual vehicle battery in a normal range can be rapidly determined during real-time charging, so that basic reference data is provided for the subsequent management of optimal charging. Meanwhile, aiming at the occurrence of accidents and abnormal data in the charging process, a pre-charging fault type database for analysis and comparison before charging and a charging process fault type database for analysis and comparison in the charging process are respectively established, so that the identification of abnormal states before and during charging of the vehicle battery can be fully and accurately realized, control and guidance are further provided for charging of the vehicle battery, the safety of the vehicle battery during charging is ensured, the safety of the use of the vehicle battery is improved, and the use reliability and safety of the new energy vehicle are improved.
The identification method capable of automatically identifying the abnormal state of the battery of the new energy vehicle specifically comprises the following steps:
s1: and acquiring historical normal charging data of the batteries of the same type, and performing charging performance analysis based on charging management to form a charging performance management database.
Acquiring historical normal charging data of the batteries of the same type, performing charging performance analysis based on charging management, and forming a charging performance management database, wherein the method comprises the following steps: acquiring the change data of different performance management parameters of the same type of battery in the effective service life period to form life-performance change curve data of the different performance management parameters, wherein the performance management parameters are performance parameters which change along with the service life of the battery; acquiring real-time charging data of different performance management parameters of the same type of battery during normal charging each time, determining parameter variation ranges of the different performance management parameters under corresponding real-time service lives by combining life-performance variation curve data of the different performance management parameters, and forming a performance normal range data set A of the different performance management parameters in an effective service life period n Wherein, the method comprises the steps of, wherein,n is the number of different performance management parameters, t is the time point in the effective life cycle, < >>Minimum value representing the range of performance management parameters numbered n at time point t, +.>A maximum value representing a range in which the performance management parameter numbered n is located at the time point t; performing performance correlation analysis in the effective service life period by combining different performance management parameters to form life-performance correlation change curve data; acquiring real-time charging data of different performance management parameters of the same type of battery during normal charging each time, performing correlation analysis on the different real-time charging data by combining life-performance correlation change curve data, and establishing a performance correlation change range data set B in an effective service life period, wherein B= [ B ] min ，b max ] t Wherein t is the time point in the effective life cycle, b min A minimum value, b, representing the corresponding range of performance-related changes at time t max A maximum value representing a corresponding performance-related variation range at a time point t; performance normal range dataset A incorporating all performance management parameters n And a performance correlation variation range set B, forming a charging performance management database.
The charging performance management database is mainly established for establishing a reasonable range of performance parameter changes of the same type of batteries in a service life cycle, and considering different service conditions of different vehicle batteries, the performance parameters of the battery cannot be completely consistent with a time performance change curve formed by experiment or theoretical analysis, so that a reasonable range needs to be determined based on analysis of big data, the performance of the battery in the range is in a normal state, rapid judgment can be performed when abnormal state identification of charging is performed, meanwhile, the performance in different states in the normal range can be distinguished due to the fact that the performance changes are in the range, and the battery can be further used as basic reference data for performing charging optimization. It should be noted here that, for the performance management parameters, battery charging performance is considered The state in the case of a change in the life cycle, and thus the performance management parameter should be a parameter exhibiting a regular change with the change in the life cycle as a whole, such as capacity, internal resistance, energy, power, etc., and the rated parameters of the other batteries are not the performance management parameters. Of course, for the obtained historical normal charging data, the charging data of the battery before the accident should be eliminated, and the length of the eliminated time period can be determined according to the actual needs, so as to avoid the abnormal data from being included in the battery as much as possible. It will be appreciated that the performance management parameters of the battery are not independent of each other, and that there may be a single performance management parameter that is within normal limits, but that the performance of the battery as a whole may exhibit anomalies. Thus, when establishing the charging performance management database, it is necessary to acquire a normal range under the overall performance analysis based on analysis of correlation establishment correlation of the charging performance management parameters, taking into consideration establishment of normal range data for each individual performance management parameter. For correlation analysis, various correlation factors can be determined based on big data analysis, such as establishing correlation analysis formulas for different performance management parameters α t Represents the correlation value at time t, < ->The correlation factor corresponding to the performance management parameter numbered n at time t is represented, but may be a constant value which does not change with time, a n Represents the nominal value corresponding to the performance management parameter numbered n, < ->Representing the real-time value obtained at time point t for the performance management parameter numbered n. In addition, it should be noted that, in the normal performance range data set A for establishing different performance management parameters n And performance-related variation range data set B, it is necessary to combine lifetime-performance in addition to range determination considering acquired big dataThe range adjustment is performed on the change curve data, that is, after the range is determined based on the acquired history data for a single range of performance management parameters, it is necessary to determine whether the value corresponding to the life-performance change curve data belongs to the range, and if the value corresponding to the life-performance change curve data does not belong to the range, it is necessary to expand the range by taking the value corresponding to the life-performance change curve data as a boundary, so that the range deviation caused by the error of the sampled data can be avoided, and it is necessary to acquire a reasonably accurate parameter range based on the same processing for the performance correlation change range data set B.
