CN114683847A - Abnormality recognition method, abnormality recognition system, electronic device, and medium for vehicle battery - Google Patents

Abnormality recognition method, abnormality recognition system, electronic device, and medium for vehicle battery Download PDF

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Publication number
CN114683847A
CN114683847A CN202011623667.9A CN202011623667A CN114683847A CN 114683847 A CN114683847 A CN 114683847A CN 202011623667 A CN202011623667 A CN 202011623667A CN 114683847 A CN114683847 A CN 114683847A
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mileage
abnormality
battery
vehicle battery
identifying
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CN202011623667.9A
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CN114683847B (en
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郑立华
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Aulton New Energy Automotive Technology Co Ltd
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Aulton New Energy Automotive Technology Co Ltd
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Priority to CN202011623667.9A priority Critical patent/CN114683847B/en
Priority to KR1020237025984A priority patent/KR20230125307A/en
Priority to PCT/CN2021/142981 priority patent/WO2022143878A1/en
Priority to JP2023540150A priority patent/JP2024502816A/en
Publication of CN114683847A publication Critical patent/CN114683847A/en
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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
    • 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
    • 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
    • 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
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/54—Drive Train control parameters related to batteries
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Y—INDEXING SCHEME RELATING TO ASPECTS CROSS-CUTTING VEHICLE TECHNOLOGY
    • B60Y2200/00—Type of vehicle
    • B60Y2200/90—Vehicles comprising electric prime movers
    • B60Y2200/91—Electric vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Y—INDEXING SCHEME RELATING TO ASPECTS CROSS-CUTTING VEHICLE TECHNOLOGY
    • B60Y2306/00—Other features of vehicle sub-units
    • B60Y2306/15—Failure diagnostics
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Y—INDEXING SCHEME RELATING TO ASPECTS CROSS-CUTTING VEHICLE TECHNOLOGY
    • B60Y2400/00—Special features of vehicle units
    • B60Y2400/11—Electric energy storages
    • B60Y2400/112—Batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/72—Electric energy management in electromobility

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Secondary Cells (AREA)

Abstract

The invention discloses a method, a system, electronic equipment and a medium for identifying the abnormity of a vehicle battery, wherein the method for identifying the abnormity of the vehicle battery comprises the following steps: selecting a target vehicle battery to be subjected to abnormal recognition; acquiring a first mileage corresponding to a battery management system of a target vehicle battery and a second mileage corresponding to a vehicle control unit of an electric vehicle loaded with the target vehicle battery; and identifying whether the target vehicle battery is abnormal or not according to the first mileage and the second mileage. According to the invention, based on the priori knowledge of mutual influence between the vehicle battery and the driving mileage, the first mileage corresponding to the battery management system of the target vehicle battery and the second mileage corresponding to the vehicle controller are used as the basis to identify whether the target vehicle battery is abnormal, so that the accuracy of identifying the vehicle battery abnormality is improved.

Description

Abnormality recognition method, abnormality recognition system, electronic device, and medium for vehicle battery
Technical Field
The invention belongs to the technical field of battery abnormal state identification, and particularly relates to an abnormal identification method, system, electronic equipment and medium for a vehicle battery.
Background
At present, various hidden dangers which cannot be directly identified exist in the use process of the new energy battery. The direct relationship behind these concerns is battery life and health. But these concerns are easily overlooked because the battery is still usable and there are no significant anomalies. With the continuous use of the battery, hidden dangers are accumulated, the battery can be finally scrapped early, and even faults occur in the use process of the automobile, so that greater loss is caused. However, the current technology cannot effectively and timely identify the abnormality of the battery.
Disclosure of Invention
The invention provides a method, a system, an electronic device and a medium for identifying the abnormality of a vehicle battery, aiming at overcoming the defect that the abnormality of the battery cannot be identified effectively and timely in the prior art.
The invention solves the technical problems through the following technical scheme:
the invention provides an abnormality recognition method for a vehicle battery, which comprises the following steps:
selecting a target vehicle battery to be subjected to abnormal recognition;
acquiring a first mileage corresponding to a battery management system of a target vehicle battery and a second mileage corresponding to a vehicle control unit of an electric vehicle loaded with the target vehicle battery;
and identifying whether the target vehicle battery is abnormal or not according to the first mileage and the second mileage.
In the scheme, based on the priori knowledge that the vehicle battery and the driving mileage have mutual influence, the first mileage corresponding to the battery management system of the target vehicle battery and the second mileage corresponding to the vehicle control unit are used as the basis to identify whether the target vehicle battery is abnormal or not, and the accuracy of vehicle battery abnormality identification is improved.
Preferably, the acquiring a first mileage corresponding to the battery management system of the target vehicle battery and a second mileage corresponding to the vehicle control unit of the electric vehicle on which the target vehicle battery is loaded includes:
acquiring a first accumulated driving mileage recorded by a battery management system of a target vehicle battery when the target vehicle battery is mounted on the electric automobile, and a second accumulated driving mileage recorded by the battery management system of the target vehicle battery when the target vehicle battery is dismounted from the electric automobile;
determining a first mileage according to the first accumulated mileage and the second accumulated mileage;
acquiring a third accumulated driving mileage recorded by a vehicle control unit of the electric vehicle when the target vehicle battery is mounted on the electric vehicle, and a fourth accumulated driving mileage recorded by the vehicle control unit of the electric vehicle when the target vehicle battery is dismounted from the electric vehicle;
determining a second mileage according to the third accumulated mileage and the fourth accumulated mileage;
the battery management system and the vehicle control unit respectively record the accumulated driving mileage.
In the technical scheme, based on the priori knowledge of the replaceable vehicle battery of the electric vehicle, the mileage from the time when the target vehicle battery is mounted on the electric vehicle to the time when the target vehicle battery is dismounted from the electric vehicle is taken as a data source, and the data sources from the two different sources are taken as the basis to identify whether the target vehicle battery is abnormal or not, so that the accuracy and the reliability of identifying the abnormal vehicle battery can be improved.
Preferably, the identifying whether the target vehicle battery is abnormal according to the first mileage and the second mileage includes:
when the first mileage exceeds the nominal mileage of the target vehicle battery, identifying the target vehicle battery as abnormal, wherein the abnormality belongs to an abnormality type of a first main class;
and identifying the subclass to which the abnormality belongs under the first main class according to the second mileage.
