CN114966404A

CN114966404A - Method, device, medium and prompt terminal for detecting charging abnormality of new energy equipment

Info

Publication number: CN114966404A
Application number: CN202110192368.2A
Authority: CN
Inventors: 朱诗严; 鞠强; 潘博存; 项宝庆
Original assignee: Qingdao Telai Big Data Co ltd; Qingdao Teld New Energy Technology Co Ltd
Current assignee: Qingdao Telai Big Data Co ltd; Qingdao Teld New Energy Technology Co Ltd
Priority date: 2021-02-20
Filing date: 2021-02-20
Publication date: 2022-08-30

Abstract

The application discloses a method, a device, a medium and a prompt terminal for detecting charging abnormity of new energy equipment. Calculating secondary reference charging process data according to the primary reference charging process data, and calculating secondary actual charging process data according to the primary actual charging process data. And finally, determining a safety threshold corresponding to the secondary reference charging process data by using an anomaly detection method based on the corresponding relation between the secondary reference charging process data and time. The safety threshold is obtained by referring to the charging process data once and is real data, so that the obtained safety threshold can more accurately reflect the current charging state of the new energy device to be detected. In addition, the secondary reference charging process data can reflect the dynamic development of variables, so that the obtained safety threshold can identify charging abnormity in time.

Description

Method, device, medium and prompt terminal for detecting charging abnormality of new energy equipment

Technical Field

The application relates to the technical field of new energy, in particular to a method, a device, a medium and a prompt terminal for detecting charging abnormality of new energy equipment.

Background

The new energy device mentioned in the present application is a device that supplies kinetic energy by a battery, for example, an automobile using a battery, hereinafter referred to as an electric automobile. With the rapid development of new energy devices, battery charging technologies are receiving more and more attention, and the safety of charging is the key point of attention.

In order to prevent charging accidents caused by excessive temperature of the battery during charging of the battery, manufacturers generally perform a test experiment on the battery, obtain safety thresholds of various safe charging variables of the battery through the test experiment, and write the safety thresholds into a Battery Management System (BMS). During the charging process, the magnitude relation between the current parameter of the battery and the set safety threshold is compared to determine whether the battery is abnormally charged.

Obviously, the safety threshold obtained by the test experiment is usually fixed, and the corresponding threshold is constantly changed along with the change of the battery performance, and if a fixed safety threshold is used as the abnormality judgment standard, the judgment result is inevitably inaccurate. Furthermore, the existing safety thresholds usually quantify a static characteristic, such as the temperature of the power storage battery, without considering the dynamic development of variables, so that charging anomalies cannot be identified in time.

Therefore, in the charging process of the new energy device, how to accurately and timely identify the charging abnormality is a problem to be solved urgently by a person skilled in the art.

Disclosure of Invention

The application aims to provide a method, a device, a medium and a prompt terminal for detecting charging abnormity of new energy equipment, which are used for accurately and timely identifying the charging abnormity in the charging process of the new energy equipment.

In order to solve the technical problem, the application provides a method for detecting charging abnormality of new energy equipment, which includes:

determining the type of the new energy equipment to be detected;

selecting a plurality of new energy devices under the type as analysis objects;

acquiring primary reference charging process data of the analysis object, which is matched with the analysis object within a preset time range, wherein the primary reference charging process data is data generated by the analysis object in a charging process;

calculating secondary reference charging process data which are corresponding to each variable and used for representing variable variation trend according to the primary reference charging process data;

calculating secondary actual charging process data which are corresponding to each variable and used for representing variable variation trend according to the primary actual charging process data of the new energy equipment to be detected; the primary actual charging process data is data generated in the current charging process of the new energy equipment to be detected;

and determining a safety threshold corresponding to the secondary reference charging process data by using an anomaly detection method based on the corresponding relation between the secondary reference charging process data and time, wherein the safety threshold is used for being compared with secondary actual charging process data of the new energy equipment to be detected as a comparison object so as to determine that the new energy equipment to be detected is abnormally charged.

Preferably, the selecting a plurality of new energy devices under the type as analysis objects includes:

and selecting a plurality of new energy devices of the same region and/or the same vehicle age under the type as the analysis object.

Preferably, the determining a safety threshold corresponding to the secondary reference charging process data by using an anomaly detection method based on the correspondence relationship between the secondary reference charging process data and time includes:

and determining the safety threshold corresponding to each variable in the secondary reference charging process data by using a statistical analysis method or a cluster analysis method.

Preferably, the statistical analysis method includes a normal distribution statistical method and a mean value method, and the cluster analysis method includes a gaussian mixture clustering method.

Preferably, the acquiring primary reference charging process data of the analysis object, which is matched with the analysis object within a preset time range, includes:

acquiring a plurality of target charging orders of the analysis object, which are matched with the analysis object, within a preset time range;

extracting the primary reference charging process data from each of the target charging orders.

Preferably, the method further comprises the following steps:

and determining the health condition corresponding to the deviation degree of the secondary actual charging process data and the safety threshold according to the corresponding relation between the preset deviation degree and the health condition.

Preferably, the determining the health condition corresponding to the deviation degree of the secondary actual charging process data from the safety threshold according to the preset corresponding relationship between the deviation degree and the health condition includes:

acquiring a plurality of historical charging orders of the new energy equipment to be detected within a preset time;

acquiring primary historical charging process data from each historical charging order;

calculating secondary historical charging process data which are corresponding to all variables and used for representing variable variation trends according to the primary historical charging process data;

calculating an average value corresponding to each variable in the secondary historical charging process data to serve as an actual average value;

calculating a reference average value corresponding to each variable of the secondary reference charging process data in the preset time;

determining the variable deviation degree of the actual average value corresponding to the same variable and the safety threshold value;

and determining the actual health grade corresponding to the variable deviation degree according to the preset corresponding relation between the variable deviation degree and the health grade.

determining actual scoring data of each variable in the secondary actual charging process data according to a preset scoring model corresponding to each variable;

determining an actual health grade corresponding to the actual score data according to a preset corresponding relation between the score data and the health grade;

the establishing process of the scoring model comprises the following steps:

dividing intervals according to a plurality of interval ranges formed by the average value and the variance corresponding to each variable in the secondary reference charging process data;

and establishing a corresponding relation between the deviation degree and the score data according to the deviation degree of the actual value of each variable and the critical value of the corresponding interval.

