WO2022174698A1

WO2022174698A1 - Health status evaluation method and apparatus for new energy device, medium and prompt terminal

Info

Publication number: WO2022174698A1
Application number: PCT/CN2022/071525
Authority: WO
Inventors: 鞠强; 朱诗严; 潘博存; 项宝庆
Original assignee: 青岛特来电新能源科技有限公司
Priority date: 2021-02-20
Filing date: 2022-01-12
Publication date: 2022-08-25
Also published as: CN114966447A

Abstract

A health status evaluation method and apparatus for a new energy device, a medium, and a prompt terminal. First, a type of a new energy device to be tested is determined, and then a matched primary reference charging process data of a plurality of new energy devices of that type within a preset time range is selected. Secondary reference charging process data is calculated according to the primary reference charging process data, and secondary actual charging process data is calculated according to the primary actual charging process data. Finally, on the basis of correlation between the primary reference charging process data and the secondary reference charging process data, and time, the respective corresponding primary safety threshold and secondary safety threshold are respectively determined using an anomaly detection method. Moreover, the health status of the new energy device to be tested can be determined according to the degree to which the primary actual charging process data and the secondary actual charging process data deviate from the respective safety threshold. With the application of the technical solution, a prompt about the health status of the current device can be given to a user in time.

Description

Health status evaluation method, device, medium and prompt terminal of new energy equipment

This application claims the priority of the Chinese patent application filed on February 20, 2021 with the application number 202110192496.7 and the invention titled "Method, Device, Medium and Prompting Terminal for Health Status Evaluation of New Energy Equipment", all of which The contents are incorporated herein by reference.

technical field

The present application relates to the technical field of new energy, and in particular, to a method, device, medium and prompt terminal for evaluating the health status of new energy equipment.

Background technique

The new energy devices mentioned in this application are devices that provide kinetic energy through batteries, for example, vehicles using batteries, hereinafter referred to as electric vehicles. With the rapid development of new energy equipment, more and more attention has been paid to the charging technology of batteries, among which the safety of charging is the focus of attention.

In order to prevent the battery from charging accidents due to factors such as high battery temperature during the charging process, the manufacturer usually tests the battery. Through the test experiment, the safety thresholds of various variables for the safe charging of the battery are obtained and written into the battery management system (BMS). ). During the charging process, the relationship between the current parameters of the battery and the set safety threshold is compared to determine whether the battery is charged abnormally.

Obviously, the above method can only identify the abnormality of charging, and the specific degree of abnormality cannot be judged. Therefore, for the user, the health status of the power battery cannot be determined, so it brings many uncertain factors to the subsequent use, and the user experience Feel bad. In addition, the safety threshold obtained in the test experiment is usually fixed, and with the change of battery performance, the corresponding threshold is also constantly changing. If a fixed safety threshold is used as the abnormality judgment standard, the judgment result will inevitably be inaccurate. Moreover, the existing safety thresholds usually quantify a static feature, such as the temperature of the power battery, without considering the dynamic development of variables, so it is impossible to identify abnormal charging in time.

It can be seen that how to evaluate the health status of the power battery of the new energy equipment and accurately and timely identify the abnormal charging is an urgent problem to be solved by those skilled in the art.

SUMMARY OF THE INVENTION

The purpose of the present application is to provide a method, device, medium and prompting terminal for evaluating the health status of new energy equipment, which are used to evaluate the health status of the power battery of the new energy equipment and to accurately and timely identify abnormal charging.

In order to solve the above-mentioned technical problems, the present application provides a method for evaluating the health status of new energy equipment, including:

Determine the type of new energy equipment to be tested;

Select multiple new energy equipment sets under the type as analysis objects;

Acquire primary reference charging process data that matches the analysis object within a preset time range of the analysis object, where the primary reference charging process data is data generated by the analysis object during the charging process;

Calculate, according to the primary reference charging process data, secondary reference charging process data corresponding to each variable and used to characterize the variation trend of the variables;

According to the actual charging process data of the new energy equipment to be detected, the second actual charging process data corresponding to each variable and used to represent the variation trend of the variables is calculated; The data generated during the current charging process of the device;

Based on the corresponding relationship between the primary reference charging process data and the secondary reference charging process data and time, the abnormality detection method is used to determine the first safety value corresponding to the primary reference charging process data and the secondary reference charging process data. a threshold value and a second safety threshold value, the first safety threshold value and the second safety threshold value are used as comparison objects to be compared with the actual primary charging process data and the secondary actual charging process data of the new energy device to be detected, respectively, to determine that the charging of the new energy equipment to be detected is abnormal;

According to the preset corresponding relationship between the degree of deviation and the state of health, determine the degree of deviation between the actual charging process data and the primary safety threshold and the degree of deviation between the actual charging process data and the secondary safety threshold. corresponding health status.

Preferably, the selection of multiple new energy equipment sets under the type as analysis objects includes:

Select multiple new energy devices in the same area and/or the same age under the type as the analysis object.

Preferably, based on the corresponding relationship between the primary reference charging process data and the secondary reference charging process data and time, the abnormality detection method is used to determine the respective primary reference charging process data and the secondary reference charging process data. The corresponding first safety threshold and second safety threshold include:

Use a statistical analysis method or a cluster analysis method to determine the primary safety threshold corresponding to each variable in the primary reference charging process data and the secondary reference charging process data corresponding to each variable safety threshold.

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 obtaining the primary reference charging process data that matches the analysis object within a preset time range of the analysis object includes:

Acquiring a plurality of target charging orders that match the analysis object within a preset time range of the analysis object;

The primary reference charging process data is extracted from each of the target charging orders.

Preferably, according to the preset corresponding relationship between the degree of deviation and the state of health, the degree of deviation between the first actual charging process data and the first safety threshold and the second actual charging process data and the second safety threshold are determined. Health conditions corresponding to the degree of deviation from the threshold include:

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

Obtain a historical charging process data from each of the historical charging orders, and calculate the average value corresponding to each variable in the historical charging process data as an actual average value;

calculating the primary reference average value corresponding to each variable of the primary reference charging process data within the predetermined time;

Determining the degree of deviation of the primary variable between the primary actual average value corresponding to the same variable and the primary safety threshold;

determining the primary actual health level corresponding to the primary variable deviation degree according to the preset correspondence between the variable deviation degree and the health level;

Calculate the secondary historical charging process data corresponding to each variable and used to characterize the variation trend of the variable according to the primary historical charging process data;

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

calculating the secondary reference average value corresponding to each variable of the secondary reference charging process data within the predetermined time;

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

Determine the secondary actual health level corresponding to the deviation degree of the secondary variable according to the corresponding relationship between the deviation degree of the variable and the health level;

The health status of the new energy equipment to be detected is determined according to the primary actual health level and the secondary actual health level.

determining the first-time actual scoring data of each variable in the first-time actual charging process data according to the first-time scoring model corresponding to each preset variable;

determining a primary actual health level corresponding to the primary actual scoring data according to the preset correspondence between the score data and the health level;

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

Determine the secondary actual health level corresponding to the secondary actual score data according to the preset correspondence between the score data and the health level;

Wherein, the establishment process of described primary scoring model comprises the following steps:

Perform interval division according to a plurality of interval ranges formed by the average value and variance corresponding to each variable in the primary reference charging process data;

According to the degree of deviation between the actual value of each variable and the critical value of the corresponding interval, the corresponding relationship between the degree of deviation and the score data is established;

Wherein, the establishment process of described secondary scoring model comprises the following steps:

Perform interval division according to multiple interval ranges formed by the average value and variance corresponding to each variable in the secondary reference charging process data;

According to the degree of deviation between the actual value of each variable and the critical value of the corresponding interval, the corresponding relationship between the degree of deviation and the score data is established.