Preferably, identifying the sub-category to which the anomaly belongs under the first main category according to the second mileage includes:
when the second mileage is empty, identifying the abnormal type of the first subclass divided under the first main class;
when the second mileage does not exceed the preset mileage, identifying the abnormal type of the second subclass which is divided under the first main class and is abnormal;
when the second mileage exceeds the preset mileage and is matched with the first mileage, identifying an abnormal type of an abnormal third subclass divided under the first main class;
and when the second mileage exceeds the preset mileage and is not matched with the first mileage, identifying the exception type of the fourth subclass divided under the first main class.
Preferably, the identifying whether the target vehicle battery is abnormal according to the first mileage and the second mileage further includes:
when the first mileage does not exceed the preset mileage, identifying that the target vehicle battery is abnormal;
when the first mileage is preset mileage, identifying an abnormal type of which the abnormality belongs to a second main class, and identifying a subclass of which the abnormality belongs to the second main class according to the second mileage;
and when the first mileage is less than the preset mileage, identifying an abnormal type of the abnormality belonging to a third main class, and identifying a subclass of the abnormality belonging to the third main class according to the second mileage.
Preferably, when the first mileage is a preset mileage, identifying an abnormal type of the abnormality belonging to the second main class, and identifying a sub-class of the abnormality belonging to the second main class according to the second mileage includes:
when the first mileage is a preset mileage and the second mileage is empty, identifying an abnormal type of the first subclass, which is divided under the second main class, as an abnormal type;
when the first mileage is a preset mileage and the second mileage does not exceed the preset mileage, identifying an abnormal type of a second subclass which is divided under a second main class;
when the first mileage is a preset mileage and the second mileage exceeds the preset mileage, identifying an abnormal type of an abnormal class belonging to a third subclass divided under the second main class;
when the first mileage is less than the preset mileage, identifying an abnormal type of the abnormality belonging to a third main class, and identifying a subclass of the abnormality belonging to the third main class according to the second mileage, including:
when the first mileage is smaller than the preset mileage and the second mileage is empty, identifying the abnormal type of the first subclass which is divided under the third main class;
when the first mileage is less than the preset mileage and the second mileage does not exceed the preset mileage, identifying an abnormal type of the second subclass, which is divided under the third main class, as an abnormal type;
and when the first mileage is less than the preset mileage and the second mileage exceeds the preset mileage, identifying an abnormal type of the third subclass which is divided under the third main class.
According to the technical scheme, the method for dividing the abnormal type of the target vehicle battery based on the numerical value and the numerical value relation of the first mileage and the second mileage specifically is provided, and the accuracy of vehicle battery abnormality identification is improved. And by the division and identification of subclasses, the abnormal reasons can be accurately positioned subsequently.
Preferably, the abnormality recognition method for a vehicle battery further includes:
and processing matched with the abnormality type of the abnormality to which the abnormality belongs is carried out on the target vehicle battery.
In the technical scheme, the battery for the target vehicle is processed in a manner of being matched with the abnormal type to which the abnormality belongs, so that the battery for the target vehicle can be reasonably processed, the abnormality can be timely processed to avoid safety problems, and the battery for the target vehicle can be used to the best to avoid resource waste.
Preferably, the abnormality recognition method for a vehicle battery further includes:
and optimizing an algorithm for recording the accumulated driving mileage by the battery management system according to the abnormal type of the abnormality, so that the accumulated driving mileage recorded by the battery management system is used for replacing or verifying the accumulated driving mileage recorded by the vehicle control unit for resource transfer during battery replacement.
In the technical scheme, the algorithm for recording the accumulated traveled distance by the battery management system is optimized according to the abnormal type of the abnormality, so that the accuracy of recording the accumulated traveled distance by the battery management system can be improved, the accumulated traveled distance recorded by the battery management system can be directly adopted when the traveled distance needs to be used in data processing such as resource transfer during subsequent battery replacement, or the accumulated traveled distance recorded by the battery management system can be adopted to check the accumulated traveled distance recorded by the whole controller, the dependence on data provided by an external third party is reduced, the cost is reduced, and the safety and the privacy of the data can be guaranteed.
The invention also provides an abnormality recognition system for the vehicle battery, which comprises a selection unit, an acquisition unit and a recognition unit;
the selecting unit is used for selecting a target vehicle battery to be subjected to abnormity identification;
the acquisition unit is used for acquiring a first mileage corresponding to a battery management system of a target vehicle battery and a second mileage corresponding to a vehicle control unit of the electric vehicle loaded with the target vehicle battery;
the identification unit is used for identifying whether the target vehicle battery is abnormal or not according to the first mileage and the second mileage.
In the scheme, based on the priori knowledge of mutual influence between the vehicle battery and the driving mileage, the first mileage corresponding to the battery management system of the target vehicle battery and the second mileage corresponding to the vehicle control unit are used as the basis for identifying whether the target vehicle battery is abnormal, so that the accuracy of identifying the vehicle battery abnormality is improved.
Preferably, the obtaining unit is further configured to obtain a first accumulated driving distance recorded by a battery management system of the target vehicle battery when the target vehicle battery is mounted on the electric vehicle, and a second accumulated driving distance recorded by the battery management system of the target vehicle battery when the target vehicle battery is dismounted from the electric vehicle;
the acquisition unit is also used for determining the first mileage according to the first accumulated mileage and the second accumulated mileage;
the acquisition unit is further used for acquiring a third accumulated driving distance recorded by a vehicle control unit of the electric vehicle when the target vehicle battery is mounted on the electric vehicle, and a fourth accumulated driving distance recorded by the vehicle control unit of the electric vehicle when the target vehicle battery is dismounted from the electric vehicle;
the acquisition unit is also used for determining a second mileage according to the third accumulated mileage and the fourth accumulated mileage;
the battery management system and the vehicle control unit respectively record the accumulated travel mileage.
In the technical scheme, based on the priori knowledge of the replaceable vehicle battery of the electric vehicle, the mileage from the time when the target vehicle battery is mounted on the electric vehicle to the time when the target vehicle battery is dismounted from the electric vehicle is taken as a data source, and the data sources from the two different sources are taken as the basis to identify whether the target vehicle battery is abnormal or not, so that the accuracy and the reliability of identifying the abnormal vehicle battery can be improved.
Preferably, the identification unit is further configured to identify the target vehicle battery as abnormal when the first mileage exceeds a nominal mileage of the target vehicle battery, and the abnormality belongs to an abnormality type of the first main class;
the identifying unit is further used for identifying the subclasses to which the abnormality belongs under the first main class according to the second mileage.