Preferably, after the determining the safety threshold corresponding to the secondary reference charging process data by using an anomaly detection method based on the correspondence relationship between the secondary reference charging process data and time, the method further includes:

and establishing a safety file of the new energy equipment to be detected according to the corresponding relation among the safety threshold, the identity information of the new energy equipment to be detected and the charging starting information.

Preferably, the method further comprises the following steps:

after actual charging starting information of the new energy equipment to be detected, which is sent by charging equipment, is obtained, the corresponding safety threshold and the identity information of the new energy equipment to be detected are searched from the safety file according to the actual charging starting information;

and sending the safety threshold value to the charging equipment so that the charging equipment can determine that the new energy equipment to be detected is abnormal in charging under the condition that the secondary actual charging process data exceeds the safety threshold value or the original threshold value output by the BMS.

In order to solve the technical problem, the present application further provides a device for detecting charging abnormality of new energy equipment, including a memory for storing a computer program;

and the processor is used for realizing the steps of the method for detecting the charging abnormity of the new energy device when the computer program is executed.

In order to solve the technical problem, the present application further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the method for detecting charging abnormality of a new energy device are implemented.

In order to solve the technical problem, the present application further provides a prompt terminal for abnormal charging of new energy devices, including:

a memory for storing a computer program;

a processor for implementing the following steps when executing the computer program:

receiving a charging abnormity detection result of the new energy equipment to be detected;

outputting charging abnormity prompt information;

wherein the charging abnormality detection result is obtained by the steps of:

determining the type of the new energy equipment to be detected;

selecting a plurality of new energy devices under the type as analysis objects;

acquiring primary reference charging process data matched with the analysis object within a preset time range, wherein the primary reference charging process data is data generated by the analysis object in a charging process;

and determining a safety threshold corresponding to the secondary reference charging process data by using an anomaly detection method based on the corresponding relation between the secondary reference charging process data and time, wherein the safety threshold is used for being compared with the secondary actual charging process data of the new energy equipment to be detected as a comparison object so as to determine that the new energy equipment to be detected is abnormally charged and generate a charging anomaly detection result.

The method for detecting charging abnormality of the new energy device includes the steps of firstly determining the type of the new energy device to be detected, then selecting a plurality of new energy devices in the type as analysis objects, and then acquiring primary reference charging process data, matched with the analysis objects, of the analysis objects within a preset time range. And calculating secondary reference charging process data according to the primary reference charging process data, and calculating secondary actual charging process data according to the primary actual charging process data of the new energy equipment to be detected. And finally, determining a safety threshold corresponding to the secondary reference charging process data by using an anomaly detection method based on the corresponding relation between the secondary reference charging process data and time, wherein the safety threshold is used for being compared with secondary actual charging process data of the new energy equipment to be detected as a comparison object so as to determine that the new energy equipment to be detected is abnormally charged. Therefore, when the technical scheme is applied, the safety threshold is obtained through the primary reference charging process data of the new energy equipment of the same type as the new energy equipment to be detected within the preset time range, and the primary reference charging process data is real data, so that the obtained safety threshold can accurately reflect the current charging state of the new energy equipment to be detected, and compared with a fixed threshold, the safety threshold obtained by the technical scheme can improve the accuracy of charging abnormity detection. In addition, the secondary reference charging process data can reflect the dynamic development of the variable, so that the obtained safety threshold can quantify the dynamic development of the variable, and the charging abnormity can be identified in time.

In addition, the detection device and the medium for the charging abnormity of the new energy equipment and the prompt terminal for the charging abnormity of the new energy equipment correspond to the method, and the effect is the same as that of the method.

Drawings

In order to more clearly illustrate the embodiments of the present application, the drawings needed for the embodiments 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 that other drawings can be obtained by those skilled in the art without inventive effort.

Fig. 1 is a structural diagram of a charging management system of an electric vehicle according to an embodiment of the present disclosure;

fig. 2 is a flowchart of a method for detecting charging abnormality of new energy equipment according to an embodiment of the present application;

FIG. 3 is a diagram illustrating a normal distribution curve of a maximum temperature rise rate according to an embodiment of the present disclosure;

FIG. 4 is a diagram illustrating a normal distribution curve of a maximum temperature difference according to an embodiment of the present disclosure;

FIG. 5 is a diagram illustrating a normal distribution curve of a maximum SOC variation rate according to an embodiment of the present application;

FIG. 6 is a schematic diagram of a normal distribution curve of a maximum pressure difference according to an embodiment of the present application;

fig. 7 is a structural diagram of a device for detecting charging abnormality of new energy equipment according to an embodiment of the present application;

fig. 8 is a structural diagram of a device for detecting charging abnormality of new energy equipment according to another embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without any creative effort belong to the protection scope of the present application.

