CN111025159B - Method and device for detecting abnormality of electric vehicle battery, intelligent device and storage medium - Google Patents
Method and device for detecting abnormality of electric vehicle battery, intelligent device and storage medium Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
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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
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
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- 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
Abstract
The application discloses an electric vehicle battery abnormity detection method, an electric vehicle battery abnormity detection device, intelligent equipment and a storage medium, wherein the method comprises the following steps: acquiring charging data of the electric vehicle; determining whether the charging data has preset charging curve characteristics, wherein the preset charging curve characteristics comprise pulses with amplitudes larger than or equal to a preset amplitude threshold value, the number of the pulses is larger than or equal to a preset number threshold value, or the ratio of the pulses in the total number of pulses in a set period of a charging stage reaches a preset percentage; and if the charging data does not have the preset charging curve characteristic, judging that the battery of the electric vehicle is abnormal. The application can realize the detection of the abnormity of the electric vehicle battery, and can timely know whether the electric vehicle battery is abnormal or not, thereby reducing the potential safety hazard of the charging process and improving the safety of the charging process of the electric vehicle.
Description
Technical Field
The application belongs to the technical field of electric vehicles, and particularly relates to a method and a device for detecting battery abnormity of an electric vehicle, intelligent equipment and a storage medium.
Background
With the continuous development and progress of society, the application of the electric vehicle is more and more extensive. In the field of transportation, motorization of vehicles has gradually become a trend. At present, the electric motor car user can use the electric pile of filling of charging station to charge for the electric motor car, and after accomplishing corresponding order payment of charging, fill the corresponding socket of electric pile and will energize, and at this moment, the electric motor car user can be connected to the corresponding socket of filling electric pile with the electric motor car through adapter, the charging wire of electric motor car to charge to the electric motor car.
In the charging process of the electric vehicle, the charging safety is very important. The existing electric vehicle charging pile is only provided with a charging socket. The battery of the electric vehicle is generally placed in the electric vehicle, and the user of the electric vehicle generally cannot see the battery, and cannot judge whether the battery is abnormal even if the user sees the battery occasionally. In addition, the electric motor car user generally does not care about the battery health status of electric motor car, generally does not detect before charging to the electric motor car, and direct access electric motor car fills electric pile and charges. Therefore, if the abnormity of the battery of the electric vehicle cannot be found in time, potential safety hazards can be brought to the charging process of the electric vehicle.
Disclosure of Invention
The embodiment of the application provides an electric vehicle battery abnormity detection method, device, intelligent equipment and storage medium, and aims to solve the problems that in the prior art, an electric vehicle user cannot timely and effectively find abnormity of an electric vehicle battery, and potential safety hazards can be brought to the charging process of an electric vehicle.
In a first aspect, an embodiment of the present application provides an electric vehicle battery abnormality detection method, including:
acquiring charging data of the electric vehicle;
determining whether the charging data has preset charging curve characteristics, wherein the preset charging curve characteristics comprise pulses with amplitudes larger than or equal to a preset amplitude threshold value, the number of the pulses is larger than or equal to a preset number threshold value, or the ratio of the pulses in the total number of pulses in a set period of a charging stage reaches a preset percentage;
and if the charging data does not have the preset charging curve characteristic, judging that the battery of the electric vehicle is abnormal.
In a possible implementation manner of the first aspect, the pulse includes a first pulse and a second pulse, and the step of determining whether the charging data has a preset charging curve characteristic includes:
determining the number of the first pulses and the number of the second pulses in a preset threshold interval, wherein the first pulses are pulses with a first pulse threshold equal to a first preset current threshold, and the second pulses are pulses with a second pulse threshold equal to a first preset voltage threshold;
if the sum of the number of the first pulses and the number of the second pulses is greater than or equal to a preset pulse total number threshold value, determining that the charging data has a preset charging curve characteristic;
and if the sum of the number of the first pulses and the number of the second pulses is zero, determining that the charging data does not have the preset charging curve characteristic.
In a possible implementation manner of the first aspect, the step of determining the number of the first pulses and the number of the second pulses includes:
performing differential calculation on the charging current data to obtain a first differential matrix;
determining the number of first differential matrixes in a preset current threshold interval in the first differential matrixes;
determining the number of the first pulses according to the number of the first differential matrixes;
performing differential calculation on the charging voltage data to obtain a second differential matrix;
determining the number of second differential matrixes in a preset voltage threshold interval in the second differential matrixes;
and determining the number of the second pulses according to the number of the second difference matrixes.
In one possible implementation manner of the first aspect, the method for detecting abnormality of a battery of an electric vehicle further includes:
if the sum of the number of the first pulses and the number of the second pulses is greater than or equal to a preset pulse total number threshold value, determining that the preset charging curve characteristic of the charging data is a first characteristic type;
if the number of the first pulses is greater than or equal to a first preset pulse number threshold, or the number of the second pulses is greater than or equal to a first preset pulse number threshold, determining that the preset charging curve characteristic of the charging data is a second characteristic type;
if the proportion of the first pulse in the total pulse number in the set period of the charging stage belongs to a first preset percentage threshold interval, or if the proportion of the second pulse in the total pulse number in the set period of the charging stage belongs to the first preset percentage threshold interval, determining that the preset charging curve characteristic of the charging data is a third characteristic type;
and if the ratio of the first pulse to the total pulse number in the set period of the charging stage is greater than or equal to a second preset percentage threshold, or the ratio of the second pulse to the total pulse number in the set period of the charging stage is greater than or equal to the second preset percentage threshold, determining that the preset charging curve characteristic of the charging data is a fourth characteristic type.