Preferably, when the second mileage is empty, the identifying unit is further configured to identify an anomaly type that the anomaly belongs to a first sub-class divided under the first main class;
when the second mileage does not exceed the preset mileage, the identification unit is further used for identifying the abnormal type of the second subclass which is divided under the first main class;
when the second mileage exceeds the preset mileage and is matched with the first mileage, the identification unit is further used for identifying the abnormality type of the third subclass which is divided under the first main class;
when the second mileage exceeds the preset mileage and is not matched with the first mileage, the identification unit is further configured to identify an abnormality type in which the abnormality belongs to a fourth subclass divided under the first main class.
Preferably, when the first mileage is less than the preset mileage and the second mileage is empty, the identifying unit is further configured to identify an abnormality type that the abnormality belongs to a first subclass divided under a third main class;
when the first mileage is less than the preset mileage and the second mileage does not exceed the preset mileage, the identification unit is further configured to identify an abnormality type that the abnormality belongs to a second subclass divided under a third main class;
when the first mileage is less than the preset mileage and the second mileage exceeds the preset mileage, the identification unit is further configured to identify an abnormality type that the abnormality belongs to a third subclass divided under a third main class.
In the technical scheme, a mode of dividing the abnormal type of the target vehicle battery based on the numerical values and the numerical value relationship of the first mileage and the second mileage is provided, and the accuracy of vehicle battery abnormality identification is improved. And by the division and identification of subclasses, the abnormal reasons can be accurately positioned subsequently.
Preferably, the abnormality recognition system for a vehicle battery further includes a processing unit;
the processing unit is used for processing the target vehicle battery and matching with the abnormal type of the abnormality.
In the technical scheme, the battery for the target vehicle is processed in a manner of being matched with the abnormal type to which the abnormality belongs, so that the battery for the target vehicle can be reasonably processed, the abnormality can be timely processed to avoid safety problems, and the battery for the target vehicle can be used to the best to avoid resource waste.
Preferably, the abnormality recognition system for a vehicle battery further includes an optimization unit;
the optimization unit is used for optimizing an algorithm of the battery management system for recording the accumulated traveled distance according to the abnormal type of the abnormality, so that the accumulated traveled distance recorded by the battery management system is used for replacing or verifying the accumulated traveled distance recorded by the vehicle control unit for resource transfer during battery replacement.
In the technical scheme, the algorithm for recording the accumulated traveled distance by the battery management system is optimized according to the abnormal type of the abnormality, so that the accuracy of recording the accumulated traveled distance by the battery management system can be improved, the accumulated traveled distance recorded by the battery management system can be directly adopted when the traveled distance needs to be used in data processing such as resource transfer during subsequent battery replacement, or the accumulated traveled distance recorded by the battery management system can be adopted to check the accumulated traveled distance recorded by the whole controller, the dependence on data provided by an external third party is reduced, the cost is reduced, and the safety and the privacy of the data can be guaranteed.
The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the computer program to realize the abnormality identification method of the vehicle battery.
The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, implements the steps of the abnormality recognition method of a vehicle battery of the present invention.
The positive progress effects of the invention are as follows: according to the invention, based on the priori knowledge of mutual influence between the vehicle battery and the driving mileage, the first mileage corresponding to the battery management system of the target vehicle battery and the second mileage corresponding to the vehicle controller are used as the basis to identify whether the target vehicle battery is abnormal, so that the accuracy of identifying the vehicle battery abnormality is improved.
Drawings
Fig. 1 is a flowchart of an abnormality recognition method for a vehicle battery according to embodiment 1 of the present invention.
Fig. 2 is a flowchart of an abnormality recognition method for a vehicle battery according to embodiment 4 of the present invention.
Fig. 3 is a flowchart of an abnormality recognition method for a vehicle battery according to embodiment 5 of the present invention.
Fig. 4 is a schematic structural diagram of an electronic device according to embodiment 5 of the present invention.
Fig. 5 is a schematic configuration diagram of an abnormality recognition system for a vehicle battery according to embodiment 8 of the present invention.
Fig. 6 is a schematic configuration diagram of an abnormality recognition system for a vehicle battery according to embodiment 11 of the present invention.
Fig. 7 is a schematic configuration diagram of an abnormality recognition system for a vehicle battery according to embodiment 12 of the present invention.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the invention thereto.
Example 1
The present embodiment provides a method for identifying an abnormality of a vehicle battery. Referring to fig. 1, the abnormality recognition method of the vehicle battery includes the steps of:
and step S1, selecting the target vehicle battery to be subjected to the abnormality recognition.
Step S2, a first mileage corresponding to the battery management system of the target vehicle battery and a second mileage corresponding to the vehicle control unit of the electric vehicle to which the target vehicle battery is loaded are obtained.
And step S3, identifying whether the target vehicle battery is abnormal according to the first mileage and the second mileage.
In specific implementation, first, in step S1, a target vehicle battery to be subjected to abnormality recognition is selected.
Then, in step S2, a first accumulated driving distance recorded by the battery management system of the target vehicle battery when the target vehicle battery is mounted on the electric vehicle and a second accumulated driving distance recorded by the battery management system of the target vehicle battery when the target vehicle battery is dismounted from the electric vehicle are obtained; determining a first mileage according to the first accumulated mileage and the second accumulated mileage; acquiring a third accumulated driving mileage recorded by a vehicle control unit of the electric vehicle when the target vehicle battery is mounted on the electric vehicle, and a fourth accumulated driving mileage recorded by the vehicle control unit of the electric vehicle when the target vehicle battery is dismounted from the electric vehicle; and determining the second mileage according to the third accumulated mileage and the fourth accumulated mileage. The battery management system and the vehicle control unit respectively record the accumulated travel mileage.
In the above implementable manner, the battery management system and the vehicle control unit respectively and independently record the accumulated traveled distance, so that the comparison of the accumulated traveled distances recorded by the battery management system and the vehicle control unit has practical significance. However, the battery management system and the vehicle control unit may record the accumulated driving mileage independently in the same or different manners.
Then, in step S3, it is recognized whether the target vehicle battery is abnormal or not based on the first mileage and the second mileage.
The method for identifying the abnormality of the vehicle battery in the embodiment identifies whether the target vehicle battery is abnormal or not according to the first mileage corresponding to the battery management system of the target vehicle battery and the second mileage corresponding to the vehicle controller based on the priori knowledge that the vehicle battery and the driving mileage have mutual influence, so that the accuracy of identifying the abnormality of the vehicle battery is improved.
Example 2
On the basis of embodiment 1, the present embodiment provides a method for identifying an abnormality of a vehicle battery.