S2: and acquiring historical charging fault data of the batteries of the same type, carrying out fault identification analysis in combination with a charging performance management database, respectively establishing a pre-charging fault type database and a charging process fault type database, and adjusting the charging performance management database to form an adjusted charging performance management database.
The method comprises the steps of obtaining historical charging fault data of batteries of the same type, carrying out fault identification analysis in combination with a charging performance management database, respectively establishing a pre-charging fault type database and a charging process fault type database, and adjusting the charging performance management database to form an adjusted charging performance management database, and comprises the following steps: acquiring pre-accident charging information data in historical charging fault data, analyzing the charging data before accident occurrence, and establishing a pre-charging fault type database; acquiring process abnormal charging information data in historical charging fault data, and carrying out charging data analysis based on different abnormal conditions by combining a charging performance management database to form a charging process fault type database; and adjusting the charging performance management database according to the fault type database in the charging process to form an adjusted charging performance management database.
When the battery is charged, the identification of the abnormal state is carried out, the corresponding possible characterization range of the performance management parameter under the abnormal state is required to be established, and the safety influence of different abnormal states on the use of the battery is considered, so that the abnormal state is divided into the abnormal state judgment before charging and the abnormal state judgment in the charging process, and then the pre-charging fault type database and the charging process fault type database are correspondingly established. For the fault type database before charging, the performance management parameter abnormal data obtained before accidents with higher danger levels such as nature occur are mainly aimed at, a corresponding database is established, and further, the previous confirmation is carried out before charging, so that the situation of safety accidents caused by corresponding accidents in the subsequent charging and using processes is avoided. For the fault type database in the charging process, the possible analysis and processing of performance management data in abnormal conditions including hardware connection, system warning and no influence on safe use are performed, so that data guidance is provided for ensuring normal charging or subsequent battery use maintenance and timely presence fault combing. Of course, it can be understood that, considering the characteristics of big data analysis, for an accident fault, the obtained range of the performance management parameter does not substantially coincide with the range data in the charging performance management database, but for a situation that the range data in the charging performance management database coincides with the range of the abnormal data obtained in the charging process, the data range of the charging performance management database needs to be adjusted based on the determined range after the charging process fault type database is determined, so as to more conservatively identify the charging and running states of the battery, and improve the reliability of the abnormal identification.