In a possible implementation manner of the first aspect, before the step of determining whether the charging data has a preset charging curve characteristic, the method includes:
acquiring the charging time of the electric vehicle;
and if the charging time is greater than or equal to a preset charging time threshold, executing the step of determining whether the charging data has the characteristics of a preset charging curve.
In a possible implementation manner of the first aspect, before the step of acquiring the charging data of the electric vehicle, the method further includes:
acquiring charging order data of a user of the electric vehicle;
detecting whether the electric vehicle has been analyzed according to the charging order data;
and if the electric vehicle is not analyzed, executing the step of acquiring the charging data of the electric vehicle.
In one possible implementation manner of the first aspect, after the step of determining that there is an abnormality in the battery of the electric vehicle, the method further includes:
generating prompt information and/or suggestion information of battery abnormity according to the charging data;
and sending the prompt information and/or the suggestion information to a mobile terminal of a user of the electric vehicle to instruct the mobile terminal to present the prompt information and/or the suggestion information to the user of the electric vehicle.
In a second aspect, an embodiment of the present application provides an apparatus for detecting abnormality of a battery of an electric vehicle, including:
the data acquisition unit is used for acquiring charging data of the electric vehicle;
the characteristic determining unit is used for determining whether the charging data has preset charging curve characteristics, wherein the preset charging curve characteristics comprise pulses with amplitudes larger than or equal to a preset amplitude threshold value, the number of the pulses is larger than or equal to a preset number threshold value, or the ratio of the pulses in the total pulse number in a set period of a charging stage reaches a preset percentage;
and the battery detection unit is used for judging that the battery of the electric vehicle is abnormal if the charging data does not have the preset charging curve characteristic.
In a third aspect, an embodiment of the present application provides a smart device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method for detecting abnormality of an electric vehicle battery according to any one of the above first aspects when executing the computer program.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for detecting abnormality of an electric vehicle battery according to any one of the first aspect is implemented.
In a fifth aspect, the present application provides a computer program product, when the computer program product runs on a smart device, the smart device is caused to execute the method for detecting abnormality of an electric vehicle battery according to any one of the first aspect.
In the embodiment of the application, whether the battery of the electric vehicle is abnormal or not is judged by acquiring the charging data of the electric vehicle and determining whether the charging data has the preset charging curve characteristic or not, the preset charging curve characteristic comprises pulses with amplitudes larger than or equal to the preset amplitude threshold value, the number of the pulses is larger than or equal to the preset number threshold value, or the ratio of the pulses in the total pulse number in the set period of the charging stage reaches the preset percentage, if the charging data does not have the preset charging curve characteristic, the battery of the electric vehicle is judged to be abnormal, so that the abnormality of the battery of the electric vehicle can be found in time, the potential safety hazard of the charging process of the electric vehicle is reduced, and the safety of the charging process of the electric vehicle is further improved.
It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic block diagram of a structure of an electric vehicle charging system provided in an embodiment of the present application;
fig. 2 is a schematic block diagram of a flow of an electric vehicle battery abnormality detection method according to an embodiment of the present application;
fig. 3 is another schematic flow chart of a method for detecting an abnormality of a battery of an electric vehicle according to an embodiment of the present application;
fig. 4 is a schematic block diagram of a specific flow of step S202 provided in the embodiment of the present application;
fig. 5 is a schematic block diagram of another flow chart of an abnormal battery detection method for an electric vehicle according to an embodiment of the present application;
fig. 6 is a block diagram illustrating a structure of an abnormality detection device for a battery of an electric vehicle according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an intelligent device provided in an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
The following first describes a system architecture and application scenarios that may be involved in the embodiments of the present application.
Referring to fig. 1, a schematic block diagram of a structure of an electric vehicle charging system provided in an embodiment of the present application is shown, where the electric vehicle includes an electric vehicle 1, a charging station 2, a user's mobile terminal 3, and a server 4, where the charging station 2 includes a plurality of charging piles, and each charging pile includes a plurality of charging sockets. Corresponding APP can be installed in the mobile terminal 3 of the user to realize corresponding functions in the charging process, such as code scanning payment, charging order generation, charging order uploading and the like. The mobile terminal 3 of the user may be, but is not limited to, a mobile phone, a smart wearable device, a tablet computer, or the like. The electric vehicle 1 may be any type of electric vehicle, such as an electric two-wheeled vehicle or an electric four-wheeled vehicle.
For example, the electric vehicle charging process based on the electric vehicle charging system may include: after a user drives the electric vehicle to arrive at a charging station, scanning the two-dimensional code on the charging pile through a mobile phone to generate a charging order; after the corresponding socket of the charging pile supplies power, a user can connect the electric vehicle to the charging pile through the plug and the power adapter to start charging the electric vehicle; when the charging time reaches the preset charging time, the charging fee reaches the prepayment fee or the electric vehicle is full, the charging plug can be unplugged, and a charging process is completed.