Preferably, based on the corresponding relationship between the primary reference charging process data and the secondary reference charging process data and time, an abnormality detection method is used to determine the primary reference charging process data and the secondary reference charging process data. After the respective corresponding first safety threshold and second safety threshold, further include:

The safety file of the new energy device to be detected is established according to the corresponding relationship between the primary safety threshold, the secondary safety threshold, the identity information of the new energy device to be detected, and the charging start information.

Preferably, it also includes:

After obtaining the actual charging start information of the new energy device to be detected sent by the charging device, search the corresponding primary safety threshold, the secondary safety threshold and the safety file from the safety file according to the actual charging start information the identity information of the new energy equipment to be detected;

Send the primary safety threshold and the secondary safety threshold to the charging device, so that the charging device determines that the primary actual charging process data exceeds the primary security threshold, or the secondary actual charging process data exceeds the primary security threshold. When the secondary safety threshold or the primary actual charging process data exceeds the original threshold output by the BMS, it is determined that the charging of the new energy device to be detected is abnormal.

In order to solve the above-mentioned technical problems, the present application also provides a device for evaluating the health status of new energy equipment, including:

The first determination module is applied to determine the type of the new energy equipment to be detected;

a selection module, used to select multiple new energy equipment sets under the type as analysis objects;

an acquisition module, configured to acquire the primary reference charging process data matching the analytical object within a preset time range of the analytical object, and the primary reference charging process data is the data generated by the analytical object during the charging process ;

a first processing module, configured to calculate, according to the primary reference charging process data, secondary reference charging process data corresponding to each variable and used to characterize the variation trend of the variables;

The second processing module is configured to calculate the secondary actual charging process data corresponding to each variable and used to represent the variation trend of the variables according to the primary actual charging process data of the new energy equipment to be detected; wherein, the primary actual charging process data is: data generated during the current charging process of the new energy device to be detected;

a second determination module, configured to determine the primary reference charging process data and the secondary reference charging process data by using an abnormality detection method based on the corresponding relationship between the primary reference charging process data and the secondary reference charging process data and time The first safety threshold value and the second safety threshold value corresponding to the data, the first safety threshold value and the second safety threshold value are used as comparison objects with the actual first charging process data and the second charging process data of the new energy equipment to be detected, respectively. The actual charging process data is compared to determine that the charging of the new energy equipment to be detected is abnormal;

The third determination module is configured to determine, according to the preset corresponding relationship between the degree of deviation and the state of health, the degree of deviation between the first actual charging process data and the first safety threshold, and the second actual charging process data and the second The health status corresponding to the degree of deviation from the secondary safety threshold.

In order to solve the above technical problems, the present application also provides a device for evaluating the health status of new energy equipment, including a memory for storing a computer program;

The processor is configured to implement the steps of the method for evaluating the health condition of the new energy equipment when executing the computer program.

In order to solve the above technical problems, the present application also provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the health status of the new energy device is realized. The steps of the assessment method.

In order to solve the above technical problems, the present application also provides a health status prompt terminal for new energy equipment, including:

memory for storing computer programs;

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

receiving the health status of the new energy device to be detected and the abnormal charging detection result corresponding to the abnormal charging of the new energy device to be detected;

outputting the health status, and in the case of receiving the abnormal charging detection result, outputting abnormal charging prompt information;

Wherein, the abnormal charging detection result and the state of health are obtained through the following steps:

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

Select multiple new energy equipment sets under the type as analysis objects;

Obtain the primary reference charging process data that matches the analytical object within a preset time range of the analytical object, and the primary reference charging process data is the data generated by the analytical object during the charging process;

The method for evaluating abnormal charging of new energy equipment provided by this application firstly determines the type of new energy equipment to be detected, then selects multiple new energy equipment under this type as analysis objects, and then obtains the analysis objects within a preset time range. , The primary reference charging process data that matches the analysis object. The secondary reference charging process data is calculated according to the primary reference charging process data, and the secondary actual charging process data is calculated according to the primary actual charging process data. Finally, based on the corresponding relationship between the primary charging process data and the secondary reference charging process data and time, the anomaly detection method is used to determine the primary and secondary safety thresholds corresponding to the primary reference charging process data and the secondary reference charging process data, respectively. It is used as a comparison object to compare with the actual charging process data of the first time and the actual charging process data of the second time, so as to determine the abnormal charging of the new energy equipment to be detected. In addition, the health status of the new energy equipment to be detected can be determined according to the degree of deviation between the primary actual charging process data and the secondary actual charging process data and the respective safety thresholds. It can be seen that, applied to the technical solution, a prompt of the current health status of the user's device can be given in time, the user experience is improved, and serious consequences caused by charging when the health status is poor can be avoided. In addition, the primary safety threshold and the secondary safety threshold are obtained through the primary reference charging process data, and the primary reference charging process data are real data, so compared with the fixed threshold, the primary safety threshold and secondary security threshold obtained by this technical solution are The sub-safety threshold can improve the accuracy of charging abnormality detection. Finally, the secondary reference charging process data can reflect the dynamic development of variables, so the obtained secondary safety threshold can quantify the dynamic development of variables and identify charging abnormalities in time.

In addition, the apparatus for evaluating the health status of the new energy equipment, the medium and the prompting terminal provided by the present application correspond to the above method, and the effects are the same as above.

Description of drawings

In order to describe the embodiments of the present application more clearly, the following will briefly introduce the drawings that are used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, which are not relevant to ordinary skills in the art. As far as personnel are concerned, other drawings can also be obtained from these drawings on the premise of no creative work.

FIG. 1 is a structural diagram of a charging management system for an electric vehicle provided by an embodiment of the application;

2 is a flowchart of a method for evaluating the health status of a new energy device provided by an embodiment of the present application;

3 is a schematic diagram of a normal distribution curve of a maximum temperature provided in an embodiment of the present application;

4 is a schematic diagram of a normal distribution curve of a maximum temperature rise rate provided by an embodiment of the present application;

5 is a schematic diagram of a normal distribution curve of a maximum temperature difference provided by an embodiment of the present application;

6 is a schematic diagram of a normal distribution curve of a maximum SOC change rate provided by an embodiment of the present application;

7 is a schematic diagram of a normal distribution curve of a maximum differential pressure provided by an embodiment of the present application;

8 is a structural diagram of a device for evaluating the health status of a new energy device provided by an embodiment of the present application;

FIG. 9 is a structural diagram of an apparatus for evaluating a health condition of a new energy device according to another embodiment of the present application.

Detailed ways

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. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative work fall within the protection scope of the present application.