The method for acquiring pre-accident charging information data in historical charging fault data, analyzing the charging data before accident occurrence, and establishing a pre-charging fault type database comprises the following steps: acquiring service life and accident charging information of corresponding different performance management parameters in pre-accident charging information data to form a parameter accident range set C of the different performance management parameters in the effective service life period n Wherein Minimum value representing the range of performance management parameters numbered n at time point t, +.>A maximum value representing a range in which the performance management parameter numbered n is located at the time point t; carrying out correlation analysis on service life and accident charging information of corresponding different performance management parameters in the pre-accident charging information data to form a correlation accident range set D in the effective service life period, wherein D= [ D ] min ，d max ] t ，d min A minimum value d representing a corresponding correlation event range at time t max A maximum value representing a corresponding correlation event range at a time point t; parameter incident scope set C combining all performance management parameters n And a correlation accident range set D, forming a pre-charging fault type database.
The establishment of the fault type database before charging also considers two aspects of a single performance management parameter range and an overall correlation analysis range. Because the range of the performance management data corresponding to the accident generally deviates from the normal performance management data range, the range of the reasonable performance management parameters and the range of the correlation analysis corresponding to the accident state can be obtained without further screening and analyzing the acquired accident charging information data.
Acquiring process abnormal charging information data in historical charging fault data, and carrying out charging data analysis based on different abnormal conditions by combining a charging performance management database to form a charging process fault type database, wherein the charging process fault type database comprises the following steps: acquiring service life and abnormal charging information of corresponding different performance management parameters in process abnormal charging information data, and forming an initial abnormal information range set of the different performance management parameters in a service life period; the initial abnormal information range set of different performance management parameters is matched with the corresponding performance normal range data set A in the charging performance management database n Performing contrast adjustment to form an adjustment abnormal information range set; the adjustment abnormal information range set of different performance management parameters is combined with the corresponding parameter accident range set C in the fault type database before charging n Performing contrast adjustment to form a reasonable abnormal information range set E n Wherein, the method comprises the steps of, wherein, represents the minimum value of the range in which the performance management parameter numbered n is located at the time point t,a maximum value representing a range in which the performance management parameter numbered n is located at the time point t; acquiring service life and abnormal charging information of corresponding different performance management parameters in abnormal charging information data in the process, and performing correlation analysis to form an initial correlation abnormal range set in an effective service life period; comparing and adjusting the initial correlation abnormal range set with a corresponding performance correlation variation range data set B in a charging performance management database to form an adjustment correlation abnormal range set; comparing and adjusting the adjustment correlation abnormal range set with the correlation accident range set D in the pre-charging fault type database to form a reasonable correlation abnormal range set F, wherein F= [ F ] min ，f max ] t ，f min Minimum value representing reasonable correlation anomaly range corresponding to time point t, f max A maximum value representing a corresponding reasonable correlation anomaly range at a time point t; reasonable anomaly information Range set E incorporating all Performance management parameters n And a reasonable correlation abnormal range set F is used for forming a fault type database in the charging process.
In the abnormal charge information, the range of the acquired performance management parameter in the abnormal state is between the range of the normal charge performance management parameter and the range of the performance management parameter corresponding to the accident information, and thus the ranges of the performance management parameters may overlap with each other. In order to improve the reliability of anomaly identification and avoid missing an anomaly state, when the establishment of the fault type database in the charging process is carried out, after an initial range is established based on original anomaly data, the initial range is also compared and adjusted with the range of corresponding normal performance management parameters, the position where the intersection exists with the range of the normal performance management parameters is also classified into the range of the anomaly data, the comparison and adjustment are carried out with the corresponding data range in the fault type database before charging, and the part where the intersection exists is removed and is integrated into the fault type database before charging, so that the reliability of the identification of the accident fault can be improved.
According to the charging process fault type database, the charging performance management database is adjusted to form an adjusted charging performance management database, which comprises the following steps: reasonable anomaly information Range set E for different performance management parameters n And a performance normal range data set A of different performance management parameters in a charging performance management database n The following comparative adjustment is performed to form an adjustment performance normal range data set G of different performance management parameters n : if the information range is reasonable E n And corresponding performance normal range dataset A n If there is intersection, G n ＝A n -(E n ∩A n ) On the contrary, G n ＝A n The method comprises the steps of carrying out a first treatment on the surface of the The reasonable correlation abnormal range set F and the performance correlation variation range data set B in the charging performance management database are subjected to the following comparative adjustment to form an adjustment performance correlation variation range data set H: if the reasonable correlation abnormal range set F and the performance correlation variation range data set B are intersected, H=B- (F n B), otherwise H=B; adjusting Performance Normal Range data set G in combination with all Performance management parameters n And adjusting the performance correlation variation range data set H to form an adjusted charging performance management database.