The core of the application is to provide a method, a device, a medium and a prompt terminal for detecting charging abnormity of new energy equipment. The new energy device proposed by the present application may be an electric vehicle or other electric devices, and the electric vehicle is taken as an example hereinafter. The method for detecting charging abnormality of the new energy device can be applied to a charging cloud platform or a charging device, and can also be an unmanned vehicle management platform (suitable for unmanned vehicles). Hereinafter, a method for detecting charging abnormality of the new energy device is applied to the charging cloud platform for explanation. The charging cloud platform is in communication connection with the charging equipment and is used for managing the plurality of charging equipment in a unified mode. In general, a charging cloud platform realizes corresponding functions by mutual cooperation of a plurality of computers. The charging equipment generally has two hardware composition modes, one is that a charger and a charging terminal are integrally arranged, the size is large, the charging equipment is often used in a high-speed service area and other fast charging scenes, the other is that the charger and the charging terminal are separately arranged, one charger can be in communication connection with a plurality of charging terminals and is used for managing the plurality of charging terminals in a unified manner. Because the charger and the charging terminal are arranged in a split manner, the charging terminal is small in size, can directly perform data interaction with the electric vehicle, is simple in function, generally sends acquired vehicle data to the corresponding charger, completes complex data operation by the charger, and then returns an operation result to the charging terminal. Fig. 1 is a structural diagram of a charging management system of an electric vehicle according to an embodiment of the present application. As shown in fig. 1, the charging management system includes a charging cloud platform and a plurality of charging devices in communication connection with the charging cloud platform, where the charging devices acquire relevant data of the electric vehicle, for example, charging start information, and send the charging start information to the charging cloud platform, and the charging cloud platform identifies the device model according to the charging start information, so as to perform relevant calculation on charging process data of other new energy devices of the same type as the charging device to obtain a safety threshold. It should be noted that fig. 1 is only a specific application scenario, and does not represent that the charging cloud platform must detect charging anomalies of the new energy device.

The description is given above for a hardware usage scenario corresponding to the method for detecting charging abnormality of the new energy device provided by the application. An embodiment of a method for detecting charging abnormality of a new energy device is described below. In order that those skilled in the art will better understand the disclosure, the following detailed description is given with reference to the accompanying drawings.

Fig. 2 is a flowchart of a method for detecting charging abnormality of new energy device according to an embodiment of the present application. As shown in fig. 2, the method includes:

s10: and determining the type of the new energy equipment to be detected.

The new energy device to be detected mentioned in this embodiment is one of the new energy devices, and the purpose of determining the type of the new energy device to be detected is to select a plurality of new energy devices in the type as analysis objects.

S11: and selecting a plurality of new energy devices under the type as analysis objects.

It should be noted that the analysis object is at least a device of the same type as the new energy device to be detected, in this embodiment, the analysis object may be of the same type as the new energy device to be detected, or of the same type as the new energy device to be detected plus the same age of the vehicle, and the purpose of selecting a plurality of new energy devices of the same type as the analysis object is to ensure that the obtained reference charging process data can accurately reflect the charging state of the new energy device to be detected, so that the detection result is more accurate. In a preferred embodiment, a plurality of new energy devices of the same region and/or the same age in the same type are selected as the analysis objects.

S12: and acquiring primary reference charging process data of the analysis object, which is matched with the analysis object, in a preset time range. The charging process data mentioned in the present application is data generated by any new energy device during the charging process. The charging process data is from a charging cloud platform and charging equipment and comprises charging system data and charging data, the charging system data mainly comprises charging pile/charging terminal data, user data and vehicle data which are stored in the cloud platform system supporting charging services, and the charging data is acquired from a vehicle by the charging equipment in the charging process (a handshaking stage, a parameter configuration stage, a charging stage and charging ending). The primary reference charging process data is data generated by the analysis object in the charging process. The primary reference charging process data and the primary actual charging process data mentioned below are both one of the charging process data, that is, data generated by the new energy device during the charging process. For the purpose of distinction, the data generated by the new energy device to be detected in the current charging process is referred to as primary actual charging process data, and the charging process data of the new energy device (analysis object) of the same type as the new energy device to be detected is referred to as primary reference charging process data and used as reference data.

Correspondingly, the primary reference charging process data can be the charging process data of the new energy equipment of the same type as the new energy equipment to be detected, or the charging process data of the new energy equipment of the same type and the same vehicle age as the new energy equipment to be detected. Taking an electric vehicle as an example, the reference charging process data may be data generated by the following new energy device in the charging process:

(1) the same vehicle type + a certain past time period/current time;

(2) the same vehicle type + the same region (as a city) + a certain period of time in the past/the current time;

(3) the same model + the same age + a certain period of time in the past/the current time.

For example, if the type of the new energy device to be detected is biddie EV450, the multiple new energy devices in the types selected as the analysis objects may be: electric vehicles of 2 years of age were obtained as analysis targets in the Chengdu region, BYD EV450, from 10 months 1 day to 10 months 30 days in 2020.

As a preferred embodiment, the primary reference charging process data includes the maximum temperature of the power storage battery, the minimum temperature of the power storage battery, the SOC of the power storage battery, the maximum voltage of the unit cell, the minimum voltage of the unit cell, the number of the maximum voltage of the unit cell, the maximum temperature monitoring point number, and the minimum temperature monitoring point number. Note that the SOC of the power storage battery mentioned in the present embodiment includes the SOC at the time of normal charging and also includes the SOC at the time of termination of imbalance abnormality. The SOC at the time of termination of the imbalance abnormality is the charging process data, and is the SOC at the time of completion of charging after the occurrence of the charging abnormality. The SOC at the time of the imbalance abnormal termination is the battery SOC at the time of the abnormal termination of the power storage battery due to the imbalance, and the reason for the abnormal termination having a large relationship with the imbalance is that the cell voltage of the new energy device reaches the target value and the power storage battery reaches the target SOC. In a specific embodiment, the more variables in the one-time reference charging process data, the more accurate the charging abnormality detection result. On the basis, the secondary reference charging process data comprises the maximum temperature difference of the power storage battery, the maximum pressure difference of the power storage battery, the maximum temperature rise rate of the power storage battery, the maximum SOC change rate of the power storage battery, the maximum voltage change rate of the single battery, the fragrance entropy value of the number of the highest temperature monitoring point, the fragrance entropy value of the number of the lowest temperature monitoring point and the fragrance entropy value of the number of the highest voltage of the single battery.