In the charging process of the electric vehicle, the charging pile can record the charging data of the electric vehicle in real time, report the charging data to the charging pile management platform, and store the charging data to the database. The database may be a MongoDB non-relational database, and the charging data generally includes charging current data, charging voltage data, charging power data, and the like. In addition, the charging order data of the user can be uploaded to the server for storage.
Based on the system architecture shown in fig. 1, the server may analyze charging data corresponding to a certain charging order of a certain user through the recorded data to determine whether the battery of the electric vehicle of the user is abnormal, and if it is analyzed that the battery of the electric vehicle of the certain user is abnormal, prompt information may be generated to warn the user about the health condition of the battery of the electric vehicle. For example, after a user completes a charging order, the server acquires telemetering data of a charging pile corresponding to the electric vehicle according to the charging order of the user so as to acquire charging data of the electric vehicle of the user during charging, and then judges whether the charging data has a preset charging curve characteristic or not, and if not, the battery of the user is judged to be abnormal; and then, the server generates abnormal prompt information according to the preset charging curve characteristics of the charging data, the abnormal prompt information is sent to a user mobile phone, and the abnormal prompt information is displayed to the user through the user mobile phone APP, so that the user can timely know the battery health condition of the electric vehicle.
Of course, the embodiments of the present application may not be based on the above system architecture or application scenario, and the purposes of the embodiments of the present application may also be achieved.
The technical solutions provided in the embodiments of the present application will be described below by specific embodiments.
Fig. 2 shows an implementation flow of an electric vehicle battery abnormality detection method provided by an embodiment of the present application, where the method flow includes steps S201 to S203. The specific realization principle of each step is as follows:
step S201: and acquiring charging data of the electric vehicle.
In the embodiment of the present application, the charging data generally refers to data of a charging process of the electric vehicle. Specifically, based on the system architecture or the application scenario shown in fig. 1, a charging process refers to a process corresponding to one charging order, that is, the charging data is charging data corresponding to one charging order, and at this time, the charging data is reported by the charging pile. In the embodiment of the application, the telemetering data uploaded by the corresponding charging pile can be acquired through charging order data of a user of the electric vehicle, wherein the telemetering data comprises charging data, and the charging data comprises charging current data, charging voltage data, charging power data and the like. The charging order data comprises information such as a user unique identifier, charging start time, charging end time, a charging pile number and the like. Specifically, after a user completes a charging order, the server searches telemetering data reported by a corresponding charging pile from a database according to information of a user unique identifier, charging start time, charging end time, a charging pile number and the like in the charging order, and then searches charging data corresponding to the user unique identifier, the charging start time, the charging end time and the like from the telemetering data so as to obtain charging data corresponding to the current charging of the user.
It should be noted that the technical solution of the embodiment of the present application may not be applied to the system architecture or the application scenario shown in fig. 1, and in this case, the charging data may be charging data recorded when an electric vehicle is charged by a certain charging device.
As an embodiment of the present application, as shown in fig. 3, before the step S201, the method for detecting abnormality of a battery of an electric vehicle further includes: ,
a1: charging order data of a user of the electric vehicle is acquired. Specifically, after a user generates a charging order through a mobile phone or other terminal equipment, the user terminal equipment uploads the charging order to the server. The charging order data comprises but is not limited to information such as a user ID, an order electric quantity, a user mobile phone number, an order duration, an equipment ID of a charging pile, a socket serial number of the charging pile, an order ending reason code, an equipment type of the charging pile, an order starting time, an order ending time, a site ID of the charging pile, a site name of the charging pile, a box delivery number of the charging pile and the like.
A2: and detecting whether the electric vehicle is analyzed or not according to the charging order data. Specifically, after receiving the charging order data of the user, the server determines whether the electric vehicle of the user has been subjected to intelligent analysis of the overcharge curve based on unique identification information such as a user ID of the charging order data, and if the electric vehicle of the user has been analyzed, generates prompt information according to a previous analysis result, and sends the prompt information to the user terminal device. And if the order is not analyzed, acquiring the charging data corresponding to the order for intelligent analysis.
As an embodiment, after an intelligent analysis of an overcharge curve is performed on an electric vehicle of a user, recording and marking the charge order data, establishing a corresponding relationship between the charge order data and the recording and marking, and if the record and marking exist in the charge order data, determining that the electric vehicle has performed the intelligent analysis of the overcharge curve; and if the record mark does not exist in the charging order data, determining that the intelligent analysis of the overcharge curve of the electric vehicle is not carried out.
A3: if the electric vehicle is not analyzed, the step S201 is executed.
Specifically, the server searches corresponding telemetering data from a database of the charging pile management platform according to a user ID, charging pile information, charging site information, order start and/or end time and the like in the charging order data, wherein the telemetering data generally comprises charging current data and charging voltage data.
Optionally, the record mark includes a mark time, if the record mark exists in the charging order data, a duration between the mark time and the current time is determined, and if the duration is greater than or equal to a preset duration, it is determined that the electric vehicle needs to perform intelligent charging curve analysis again, so that it is ensured that the intelligent charging curve analysis can be effectively performed on the electric vehicle of the user when the battery of the electric vehicle is replaced by the user.
Step S202: and determining whether the charging data has preset charging curve characteristics, wherein the preset charging curve characteristics comprise pulses with amplitudes larger than or equal to a preset amplitude threshold value, the number of the pulses is larger than or equal to a preset number threshold value, or the ratio of the pulses in the total number of pulses in a set period of a charging stage reaches a preset percentage.