Of course, when the adjustment of the overlapping data range is considered in the establishment of the charging process fault type database, the range adjustment of the range data in the affected charging performance management database is needed to ensure that the data range and the state corresponding to the abnormal identification have uniqueness. The reliability and the high efficiency of the abnormal state identification are improved.
S3: and acquiring real-time charging access information, and performing fault detection according to the pre-charging fault type database to form pre-charging fault detection result information.
Acquiring real-time charging access information, performing fault detection according to a pre-charging fault type database to form pre-charging fault detection result information, and comprising: acquiring the service life of the battery of the real-time charging access information and the corresponding access values of different performance management parameters, and performing correlation analysis on the access values of the different performance management parameters to form a use correlation value; the access value of different performance management parameters is combined with the corresponding parameter accident range set C in the fault type database before charging n One-to-one comparison is carried out, the using correlation value is compared with a correlation accident range set D in a fault type database before charging, and the following analysis and judgment are carried out: if the access value of the performance management parameter belongs to the corresponding parameter accident range set C n Forming fault detection abnormal information before charging and judging that the detection result is abnormal; if the using correlation value belongs to the correlation accident range set D, forming fault detection abnormal information before charging, and judging that the detection result is abnormal; if the access values of the performance management parameters do not belong to the corresponding parameter accident range set C n And if the using correlation value does not belong to the correlation accident range set D, forming the fault detection normal information before charging, and judging that the detection result is normal.
After the pre-charging fault type database is established, the comparison judgment before charging can be directly carried out according to the data of the performance management parameters acquired by facts. Here, since the occurrence of the accident abnormal state has a large influence on the safety of the battery and the personnel, the accident abnormal state is strictly recognized, and the detection result is considered to be abnormal only when any one of the items does not reach the criterion.
S4: and when the data of the fault detection result before charging is displayed normally, performing optimal charging management control.
When the data of the fault detection result before charging is displayed normally, performing optimal charging management control, including: when the data of the fault detection result before charging shows normal, charging management control based on prolonging the service life of the battery is performed when the battery is charged.
The battery can be normally charged after the fault detection before charging shows a normal result, the reliability of the battery is considered, the use quality of a battery product is increased, and the charging management which considers prolonging the service life of the battery, such as adjustment of a charging strategy, adjustment of a charging capacity and the like, can be performed based on the current data of the battery during charging. In addition, the adaptive charge control adjustment can be performed according to the position of the performance parameter of the secondary battery in the normal charge performance management parameter range data so as to achieve the optimal state value in the data range.
S5: and acquiring real-time charging process information in the process of optimizing the charging management control, and performing process monitoring by combining a charging process fault type database to form charging process real-time monitoring result information.
Acquiring real-time charging process information in the process of optimizing charging management control, and performing process monitoring by combining a charging process fault type database, wherein the method comprises the following steps: acquiring the service life of the battery of the real-time charging process information and corresponding process values of different performance management parameters, and performing correlation analysis on the process values of the different performance management parameters to form a process correlation value; setting a duration threshold T, and setting a process value of different performance management parameters and a corresponding reasonable abnormal information range set E in a charging process fault type database n One-to-one comparison is carried out, the process correlation value is compared with a reasonable correlation abnormal range set F in a charging process fault type database, and the following analysis and judgment are carried out: if the process value of the performance management parameter belongs to the corresponding reasonable abnormal information range set E n And if the duration exceeds the duration threshold T, forming abnormal information of the charging process; if the process correlation value belongs to a reasonable correlation abnormal range set F and the duration exceeds a duration threshold T, forming abnormal information of the charging process; if the process values of the performance management parameters do not belong to the corresponding reasonable abnormal information range set E n And the process correlation value does not belong to the reasonable correlation abnormal range set F, so that normal information of the charging process is formed; if the process value of the performance management parameter belongs to the corresponding reasonable abnormal information range set E n And last forIf the duration does not exceed the duration threshold T, normal information of the charging process is formed; if the process correlation value belongs to the reasonable correlation abnormal range set F and the duration time does not exceed the duration time threshold T, normal information of the charging process is formed.