1) Maximum temperature difference of power storage battery

The temperature difference is the difference value of the highest temperature and the lowest temperature of the battery at the same moment in the charging process, and is obtained from the highest temperature of the power storage battery and the lowest temperature of the power storage battery. The maximum temperature difference refers to the maximum value of the temperature difference in one charging process.

2) Maximum pressure differential of power accumulator

The maximum voltage difference refers to the difference between the highest voltage of the single battery and the lowest voltage of the single battery after the end of one charging process.

3) Maximum rate of temperature rise of power storage battery

The temperature rise rate refers to the amount of change in the maximum temperature of the battery at a certain frequency (milliseconds, seconds, minutes) during charging. The maximum temperature rise rate refers to the maximum value of the temperature rise rate in one charging process.

4) Maximum rate of change of SOC of power storage battery

The SOC change rate refers to a rate of change of SOC transmitted from the BMS at a specific frequency (milliseconds, seconds, minutes) during one charge. The maximum SOC change rate refers to the maximum value of the SOC change rate in one charging process.

5) Maximum rate of change of cell voltage

The cell voltage change rate refers to the amount of change at a specific frequency (msec, sec, min) of the highest cell voltage transmitted by the BMS during charging. The maximum change rate of the voltage of the single battery refers to the maximum value of the voltage change rate of the single battery in one charging process.

6) Entropy of fragrance of maximum temperature monitoring point number

Based on the highest temperature detection point number obtained according to specific frequency (millisecond, second and minute) in the primary charging process, the fragrance concentration entropy value of the highest temperature detection point number is calculated by combining a fragrance concentration entropy algorithm.

7) Aroma concentration entropy value of lowest temperature monitoring point number

Based on the lowest temperature detection point number obtained according to specific frequency (millisecond, second and minute) in the primary charging process, the fragrance concentration entropy value of the lowest temperature detection point number is calculated by combining a fragrance concentration entropy algorithm.

8) Fragrance entropy value of serial number of highest voltage of single battery

Based on the number of the single battery highest voltage detection point obtained according to specific frequency (millisecond, second and minute) in the primary charging process, the fragrance concentration entropy value of the number where the single battery highest voltage is located is calculated by combining a fragrance concentration entropy algorithm.

It can be understood that the fragrance entropy value is the discrete degree of the number of the highest temperature monitoring point and the number of the highest voltage of the single battery in the charging process, and the lower the discrete degree, the higher the possibility of charging abnormity appears.

In addition, the primary reference charging process data acquired in this step may be acquired online after the charging start information of the new energy to be detected is acquired, or may be stored in a local database in advance, and directly called from the local database after the charging start information of the new energy to be detected is acquired. It can be understood that the primary reference charging process data may be historical data or real-time data if the primary reference charging process data is acquired online after the charging start information of the new energy to be detected is acquired, and the primary reference charging process data is historical data if the primary reference charging process data is directly called from a local database after the charging start information of the new energy to be detected is acquired.

S13: and calculating secondary reference charging process data which are corresponding to the variables and used for representing the variable variation trend according to the primary reference charging process data.

The secondary reference charging process data is obtained according to the primary reference charging process data and is used for representing variable variation trends, such as variable variation, gradient variation and the like. It is to be understood that the number of variables included in the primary reference charging process data may be the same as or different from the number of variables included in the secondary reference charging process data, but the types of variables are necessarily different, as described in detail below.

S14: and calculating secondary actual charging process data which are corresponding to each variable and used for representing the variable variation trend according to the primary actual charging process data of the new energy equipment to be detected.

The primary actual charging process data is data generated in the current charging process of the new energy device to be detected. The secondary actual charging process data is obtained according to the primary actual charging process data and is used for representing variable variation trends, such as variable variation, gradient variation, discrete degree and the like. It should be noted that the method of obtaining the secondary reference charging process data from the primary reference charging process data is the same as the method of obtaining the secondary actual charging process data from the primary actual charging process data. It is to be understood that the number of variables included in the primary reference charging process data may be the same as or different from the number of variables included in the secondary reference charging process data, but the types of variables are necessarily different, as described in detail below.

S15: and determining a safety threshold corresponding to the secondary reference charging process data by using an anomaly detection method based on the corresponding relation between the secondary reference charging process data and time, wherein the safety threshold is used as a comparison object to be compared with the secondary actual charging process data of the new energy equipment to be detected so as to determine that the new energy equipment to be detected is abnormally charged.

It should be noted that, in this embodiment, the calculation method of the safety threshold is not limited, and may be determined by using a statistical analysis method or a cluster analysis method. The safety threshold value in the step and the existing fixed threshold value obtained through experiments are used for measuring whether charging is abnormal, but the safety threshold value in the step is obtained through real data of the new energy equipment of the same type as the new energy equipment to be detected in the charging process, so that the charging state of the equipment of the same type can be truly reflected.

The new energy device to be detected is divided into a handshake stage, a parameter configuration stage, a charging stage and a charging end stage in the charging process, and the actual charging process data can be data of one or all of the four stages. The safety threshold is determined by the charging process data of the new energy equipment with the same type as the new energy equipment to be detected, so that the safety threshold can be used as a detection standard for the abnormity of the new energy equipment to be detected. And determining that the new energy equipment to be detected is abnormal in charging as long as the data of the secondary actual charging process exceeds the safety threshold.