Specifically, the preset charging curve feature refers to a feature calibrated manually in advance, that is, a feature summarized according to a charging curve characteristic of each charging data by analyzing charging data of the electric vehicle in advance. In the embodiment of the present application, the preset charging curve is characterized by an "oscillating" charging curve. When certain charging data has the characteristics of the preset charging curve, judging that the charging current curve corresponding to the charging data is an oscillating charging curve; when the charging current curve of the charging data is judged to be an 'oscillation' type charging curve, the electric vehicle battery corresponding to the charging data is presumed to be normal; when certain charging data does not have the preset charging curve characteristic, the fact that the electric vehicle battery corresponding to the charging data is abnormal is presumed. That is, in the embodiment of the present application, determining whether the charging curve corresponding to the charging data is an "oscillating" charging curve is equivalent to determining whether the charging data has a preset charging curve characteristic. It should be noted that, in a specific application, the charging data does not need to be converted into a charging curve, but the intelligent analysis may be performed based on the charging data.
In this embodiment of the application, the preset amplitude threshold may be 0.3A, the preset number threshold may be 10, and the preset percentage may be 20%. For example, when the amplitude of the pulse in the charging current curve corresponding to the charging data is greater than or equal to 0.3A, and the number of pulses with the amplitude greater than or equal to 0.3A is 10, it may be determined that the charging data has the preset charging curve characteristic, and the charging curve corresponding to the charging data is of an "oscillating" type.
As an embodiment of the present application, the pulses include a first pulse and a second pulse, and as shown in fig. 4, the step S202 specifically includes:
b1: and determining the number of the first pulses and the number of the second pulses in a preset threshold interval, wherein the first pulses are pulses with a first pulse threshold equal to a first preset current threshold, and the second pulses are pulses with a second pulse threshold equal to a first preset voltage threshold. Illustratively, the first pulse is a small pulse of the charging current data, and the second pulse is a large pulse of the charging voltage data. If the charging data is charging current data, the small pulse threshold is equal to the current small wave threshold (0.29A), and the large pulse threshold is equal to the current large wave threshold (0.5A); if the charging data is charging voltage data, the small pulse threshold is equal to the voltage small wave threshold (10V) and the large pulse threshold is equal to the voltage large wave threshold (20V). Thus, the first pulse is a current pulse with a small pulse threshold equal to a first preset current threshold, which may be 0.29A; the second pulse is a voltage pulse with a large pulse threshold equal to a second preset voltage threshold, and the first preset voltage threshold may be 20V.
Specifically, the charging data packet includes charging current data and charging voltage data, the preset threshold interval includes a preset current threshold interval and a preset voltage threshold interval, and the step B1 specifically includes:
b11: and carrying out differential calculation on the charging current data to obtain a first differential matrix. Specifically, the charging current data is composed of current data of multiple points, and the current data of the previous point is subtracted from the current data of the next point in sequence according to the time sequence to obtain current data after difference calculation. Optionally, the charging current data may be median filtered before the difference calculation to make the charging data smoother.
B12: and determining the number of the first differential matrixes in a preset current threshold interval in the first differential matrixes. The predetermined current threshold interval may be [0.29A, 0.5A ].
B13: and determining the number of the first pulses according to the number of the first difference matrixes. In this embodiment, the number of the first differential matrices is the number of the first pulses.
B14: and carrying out differential calculation on the charging voltage data to obtain a second differential matrix. Specifically, the charging voltage data is composed of multi-point voltage data, and the voltage data of the previous point is subtracted from the voltage data of the next point in sequence according to the time sequence to obtain voltage data after difference calculation. Optionally, the charging voltage data may be median filtered before the differential calculation to make the charging data smoother.
B15: and determining the number of second differential matrixes in a preset voltage threshold interval in the second differential matrixes. The preset voltage threshold interval may be [10V, 20V ].
B16: and determining the number of the second pulses according to the number of the second difference matrixes. In this embodiment, the number of the second differential matrices is the number of the second pulses.
B2: and if the sum of the number of the first pulses and the number of the second pulses is greater than or equal to a preset pulse total number threshold value, determining that the charging data has a preset charging curve characteristic.
B3: and if the sum of the number of the first pulses and the number of the second pulses is zero, determining that the charging data does not have the preset charging curve characteristic. For example, if the sum of the number of the differential matrixes of the first pulse in the interval [0.29A, 0.5A ] and the number of the differential matrixes of the second pulse in the interval [10V, 20V ] is greater than or equal to 5, it is determined that the charging data has the preset charging curve characteristic, that is, the charging curve for the charging data pair is an "oscillating" type charging curve; and if the sum of the number of the differential matrixes of the first pulse in the interval [0.29A, 0.5A ] and the number of the differential matrixes of the second pulse in the interval [10V, 20V ] is zero, determining that the charging data does not have the preset charging curve characteristic, namely that the charging data is not an oscillating charging curve for the charging curve.
In the embodiment of the present application, the number of pulses of the charging curve corresponding to the charging data may be calculated by using a differential matrix. Specifically, the charging current data is subjected to differential calculation to obtain a differential matrix, the number of the differential matrix in a preset current threshold interval in the differential matrix is determined, and finally the number of the pulses is determined according to the number of the differential matrix. Optionally, before performing the difference calculation, the charge data may be subjected to median filtering to make the charge data smoother, and then the charge data after the median filtering may be subjected to the difference calculation. Of course, the median filtering process for the charging data is an optional operation and is not necessary.