The core of the present application is to provide a method, device, medium and prompt terminal for evaluating the health status of new energy equipment. The new energy device proposed in this application may be an electric vehicle or other electric device, and the following description will take an electric vehicle as an example. The method for evaluating the health status of new energy equipment mentioned in the embodiments of the present application can be applied to a charging cloud platform or charging equipment, and can also be an unmanned vehicle management platform (applicable to unmanned vehicles). In the following, the evaluation method of abnormal charging of new energy equipment is applied to the charging cloud platform for description. Among them, the charging cloud platform is connected in communication with the charging equipment for unified management of multiple charging equipment. Usually, the charging cloud platform consists of multiple computers cooperating with each other to achieve corresponding functions. There are usually two types of hardware components for charging equipment. One is that the charger and the charging terminal are set together, which is relatively large and is often used in fast charging scenarios such as high-speed service areas. The other is the separate setting of the charging machine and the charging terminal. One charger can be connected with multiple charging terminals for unified management of multiple charging terminals. Because the charger and the charging terminal are set separately, the charging terminal is small in size and directly interacts with the electric vehicle. The function is relatively simple. Usually, the acquired vehicle data is sent to the corresponding charger, and the charger completes the more complex tasks. Data operation, and then return the operation result to the charging terminal. FIG. 1 is a structural diagram of a charging management system for an electric vehicle provided by an embodiment of the present application. As shown in Figure 1, the charging management system includes a charging cloud platform, a plurality of charging devices connected to the charging cloud platform, and the charging devices obtain relevant data of the electric vehicle, such as charging start information, and send the charging start information to the charging cloud. The platform, the charging cloud platform identifies the device model according to the charging startup information, so as to perform relevant calculations on the charging process data matching the device signal to obtain the safety threshold. It should be noted that Figure 1 is only a specific application scenario, and does not mean that the charging cloud platform must realize the detection of abnormal charging of new energy equipment.

The hardware usage scenarios corresponding to the method for evaluating the health status of the new energy equipment provided by the present application are described above. Embodiments of the evaluation method for abnormal charging of new energy equipment will be described below. In order to make those skilled in the art better understand the solution of the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

FIG. 2 is a flowchart of a method for evaluating a health condition of a new energy device provided by an embodiment of the present application. As shown in Figure 2, the method includes:

S10: Determine the type of 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 multiple new energy devices of this type as analysis objects.

S11: Select multiple new energy equipment sets under the type as analysis objects.

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

S12: Acquire primary reference charging process data matching the analysis object within a preset time range of the analysis object.

The charging process data mentioned in this application is the data generated by any new energy device during the charging process. The charging process data comes from the charging cloud platform and charging equipment, including charging system data and charging data. The charging system data is mainly the charging pile/charging terminal data, user data, and vehicle data stored in the cloud platform system that supports the charging business. The charging data is Obtained from the vehicle by the charging device during the charging process (handshake phase, parameter configuration phase, charging phase, charging end). The primary reference charging process data is the data generated by the analysis object during the charging process. The one-time reference charging process data and the one-time actual charging process data mentioned below are both types of charging process data, that is, data generated by the new energy device during the charging process. Just for the purpose of distinction, the data generated by the new energy equipment to be detected during the current charging process is called the actual charging process data, and the charging process of the new energy equipment (analysis object) of the same model as the new energy equipment to be detected The data is called primary reference charging process data, and is used as reference data.

Correspondingly, the one-time reference charging process data can be the charging process data of the new energy device of the same type as the new energy device to be detected, or the charging process of the new energy device of the same type + the same age as the new energy device to be detected. data etc. Taking an electric vehicle as an example, the reference charging process data can be the data generated by the following new energy equipment during the charging process:

(1) The same model + a certain time period in the past/current moment;

(2) The same model + the same area (like a city) + a certain time period in the past/current moment;

(3) Same model + same age + past time period/current moment.

For example, if the type of new energy equipment to be detected is BYD EV450, then multiple new energy equipment under the type selected as the analysis objects can be: BYD EV450 in Chengdu area from October 1st to October 30th, 2020, 2 years The age of the electric vehicle is used as the analysis object.

As a preferred embodiment, the primary reference charging process data includes the highest temperature of the power battery, the lowest temperature of the power battery, the SOC of the power battery, the highest voltage of the single battery, the lowest voltage of the single battery, the number of the highest voltage of the single battery, the highest Temperature monitoring point number and minimum temperature monitoring point number. It should be noted that the SOC of the traction battery mentioned in this embodiment includes the SOC during normal charging, and also includes the SOC when the imbalance is abnormally terminated. The SOC at the time of abnormal termination of imbalance belongs to the charging process data, but after abnormal charging occurs, the SOC at the end of charging is reversely analyzed. The SOC at the time of abnormal termination of imbalance is the battery SOC when the power battery is abnormally terminated due to imbalance. The battery reaches the target SOC termination. In a specific embodiment, the more variables in the one-time reference charging process data, the more accurate the abnormal charging detection result. On this basis, the secondary reference charging process data includes the maximum temperature difference of the power battery, the maximum pressure difference of the power battery, the maximum temperature rise rate of the power battery, the maximum SOC change rate of the power battery, the maximum change rate of the single cell voltage, the maximum The fragrance entropy value of the temperature monitoring point number, the fragrance entropy value of the lowest temperature monitoring point number, and the fragrance entropy value of the number where the highest voltage of the single battery is located.

1) The maximum temperature difference of the power battery

The temperature difference refers to the difference between the maximum temperature and the minimum temperature of the battery at the same time during the charging process, which is obtained from the maximum temperature of the power battery and the minimum temperature of the power battery. The maximum temperature difference refers to the maximum temperature difference during one charge.

2) The maximum pressure difference of the power battery

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

3) The maximum temperature rise rate of the power battery

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

4) The maximum SOC change rate of the power battery

The rate of SOC change refers to the rate of change of the SOC transmitted by 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 during one charge.

5) Maximum rate of change of single cell voltage

The rate of change of the single cell voltage refers to the amount of change in the highest voltage of the single cell transmitted by the BMS at a specific frequency (milliseconds, seconds, minutes) during the charging process. The maximum rate of change of the cell voltage refers to the maximum value of the rate of change of the cell voltage during one charge.

6) Fragrance entropy value of the highest temperature monitoring point number

Based on the highest temperature detection point number obtained at a specific frequency (milliseconds, seconds, minutes) during a charging process, combined with the fragrance entropy algorithm, the fragrance entropy value of the highest temperature monitoring point number is calculated.

7) Fragrance entropy value of the lowest temperature monitoring point number

Based on the lowest temperature detection point number obtained at a specific frequency (milliseconds, seconds, minutes) during a charging process, combined with the fragrance entropy algorithm, the fragrance entropy value of the lowest temperature monitoring point number is calculated.

8) Fragrance entropy value of the number where the highest voltage of the single battery is located

Based on the detection point number of the highest voltage of the single battery obtained at a specific frequency (milliseconds, seconds, minutes) during a charging process, combined with the fragrance entropy algorithm to calculate the fragrance entropy value of the number where the highest voltage of the single battery is located.

It can be understood that the Xiangnon entropy value can be used to see the degree of dispersion between the number of the highest temperature monitoring point and the number of the highest voltage of the single battery during the charging process. The lower the degree of dispersion, the greater the possibility of abnormal charging.

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

S13: Calculate secondary reference charging process data corresponding to each variable and used to characterize the variation trend of the variable 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 to characterize the variation trend of variables, such as variable variation, gradient variation, and the like. It can be understood that the number of variables contained in the primary reference charging process data and the number of variables contained in the secondary reference charging process data may be the same or different, but the types of variables are necessarily different, which will be described in detail below.

S14: Calculate secondary actual charging process data corresponding to each variable and used to characterize the variation trend of the variables according to the primary actual charging process data of the new energy equipment to be detected.