The method for detecting charging abnormality of the new energy device includes the steps of determining the type of the new energy device to be detected, selecting a plurality of new energy devices in the type as analysis objects, and acquiring primary reference charging process data, matched with the analysis objects, of the analysis objects within a preset time range. And calculating secondary reference charging process data according to the primary reference charging process data, and calculating secondary actual charging process data according to the primary actual charging process data of the new energy equipment to be detected. And finally, determining a safety threshold corresponding to the secondary reference charging process data by using an anomaly detection method based on the corresponding relation between the secondary reference charging process data and time, wherein the safety threshold is used for being compared with secondary actual charging process data of the new energy equipment to be detected as a comparison object so as to determine that the new energy equipment to be detected is abnormally charged. Therefore, when the technical scheme is applied, the safety threshold is obtained through the primary reference charging process data of the new energy equipment of the same type as the new energy equipment to be detected within the preset time range, and the primary reference charging process data is real data, so that the obtained safety threshold can accurately reflect the current charging state of the new energy equipment to be detected, and compared with a fixed threshold, the safety threshold obtained by the technical scheme can improve the accuracy of charging abnormity detection. In addition, the secondary reference charging process data can reflect the dynamic development of the variable, so that the obtained safety threshold can quantify the dynamic development of the variable, and the charging abnormity can be identified in time.

On the basis of the above embodiment, determining the safety threshold corresponding to the secondary reference charging process data by using the anomaly detection method based on the correspondence between the secondary reference charging process data and the time includes:

and determining the safety threshold corresponding to each variable in the secondary reference charging process data by using a statistical analysis method or a cluster analysis method. As a preferred embodiment, the statistical analysis method includes a normal distribution statistical method and a mean value method, and the cluster analysis method includes a gaussian mixture clustering method. The corresponding method is described in detail below.

1. Determining safety threshold corresponding to highest temperature rise rate in secondary reference charging process data by utilizing normal distribution statistical method

(1) Selecting secondary reference charging process data in a charging order of new energy equipment of the same type as the new energy equipment to be detected within the past 30 days as sample data;

(2) acquiring a temperature rise rate for each charging order;

(3) calculating the average value mu and the standard deviation sigma of the highest temperature rise rate of all orders;

(4) according to the "3 σ" principle of normal distribution: the interval (μ -3 σ, μ +3 σ) is the actually possible value interval of the random variable X, and the probability that X falls outside the interval is less than three thousandths, which is generally considered to be not occurred in the practical problem. If the variable exceeds three thousandths, the anomaly point is reached. The threshold for the highest temperature is μ +3 σ, the highest temperature safety threshold. Fig. 3 is a schematic diagram of a normal distribution curve of a maximum temperature rise rate according to an embodiment of the present application. As shown in fig. 3, the point a, the point B, the point C, and the point D are the highest temperature rise rates of the new energy device to be detected, respectively, where the highest temperature rise rates of the point a and the point B are normal, and the highest temperature rise rates of the point C and the point D are abnormal.

2. Determining safety threshold corresponding to maximum temperature difference in multiple groups of secondary reference charging process data by utilizing normal distribution statistical method

(2) acquiring the maximum temperature difference for each order;

(3) calculating the average value mu and the standard deviation sigma of the maximum temperature difference of all orders;

(4) according to the "3 σ" principle of normal distribution: the interval (μ -3 σ, μ +3 σ) is the actually possible value interval of the random variable X, and the probability that X falls outside the interval is less than three thousandths, which is generally considered to be not occurred in the practical problem. If the variable exceeds three thousandths, the anomaly point is reached. The threshold for the highest temperature is μ +3 σ, the highest temperature safety threshold. Fig. 4 is a schematic diagram of a normal distribution curve of a maximum temperature difference according to an embodiment of the present disclosure. As shown in fig. 4, the point a, the point B, the point C, the point D, and the point E are the maximum temperature difference of the new energy device to be detected, respectively, where the maximum temperature difference of the point a is normal, and the maximum temperature differences of the point B, the point C, the point D, and the point E are abnormal.

Similarly, according to the method, the safety threshold of each variable in the secondary reference charging process data can be obtained, and details are not repeated in this embodiment. Fig. 5 is a schematic diagram of a normal distribution curve of a maximum SOC change rate according to an embodiment of the present application. As shown in fig. 5, the point a, the point B, the point C, and the point D are maximum SOC change rates of the new energy device to be detected, respectively, where the maximum SOC change rates of the point a, the point B, and the point C are normal, and the maximum SOC change rate of the point D is abnormal. Fig. 6 is a schematic diagram of a normal distribution curve of a maximum pressure difference according to an embodiment of the present application. As shown in fig. 6, point a is the maximum differential pressure of the new energy device to be detected, and the maximum differential pressure of point a is abnormal.

3. Determining safety threshold corresponding to highest temperature rise rate in secondary reference charging process data by using Gaussian mixture clustering method

(1) Selecting a charging order of the new energy equipment with the same type as the new energy equipment to be detected within 30 days as sample data;

(2) acquiring a highest temperature rise value per minute for each order, and then acquiring a maximum value of the highest temperature rise value of each order;

(3) establishing a Gaussian mixture model with the highest temperature rise rate according to the determined hyper-parameter (the clustering number is 3);

(4) and performing clustering calculation on the secondary actual charging process data through the obtained Gaussian mixture model, wherein the clustering result comprises 3 types, the 1 st type mu-3 sigma, the 3 rd type mu +3 sigma and the rest 2 types, and if the probability of the 3 rd type is greater than 50%, the secondary actual charging process data are represented to exceed a safety threshold.

Further, acquiring the primary reference charging process data of the analysis object, which is matched with the analysis object, within the preset time range includes:

acquiring a plurality of target charging orders of an analysis object, which are matched with the analysis object, within a preset time range;

primary reference charging process data is extracted from each target charging order.

For the charging cloud platform, in the charging process of the new energy device, a charging order corresponding to the current charging is generated, and the charging order contains the primary reference charging process data, so that the mode of acquiring the primary reference charging process data by using the charging order is simple, and additional hardware is not required or additional equipment resources are not occupied.