Optionally, if the number of the first pulses is greater than or equal to a first preset pulse number threshold, or the number of the second pulses is greater than or equal to a first preset pulse number threshold, it is determined that the charging data has a preset charging curve characteristic.
Optionally, if the percentage of the first pulse in the total pulse number in the charging phase setting period belongs to a first preset percentage threshold interval, or if the percentage of the second pulse in the total pulse number in the charging phase setting period belongs to the first preset percentage threshold interval, it is determined that the charging data has a preset charging curve characteristic.
In fact, a complete charging process generally includes a first phase, a second phase and a third phase, wherein the first phase refers to a steady charging of current and voltage, the second phase refers to a descending charging with a constant voltage and a small current, and the third phase refers to a trickle charging with a constant voltage and a small current until the current voltage is zero. In the embodiment of the present application, whether the charging data of the second stage is the charging data of the first stage or not may be determined in the above manner.
As an embodiment of the present application, as shown in fig. 5, before step S202, the method further includes:
c1: and acquiring the charging time of the electric vehicle.
C2: and if the charging time is greater than or equal to a preset charging time threshold, executing the step of determining whether the charging data has the characteristics of a preset charging curve. The preset charging time threshold is 30 minutes.
In this embodiment, in order to make intelligent analysis of charging data of an electric vehicle more accurate and reliable, the charging data of the electric vehicle with the charging duration greater than or equal to the preset charging duration threshold is intelligently analyzed, that is, the charging data meeting the quantity required by the intelligent analysis is acquired, so that the accuracy of the intelligent analysis is improved.
Step S203: and if the charging data does not have the preset charging curve characteristic, judging that the battery of the electric vehicle is abnormal.
Specifically, by intelligently analyzing the charging data, if the charging data has the characteristics of the preset charging curve, or the charging current curve of the charging data is an oscillating curve, the battery of the electric vehicle is judged to be normal; and if the charging data does not have the preset charging curve characteristic, judging that the battery of the electric vehicle is possibly abnormal. The abnormality of the battery of the electric vehicle includes, but is not limited to, battery aging, battery thermal runaway.
In the embodiment of the application, whether the battery of the electric vehicle is abnormal or not is judged by acquiring the charging data of the electric vehicle and determining whether the charging data has the preset charging curve characteristic or not, the preset charging curve characteristic comprises pulses with amplitudes larger than or equal to the preset amplitude threshold value, the number of the pulses is larger than or equal to the preset number threshold value, or the ratio of the pulses in the total pulse number in the set period of the charging stage reaches the preset percentage, if the charging data does not have the preset charging curve characteristic, the battery of the electric vehicle is judged to be abnormal or not, so that whether the battery of the electric vehicle is abnormal or not can be known in time, the potential safety hazard of the charging process of the electric vehicle is reduced, and the safety of the charging process of the electric vehicle.
As an embodiment of the present application, after the step of determining that there is an abnormality in the battery of the electric vehicle, the method further includes:
d1: and generating prompt information and/or suggestion information of battery abnormity according to the charging data. Optionally, a feature type of a preset charging curve feature of the charging data is determined, and prompt information and/or suggestion information of battery abnormity is generated according to the feature type.
Specifically, if the sum of the number of the first pulses and the number of the second pulses is greater than or equal to a preset pulse total number threshold, determining that the preset charging curve characteristic of the charging data is a first characteristic type; if the number of the first pulses is greater than or equal to a first preset pulse number threshold, or the number of the second pulses is greater than or equal to a first preset pulse number threshold, determining that the preset charging curve characteristic of the charging data is a second characteristic type; if the proportion of the first pulse in the total pulse number in the set period of the charging stage belongs to a first preset percentage threshold interval, or if the proportion of the second pulse in the total pulse number in the set period of the charging stage belongs to the first preset percentage threshold interval, determining that the preset charging curve characteristic of the charging data is a third characteristic type; and if the ratio of the first pulse to the total pulse number in the set period of the charging stage is greater than or equal to a second preset percentage threshold, or the ratio of the second pulse to the total pulse number in the set period of the charging stage is greater than or equal to the second preset percentage threshold, determining that the preset charging curve characteristic of the charging data is a fourth characteristic type. The first characteristic type is large and small oscillation, the second characteristic type is sparse oscillation, the third characteristic type is partial oscillation, and the fourth characteristic type is full oscillation.
Illustratively, the number of small pulses is the number of differential matrixes between current 0.29 and 0.5, the number of large pulses is the number of differential matrixes with voltage more than 20, the large pulses and the small pulses have no oscillation when the number of large pulses and the small pulses is 0, and the large oscillation and the small oscillation occur if the number of large pulses and the small pulses is more than or equal to 5; if the number of the small pulses is more than or equal to 4, the small pulses are sparse pulses, namely sparse oscillation, or if the number of the large pulses is more than or equal to 4, the large pulses are sparse pulses, namely sparse oscillation; if the proportion of the small pulse is 10% -30%, the small pulse is a part of small pulses, namely partial oscillation, or if the proportion of the large pulse is 10% -30%, the large pulse is a part of large pulses, namely partial oscillation; if the small pulse ratio is greater than or equal to 30%, the pulse is a full small pulse, namely the full oscillation, or if the large pulse ratio is greater than or equal to 30%, the pulse is a full large pulse, namely the full oscillation.