In specific implementation, first, in step S1, a target vehicle battery to be subjected to abnormality recognition is selected.
Then, in step S2, a first accumulated driving distance recorded by the battery management system of the target vehicle battery when the target vehicle battery is mounted on the electric vehicle and a second accumulated driving distance recorded by the battery management system of the target vehicle battery when the target vehicle battery is dismounted from the electric vehicle are obtained; determining a first mileage according to the first accumulated mileage and the second accumulated mileage; acquiring a third accumulated driving mileage recorded by a vehicle control unit of the electric vehicle when the target vehicle battery is mounted on the electric vehicle, and a fourth accumulated driving mileage recorded by the vehicle control unit of the electric vehicle when the target vehicle battery is dismounted from the electric vehicle; and determining the second mileage according to the third accumulated mileage and the fourth accumulated mileage. The battery management system and the vehicle control unit respectively record the accumulated travel mileage.
Based on the priori knowledge of the replaceable vehicle battery of the electric vehicle, the mileage from the time when the target vehicle battery is mounted on the electric vehicle to the time when the target vehicle battery is dismounted from the electric vehicle is taken as a data source, and the data sources from the two different sources are used as the basis for identifying whether the target vehicle battery is abnormal or not, so that the accuracy and the reliability of the abnormal identification of the vehicle battery can be improved.
Next, in step S3, when the first mileage exceeds the nominal mileage of the target vehicular battery, identifying the target vehicular battery as abnormal, and the abnormality being of an abnormality type of a first main class; and identifying the subclass to which the abnormality belongs under the first main class according to the second mileage. The nominal mileage is the vehicle battery's own attributes.
The specific way to identify subclasses is as follows:
and when the second mileage is empty, identifying the exception type of the exception belonging to a first sub-class divided under the first main class. The logic to identify the exception type of the first subclass is: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is larger than a nominal mileage and is not in accordance with an actual situation, and meanwhile, real-time vehicle information of the vehicle cannot be associated, so that the first subclass of abnormal type is identified.
And when the second mileage does not exceed the preset mileage, identifying the abnormality type of the second subclass which is divided under the first main class. The logic to identify the exception type of the second subclass is: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is larger than a nominal mileage and does not accord with an actual situation, and meanwhile, in a related real-time second mileage, the second mileage does not exceed a preset mileage and does not accord with the actual situation, so that the abnormal type of the second subclass is identified. In an alternative embodiment, the preset mileage is 0.
And when the second mileage exceeds the preset mileage and is matched with the first mileage, identifying the abnormal type of the third subclass which is divided under the first main class. In one aspect of this embodiment, if the difference between the second mileage and the first mileage is less than or equal to the preset percentage, the second mileage is considered to be matched with the first mileage. As an alternative embodiment, the preset percentage is 10%. The logic to identify the exception type of the third subclass is: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is larger than a nominal mileage and is not in line with an actual situation, and meanwhile, in a related real-time second mileage, the difference between the real-time second mileage and the first mileage is not much, so that the abnormal type of the second subclass is identified.
And when the second mileage exceeds the preset mileage and is not matched with the first mileage, identifying the exception type of the fourth subclass divided under the first main class. In one aspect of this embodiment, if the difference between the second mileage and the first mileage is greater than a predetermined percentage, the second mileage is considered not to match the first mileage. As an alternative embodiment, the preset percentage is 10%. The logic to identify the exception type of the fourth subclass is: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is larger than a nominal mileage and does not accord with an actual condition, and meanwhile, in a related real-time second mileage, the real-time second mileage is more different from the first mileage and does not accord with the actual condition, so that the abnormal type of the second subclass is identified.
The abnormality identification method for the vehicle battery of the embodiment provides a way of specifically dividing the abnormality type of the target vehicle battery based on the numerical values and the numerical value relationship of the first mileage and the second mileage, and improves the accuracy of the abnormality identification for the vehicle battery. And by the division and identification of subclasses, the abnormal reasons can be accurately positioned subsequently.
Example 3
On the basis of embodiment 1, the present embodiment provides a method for identifying an abnormality of a vehicle battery.
In specific implementation, first, in step S1, a target vehicle battery to be subjected to abnormality recognition is selected.
Then, in step S2, a first accumulated driving distance recorded by the battery management system of the target vehicle battery when the target vehicle battery is mounted on the electric vehicle and a second accumulated driving distance recorded by the battery management system of the target vehicle battery when the target vehicle battery is dismounted from the electric vehicle are obtained; determining a first mileage according to the first accumulated mileage and the second accumulated mileage; acquiring a third accumulated driving mileage recorded by a vehicle control unit of the electric vehicle when the target vehicle battery is mounted on the electric vehicle, and a fourth accumulated driving mileage recorded by the vehicle control unit of the electric vehicle when the target vehicle battery is dismounted from the electric vehicle; and determining the second mileage according to the third accumulated mileage and the fourth accumulated mileage. The battery management system and the vehicle control unit respectively record the accumulated travel mileage.
Next, in step S3, when the first mileage does not exceed the preset mileage, recognizing the target vehicle battery as abnormal; when the first mileage is a preset mileage, identifying an abnormal type of the abnormality belonging to a second main class, and identifying a subclass of the abnormality belonging to the second main class according to the second mileage; and when the first mileage is less than the preset mileage, identifying an abnormal type of the abnormality belonging to a third main class, and identifying a subclass of the abnormality belonging to the third main class according to the second mileage.
The way of identifying the sub-class under the second main class is specifically as follows:
and when the first mileage is a preset mileage and the second mileage is empty, identifying the abnormal type of the first subclass which is divided under the second main class. The identification logic is as follows: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is absent, the actual situation is not met, and meanwhile, real-time vehicle information of a vehicle is not related, so that the abnormal type of a first sub-class divided under a second main class is identified. In an alternative embodiment, the preset mileage is 0.
And when the first mileage is a preset mileage and the second mileage does not exceed the preset mileage, identifying the abnormality type of the second subclass which is divided under the second main class. The identification logic is as follows: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is equal to a preset mileage and is not in accordance with an actual condition, and meanwhile, in a related real-time second mileage, a second mileage is less than or equal to the preset mileage and is not in accordance with the actual condition, so that the abnormal type of a second subclass divided under a second main class is identified.
And when the first mileage is a preset mileage and the second mileage exceeds the preset mileage, identifying an abnormal type of the third subclass which is divided under the second main class. The identification logic is as follows: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is equal to a preset mileage and is not in line with an actual situation, and a real-time second mileage related to the first mileage is normal, so that an abnormal type which is abnormal and belongs to a third subclass divided under a second main class is identified.