In the above embodiment, whether the new energy device to be detected is abnormal in charging can be determined according to whether the secondary actual charging process data exceeds the safety threshold. On this basis, in the embodiment, according to the preset corresponding relationship between the deviation degree and the health condition, the health condition corresponding to the deviation degree between the secondary actual charging process data and the safety threshold is determined, so that the actual health level is determined to evaluate the new energy device to be detected from multiple aspects.

As a preferred embodiment, determining the health condition corresponding to the deviation degree of the secondary actual charging process data from the safety threshold according to the preset corresponding relationship between the deviation degree and the health condition includes:

calculating a reference average value corresponding to each variable of the secondary reference charging process data in a preset time;

It should be noted that the method of obtaining the secondary historical charging process data from the primary historical charging process data is the same as the method of obtaining the secondary actual charging process data from the primary actual charging process data. The primary historical charging process data in this embodiment is also one of the charging process data, but is only the charging process data corresponding to the historical charging order. The historical charging order is the order of the new energy device to be detected, so the historical charging process data is the real data of the new energy device to be detected, the actual average value obtained through the data is used as a health assessment of the new energy device to be detected, similarly, the reference average value obtained through the reference charging process data is used as a reference standard, and if the deviation degree of the actual average value and the reference average value is large, the risk of becoming high-risk equipment is large. It can be understood that the corresponding relationship between the variable deviation degree and the health level can be obtained according to an empirical value or other calibration manners, which is not described in detail in this embodiment.

In the embodiment, on the basis of identifying the charging abnormality of the new energy device to be detected, the health condition of the new energy to be detected can be evaluated, the health condition can be quantified, a user can know the charging trend of the power storage battery in advance through the actual health grade, and the user experience is improved.

Corresponding to the previous embodiment, in this embodiment, the health condition of the new energy device to be detected is quantitatively evaluated by another method, and a scoring model is first established, where the establishing process of the scoring model includes the following steps:

and carrying out interval division according to a plurality of interval ranges formed by the average value and the variance corresponding to each variable in the secondary reference charging process data. For example, the variable is the maximum temperature difference, divided into three levels, good, medium and bad, and the interval includes: (0, μ), (μ, μ +3 σ), (μ +3 σ, and ∞).

And establishing a corresponding relation between the deviation degree and the score data according to the deviation degree of the actual value of each variable and the critical value of the corresponding interval. Wherein, the critical values of the corresponding interval are 0, mu and mu +3 sigma. In a specific implementation, different levels are quantitatively scored according to a data mining method. For example, the maximum temperature difference is further modified based on the method of Zscore, and 3 σ is used as the critical value of the high-risk vehicle. The score data may be 0-100, and it is understood that the degree of deviation of the actual value from the threshold value of the corresponding interval is in a negative correlation with the score data, i.e. the greater the degree of deviation of the actual value from the threshold value of the corresponding interval, the lower the score data is, and the smaller the degree of deviation of the actual value from the threshold value of the corresponding interval, the higher the score data is. For example, when μ +3 σ is 60 points and (0, μ) is 100 points (good), 60 points < (μ, μ +3 σ) < 100 points (middle), (μ +3 σ, ∞) < 60 points (difference). If the actual variable falls within (μ, μ +3 σ), a specific score is derived according to the deviation procedure from the critical values μ, μ +3 σ.

The health condition evaluation of the new energy equipment to be detected by utilizing the scoring model specifically comprises the following steps:

and determining the actual health grade corresponding to the actual score data according to the preset corresponding relation between the score data and the health grade.

It can be understood that the charging protection mechanism can be determined according to actual conditions, for example, when the new energy device to be detected is located in a high-risk station such as an oil and gas station, all charging devices in the area are controlled to limit current; or only controlling the charging equipment where the new energy equipment to be detected is located to carry out current limiting. In addition, different SOC limit values may be set according to different scenarios, or charging devices in a designated area (such as a city) may be controlled to limit current, and so on, which are not described in detail in this embodiment.

On the basis of the foregoing embodiment, in order to facilitate uniform management of subsequent data, in this embodiment, after determining the safety threshold corresponding to the secondary reference charging process data, the method further includes:

and establishing a safety file of the new energy equipment to be detected according to the corresponding relation among the safety threshold value, the identity information of the new energy equipment to be detected and the charging starting information.

Further, in order to reduce the data calculation amount and improve the detection speed, after the actual charging start information of the new energy device to be detected, which is sent by the charging device, is acquired, the corresponding safety threshold and the identity information of the new energy device to be detected are searched from the safety file according to the actual charging start information;

and sending a safety threshold value to the charging equipment so that the charging equipment determines that the new energy equipment to be detected is abnormal in charging under the condition that the secondary actual charging process data exceeds the safety threshold value or the original threshold value output by the BMS.

Due to the fact that the safety file is established, after the charging cloud platform obtains the actual charging starting information, the corresponding safety threshold value can be found from the safety file and directly sent to the charging equipment, and online computing time is saved. It is to be understood that, for the charging cloud platform, it may also calculate a new safety threshold based on the latest one-time reference charging process data, and determine the actual charging process data according to the new safety threshold. In this embodiment, increase the detection of battery charging outfit to charging abnormity, realize charging cloud platform side protection and battery charging outfit side protection, realize the division of labor cooperation on big data layer and equipment layer, exert respective advantage. In addition, for the charging equipment, on one hand, the safety threshold sent by the charging cloud platform is received, on the other hand, the fixed threshold sent by the BMS is also obtained, the two thresholds are utilized to judge the primary actual charging process data and the secondary actual charging process data, and the problem that one threshold is lost or inaccurate in the transmission process to cause the inaccurate detection result is prevented.

In the foregoing embodiment, a detailed description is given of a method for detecting charging abnormality of new energy equipment, and the present application also provides an embodiment corresponding to a device for detecting charging abnormality of new energy equipment. It should be noted that the present application describes the embodiments of the apparatus portion from two perspectives, one is based on the functional module, and the other is based on the hardware structure.