The data of an actual charging process is the data generated during the current charging process of the new energy device to be detected. The second actual charging process data is obtained according to the first actual charging process data, and is used to characterize the change trend of variables, such as variable variation, gradient change, and discrete degree. It should be noted that the method for obtaining the secondary reference charging process data from the primary reference charging process data is the same as the method for obtaining the secondary actual charging process data from the primary actual charging process data. It can be understood that the number of variables contained in the primary reference charging process data and the number of variables contained in the secondary reference charging process data may be the same or different, but the types of variables are necessarily different, which will be described in detail below.

S15: Based on the corresponding relationship between the primary reference charging process data and the secondary reference charging process data and time, use the abnormality detection method to determine the first safety threshold and the second safety threshold corresponding to the primary reference charging process data and the secondary reference charging process data respectively. .

In this step, the first safety threshold and the second safety threshold are used as comparison objects to be compared with the actual first charging process data and the second actual charging process data of the new energy equipment to be detected, respectively, so as to determine the abnormal charging of the new energy equipment to be detected. . It should be noted that, in this embodiment, the calculation methods of the primary safety threshold and the secondary safety threshold are not limited, and may be determined by using a statistical analysis method or a cluster analysis method. The primary and secondary safety thresholds in this step and the existing fixed thresholds obtained through experiments are both used to measure whether the charging is abnormal, but the primary and secondary safety thresholds in this step are passed and to be detected. The new energy equipment is obtained from the real data of the new energy equipment of the same type during the charging process, so it can truly reflect the charging state of the same type of equipment.

During the charging process of the new energy device to be detected, it is divided into four stages: handshake stage, parameter configuration stage, charging stage, and charging end. The actual charging process data can be data of one or all of the four stages. Since the primary safety threshold and the secondary safety threshold are determined by the charging process data of the new energy equipment of the same type to be detected, they can be used as the detection criteria for the abnormality of the new energy equipment to be detected. As long as at least one of the first actual charging process data or the second actual charging process data exceeds the corresponding safety threshold, it is determined that the charging of the new energy device to be detected is abnormal.

S16: Determine the health status corresponding to the deviation degree of the actual charging process data and the primary safety threshold and the deviation degree of the second actual charging process data and the secondary safety threshold according to the preset corresponding relationship between the deviation degree and the health condition.

It should be noted that steps S15 and S16 are independent of each other, and even if the new energy equipment to be detected does not have abnormal charging, the health status of the new energy equipment can be evaluated. In this embodiment, the actual health level of the new energy equipment to be detected is determined by the degree of deviation between the actual charging process data and the primary safety threshold, and the deviation degree between the actual charging process data and the secondary safety threshold, so that the user can grasp the equipment's health status in time. Health status.

In other embodiments, when the health status indicates that the new energy device to be detected is a high-risk device, the charging protection mechanism is triggered. It is understandable that the charging protection mechanism can be determined according to the actual situation. For example, if the new energy equipment to be detected is located in a high-risk site such as an oil and gas station, all charging equipment in the area will be controlled to limit the current; or only the new energy equipment to be detected will be controlled. The charging device where it is located is limited in current. In addition, different SOC limits may also be set according to different scenarios, or the charging equipment in a designated area (eg, Chengdu) can be controlled to limit the current, and the like will not be repeated in this embodiment.

The method for evaluating abnormal charging of a new energy device provided by this embodiment firstly determines the type of the new energy device to be detected, then selects multiple new energy devices under this type as analysis objects, and then obtains the analysis objects within a preset time range. , The primary reference charging process data that matches the analysis object. The secondary reference charging process data is calculated according to the primary reference charging process data, and the secondary actual charging process data is calculated according to the primary actual charging process data. Finally, based on the corresponding relationship between the primary charging process data and the secondary reference charging process data and time, the anomaly detection method is used to determine the primary and secondary safety thresholds corresponding to the primary reference charging process data and the secondary reference charging process data, respectively. It is used as a comparison object to compare with the actual charging process data of the first time and the actual charging process data of the second time, so as to determine the abnormal charging of the new energy equipment to be detected. In addition, the health status of the new energy equipment to be detected can be determined according to the degree of deviation between the primary actual charging process data and the secondary actual charging process data and the respective safety thresholds. It can be seen that, applied to the technical solution, a prompt of the current health status of the user's device can be given in time, the user experience is improved, and serious consequences caused by charging when the health status is poor can be avoided. In addition, the primary safety threshold and the secondary safety threshold are obtained through the primary reference charging process data, and the primary reference charging process data are real data, so compared with the fixed threshold, the primary safety threshold and secondary security threshold obtained by this technical solution are The sub-safety threshold can improve the accuracy of charging abnormality detection. Finally, the secondary reference charging process data can reflect the dynamic development of variables, so the obtained secondary safety threshold can quantify the dynamic development of variables and identify charging abnormalities in time.

On the basis of the above-mentioned embodiment, based on the corresponding relationship between the primary reference charging process data and the secondary reference charging process data and time, the abnormality detection method is used to determine the first safety value corresponding to the primary reference charging process data and the secondary reference charging process data respectively. Thresholds and second safety thresholds include:

The primary safety threshold corresponding to each variable in the primary reference charging process data and the secondary safety threshold corresponding to each variable in the secondary reference charging process data are determined 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 the clustering analysis method includes a Gaussian mixture clustering method. The corresponding methods are described in detail below.

1. Use the normal distribution statistical method to determine the safety threshold corresponding to the highest temperature in the reference charging process data

(1) Select the one-time reference charging process data in the charging order of the new energy equipment of the same type as the new energy equipment to be tested in the past 30 days as the sample data;

(2) For each order, obtain the maximum value of the maximum battery temperature per minute as the maximum temperature corresponding to this order;

(3) Calculate the average μ and standard deviation σ of the highest temperature of all orders;

(4) According to the "3σ" principle of normal distribution: the interval (μ-3σ, μ+3σ) is the actual possible value interval of the random variable X, and the probability of X falling outside this interval is less than three thousandths. It is generally assumed in the question that such an event will not occur. If the variable exceeds this three thousandths, it is an outlier. Then the maximum temperature threshold is μ+3σ, that is, the maximum temperature safety threshold. FIG. 3 is a schematic diagram of a normal distribution curve of a maximum temperature according to an embodiment of the present application. As shown in Figure 3, point A, point B, point C, point D, and point E are the maximum temperatures of the new energy equipment to be detected. The maximum temperature of E is abnormal.

It should be noted that the above maximum temperature safety threshold is only the safety threshold of the variable temperature in the one-time reference charging process data, and the safety thresholds of other variables in the reference charging data can also be calculated in the same way.

2. Use the Gaussian mixture clustering method to determine the safety threshold corresponding to the highest temperature in the multiple sets of one-time reference charging process data

(1) Select the charging orders within 30 days of the new energy equipment of the same type as the new energy equipment to be tested as the sample data;

(3) Determine the hyperparameter (number of clusters=3), establish the Gaussian mixture model of the highest temperature;

(4) Perform clustering calculation on the actual charging process data through the obtained Gaussian mixture model. The clustering results have three categories, the first category μ-3σ, the third category μ+3σ, and the rest are the second category. The probability of category 3 is greater than 50%, indicating that the actual charging process data exceeds the safety threshold.