D2: and sending the prompt information and/or the suggestion information to a mobile terminal of a user of the electric vehicle to instruct the mobile terminal to present the prompt information and/or the suggestion information to the user of the electric vehicle.
Specifically, the server may generate a prompt or advice message if it is determined that there is an abnormality in the battery of the electric vehicle. The prompting information is used for prompting a user that the electric vehicle has abnormal batteries, and the suggestion information is a countermeasure which can be adopted by the user aiming at the abnormal batteries. For example, the server generates information including prompt information and advice information, which is specifically "intelligently analyze according to a charging curve that you change little in the amount of electricity although charging for a long time, presume that there is an abnormality in the battery, and advise you to repair or replace the battery". Further, the prompt message and/or the suggestion message of the battery abnormity is generated according to the characteristic type, so that the prompt message and/or the suggestion message can be more accurate and effective.
According to the embodiment of the application, the intelligent analysis is carried out on the electric vehicle charging data reported by the charging pile, whether the battery abnormality occurs in the electric vehicle battery of the user is judged, if the battery abnormality occurs, the prompt information is generated to warn the user, the user can timely learn and pay attention to the battery health condition of the electric vehicle, the potential safety hazard in the electric vehicle charging process is reduced or eliminated, and the safety of the electric vehicle charging process is improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 6 shows a block diagram of the electric vehicle battery abnormality detection apparatus provided in the embodiment of the present application, corresponding to the electric vehicle battery abnormality detection method described in the above embodiment, and only the relevant parts to the embodiment of the present application are shown for convenience of description.
Referring to fig. 6, the electric vehicle battery abnormality detection apparatus includes: a data acquisition unit 61, a feature determination unit 62, a battery detection unit 63, wherein:
a data acquisition unit 61 for acquiring charging data of the electric vehicle;
a characteristic determining unit 62, configured to determine whether the charging data has a preset charging curve characteristic, where the preset charging curve characteristic includes pulses with an amplitude greater than or equal to a preset amplitude threshold, and the number of the pulses is greater than or equal to a preset number threshold, or a ratio of the pulses to a total number of pulses in a set period of a charging phase reaches a preset percentage;
and the battery detection unit 63 is configured to determine that the battery of the electric vehicle is abnormal if the charging data does not have the preset charging curve characteristic.
Optionally, the pulses include a first pulse and a second pulse, and the characteristic determination unit 62 includes:
the pulse number determining module is used for determining the number of the first pulses and the number of the second pulses in a preset threshold interval, wherein the first pulses are pulses with a first pulse threshold value equal to a first preset current threshold value, and the second pulses are pulses with a second pulse threshold value equal to a first preset voltage threshold value;
a charging curve characteristic first determining module, configured to determine that the charging data has a preset charging curve characteristic if a sum of the number of the first pulses and the number of the second pulses is greater than or equal to a preset pulse total number threshold;
and the second charging curve characteristic determining module is used for determining that the charging data does not have the preset charging curve characteristic if the sum of the number of the first pulses and the number of the second pulses is zero.
Optionally, the feature determining unit 62 further includes:
and a third charging curve characteristic determining module, configured to determine that the charging data has a preset charging curve characteristic if the number of the first pulses is greater than or equal to a first preset pulse number threshold, or the number of the second pulses is greater than or equal to a first preset pulse number threshold.
And the charging curve characteristic fourth determining module is used for determining that the charging data has the preset charging curve characteristic if the proportion of the first pulse in the total pulse number in the charging stage set period belongs to a first preset percentage threshold interval, or if the proportion of the second pulse in the total pulse number in the charging stage set period belongs to the first preset percentage threshold interval.
Optionally, the charging data includes charging current data and charging voltage data, and the pulse number determining module specifically includes:
the first difference calculation submodule is used for carrying out difference calculation on the charging current data to obtain a first difference matrix;
the first matrix number determining submodule is used for determining the number of first differential matrixes in a preset current threshold interval in the first differential matrix;
the first pulse number determining submodule is used for determining the number of the first pulses according to the number of the first differential matrixes;
the second difference calculation submodule is used for carrying out difference calculation on the charging voltage data to obtain a second difference matrix;
the second matrix number determining submodule is used for determining the number of second differential matrixes in a preset voltage threshold interval in the second differential matrix;
and the second pulse number determining submodule is used for determining the number of the second pulses according to the number of the second difference matrixes.
Optionally, the electric vehicle battery abnormality detection apparatus further includes:
a first feature type determining unit, configured to determine that the preset charging curve feature of the charging data is a first feature type if a sum of the number of the first pulses and the number of the second pulses is greater than or equal to a preset pulse total threshold;
a second feature type determining unit, configured to determine that the preset charging curve feature of the charging data is a second feature type if the number of the first pulses is greater than or equal to a first preset pulse number threshold, or the number of the second pulses is greater than or equal to a first preset pulse number threshold;
a third feature type determining unit, configured to determine that the preset charging curve feature of the charging data is a third feature type if the percentage of the total pulse number of the first pulse in the charging stage set period belongs to a first preset percentage threshold interval, or if the percentage of the total pulse number of the second pulse in the charging stage set period belongs to the first preset percentage threshold interval;
a fourth feature type determining unit, configured to determine that the preset charging curve feature that the charging data has is a fourth feature type if an occupation ratio of the first pulse in the total pulse number in the charging phase setting period is greater than or equal to a second preset percentage threshold, or an occupation ratio of the second pulse in the total pulse number in the charging phase setting period is greater than or equal to the second preset percentage threshold.