Fig. 7 is a structural diagram of a detection apparatus for detecting charging abnormality of new energy devices according to an embodiment of the present application. As shown in fig. 7, the apparatus for detecting charging abnormality of the new energy device based on the angle of the functional module includes:

the first determining module 10 is configured to determine the type of the new energy device to be detected;

the selection module 11 is used for selecting a plurality of new energy devices under the types as analysis objects;

the acquisition module 12 is configured to acquire primary reference charging process data of the analysis object in a preset time range, where the primary reference charging process data is data generated by the analysis object in a charging process;

the first processing module 13 is configured to calculate, according to the primary reference charging process data, secondary reference charging process data, corresponding to each variable, for representing a variation trend of the variable;

the second processing module 14 is configured to calculate, according to the primary actual charging process data of the new energy device to be detected, secondary actual charging process data, corresponding to each variable, for representing a variable change trend; the primary actual charging process data is data generated in the current charging process of the new energy equipment to be detected;

and the second determining module 15 is configured to determine, by using an anomaly detection method, a safety threshold corresponding to the secondary reference charging process data based on a corresponding relationship between the secondary reference charging process data and time, where the safety threshold is used as a comparison object to be compared with secondary actual charging process data of the new energy device to be detected, so as to determine that the new energy device to be detected is charging anomalous.

Since the embodiments of the apparatus portion and the method portion correspond to each other, please refer to the description of the embodiments of the method portion for the embodiments of the apparatus portion, which is not repeated here.

The device for detecting charging abnormality of new energy equipment provided by this embodiment determines the type of new energy equipment to be detected, selects a plurality of new energy equipment in the type as analysis objects, and acquires primary reference charging process data of the analysis objects, which is matched with the analysis objects, within a preset time range. And calculating secondary reference charging process data according to the primary reference charging process data, and calculating secondary actual charging process data according to the primary actual charging process data of the new energy equipment to be detected. And finally, determining a safety threshold corresponding to the secondary reference charging process data by using an anomaly detection method based on the corresponding relation between the secondary reference charging process data and time, wherein the safety threshold is used for being compared with secondary actual charging process data of the new energy equipment to be detected as a comparison object so as to determine that the new energy equipment to be detected is abnormally charged. Therefore, when the technical scheme is applied, the safety threshold is obtained through the primary reference charging process data of the new energy equipment of the same type as the new energy equipment to be detected within the preset time range, and the primary reference charging process data is real data, so that the obtained safety threshold can accurately reflect the current charging state of the new energy equipment to be detected, and compared with a fixed threshold, the safety threshold obtained by the technical scheme can improve the accuracy of charging abnormity detection. In addition, the secondary reference charging process data can reflect the dynamic development of the variable, so that the obtained safety threshold can quantify the dynamic development of the variable, and the charging abnormity can be identified in time.

Fig. 8 is a structural diagram of a device for detecting charging abnormality of a new energy device according to another embodiment of the present application, and as shown in fig. 8, the device for detecting charging abnormality of a new energy device includes, in terms of hardware structure: a memory 20 for storing a computer program;

and the processor 21 is configured to implement the steps of the method for detecting charging abnormality of the new energy device in the above embodiment when executing the computer program.

The device for detecting charging abnormality of the new energy device provided by this embodiment may include, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, or the like.

The processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 21 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 21 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the display screen. In some embodiments, the processor 21 may further include an AI (Artificial Intelligence) processor for processing a calculation operation related to machine learning.

The memory 20 may include one or more computer-readable storage media, which may be non-transitory. Memory 20 may also include high speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In this embodiment, the memory 20 is at least used for storing a computer program 201, wherein after being loaded and executed by the processor 21, the computer program is capable of implementing the relevant steps of the method for detecting charging abnormality of a new energy device disclosed in any one of the foregoing embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202, data 203, and the like, and the storage manner may be a transient storage manner or a permanent storage manner. Operating system 202 may include, among others, Windows, Unix, Linux, and the like. Data 203 may include, but is not limited to, primary reference charging process data, secondary reference charging process data, primary actual charging process data, secondary actual charging process data, and the like.

In some embodiments, the apparatus for detecting charging abnormality of the new energy device may further include a display screen 22, an input/output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.

Those skilled in the art will appreciate that the structure shown in fig. 8 does not constitute a limitation of the detection means for charging abnormality of the new energy device, and may include more or less components than those shown in the drawings.

The device for detecting charging abnormality of the new energy device, provided by the embodiment of the application, comprises a memory and a processor, wherein when the processor executes a program stored in the memory, the following method can be realized: firstly, determining the type of the new energy equipment to be detected, then selecting a plurality of new energy equipment under the type as analysis objects, and then acquiring primary reference charging process data of the analysis objects, which are matched with the analysis objects, within a preset time range. And calculating secondary reference charging process data according to the primary reference charging process data, and calculating secondary actual charging process data according to the primary actual charging process data of the new energy equipment to be detected. And finally, determining a safety threshold corresponding to the secondary reference charging process data by using an anomaly detection method based on the corresponding relation between the secondary reference charging process data and time, wherein the safety threshold is used for being compared with secondary actual charging process data of the new energy equipment to be detected as a comparison object so as to determine that the new energy equipment to be detected is abnormally charged. Therefore, when the technical scheme is applied, the safety threshold is obtained through the primary reference charging process data of the new energy equipment of the same type as the new energy equipment to be detected within the preset time range, and the primary reference charging process data is real data, so that the obtained safety threshold can accurately reflect the current charging state of the new energy equipment to be detected, and compared with a fixed threshold, the safety threshold obtained by the technical scheme can improve the accuracy of charging abnormity detection. In addition, the secondary reference charging process data can reflect the dynamic development of the variable, so that the obtained safety threshold can quantify the dynamic development of the variable, and the charging abnormity can be identified in time.