3. Use the normal distribution statistical method to determine the safety threshold corresponding to the highest temperature rise rate in multiple sets of secondary reference charging process data

(1) Select the secondary reference charging process data in the charging orders for the new energy equipment of the same type as the new energy equipment to be tested in the past 30 days as the sample data;

(2) Obtain the temperature rise rate for each charging order;

(3) Calculate the average μ and standard deviation σ of the highest temperature rise rate of all orders;

(4) According to the "3σ" principle of normal distribution: the interval (μ-3σ, μ+3σ) is the actual possible value interval of the random variable X, and the probability of X falling outside this interval is less than three thousandths. It is generally assumed in the question that such an event will not occur. If the variable exceeds this three thousandths, it is an outlier. Then the maximum temperature threshold is μ+3σ, that is, the maximum temperature safety threshold. FIG. 4 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 Figure 4, point A, point B, point C, and point D are respectively the maximum temperature rise rates of the new energy equipment to be tested. Among them, the maximum temperature rise rates of points A and B are normal, and points C and D are The maximum temperature rise rate is abnormal.

4. Use the normal distribution statistical method to determine the safety threshold corresponding to the maximum temperature difference in multiple sets of secondary reference charging process data

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

(3) Calculate the average μ and standard deviation σ of the maximum temperature difference of all orders;

(4) According to the "3σ" principle of normal distribution: the interval (μ-3σ, μ+3σ) is the actual possible value interval of the random variable X, and the probability of X falling outside this interval is less than three thousandths. It is generally assumed in the question that such an event will not occur. If the variable exceeds this three thousandths, it is an outlier. Then the maximum temperature threshold is μ+3σ, that is, the maximum temperature safety threshold. FIG. 5 is a schematic diagram of a normal distribution curve of a maximum temperature difference provided by an embodiment of the present application. As shown in Figure 5, point A, point B, point C, point D, and point E are the maximum temperature difference of the new energy equipment to be detected, among which, the maximum temperature difference of point A is normal, and point B, point C, point D and point The maximum temperature difference of E is constant.

Similarly, according to the above method, the safety threshold of each variable in the secondary reference charging process data can be obtained, which is not repeated in this embodiment. FIG. 6 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 Figure 6, point A, point B, point C, and point D are respectively the maximum SOC change rates of the new energy equipment to be detected. The maximum SOC rate of change is abnormal. FIG. 7 is a schematic diagram of a normal distribution curve of a maximum pressure difference provided by an embodiment of the present application. As shown in Figure 7, point A is the maximum pressure difference of the new energy equipment to be detected, and the maximum pressure difference of point A is constant.

5. Use the Gaussian mixture clustering method to determine the safety threshold corresponding to the highest temperature rise rate in multiple sets of secondary reference charging process data

(2) Obtain the maximum temperature rise value per minute for each order, and then obtain the maximum value of the maximum temperature rise value for each order;

(3) With the determined hyperparameter (number of clusters=3), a Gaussian mixture model of the highest temperature rise rate is established;

(4) Perform clustering calculation on the data of the second actual charging process through the obtained Gaussian mixture model. The clustering results have 3 categories, the first category μ-3σ, the third category μ+3σ, and the rest are the second category, If the probability of class 3 is greater than 50%, it indicates that the second actual charging process data exceeds the safety threshold.

Further, obtaining the primary reference charging process data that matches the analysis object within a preset time range of the analysis object includes:

Acquire multiple target charging orders that match the analysis object within a preset time range;

The reference charging process data is extracted once from each target charging order.

For the charging cloud platform, during the charging process of the new energy equipment, a charging order corresponding to this charging will be generated. The charging order contains the reference charging process data mentioned above, so the charging order is used to obtain a reference charging. The method of process data is relatively simple, and there is no need to add additional hardware or occupy additional equipment resources.

Two specific implementation manners of S16 are given in this application, which does not mean that there are only these two manners.

1. On the basis of the above embodiment, S16 includes:

Obtain multiple historical charging orders within a predetermined time of the new energy equipment to be tested;

Obtain a historical charging process data from each historical charging order, and calculate the average value corresponding to each variable in the historical charging process data as an actual average value;

Calculate the primary reference average value of each variable corresponding to the primary reference charging process data within a predetermined time;

Determine the degree of deviation of the primary variable between the primary actual average value corresponding to the same variable and the primary safety threshold;

Determine the primary actual health level corresponding to the primary variable deviation degree according to the corresponding relationship between the preset variable deviation degree and the health level;

Calculate the secondary historical charging process data corresponding to each variable and used to characterize the variation trend of the variables according to the primary historical charging process data;

Calculate the secondary reference average value of each variable corresponding to the secondary reference charging process data within a predetermined time;

Determine the degree of deviation of the quadratic variable between the quadratic actual average value corresponding to the same variable and the quadratic safety threshold;

Determine the secondary actual health level corresponding to the deviation degree of the secondary variable according to the corresponding relationship between the pre-set variable deviation degree and the health level;

Determine the health status of the new energy equipment to be detected according to the primary actual health level and the secondary actual health level.

It should be noted that the method for obtaining the secondary historical charging process data from the primary historical charging process data is the same as the method for obtaining the secondary actual charging process data from the primary actual charging process data. The one-time historical charging process data in this embodiment is also a type of charging process data, which is only the charging process data corresponding to the historical charging order. Since the historical charging order is the order of the new energy equipment to be tested, the historical charging process data is the real data of the new energy equipment to be tested, and the actual average value obtained through these data is used as a health assessment of the new energy equipment to be tested. , Similarly, the reference average value obtained by referring to the charging process data once is used as the reference standard. If the deviation between the actual average value and the reference average value is large, it means that the risk of becoming a high-risk device is high. It can be understood that, the corresponding relationship between the variable deviation degree and the health level may be obtained according to an empirical value or other calibration methods, which will not be described in detail in this embodiment.

In this embodiment, the health status of the new energy to be detected is evaluated to realize the quantification of the health status, and the user can know the charging trend of the power battery in advance through the actual health level, which improves the user experience.

2. In this embodiment, another method is used to quantitatively evaluate the health status of the new energy equipment to be detected. First, a scoring model must be established. The process of establishing the scoring model includes the following steps:

1) The establishment process of a scoring model includes the following steps:

The interval is divided according to multiple interval ranges formed by the mean value and variance corresponding to each variable in the primary reference charging process data. For example, the variable is the highest temperature, which is divided into three grades, namely good, medium and poor, and the interval includes: (0, μ), (μ, μ+3σ), (μ+3σ, ∞).

According to the degree of deviation between the actual value of each variable and the critical value of the corresponding interval, the corresponding relationship between the degree of deviation and the score data is established. Among them, the critical value of the corresponding interval is 0, μ, and μ+3σ. In the specific implementation, according to the data mining method, quantitative scoring is performed on different grades. For example, for the highest temperature, based on the method of Zscore, it is further corrected, and 3σ is used as the critical value of high-risk vehicles. The score data can be 0-100. It can be understood that the degree of deviation between the actual value and the critical value of the corresponding interval is negatively correlated with the score data, that is, the greater the deviation between the actual value and the critical value of the corresponding interval, the higher the score. The lower the data, the smaller the deviation of the actual value from the critical value of the corresponding interval, and the higher the score data. For example, μ+3σ is 60 points, (0, μ) is 100 points (good), then 60 points<(μ, μ+3σ)<100 points (medium), (μ+3σ, ∞)<60 points ( Difference). If the actual variable falls in (μ, μ+3σ), then a specific score is obtained according to the deviation procedure from the critical value μ, μ+3σ.