Optionally, the electric vehicle battery abnormality detection apparatus further includes:
a charging duration acquisition unit for acquiring a charging duration of the electric vehicle;
and the charging duration verification unit is used for executing the step of determining whether the charging data has the characteristics of a preset charging curve or not if the charging duration is greater than or equal to a preset charging duration threshold.
Optionally, the electric vehicle battery abnormality detection apparatus further includes:
the order data acquisition unit is used for acquiring charging order data of a user of the electric vehicle;
the analysis detection unit is used for detecting whether the electric vehicle is analyzed or not according to the charging order data;
an execution unit, configured to execute the step of obtaining charging data of the electric vehicle if the electric vehicle is not analyzed.
Optionally, the electric vehicle battery abnormality detection apparatus further includes:
the information generating unit is used for generating prompt information and/or suggestion information of battery abnormity according to the charging data;
and the information presentation unit is used for sending the prompt information and/or the suggestion information to a mobile terminal of a user of the electric vehicle so as to instruct the mobile terminal to present the prompt information and/or the suggestion information to the user of the electric vehicle.
In the embodiment of the application, whether the battery of the electric vehicle is abnormal or not is judged by acquiring the charging data of the electric vehicle and determining whether the charging data has the preset charging curve characteristic or not, the preset charging curve characteristic comprises pulses with amplitudes larger than or equal to the preset amplitude threshold value, the number of the pulses is larger than or equal to the preset number threshold value, or the ratio of the pulses in the total pulse number in the set period of the charging stage reaches the preset percentage, if the charging data does not have the preset charging curve characteristic, the battery of the electric vehicle is judged to be abnormal, so that whether the battery of the electric vehicle is abnormal or not can be known in time, the potential safety hazard of the charging process of the electric vehicle is reduced, and the safety of the charging process of the electric vehicle is further improved.
The device for detecting the battery abnormity of the electric vehicle has the function of realizing the method for detecting the battery abnormity of the electric vehicle, the function can be realized by hardware, and can also be realized by executing corresponding software by hardware, the hardware or the software comprises one or more modules corresponding to the function, and the modules can be software and/or hardware.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
Embodiments of the present application also provide a computer-readable storage medium, which stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of any one of the electric vehicle battery abnormality detection methods shown in fig. 1 to 5 are implemented.
The embodiment of the present application further provides an intelligent device, which includes a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, where the processor executes the computer readable instructions to implement the steps of any one of the methods for detecting abnormality of an electric vehicle battery as shown in fig. 1 to 5.
The embodiment of the present application also provides a computer program product, which when running on a server, causes the server to execute the steps of implementing any one of the electric vehicle battery abnormality detection methods as shown in fig. 1 to 5.
Fig. 7 is a schematic diagram of an intelligent device provided in an embodiment of the present application. As shown in fig. 7, the smart device 7 of this embodiment includes: a processor 70, a memory 71, and computer readable instructions 72 stored in the memory 71 and executable on the processor 70. The processor 70, when executing the computer readable instructions 72, implements the steps in the various electric vehicle battery abnormality detection method embodiments described above, such as steps S201-S203 shown in fig. 2. Alternatively, the processor 70, when executing the computer readable instructions 72, implements the functions of the modules/units in the above-described device embodiments, such as the functions of the units 61 to 63 shown in fig. 6.
Illustratively, the computer readable instructions 72 may be partitioned into one or more modules/units that are stored in the memory 71 and executed by the processor 70 to accomplish the present application. The one or more modules/units may be a series of computer-readable instruction segments capable of performing specific functions, which are used to describe the execution process of the computer-readable instructions 72 in the smart device 7.
The intelligent device 7 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The intelligent device 7 may include, but is not limited to, a processor 70, a memory 71. It will be appreciated by those skilled in the art that fig. 7 is merely an example of the smart device 7, and does not constitute a limitation of the smart device 7, and may include more or less components than those shown, or combine certain components, or different components, for example, the smart device 7 may also include input-output devices, network access devices, buses, etc.
The Processor 70 may be a CentraL Processing Unit (CPU), other general purpose Processor, a DigitaL SignaL Processor (DSP), an AppLication Specific Integrated Circuit (ASIC), an off-the-shelf ProgrammabLe Gate Array (FPGA) or other ProgrammabLe logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 71 may be an internal storage unit of the smart device 7, such as a hard disk or a memory of the smart device 7. The memory 71 may also be an external storage device of the Smart device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure DigitaL (SD) Card, a FLash memory Card (FLash Card), and the like, which are provided on the Smart device 7. Further, the memory 71 may also include both an internal storage unit and an external storage device of the smart device 7. The memory 71 is used to store the computer readable instructions and other programs and data required by the smart device. The memory 71 may also be used to temporarily store data that has been output or is to be output.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. An abnormality detection method for a battery of an electric vehicle, characterized by comprising:
acquiring charging data of the electric vehicle;
determining whether the charging data has preset charging curve characteristics, wherein the preset charging curve characteristics comprise pulses with amplitudes larger than or equal to a preset amplitude threshold value, the number of the pulses is larger than or equal to a preset number threshold value, or the ratio of the pulses in the total number of pulses in a set period of a charging stage reaches a preset percentage; the pulses comprise a first pulse and a second pulse, and the step of determining whether the charging data has a preset charging curve characteristic comprises: determining the number of the first pulses and the number of the second pulses in a preset threshold interval, wherein the first pulses are pulses with a first pulse threshold equal to a first preset current threshold, and the second pulses are pulses with a second pulse threshold equal to a first preset voltage threshold; if the sum of the number of the first pulses and the number of the second pulses is zero, determining that the charging data does not have the preset charging curve characteristic;
and if the charging data does not have the preset charging curve characteristic, judging that the battery of the electric vehicle is abnormal.