The application also provides a corresponding embodiment of the computer readable storage medium. The computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps as set forth in the above-mentioned method embodiments.

It is to be understood that if the method in the above embodiments is implemented in the form of software functional units and sold or used as a stand-alone product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application may be substantially or partially implemented in the form of a software product, which is stored in a storage medium and executes all or part of the steps of the methods of the embodiments of the present application, or all or part of the technical solutions. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

Finally, the application also provides a new energy device charging abnormity prompting terminal, which comprises a memory and a control module, wherein the memory is used for storing a computer program;

and outputting the charging abnormity prompt information.

The charging abnormity detection result is obtained through the following steps:

determining the type of the new energy equipment to be detected;

selecting a plurality of new energy devices of the same type as analysis objects;

acquiring primary reference charging process data of an analysis object in a preset time range, wherein the primary reference charging process data is data generated by the analysis object in a charging process;

calculating secondary reference charging process data which are corresponding to each variable and used for representing the variable variation trend according to the primary reference charging process data;

and determining a safety threshold corresponding to the secondary reference charging process data by using an anomaly detection method based on the corresponding relation between the secondary reference charging process data and time, wherein the safety threshold is used for being compared with secondary actual charging process data of the new energy equipment to be detected as a comparison object so as to determine that the new energy equipment to be detected is abnormally charged and generate a charging anomaly detection result.

It can be understood that the terminal for prompting charging abnormality of the new energy device provided by the embodiment may include, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, or the like. In general, the charging abnormality detection result is obtained by the charging cloud platform or the charging terminal mentioned above, and the devices are in communication connection with the prompt terminal, so that the charging abnormality detection result is sent to the prompt terminal, and the prompt terminal receives and outputs the charging abnormality prompt information, so that a user can check the charging abnormality prompt information in time.

The method, the device, the medium and the prompt terminal for detecting the charging abnormality of the new energy device are described in detail above. The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, without departing from the principle of the present application, the present application can also make several improvements and modifications, and those improvements and modifications also fall into the protection scope of the claims of the present application.

It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

Claims

1. A method for detecting charging abnormality of new energy equipment is characterized by comprising the following steps:

determining the type of the new energy equipment to be detected;

selecting a plurality of new energy devices under the type as analysis objects;

2. The detection method according to claim 1, wherein the selecting a plurality of new energy devices of the type as analysis objects comprises:

3. The detection method according to claim 2, wherein the determining the safety threshold corresponding to the secondary reference charging process data by using an anomaly detection method based on the correspondence relationship between the secondary reference charging process data and time comprises:

4. The detection method according to claim 3, wherein the statistical analysis method comprises a normal distribution statistical method and a mean value method, and the cluster analysis method comprises a Gaussian mixture clustering method.

5. The detection method according to claim 1, wherein the obtaining of the primary reference charging process data of the analysis object matching with the analysis object within a preset time range comprises:

6. The detection method according to any one of claims 1 to 5, further comprising:

7. The detection method according to claim 6, wherein the determining the health condition corresponding to the deviation degree of the secondary actual charging process data from the safety threshold according to the preset corresponding relationship between the deviation degree and the health condition comprises:

8. The detection method according to claim 6, wherein the determining the health condition corresponding to the deviation degree of the secondary actual charging process data from the safety threshold according to the preset corresponding relationship between the deviation degree and the health condition comprises:

the establishing process of the scoring model comprises the following steps:

9. The method according to any one of claims 1 to 5, wherein after determining the safety threshold corresponding to the secondary reference charging process data by using an abnormality detection method based on the correspondence relationship between the secondary reference charging process data and time, the method further comprises:

10. The detection method according to claim 9, further comprising:

11. The utility model provides a detection apparatus for new energy equipment charging is unusual, its characterized in that includes:

the first determining module is used for determining the type of the new energy equipment to be detected;

the selection module is used for selecting the new energy equipment under the type as an analysis object;

the acquisition module is used for acquiring primary reference charging process data, matched with the analysis object, of the analysis object within a preset time range, wherein the primary reference charging process data are data generated by the analysis object in a charging process;

the first processing module is used for calculating secondary reference charging process data which are corresponding to all variables and used for representing variable variation trend according to the primary reference charging process data;

the second processing module is used for calculating secondary actual charging process data which are corresponding to all variables and used for representing variable change trends according to the primary actual charging process data of the new energy equipment to be detected; the primary actual charging process data are data generated in the current charging process of the new energy equipment to be detected;

and the second determining module is used for determining a safety threshold corresponding to the secondary reference charging process data by using an anomaly detection method based on the corresponding relation between the secondary reference charging process data and time, wherein the safety threshold is used for being used as a comparison object to be compared with the secondary actual charging process data of the new energy equipment to be detected so as to determine that the new energy equipment to be detected is abnormally charged.

12. The device for detecting the charging abnormity of the new energy equipment is characterized by comprising a memory, a storage unit and a control unit, wherein the memory is used for storing a computer program;

a processor for implementing the steps of the method for detecting charging anomalies of a new energy device according to any one of claims 1 to 10 when the computer program is executed.

13. A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when executed by a processor, the computer program implements the steps of the method for detecting charging abnormality of a new energy device according to any one of claims 1 to 10.

14. The utility model provides a suggestion terminal that new energy equipment charges unusually, its characterized in that includes:

a memory for storing a computer program;

outputting charging abnormity prompt information;

wherein the charging abnormality detection result is obtained by the steps of:

determining the type of the new energy equipment to be detected;

selecting a plurality of new energy devices under the type as analysis objects;

acquiring primary reference charging process data, matched with the analysis object, of the analysis object within a preset time range, wherein the primary reference charging process data are data generated by the analysis object in a charging process;

calculating secondary reference charging process data which are corresponding to all variables and used for representing variable variation trends according to the primary reference charging process data;