The specific way of identifying the subclass to which the abnormality belongs under the third main class according to the second mileage is as follows:
and when the first mileage is less than the preset mileage and the second mileage is empty, identifying the abnormal type of the first subclass which is divided under the third main class. The identification logic is as follows: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is smaller than a preset mileage, the record is not in line with the actual situation, and meanwhile, real-time vehicle information of the vehicle cannot be correlated, so that the abnormal type of the first subclass divided under the third main class is identified. In an alternative embodiment, the preset mileage is 0.
And when the first mileage is less than the preset mileage and the second mileage does not exceed the preset mileage, identifying the abnormal type of the second subclass which is divided under the third main class. The identification logic is as follows: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is smaller than a preset mileage and is not in accordance with an actual condition, and meanwhile, in a related real-time second mileage, a second mileage is smaller than or equal to the preset mileage and is not in accordance with the actual condition, so that the abnormal type of a second subclass divided under a third main class is identified.
And when the first mileage is less than the preset mileage and the second mileage exceeds the preset mileage, identifying an abnormal type of the third subclass which is divided under the third main class. The identification logic is as follows: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is smaller than a preset mileage and is not in line with an actual situation, and a real-time second mileage related to the first mileage is normal, so that an abnormal type which is abnormal and belongs to a third subclass divided under a third main class is identified.
The abnormity identification method of the vehicle battery provides a mode of dividing the abnormity type of the target vehicle battery based on the numerical values and the numerical value relationship of the first mileage and the second mileage, and improves the accuracy of abnormity identification of the vehicle battery. And by the division and identification of subclasses, the abnormal reasons can be accurately positioned subsequently.
According to the abnormity identification method of the vehicle battery, various abnormity types of the battery are identified by collecting battery data and automobile data in the running process of the automobile, combining the attribute data of the battery and comprehensively considering a plurality of data, the abnormity of the battery and the abnormity appearing in the using process are covered, the real condition of the battery is reflected in time, and timely and effective reference information is provided for enterprise operation.
Example 4
This embodiment provides a method for identifying an abnormality of a vehicle battery on the basis of any one of embodiments 1 to 3. Referring to fig. 2, the method for identifying an abnormality of a vehicle battery includes:
and step S1, selecting the target vehicle battery to be subjected to the abnormality recognition.
Step S2, a first mileage corresponding to the battery management system of the target vehicle battery and a second mileage corresponding to the vehicle control unit of the electric vehicle to which the target vehicle battery is loaded are obtained.
And step S3, identifying whether the target vehicle battery is abnormal according to the first mileage and the second mileage.
Step S4 is a step of processing the target vehicle battery to match the abnormality type to which the abnormality belongs.
In step S4, the vehicle battery is checked for an evaluation according to the type of abnormality to which the vehicle battery belongs, and the vehicle battery is discarded or reused as an energy storage battery.
The method for identifying the abnormality of the vehicle battery carries out processing matched with the abnormality type of the abnormality on the target vehicle battery, so that the target vehicle battery can be reasonably processed, the abnormality can be timely processed to avoid safety problems, and the method can make the best use of the abnormality to avoid resource waste.
Example 5
This embodiment provides a method for identifying an abnormality of a vehicle battery on the basis of any one of embodiments 1 to 3. Referring to fig. 3, the method for identifying an abnormality of a vehicle battery includes:
and step S1, selecting the target vehicle battery to be subjected to the abnormality recognition.
Step S2, a first mileage corresponding to the battery management system of the target vehicle battery and a second mileage corresponding to the vehicle control unit of the electric vehicle to which the target vehicle battery is loaded are obtained.
And step S3, identifying whether the target vehicle battery is abnormal according to the first mileage and the second mileage.
And S5, optimizing an algorithm for recording the accumulated traveled distance by the battery management system according to the abnormal type of the abnormality, so that the accumulated traveled distance recorded by the battery management system is used for replacing or verifying the accumulated traveled distance recorded by the vehicle control unit for resource transfer during battery replacement.
According to the method for identifying the abnormality of the vehicle battery, the algorithm for recording the accumulated traveled distance by the battery management system is optimized according to the type of the abnormality to which the abnormality belongs, and the accuracy for recording the accumulated traveled distance by the battery management system can be improved, so that the accumulated traveled distance recorded by the battery management system can be directly adopted when the traveled distance needs to be used in data processing such as resource transfer during subsequent battery replacement, or the accumulated traveled distance recorded by the battery management system can be adopted to check the accumulated traveled distance recorded by the whole controller, the dependence on data provided by an external third party is reduced, the cost is reduced, and the safety and the privacy of the data can be guaranteed.
Example 6
Fig. 4 is a schematic structural diagram of an electronic device provided in this embodiment. The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the abnormality recognition method for the vehicle battery according to any one of embodiments 1 to 5 when executing the program. The electronic device 30 shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
The electronic device 30 may be embodied in the form of a general purpose computing device, which may be, for example, a server device. The components of the electronic device 30 may include, but are not limited to: the at least one processor 31, the at least one memory 32, and a bus 33 connecting the various system components (including the memory 32 and the processor 31).
The bus 33 includes a data bus, an address bus, and a control bus.
The memory 32 may include volatile memory, such as Random Access Memory (RAM)321 and/or cache memory 322, and may further include Read Only Memory (ROM) 323.
Memory 32 may also include a program/utility 325 having a set (at least one) of program modules 324, such program modules 324 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The processor 31 executes various functional applications and data processing, such as the abnormality recognition method for a vehicle battery according to any one of embodiments 1 to 5 of the present invention, by running a computer program stored in the memory 32.
The electronic device 30 may also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). Such communication may be through input/output (I/O) interfaces 35. Also, model-generating device 30 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via network adapter 36. As shown, network adapter 36 communicates with the other modules of model-generating device 30 via bus 33. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the model-generating device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 7
The present embodiment provides a computer-readable storage medium on which a computer program is stored, the program implementing the steps of the abnormality recognition method for a vehicle battery according to any one of embodiments 1 to 5 when executed by a processor.
More specific examples that may be employed by the readable storage medium include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible implementation, the present invention may also be implemented in the form of a program product including program code for causing a terminal device to execute steps of implementing the abnormality recognition method for a vehicle battery of any one of embodiments 1 to 5, when the program product is run on the terminal device.
Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may be executed entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a remote device or entirely on the remote device.
Example 8
The present embodiment provides an abnormality recognition system for a vehicle battery. Referring to fig. 5, the abnormality recognition system for a vehicle battery includes an extracting unit 201, an acquiring unit 202, and a recognizing unit 203.