2. The method of claim 1, wherein the step of determining whether the charging data has a predetermined charging profile characteristic comprises:
and if the sum of the number of the first pulses and the number of the second pulses is greater than or equal to a preset pulse total number threshold value, determining that the charging data has a preset charging curve characteristic.
3. The method of claim 1, wherein said charging data includes charging current data and charging voltage data, and said step of determining the number of said first pulses and the number of said second pulses comprises:
performing differential calculation on the charging current data to obtain a first differential matrix;
determining the number of first differential matrixes in a preset current threshold interval in the first differential matrixes;
determining the number of the first pulses according to the number of the first differential matrixes;
performing differential calculation on the charging voltage data to obtain a second differential matrix;
determining the number of second differential matrixes in a preset voltage threshold interval in the second differential matrixes;
and determining the number of the second pulses according to the number of the second difference matrixes.
4. The method of claim 1, wherein the electric vehicle battery abnormality detection method further comprises:
if the sum of the number of the first pulses and the number of the second pulses is greater than or equal to a preset pulse total number threshold value, determining that the preset charging curve characteristic of the charging data is a first characteristic type;
if the number of the first pulses is greater than or equal to a first preset pulse number threshold, or the number of the second pulses is greater than or equal to a first preset pulse number threshold, determining that the preset charging curve characteristic of the charging data is a second characteristic type;
if the proportion of the first pulse in the total pulse number in the set period of the charging stage belongs to a first preset percentage threshold interval, or if the proportion of the second pulse in the total pulse number in the set period of the charging stage belongs to the first preset percentage threshold interval, determining that the preset charging curve characteristic of the charging data is a third characteristic type;
and if the ratio of the first pulse to the total pulse number in the set period of the charging stage is greater than or equal to a second preset percentage threshold, or the ratio of the second pulse to the total pulse number in the set period of the charging stage is greater than or equal to the second preset percentage threshold, determining that the preset charging curve characteristic of the charging data is a fourth characteristic type.
5. The method of claim 1, wherein prior to the step of determining whether the charging data has a preset charging profile characteristic, comprising:
acquiring the charging time of the electric vehicle;
and if the charging time is greater than or equal to a preset charging time threshold, executing the step of determining whether the charging data has the characteristics of a preset charging curve.
6. The method of claim 1, further comprising, prior to the step of obtaining charging data for the electric vehicle:
acquiring charging order data of a user of the electric vehicle;
detecting whether the electric vehicle has been analyzed according to the charging order data;
and if the electric vehicle is not analyzed, executing the step of acquiring the charging data of the electric vehicle.
7. The method according to any one of claims 1 to 6, further comprising, after the step of determining that there is an abnormality in the battery of the electric vehicle:
generating prompt information and/or suggestion information of battery abnormity according to the charging data;
and sending the prompt information and/or the suggestion information to a mobile terminal of a user of the electric vehicle to instruct the mobile terminal to present the prompt information and/or the suggestion information to the user of the electric vehicle.
8. An abnormality detection device for a battery of an electric vehicle, comprising:
the data acquisition unit is used for acquiring charging data of the electric vehicle;
the characteristic determining unit is used for determining whether the charging data has preset charging curve characteristics, wherein the preset charging curve characteristics comprise pulses with amplitudes larger than or equal to a preset amplitude threshold value, the number of the pulses is larger than or equal to a preset number threshold value, or the ratio of the pulses in the total pulse number in a set period of a charging stage reaches a preset percentage; the pulse includes a first pulse and a second pulse, and the characteristic determination unit includes: the pulse number determining module is used for determining the number of the first pulses and the number of the second pulses in a preset threshold interval, wherein the first pulses are pulses with a first pulse threshold value equal to a first preset current threshold value, and the second pulses are pulses with a second pulse threshold value equal to a first preset voltage threshold value;
a second charging curve characteristic determining module, configured to determine that the charging data does not have a preset charging curve characteristic if a sum of the number of the first pulses and the number of the second pulses is zero;
and the battery detection unit is used for judging that the battery of the electric vehicle is abnormal if the charging data does not have the preset charging curve characteristic.
9. A smart device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of detecting abnormality in a battery of an electric vehicle according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the electric vehicle battery abnormality detection method according to any one of claims 1 to 7.
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| CN113799650B (en) * | 2021-10-18 | 2024-01-09 | 广州小鹏汽车科技有限公司 | Battery data processing method and device |
| CN115864295A (en) * | 2022-11-28 | 2023-03-28 | 富满微电子集团股份有限公司 | Method, circuit and chip for judging abnormal protection |
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