The establishment of the fault type database in the charging process is to identify and monitor the abnormal state in the charging process, wherein the degree of deviation of the data of the abnormal state in the charging process from the data range of the normal performance management parameter is considered to be small, so that the time threshold is set as an important investigation item for judgment, and the abnormal state of the battery needs to be determined under the condition that the abnormal data range is met and the time threshold is reached. Of course, it can be appreciated that the determination of the abnormal state provides a data basis for subsequent fault handling to better and more efficiently implement maintenance and technical handling of the battery.
The invention also provides a charging pile capable of automatically identifying the abnormal state of the battery of the new energy vehicle, and the identification method capable of automatically identifying the abnormal state of the battery of the new energy vehicle provided by the invention comprises the following steps: the data acquisition unit is used for acquiring real-time charging access information and real-time charging process information of the vehicle battery; the database unit is used for storing, updating and adjusting a charging management database, a pre-charging fault type database and a charging process fault type database; the data transmission unit is used for acquiring the real-time charging access information and the real-time charging process information acquired by the data acquisition unit, uploading the real-time charging access information and the real-time charging process information, and downloading and updating the historical normal charging data and the historical charging fault data; the analysis processing unit is used for acquiring historical normal charging data of the data transmission unit, performing charging performance analysis based on charging management to form a charging performance management database, acquiring historical charging fault data of the data transmission unit, performing fault identification analysis by combining the charging performance management database, respectively establishing a pre-charging fault type database and a charging process fault type database, adjusting the charging performance management database to form an adjusted charging performance management database, performing fault detection on real-time charging access information of the data transmission unit to form pre-charging fault detection result information, performing process monitoring on the real-time charging process information of the data transmission unit to form charging process real-time monitoring result information.
The charging pile fully collects basic big data required by abnormality identification analysis through the data collection unit, gathers and synthesizes the data according to the data transmission unit, and meanwhile, the database unit completes the storage of various databases for abnormality identification. In addition, the analysis processing unit can be used for real-time retrieval of the database and real-time data to perform recognition analysis of abnormal states, real-time and efficient completion of recognition analysis processing is achieved, and a material basis is provided for accurate and efficient recognition of abnormal states of the vehicle battery.
In summary, the charging pile with the ordered charging function and the identification method capable of automatically identifying the abnormal state of the battery of the new energy vehicle provided by the embodiment of the invention have the beneficial effects that:
according to the method, the normal charging data of the batteries of the same type are obtained, the charging performance management database based on big data is built, and the position of the performance state of the individual vehicle battery in a normal range can be rapidly determined during real-time charging, so that basic reference data is provided for the subsequent management of optimal charging. Meanwhile, aiming at the occurrence of accidents and abnormal data in the charging process, a pre-charging fault type database for analysis and comparison before charging and a charging process fault type database for analysis and comparison in the charging process are respectively established, so that the identification of abnormal states before and during charging of the vehicle battery can be fully and accurately realized, control and guidance are further provided for charging of the vehicle battery, the safety of the vehicle battery during charging is ensured, the safety of the use of the vehicle battery is improved, and the use reliability and safety of the new energy vehicle are improved.