The selecting unit 201 is used for selecting a target vehicle battery to be subjected to abnormality identification; the obtaining unit 202 is configured to obtain a first mileage corresponding to a battery management system of a target vehicle battery and a second mileage corresponding to a vehicle control unit of an electric vehicle to which the target vehicle battery is loaded; the identification unit 203 is configured to identify whether the target vehicle battery is abnormal according to the first mileage and the second mileage.
In specific implementation, first, the selection unit 201 selects a target vehicle battery to be subjected to abnormality identification.
Then, the obtaining unit 202 obtains a first accumulated driving distance recorded by a battery management system of the target vehicle battery when the target vehicle battery is mounted on the electric vehicle, and a second accumulated driving distance recorded by the battery management system of the target vehicle battery when the target vehicle battery is dismounted from the electric vehicle; the obtaining unit 202 determines a first mileage according to the first accumulated mileage and the second accumulated mileage; the obtaining unit 202 obtains a third accumulated driving distance recorded by a vehicle control unit of the electric vehicle when the target vehicle battery is mounted on the electric vehicle, and a fourth accumulated driving distance recorded by the vehicle control unit of the electric vehicle when the target vehicle battery is dismounted from the electric vehicle; the acquisition unit 202 determines the second mileage based on the third accumulated mileage and the fourth accumulated mileage. The battery management system and the vehicle control unit respectively record the accumulated travel mileage.
In the above implementation manner, the battery management system and the vehicle control unit respectively and independently record the accumulated traveled mileage, so that there is practical significance in comparing the accumulated traveled mileage recorded by the battery management system and the vehicle control unit. However, the battery management system and the vehicle control unit may record the accumulated driving mileage independently in the same or different manners.
Then, the identifying unit 203 identifies whether the target vehicular battery is abnormal or not based on the first mileage and the second mileage.
The abnormality recognition system for the vehicle battery in the embodiment recognizes whether the target vehicle battery is abnormal or not according to the first mileage corresponding to the battery management system of the target vehicle battery and the second mileage corresponding to the vehicle controller based on the priori knowledge that the vehicle battery and the driving mileage have mutual influence, so that the accuracy of abnormality recognition of the vehicle battery is improved.
Example 9
On the basis of embodiment 8, the present embodiment provides an abnormality recognition system for a vehicle battery.
In specific implementation, first, the selecting unit 201 selects a target vehicle battery to be subjected to abnormality identification.
Then, the obtaining unit 202 obtains a first accumulated driving distance recorded by a battery management system of the target vehicle battery when the target vehicle battery is mounted on the electric vehicle, and a second accumulated driving distance recorded by the battery management system of the target vehicle battery when the target vehicle battery is dismounted from the electric vehicle; the obtaining unit 202 determines a first mileage according to the first accumulated mileage and the second accumulated mileage; the obtaining unit 202 obtains a third accumulated driving distance recorded by a vehicle control unit of the electric vehicle when the target vehicle battery is mounted on the electric vehicle, and a fourth accumulated driving distance recorded by the vehicle control unit of the electric vehicle when the target vehicle battery is dismounted from the electric vehicle; the acquisition unit 202 determines the second mileage based on the third accumulated mileage and the fourth accumulated mileage. The battery management system and the vehicle control unit respectively record the accumulated travel mileage.
Based on the priori knowledge of the replaceable vehicle battery of the electric vehicle, the mileage from the time when the target vehicle battery is mounted on the electric vehicle to the time when the target vehicle battery is dismounted from the electric vehicle is taken as a data source, and the data sources from the two different sources are used as the basis for identifying whether the target vehicle battery is abnormal or not, so that the accuracy and the reliability of the abnormal identification of the vehicle battery can be improved.
Next, when the first mileage exceeds the nominal mileage of the target vehicular battery, the identifying unit 203 identifies the target vehicular battery as abnormal, and the abnormality belongs to an abnormality type of the first main class; the identifying unit 203 identifies a sub-class to which the abnormality belongs under the first main class according to the second mileage. The nominal mileage is the vehicle battery's own attributes.
The specific way in which the identifying unit 203 identifies the subclasses is as follows:
when the second mileage is empty, the identifying unit 203 identifies an abnormality type in which the abnormality belongs to a first sub-class divided under the first main class. The logic to identify the exception type of the first subclass is: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is larger than a nominal mileage and is not in accordance with an actual situation, and meanwhile, real-time vehicle information of the vehicle cannot be associated, so that the first subclass of abnormal type is identified.
When the second mileage does not exceed the preset mileage, the identifying unit 203 identifies an abnormality type that the abnormality belongs to a second sub-class divided under the first main class. The logic to identify the exception type of the second subclass is: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is larger than a nominal mileage and does not accord with an actual situation, and meanwhile, in a related real-time second mileage, the second mileage does not exceed a preset mileage and does not accord with the actual situation, so that the abnormal type of the second subclass is identified. In an alternative embodiment, the preset mileage is 0.
When the second mileage exceeds the preset mileage and matches the first mileage, the identifying unit 203 identifies an abnormality type in which the abnormality belongs to a third subclass divided under the first main class. In one aspect of this embodiment, if the difference between the second mileage and the first mileage is less than or equal to the preset percentage, the second mileage is considered to be matched with the first mileage. As an alternative embodiment, the preset percentage is 10%. The logic to identify the exception type of the third subclass is: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is larger than a nominal mileage and is not in line with an actual situation, and meanwhile, in a related real-time second mileage, the difference between the real-time second mileage and the first mileage is not much, so that the abnormal type of the second subclass is identified.
When the second mileage exceeds the preset mileage and does not match the first mileage, the identifying unit 203 identifies an abnormality type in which an abnormality belongs to a fourth subclass divided under the first main class. In one aspect of this embodiment, if the difference between the second mileage and the first mileage is greater than a predetermined percentage, the second mileage is considered not to match the first mileage. As an alternative embodiment, the preset percentage is 10%. The logic to identify the exception type of the fourth subclass is: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is larger than a nominal mileage and does not accord with an actual condition, and meanwhile, in a related real-time second mileage, the real-time second mileage is more different from the first mileage and does not accord with the actual condition, so that the abnormal type of the second subclass is identified.
The abnormality recognition system for the vehicle battery of the embodiment provides a way of specifically dividing the abnormality type of the target vehicle battery based on the numerical values and the numerical value relationship of the first mileage and the second mileage, and improves the accuracy of abnormality recognition for the vehicle battery. And by the division and identification of subclasses, the abnormal reasons can be accurately positioned subsequently.