2) The establishment process of the secondary scoring model includes the following steps:

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

According to the degree of deviation between the actual value of each variable and the critical value of the corresponding interval, the corresponding relationship between the degree of deviation and the score data is established. Among them, the critical value of the corresponding interval is 0, μ, and μ+3σ. In the specific implementation, according to the data mining method, quantitative scoring is performed on different grades. For example, for the maximum temperature difference, it is further corrected based on the method of zscore, and 3σ is used as the critical value of high-risk vehicles. The score data can be 0-100. It can be understood that the degree of deviation between the actual value and the critical value of the corresponding interval is negatively correlated with the score data, that is, the greater the deviation between the actual value and the critical value of the corresponding interval, the higher the score. The lower the data, the smaller the deviation of the actual value from the critical value of the corresponding interval, and the higher the score data. For example, μ+3σ is 60 points, (0, μ) is 100 points (good), then 60 points<(μ, μ+3σ)<100 points (medium), (μ+3σ, ∞)<60 points ( Difference). If the actual variable falls in (μ, μ+3σ), then a specific score is obtained according to the deviation procedure from the critical value μ, μ+3σ.

On the basis of the above embodiment, S16 includes:

According to the pre-set primary scoring model corresponding to each variable, the primary actual scoring data of each variable in the actual charging process data is determined;

Determine an actual health level corresponding to the actual score data according to the correspondence between the preset score data and the health level;

According to the preset secondary scoring model corresponding to each variable, the secondary actual scoring data of each variable in the secondary actual charging process data is determined;

The secondary actual health level corresponding to the secondary actual score data is determined according to the preset correspondence between the score data and the health level.

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

According to the corresponding relationship between the primary security threshold, the secondary security threshold, the identity information of the new energy device to be detected, and the charging start information, a security file of the new energy device to be detected is established.

Further, in order to reduce the amount of data calculation and improve the detection speed, after obtaining the actual charging start information of the new energy device to be detected sent by the charging device, the corresponding primary safety threshold, The secondary security threshold and the identity information of the new energy equipment to be detected;

Send the primary safety threshold and the secondary safety threshold to the charging device so that the charging device determines that the first actual charging process data exceeds the primary safety threshold, or the second actual charging process data exceeds the secondary safety threshold, or the first actual charging process data exceeds the BMS output In the case of the original threshold, it is determined that the charging of the new energy equipment to be detected is abnormal.

Since the security file is established, after the charging cloud platform obtains the actual charging startup information, it can find the corresponding primary and secondary security thresholds from the security file and send them directly to the charging device, saving online computing time. It can be understood that, for the charging cloud platform, it can also calculate a new safety threshold based on the latest reference charging process data, and judge the actual charging process data according to the new safety threshold. In this embodiment, the detection of abnormal charging by the charging device is added, the protection on the side of the charging cloud platform and the protection on the side of the charging device are realized, the division of labor between the big data layer and the device layer is realized, and their respective advantages are exerted. In addition, for the charging device, on the one hand, it receives the primary safety threshold and the secondary safety threshold sent by the charging cloud platform, and on the other hand, it also obtains the fixed threshold sent by the BMS. The charging process data is judged to prevent one of the thresholds from being lost or inaccurate during the transmission process, resulting in inaccurate detection results.

In the above embodiments, the method for evaluating the health condition of new energy equipment is described in detail, and the present application also provides embodiments corresponding to the apparatus for evaluating the health condition of new energy equipment. It should be noted that this application describes the embodiments of the device part from two perspectives, one is based on the perspective of functional modules, and the other is based on the perspective of hardware structure.

FIG. 8 is a structural diagram of an apparatus for evaluating a health condition of a new energy device according to an embodiment of the present application. As shown in Figure 8, based on the perspective of functional modules, the health status evaluation device of new energy equipment includes:

The first determination module 10 is applied to determine the type of the new energy equipment to be detected;

The selection module 11 is used to select a plurality of new energy equipment sets under this type as analysis objects;

The acquiring module 12 is configured to acquire the primary reference charging process data matching the analytical object within the preset time range, and the primary reference charging process data is the data generated by the analytical object during the 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 and used to characterize the variation trend of the variables;

The second processing module 14 is configured to calculate the secondary actual charging process data corresponding to each variable and used to represent the variation trend of the variables according to the primary actual charging process data of the new energy equipment to be detected; wherein, the primary actual charging process data is the data of the primary charging process to be detected. Data generated during the current charging process of new energy equipment;

The second determination module 15 is configured to, based on the corresponding relationship between the primary reference charging process data and the secondary reference charging process data and time, use an abnormality detection method to determine the first safety value corresponding to the primary reference charging process data and the secondary reference charging process data respectively. The threshold value and the second safety threshold value, the first safety threshold value and the second safety threshold value are used as comparison objects to be compared with the actual first charging process data and the second actual charging process data of the new energy equipment to be detected, respectively, to determine the new energy to be detected. The device is abnormally charged;

The third determination module 16 is configured to determine, according to the preset corresponding relationship between the degree of deviation and the state of health, the degree of deviation between the actual charging process data and the primary safety threshold and the degree of deviation between the actual charging process data and the secondary safety threshold. corresponding health status.

Since the embodiment of the apparatus part corresponds to the embodiment of the method part, for the embodiment of the apparatus part, please refer to the description of the embodiment of the method part, which will not be repeated here.

The apparatus for evaluating abnormal charging of new energy equipment provided by this embodiment firstly determines the type of new energy equipment to be detected, then selects multiple new energy equipment under this type as analysis objects, and then obtains the analysis objects within a preset time range. , The primary reference charging process data that matches the analysis object. The secondary reference charging process data is calculated according to the primary reference charging process data, and the secondary actual charging process data is calculated according to the primary actual charging process data. Finally, based on the corresponding relationship between the primary charging process data and the secondary reference charging process data and time, the anomaly detection method is used to determine the primary and secondary safety thresholds corresponding to the primary reference charging process data and the secondary reference charging process data, respectively. It is used as a comparison object to compare with the actual charging process data of the first time and the actual charging process data of the second time, so as to determine the abnormal charging of the new energy equipment to be detected. In addition, the health status of the new energy equipment to be detected can be determined according to the degree of deviation between the primary actual charging process data and the secondary actual charging process data and the respective safety thresholds. It can be seen that, applied to the technical solution, a prompt of the current health status of the user's device can be given in time, the user experience is improved, and serious consequences caused by charging when the health status is poor can be avoided. In addition, the primary safety threshold and the secondary safety threshold are obtained through the primary reference charging process data, and the primary reference charging process data are real data, so compared with the fixed threshold, the primary safety threshold and secondary security threshold obtained by this technical solution are The sub-safety threshold can improve the accuracy of charging abnormality detection. Finally, the secondary reference charging process data can reflect the dynamic development of variables, so the obtained secondary safety threshold can quantify the dynamic development of variables and identify charging abnormalities in time.

FIG. 9 is a structural diagram of an apparatus for evaluating the health status of new energy equipment provided by another embodiment of the present application. As shown in FIG. 9 , based on the hardware structure, the apparatus for evaluating the health status of new energy equipment includes: a memory 20 for store computer programs;

The processor 21 is configured to implement the steps of the method for evaluating the health condition of the new energy equipment in the above-mentioned embodiment when executing the computer program.

The apparatus for evaluating the health status of the new energy equipment provided in this embodiment may include, but is not limited to, a smart phone, a tablet computer, a notebook computer, or a desktop computer.

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 can use at least one hardware form among DSP (Digital Signal Processing, digital signal processing), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array, programmable logic array) accomplish. The processor 21 may also include a main processor and a co-processor. The main processor is a processor used to process data in the wake-up state, also called CPU (Central Processing Unit, central processing unit); the co-processor 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, image processor), and the GPU is used for rendering and drawing the content that needs to be displayed on the display screen. In some embodiments, the processor 21 may further include an AI (Artificial Intelligence, artificial intelligence) processor, where the AI processor is used to process computing operations related to machine learning.