The charging pile fully collects basic big data required by abnormality identification analysis through the data collection unit, gathers and synthesizes the data according to the data transmission unit, and meanwhile, the database unit completes the storage of various databases for abnormality identification. In addition, the analysis processing unit can be used for real-time retrieval of the database and real-time data to perform recognition analysis of abnormal states, real-time and efficient completion of recognition analysis processing is achieved, and a material basis is provided for accurate and efficient recognition of abnormal states of the vehicle battery.
In the present invention, "at least one" means one or more, and "a plurality" means two or more. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b, or c may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
Those of ordinary skill in the art will appreciate that the elements and method steps of the examples described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or as a combination of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. An identification method for automatically identifying abnormal states of a battery of a new energy vehicle, comprising the steps of:
acquiring historical normal charging data of the batteries of the same type, and performing charging performance analysis based on charging management to form a charging performance management database;
acquiring historical charging fault data of the batteries of the same type, carrying out fault identification analysis by combining the charging performance management database, respectively establishing a pre-charging fault type database and a charging process fault type database, 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 according to the pre-charging fault type database to form pre-charging fault detection result information;
when the data of the fault detection result before charging is displayed normally, optimizing charging management control;
and acquiring real-time charging process information in the process of optimizing the charging management control, and performing process monitoring by combining the charging process fault type database to form charging process real-time monitoring result information.
2. The method for automatically identifying abnormal states of a battery of a new energy vehicle according to claim 1, wherein the acquiring historical normal charging data of the same type of battery, performing charging performance analysis based on charging management, and forming a charging performance management database, comprises:
acquiring the change data of different performance management parameters of the same type of battery in an effective service life period to form life-performance change curve data of different performance management parameters, wherein the performance management parameters are performance parameters which change along with the service life of the battery;
acquiring real-time charging data of different performance management parameters of the same type of battery during normal charging each time, and determining parameter variation ranges of different performance management parameters under corresponding real-time service lives by combining the life-performance variation curve data of the different performance management parameters to form a performance normal range data set A of the different performance management parameters in the effective service life period n Wherein, the method comprises the steps of, wherein,n is the number of the different performance management parameters, t is the time point in the effective life cycle, +.>Minimum value representing the range of said performance management parameter numbered n at time point t,/->Representing the maximum value of the range in which the performance management parameter numbered n is located at the time point t;
performing performance correlation analysis in the effective service life period by combining different performance management parameters to form life-performance correlation change curve data;
acquiring real-time charging data of different performance management parameters of the same type of battery during normal charging each time, carrying out correlation analysis on the different real-time charging data by combining the life-performance correlation change curve data, and establishing a performance correlation change range data set B in the effective service life period, wherein B= [ B ] min ，b max ] t Wherein t is the time point in the effective life cycle, b min A minimum value representing the corresponding performance-related variation range at the time point t,b max a maximum value representing a corresponding performance-related variation range at a time point t;
said performance normal range data set a incorporating all of said performance management parameters n And the performance correlation variation range set B, forming the charging performance management database.
3. The method for automatically identifying abnormal states of a battery of a new energy vehicle according to claim 2, wherein the acquiring historical charging failure data of the same type of battery, performing failure identification analysis in combination with the charging performance management database, respectively establishing a pre-charging failure type database and a charging process failure type database, and adjusting the charging performance management database to form an adjusted charging performance management database, comprises:
acquiring pre-accident charging information data in the historical charging fault data, analyzing the charging data before accident occurrence, and establishing the pre-charging fault type database;
acquiring process abnormal charging information data in the historical charging fault data, and carrying out charging data analysis based on different abnormal conditions by combining the charging performance management database to form a charging process fault type database;
and adjusting the charging performance management database according to the charging process fault type database to form the charging performance adjustment management database.