Example 10
In addition to embodiment 8, the present embodiment provides an abnormality recognition system for a vehicle battery.
In specific implementation, first, the selection unit 201 selects a target vehicle battery to be subjected to abnormality identification.
Then, the obtaining unit 202 obtains a first accumulated driving mileage recorded by a battery management system of the target vehicle battery when the target vehicle battery is mounted on the electric vehicle, and a second accumulated driving mileage recorded by the battery management system of the target vehicle battery when the target vehicle battery is dismounted from the electric vehicle; determining a first mileage according to the first accumulated mileage and the second accumulated mileage; acquiring a third accumulated driving mileage recorded by a vehicle control unit of the electric vehicle when the target vehicle battery is mounted on the electric vehicle, and a fourth accumulated driving mileage recorded by the vehicle control unit of the electric vehicle when the target vehicle battery is dismounted from the electric vehicle; and determining the second mileage according to the third accumulated mileage and the fourth accumulated mileage. The battery management system and the vehicle control unit respectively record the accumulated travel mileage.
Next, when the first mileage does not exceed the preset mileage, the identification unit 203 identifies the target vehicle battery as abnormal; when the first mileage is a preset mileage, the identifying unit 203 identifies an abnormal type of the abnormality belonging to the second main class, and the identifying unit 203 identifies a subclass of the abnormality belonging to the second main class according to the second mileage; when the first mileage is less than the preset mileage, the identifying unit 203 identifies an abnormality type where the abnormality belongs to the third main class, and the identifying unit 203 identifies a sub-class where the abnormality belongs to the third main class according to the second mileage.
The way that the identifying unit 203 identifies the subclasses under the second main class is specifically as follows:
when the first mileage is a preset mileage and the second mileage is empty, the identifying unit 203 identifies an abnormality type that the abnormality belongs to the first subclass divided under the second main class. The identification logic is as follows: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is absent, the actual situation is not met, and meanwhile, real-time vehicle information of a vehicle is not related, so that the abnormal type of a first sub-class divided under a second main class is identified. In an alternative embodiment, the preset mileage is 0.
When the first mileage is the preset mileage and the second mileage does not exceed the preset mileage, the identifying unit 203 identifies an abnormality type in which the abnormality belongs to a second sub-class divided under the second main class. The identification logic is as follows: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is equal to a preset mileage and is not in accordance with an actual condition, and meanwhile, in a related real-time second mileage, a second mileage is less than or equal to the preset mileage and is not in accordance with the actual condition, so that the abnormal type of a second subclass divided under a second main class is identified.
When the first mileage is a preset mileage and the second mileage exceeds the preset mileage, the identifying unit 203 identifies an abnormality type in which the abnormality belongs to a third subclass divided under the second main class. The identification logic is as follows: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is equal to a preset mileage and is not in line with an actual situation, and a real-time second mileage related to the first mileage is normal, so that an abnormal type which is abnormal and belongs to a third subclass divided under a second main class is identified.
The specific way in which the identifying unit 203 identifies the subclass to which the abnormality belongs under the third main class according to the second mileage is as follows:
when the first mileage is less than the preset mileage and the second mileage is empty, the identifying unit 203 identifies an abnormality type in which the abnormality belongs to the first subclass divided under the third main class. The identification logic is as follows: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is smaller than a preset mileage, the record is not in line with the actual situation, and meanwhile, real-time vehicle information of the vehicle cannot be correlated, so that the abnormal type of the first subclass divided under the third main class is identified. In an alternative embodiment, the preset mileage is 0.
When the first mileage is less than the preset mileage and the second mileage does not exceed the preset mileage, the identifying unit 203 identifies an abnormality type in which the abnormality belongs to a second subclass divided under a third main class. The identification logic is as follows: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is smaller than a preset mileage and is not in accordance with an actual condition, and meanwhile, in a related real-time second mileage, a second mileage is smaller than or equal to the preset mileage and is not in accordance with the actual condition, so that the abnormal type of a second subclass divided under a third main class is identified.
When the first mileage is less than the preset mileage and the second mileage exceeds the preset mileage, the identifying unit 203 identifies an abnormality type in which the abnormality belongs to a third subclass divided under a third main class. The identification logic is as follows: in a certain battery replacement process of the battery, order data and a battery replacement record exist, a first mileage in the record is smaller than a preset mileage and is not in line with an actual situation, and a real-time second mileage related to the first mileage is normal, so that an abnormal type which is abnormal and belongs to a third subclass divided under a third main class is identified.
The abnormality identification system for the vehicle battery provides a mode of dividing the abnormality type of the target vehicle battery based on the numerical values and the numerical value relationship of the first mileage and the second mileage, and improves the accuracy of the abnormality identification of the vehicle battery. And by the division and identification of subclasses, the abnormal reasons can be accurately positioned subsequently.
The abnormity identification system of the vehicle battery of the embodiment identifies various abnormal types of the battery by collecting battery data and automobile data in the running process of the automobile, combining the attribute data of the battery and comprehensively considering a plurality of data, covers the abnormity of the battery and the abnormity appearing in the using process, timely reflects the real condition of the battery and provides timely and effective reference information for enterprise operation.
Example 11
This embodiment provides an abnormality recognition system for a vehicle battery on the basis of any one of embodiments 8 to 10. Referring to fig. 6, the abnormality recognition system for a vehicle battery further includes a processing unit 204.
The processing unit 204 performs processing matching the abnormality type to which the abnormality belongs on the target vehicle battery.
In specific implementation, the processing unit 204 checks the vehicle battery according to the type of the abnormality to which the vehicle battery belongs, so as to perform evaluation, and discard or transfer the vehicle battery to an energy storage battery or the like.
The abnormity identification system of the vehicle battery carries out processing matched with the abnormity type of the target vehicle battery, so that the target vehicle battery can be reasonably processed, abnormity can be timely processed, safety problems can be avoided, and resource waste can be avoided by making the best use of things.
Example 12
This embodiment provides an abnormality recognition system for a vehicle battery on the basis of any one of embodiments 8 to 10. Referring to fig. 7, the abnormality recognition system for a vehicle battery further includes an optimization unit 205.
The optimization unit 205 optimizes an algorithm for recording the accumulated traveled distance by the battery management system according to the type of the abnormality to which the abnormality belongs, so as to replace or verify the accumulated traveled distance recorded by the vehicle control unit for resource transfer during battery replacement by using the accumulated traveled distance recorded by the battery management system.