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 storage devices. In this embodiment, the memory 20 is at least used to store the following computer program 201 , wherein, after the computer program is loaded and executed by the processor 21 , the relevant steps of the method for evaluating abnormal charging of a new energy device disclosed in any of the foregoing embodiments can be implemented. . In addition, the resources stored in the memory 20 may also include an operating system 202, data 203, etc., and the storage mode may be short-term storage or permanent storage. The operating system 202 may include Windows, Unix, Linux, and the like. The data 203 may include but 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 evaluating abnormal charging of new energy equipment may further include a display screen 22 , an input and output interface 23 , a communication interface 24 , a power supply 25 and a communication bus 26 .

Those skilled in the art can understand that the structure shown in FIG. 9 does not constitute a limitation of the apparatus for evaluating abnormal charging of new energy equipment, and may include more or less components than those shown in the figure.

The apparatus for evaluating abnormal charging of new energy equipment provided by the embodiment of the present application includes a memory and a processor. When the processor executes a program stored in the memory, the processor can implement the following method: firstly determine the type of the new energy equipment to be detected, and then select the type of the new energy equipment to be detected. Multiple new energy devices under the type are used as the analysis objects, and then the primary reference charging process data matching the analysis objects within the preset time range is obtained. The secondary reference charging process data is calculated according to the primary reference charging process data, and the secondary actual charging process data is calculated according to the primary actual charging process data. Finally, based on the corresponding relationship between the primary charging process data and the secondary reference charging process data and time, the anomaly detection method is used to determine the primary and secondary safety thresholds corresponding to the primary reference charging process data and the secondary reference charging process data, respectively. It is used as a comparison object to compare with the actual charging process data of the first time and the actual charging process data of the second time, so as to determine the abnormal charging of the new energy equipment to be detected. In addition, the health status of the new energy equipment to be detected can be determined according to the degree of deviation between the primary actual charging process data and the secondary actual charging process data and the respective safety thresholds. It can be seen that, applied to the technical solution, a prompt of the current health status of the user's device can be given in time, the user experience is improved, and serious consequences caused by charging when the health status is poor can be avoided. In addition, the primary safety threshold and the secondary safety threshold are obtained through the primary reference charging process data, and the primary reference charging process data are real data, so compared with the fixed threshold, the primary safety threshold and secondary security threshold obtained by this technical solution are The sub-safety threshold can improve the accuracy of charging abnormality detection. Finally, the secondary reference charging process data can reflect the dynamic development of variables, so the obtained secondary safety threshold can quantify the dynamic development of variables and identify charging abnormalities in time.

The present application also provides an embodiment corresponding to a computer-readable storage medium. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by the processor, the steps described in the foregoing method embodiments are implemented.

It can be understood that, if the methods in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of software products in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, and the computer software products are stored in a storage medium , to execute all or part of the steps of the methods in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program codes .

Finally, the embodiment of the present application also provides a prompt terminal for the health status of the new energy equipment, including:

Receive the health status of the new energy device to be detected and the abnormal charging detection result corresponding to the abnormal charging of the new energy device to be detected;

Output the health status, and when receiving the abnormal charging detection result, output the abnormal charging prompt information;

Among them, the abnormal charging detection result and the health status are obtained through the following steps:

Determine the type of new energy equipment to be tested;

Select multiple new energy equipment sets under Type as analysis objects;

Acquiring primary reference charging process data matching the analysis object within a preset time range of the analysis object, and the primary reference charging process data is the data generated by the analysis object during the charging process;

Calculate the secondary reference charging process data corresponding to each variable and used to characterize the variation trend of the variable according to the primary reference charging process data;

According to the actual charging process data of the new energy equipment to be detected, the second actual charging process data corresponding to each variable and used to characterize the variation trend of the variables is calculated; wherein, the actual charging process data of the first time is the current charging process of the new energy equipment to be detected. data generated;

Based on the corresponding relationship between the primary reference charging process data and the secondary reference charging process data and time, the abnormality detection method is used to determine the first safety threshold and the second safety threshold corresponding to the primary reference charging process data and the secondary reference charging process data respectively. A safety threshold value and a second safety threshold value are used as comparison objects to be compared with the actual first charging process data and the second actual charging process data of the new energy device to be detected respectively, so as to determine that the charging of the new energy device to be detected is abnormal;

According to the corresponding relationship between the preset deviation degree and the health condition, determine the health condition corresponding to the deviation degree of the actual charging process data and the primary safety threshold and the deviation degree of the second actual charging process data and the secondary safety threshold.

It can be understood that the prompting terminal for the health status of the new energy device provided in this embodiment may include, but is not limited to, a smart phone, a tablet computer, a notebook computer, or a desktop computer. Usually, the abnormal charging detection result is obtained from the charging cloud platform or charging terminal mentioned above. These devices establish a communication connection with the prompting terminal, so that the abnormal charging detection result is sent to the prompting terminal and received by the prompting terminal. And output abnormal charging prompt information, so that users can check in time.

The health status evaluation method, device, medium and prompting terminal of new energy equipment provided by the present application are described above in detail. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the various embodiments can be referred to each other. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant part can be referred to the description of the method. It should be pointed out that for those of ordinary skill in the art, without departing from the principles of the present application, several improvements and modifications can also be made to the present application, and these improvements and modifications also fall within the protection scope of the claims of the present application.

It should also be noted that, in this specification, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply these entities or operations. There is no such actual relationship or sequence between operations. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

Claims

A method for evaluating the health status of new energy equipment, comprising:

Determine the type of new energy equipment to be tested;

Select multiple new energy equipment sets under the type as analysis objects;

Acquire primary reference charging process data that matches the analysis object within a preset time range of the analysis object, where the primary reference charging process data is data generated by the analysis object during the charging process;

Calculate, according to the primary reference charging process data, secondary reference charging process data corresponding to each variable and used to characterize the variation trend of the variables;

According to the actual charging process data of the new energy equipment to be detected, the second actual charging process data corresponding to each variable and used to represent the variation trend of the variables is calculated; Data generated during the current charging process of the device;

Based on the corresponding relationship between the primary reference charging process data and the secondary reference charging process data and time, the abnormality detection method is used to determine the first safety value corresponding to the primary reference charging process data and the secondary reference charging process data. a threshold value and a second safety threshold value, the first safety threshold value and the second safety threshold value are used as comparison objects to be compared with the actual primary charging process data and the secondary actual charging process data of the new energy device to be detected, respectively, to determine that the charging of the new energy equipment to be detected is abnormal;

According to the preset corresponding relationship between the degree of deviation and the state of health, determine the degree of deviation between the actual charging process data and the primary safety threshold and the degree of deviation between the actual charging process data and the secondary safety threshold. corresponding health status.
The evaluation method according to claim 1, wherein the selecting a plurality of new energy equipment sets under the type as analysis objects comprises:

Select multiple new energy devices in the same area and/or the same age under the type as the analysis object.
The evaluation method according to claim 2, wherein the primary reference charging process is determined by an abnormality detection method based on the corresponding relationship between the primary reference charging process data and the secondary reference charging process data and time The respective first safety threshold and second safety threshold corresponding to the data and the secondary reference charging process data include:

Use a statistical analysis method or a cluster analysis method to determine the primary safety threshold corresponding to each variable in the primary reference charging process data and the secondary reference charging process data corresponding to each variable safety threshold.
The evaluation method according to claim 3, wherein 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 evaluation method according to claim 1, wherein the obtaining the primary reference charging process data matching the analysis object within a preset time range of the analysis object comprises:

Acquiring a plurality of target charging orders that match the analysis object within a preset time range of the analysis object;

The primary reference charging process data is extracted from each of the target charging orders.
The evaluation method according to any one of claims 1 to 5, wherein the deviation of the first-time actual charging process data from the first-time safety threshold is determined according to a preset corresponding relationship between the degree of deviation and the health condition The health conditions corresponding to the degree and the degree of deviation of the secondary actual charging process data from the secondary safety threshold include:

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

Obtain a historical charging process data from each of the historical charging orders, and calculate the average value corresponding to each variable in the historical charging process data as an actual average value;

calculating the primary reference average value corresponding to each variable of the primary reference charging process data within the predetermined time;

Determining the degree of deviation of the primary variable between the primary actual average value corresponding to the same variable and the primary safety threshold;

determining the primary actual health level corresponding to the primary variable deviation degree according to the preset correspondence between the variable deviation degree and the health level;

Calculate the secondary historical charging process data corresponding to each variable and used to characterize the variation trend of the variable according to the primary historical charging process data;

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

calculating the secondary reference average value corresponding to each variable of the secondary reference charging process data within the predetermined time;

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

Determine the secondary actual health level corresponding to the deviation degree of the secondary variable according to the corresponding relationship between the deviation degree of the variable and the health level;

The health status of the new energy equipment to be detected is determined according to the primary actual health level and the secondary actual health level.
The evaluation method according to any one of claims 1 to 5, wherein the deviation of the first-time actual charging process data from the first-time safety threshold is determined according to a preset corresponding relationship between the degree of deviation and the health condition The health conditions corresponding to the degree and the degree of deviation of the secondary actual charging process data from the secondary safety threshold include:

determining the first-time actual scoring data of each variable in the first-time actual charging process data according to the first-time scoring model corresponding to each preset variable;

determining a primary actual health level corresponding to the primary actual scoring data according to the preset correspondence between the score data and the health level;

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

Determine the secondary actual health level corresponding to the secondary actual score data according to the preset correspondence between the score data and the health level;

Wherein, the establishment process of described primary scoring model comprises the following steps:

Perform interval division according to a plurality of interval ranges formed by the average value and variance corresponding to each variable in the primary reference charging process data;

According to the degree of deviation between the actual value of each variable and the critical value of the corresponding interval, the corresponding relationship between the degree of deviation and the score data is established;

Wherein, the establishment process of described secondary scoring model comprises the following steps:

Perform interval division according to multiple interval ranges formed by the average value and variance corresponding to each variable in the secondary reference charging process data;

According to the degree of deviation between the actual value of each variable and the critical value of the corresponding interval, the corresponding relationship between the degree of deviation and the score data is established.
The evaluation method according to claim 1, characterized in that, based on the corresponding relationship between the primary reference charging process data and the secondary reference charging process data and time, an abnormality detection method is used to determine the primary reference charging After the first safety threshold and the second safety threshold corresponding to the process data and the secondary reference charging process data, the following further includes:

The safety file of the new energy device to be detected is established according to the corresponding relationship between the primary safety threshold, the secondary safety threshold, the identity information of the new energy device to be detected, and the charging start information.
The evaluation method according to claim 8, further comprising:

After obtaining the actual charging start information of the new energy device to be detected sent by the charging device, search the corresponding primary safety threshold, the secondary safety threshold and the safety file from the safety file according to the actual charging start information the identity information of the new energy equipment to be detected;

Send the primary safety threshold and the secondary safety threshold to the charging device, so that the charging device determines that the primary actual charging process data exceeds the primary security threshold, or the secondary actual charging process data exceeds the primary security threshold. When the secondary safety threshold or the primary actual charging process data exceeds the original threshold output by the BMS, it is determined that the charging of the new energy device to be detected is abnormal.
A device for evaluating the health status of new energy equipment, comprising:

The first determination module is applied to determine the type of the new energy equipment to be detected;

a selection module, used to select multiple new energy equipment sets under the type as analysis objects;

an acquisition module, configured to acquire the primary reference charging process data matching the analytical object within a preset time range of the analytical object, and the primary reference charging process data is the data generated by the analytical object during the charging process ;

a first processing module, configured to calculate, according to the primary reference charging process data, secondary reference charging process data corresponding to each variable and used to characterize the variation trend of the variables;

The second processing module is configured to calculate the second actual charging process data corresponding to each variable and used to represent the variation trend of the variables according to the primary actual charging process data of the new energy equipment to be detected; wherein, the primary actual charging process data is: data generated during the current charging process of the new energy device to be detected;

a second determination module, configured to determine the primary reference charging process data and the secondary reference charging process data by using an abnormality detection method based on the corresponding relationship between the primary reference charging process data and the secondary reference charging process data and time The first safety threshold value and the second safety threshold value corresponding to the data, the first safety threshold value and the second safety threshold value are used as comparison objects with the actual first charging process data and the second charging process data of the new energy equipment to be detected, respectively. The actual charging process data is compared to determine that the charging of the new energy equipment to be detected is abnormal;

The third determining module is configured to determine, according to the preset corresponding relationship between the degree of deviation and the state of health, the degree of deviation between the first actual charging process data and the first safety threshold, and the second actual charging process data and the second The health status corresponding to the degree of deviation from the secondary safety threshold.
A device for evaluating the health status of new energy equipment, characterized by comprising a memory for storing computer programs;

The processor is configured to implement the steps of the method for evaluating the health condition of the new energy equipment according to any one of claims 1 to 9 when executing the computer program.
A computer-readable storage medium, characterized in that, a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the new energy device according to any one of claims 1 to 9 is realized The steps of the health status assessment method.
A health status prompting terminal for new energy equipment, characterized in that it includes:

memory for storing computer programs;

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

receiving the health status of the new energy device to be detected and the abnormal charging detection result corresponding to the abnormal charging of the new energy device to be detected;

outputting the health status, and in the case of receiving the abnormal charging detection result, outputting abnormal charging prompt information;

Wherein, the abnormal charging detection result and the state of health are obtained through the following steps:

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

Select multiple new energy equipment sets under the type as analysis objects;

Acquire primary reference charging process data that matches the analysis object within a preset time range of the analysis object, where the primary reference charging process data is data generated by the analysis object during the charging process;

Calculate, according to the primary reference charging process data, secondary reference charging process data corresponding to each variable and used to characterize the variation trend of the variables;

According to the actual charging process data of the new energy equipment to be detected, the second actual charging process data corresponding to each variable and used to represent the variation trend of the variables is calculated; Data generated during the current charging process of the device;

Based on the corresponding relationship between the primary reference charging process data and the secondary reference charging process data and time, the abnormality detection method is used to determine the first safety value corresponding to the primary reference charging process data and the secondary reference charging process data. a threshold value and a second safety threshold value, the first safety threshold value and the second safety threshold value are used as comparison objects to be compared with the actual primary charging process data and the secondary actual charging process data of the new energy device to be detected, respectively, to determine that the charging of the new energy equipment to be detected is abnormal;

According to the preset corresponding relationship between the degree of deviation and the state of health, determine the degree of deviation between the actual charging process data and the primary safety threshold and the degree of deviation between the actual charging process data and the secondary safety threshold. corresponding health status.