4. The method for automatically identifying abnormal states of a battery of a new energy vehicle according to claim 3, wherein the acquiring pre-accident charging information data in the historical charging failure data, performing a pre-accident based charging data analysis, and establishing the pre-charging failure type database comprises:
acquiring service life and corresponding accident charging information of different performance management parameters in the pre-accident charging information data to form parameter accidents with different performance management parameters in the effective service life cycleRange set C n WhereinMinimum value representing the range of said performance management parameter numbered n at time point t,/->Representing the maximum value of the range in which the performance management parameter numbered n is located at the time point t;
carrying out correlation analysis on service life and corresponding accident charging information with different performance management parameters in the pre-accident charging information data to form a correlation accident range set D in the effective service life period, wherein D= [ D ] min ，d max ] t ，d min A minimum value d representing a corresponding correlation event range at time t max A maximum value representing a corresponding correlation event range at a time point t;
The parameter incident scope set C combining all the performance management parameters n And the correlation accident range set D is used for forming the pre-charging fault type database.
5. The method for automatically identifying abnormal states of a battery of a new energy vehicle according to claim 4, wherein the acquiring process abnormal charging information data in the historical charging failure data and performing charging data analysis based on different abnormal conditions in combination with the charging performance management database to form the charging process failure type database comprises:
acquiring service life and corresponding abnormal charging information of different performance management parameters in process abnormal charging information data, and forming an initial abnormal information range set of the different performance management parameters in the service life period;
the initial abnormal information range set of the different performance management parameters is matched with the performance normal range data set A corresponding to the charging performance management database n Proceeding withComparing and adjusting to form an adjusting abnormal information range set;
the adjustment abnormality information range set of different performance management parameters is compared with the corresponding parameter accident range set C in the pre-charging fault type database n Performing contrast adjustment to form a reasonable abnormal information range set E n Wherein, the method comprises the steps of, wherein,minimum value representing the range of said performance management parameter numbered n at time point t,/->Representing the maximum value of the range in which the performance management parameter numbered n is located at the time point t;
acquiring service life and corresponding abnormal charging information of different performance management parameters in abnormal charging information data in the process, and performing correlation analysis to form an initial correlation abnormal range set in an effective service life period;
comparing and adjusting the initial correlation abnormal range set with the corresponding performance correlation variation range data set B in the charging performance management database to form an adjustment correlation abnormal range set;
comparing and adjusting the adjustment correlation abnormal range set with the correlation accident range set D in the pre-charging fault type database to form a reasonable correlation abnormal range set F, wherein F= [ F ] min ，f max ] t ，f min Minimum value representing reasonable correlation anomaly range corresponding to time point t, f max A maximum value representing a corresponding reasonable correlation anomaly range at a time point t;
reasonable anomaly information scope set E combining all the performance management parameters n And the reasonable correlation abnormal range set F forms the charging process fault type database.
6. The method for automatically identifying abnormal states of a battery of a new energy vehicle according to claim 5, wherein the adjusting the charging performance management database according to the charging process fault type database to form the adjusted charging performance management database comprises:
a reasonable anomaly information range set E for different performance management parameters n And said performance normal range dataset a for different ones of said performance management parameters in said charging performance management database n Performing the following comparative adjustment to form an adjustment performance normal range data set G of different performance management parameters n ：
If the reasonable abnormal information range set E n And corresponding said performance normal range data set a n If there is intersection, G n ＝A n -(E n ∩A n ) On the contrary, G n ＝A n ；
The reasonable correlation abnormal range set F and the performance correlation variation range data set B in the charging performance management database are subjected to the following comparative adjustment to form an adjustment performance correlation variation range data set H:
if the reasonable correlation abnormal range set F and the performance correlation variation range data set B are intersected, H=B- (F n B), otherwise H=B;
Adjusting Performance Normal Range data set G incorporating all of the Performance management parameters n And the adjustment performance correlation variation range data set H forms the adjustment charging performance management database.