The abnormality recognition system for the vehicle battery optimizes the algorithm for recording the accumulated traveled distance by the battery management system according to the type of the abnormality to which the abnormality belongs, and can improve the accuracy for recording the accumulated traveled distance by the battery management system, so that the accumulated traveled distance recorded by the battery management system can be directly adopted when the traveled distance needs to be used in data processing such as resource transfer during subsequent battery replacement, or the accumulated traveled distance recorded by the battery management system can be adopted to check the accumulated traveled distance recorded by the whole controller, the dependence on data provided by an external third party is reduced, the cost is reduced, and the safety and the privacy of the data can be guaranteed.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.

Claims (11)

1. An abnormality recognition method for a vehicle battery, characterized by comprising the steps of:
selecting a target vehicle battery to be subjected to abnormal recognition;
acquiring a first mileage corresponding to a battery management system of the target vehicle battery and a second mileage corresponding to a vehicle control unit of the electric vehicle loaded with the target vehicle battery;
and identifying whether the target vehicle battery is abnormal according to the first mileage and the second mileage.
2. The method for identifying an abnormality of a vehicle battery according to claim 1, wherein the acquiring a first mileage corresponding to a battery management system of the target vehicle battery and a second mileage corresponding to a vehicle control unit of an electric vehicle on which the target vehicle battery is mounted includes:
acquiring a first accumulated driving mileage recorded by a battery management system of the target vehicle battery when the target vehicle battery is mounted on the electric vehicle, and a second accumulated driving mileage recorded by the battery management system of the target vehicle battery when the target vehicle battery is dismounted from the electric vehicle;
determining a first mileage according to the first accumulated mileage and the second accumulated mileage;
acquiring a third accumulated driving distance recorded by a vehicle control unit of the electric vehicle when the target vehicle battery is mounted on the electric vehicle, and acquiring a fourth accumulated driving distance recorded by the vehicle control unit of the electric vehicle when the target vehicle battery is dismounted from the electric vehicle;
determining a second mileage according to the third accumulated mileage and the fourth accumulated mileage;
and the battery management system and the vehicle control unit respectively record the accumulated driving mileage.
3. The abnormality recognition method for the vehicular battery according to claim 2, wherein the recognizing whether the target vehicular battery is abnormal or not based on the first mileage and the second mileage includes:
when the first mileage exceeds a nominal mileage of the target vehicular battery, identifying the target vehicular battery as abnormal, and the abnormality belongs to an abnormality type of a first main class;
identifying a sub-class to which the anomaly belongs under the first main class according to the second mileage.
4. The abnormality recognition method for a vehicular battery according to claim 3, wherein said recognizing a subclass to which the abnormality belongs under the first main class based on the second mileage includes:
when the second mileage is empty, identifying the abnormality type of the abnormality belonging to a first subclass divided under the first main class;
when the second mileage does not exceed a preset mileage, identifying the abnormality type of the abnormality belonging to a second subclass divided under the first main class;
when the second mileage exceeds a preset mileage and is matched with the first mileage, identifying the abnormality type of the abnormality belonging to a third subclass divided under the first main class;
and when the second mileage exceeds a preset mileage and is not matched with the first mileage, identifying the exception type of the exception belonging to a fourth subclass divided under the first main class.
5. The abnormality recognition method for the vehicular battery according to claim 3, wherein the recognizing whether the target vehicular battery is abnormal or not based on the first mileage and the second mileage further comprises:
when the first mileage does not exceed a preset mileage, identifying that the target vehicle battery is abnormal;
when the first mileage is the preset mileage, identifying an abnormal type of the abnormality belonging to a second main class, and identifying a subclass of the abnormality belonging to the second main class according to the second mileage;
and when the first mileage is less than the preset mileage, identifying an abnormal type of the abnormality belonging to a third main class, and identifying a subclass of the abnormality belonging to the third main class according to the second mileage.
6. The method for identifying an abnormality of a vehicle battery according to claim 5, wherein the identifying an abnormality type of the abnormality belonging to a second main class when the first mileage is the preset mileage, and identifying a sub-class to which the abnormality belongs under the second main class according to the second mileage includes:
when the first mileage is the preset mileage and the second mileage is empty, identifying the abnormality type of the abnormality belonging to a first subclass divided under the second main class;
when the first mileage is the preset mileage and the second mileage does not exceed the preset mileage, identifying an abnormality type of the abnormality belonging to a second subclass divided under the second main class;
when the first mileage is the preset mileage and the second mileage exceeds the preset mileage, identifying an abnormality type of the abnormality belonging to a third subclass divided under the second main class;
when the first mileage is less than the preset mileage, identifying an exception type that the exception belongs to a third main class, and identifying a subclass that the exception belongs to the third main class according to the second mileage includes:
when the first mileage is smaller than the preset mileage and the second mileage is empty, identifying the abnormality type of the abnormality belonging to a first subclass divided under the third main class;
when the first mileage is less than the preset mileage and the second mileage does not exceed the preset mileage, identifying an abnormality type of the abnormality belonging to a second subclass divided under the third main class;
and when the first mileage is less than the preset mileage and the second mileage exceeds the preset mileage, identifying the abnormality type of the abnormality belonging to a third subclass divided under the third main class.
7. The abnormality recognition method for the vehicle battery according to any one of claims 3 to 6, characterized in that the abnormality recognition method for the vehicle battery further comprises:
and processing matched with the abnormality type of the abnormality is carried out on the target vehicle battery.
8. The abnormality recognition method for the vehicle battery according to any one of claims 3 to 6, characterized in that the abnormality recognition method for the vehicle battery further comprises:
and optimizing an algorithm for recording the accumulated traveled distance by the battery management system according to the abnormal type of the abnormality so as to replace or verify the accumulated traveled distance recorded by the vehicle control unit for resource transfer during battery replacement.
9. An abnormality recognition system for a vehicle battery is characterized by comprising a selection unit, an acquisition unit and a recognition unit;
the selection unit is used for selecting a target vehicle battery to be subjected to abnormity identification;
the acquisition unit is used for acquiring a first mileage corresponding to a battery management system of the target vehicle battery and a second mileage corresponding to a vehicle control unit of the electric vehicle loaded with the target vehicle battery;
the identification unit is used for identifying whether the target vehicle battery is abnormal or not according to the first mileage and the second mileage.
10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the abnormality recognition method for a vehicle battery according to any one of claims 1 to 8 when executing the computer program.
11. A computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing the steps of the abnormality recognition method for a vehicle battery according to any one of claims 1 to 8.
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