7. The method for automatically identifying abnormal states of a battery of a new energy vehicle according to claim 6, wherein the acquiring real-time charging access information and performing fault detection according to the pre-charging fault type database to form pre-charging fault detection result information comprises:
acquiring the service life of the battery of the real-time charging access information and corresponding access values of different performance management parameters, and performing correlation analysis on the access values of the different performance management parameters to form a use correlation value;
the access values of the different performance management parameters are combined with the corresponding parameter accident range set C in the pre-charging fault type database n Performing one-to-one comparison, namely comparing the use correlation value with the correlation accident range set D in the pre-charging fault type database, and performing the following analysis and judgment:
if the access value of the performance management parameter belongs to the corresponding parameter accident range set C n Forming fault detection abnormal information before charging and judging that the detection result is abnormal;
if the using correlation value belongs to the correlation accident range set D, forming fault detection abnormal information before charging, and judging that the detection result is abnormal;
if the access values of the performance management parameters do not belong to the corresponding parameter accident range set C n And if the using correlation value does not belong to the correlation accident range set D, forming the fault detection normal information before charging, and judging that the detection result is normal.
8. The method for automatically identifying an abnormal state of a battery of a new energy vehicle according to claim 7, wherein the performing the optimal charge management control when the pre-charge failure detection result data shows normal, comprises:
and when the data of the fault detection result before charging is normal, performing charging management control based on prolonging the service life of the battery when the battery is charged.
9. The method for automatically identifying abnormal states of a battery of a new energy vehicle according to claim 8, wherein the steps of acquiring real-time charging process information in an optimized charging management control process and performing process monitoring in combination with the charging process fault type database include:
Acquiring the service life of the battery of the real-time charging process information and corresponding process values of different performance management parameters, and performing correlation analysis on the process values of the different performance management parameters to form a process correlation value;
setting a duration threshold T, and setting process values of different performance management parameters and the corresponding reasonable abnormal information range set E in the charging process fault type database n Performing one-to-one comparison, namely comparing the process correlation value with the reasonable correlation abnormal range set F in the charging process fault type database, and performing the following analysis and judgment:
if the process value of the performance management parameter belongs to the corresponding reasonable abnormal information range set E n And if the duration exceeds the duration threshold T, forming abnormal charging process information;
if the process correlation value belongs to the reasonable correlation abnormal range set F and the duration exceeds the duration threshold T, forming abnormal charging process information;
if the process values of the performance management parameters do not belong to the corresponding reasonable abnormal information range set E n And the process correlation value does not belong to the reasonable correlation abnormal range set F, so that normal charging process information is formed;
If the process value of the performance management parameter belongs to the corresponding reasonable abnormal information range set E n And if the duration time does not exceed the duration time threshold T, forming normal information of the charging process;
and if the process correlation value belongs to the reasonable correlation abnormal range set F and the duration time does not exceed the duration time threshold T, forming normal information of the charging process.
10. A charging pile for automatically identifying abnormal state of a battery of a new energy vehicle, employing the identification method for automatically identifying abnormal state of a battery of a new energy vehicle according to any one of claims 1 to 9, characterized by comprising:
the data acquisition unit is used for acquiring real-time charging access information and real-time charging process information of the vehicle battery;
the database unit is used for storing, updating and adjusting a charging management database, a pre-charging fault type database and a charging process fault type database;
the data transmission unit is used for acquiring the real-time charging access information and the real-time charging process information acquired by the data acquisition unit, uploading the real-time charging access information and the real-time charging process information, and downloading and updating the historical normal charging data and the historical charging fault data;
the analysis processing unit is used for acquiring historical normal charging data of the data transmission unit, performing charging performance analysis based on charging management to form a charging performance management database, acquiring historical charging fault data of the data transmission unit, performing fault identification analysis by combining the charging performance management database, respectively establishing a pre-charging fault type database and a charging process fault type database, adjusting the charging performance management database to form an adjustment charging performance management database, acquiring real-time charging access information of the data transmission unit, performing fault detection to form pre-charging fault detection result information, and acquiring real-time charging process information of the data transmission unit, performing process monitoring to form charging process real-time monitoring result information.
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