WO2023273971A1

WO2023273971A1 - Battery pack leakage detection method and apparatus, electronic device, and storage medium

Info

Publication number: WO2023273971A1
Application number: PCT/CN2022/100300
Authority: WO
Inventors: 潘垂宇; 李雪; 张志�; 于春洋; 许立超; 于鹏
Original assignee: 中国第一汽车股份有限公司
Priority date: 2021-06-30
Filing date: 2022-06-22
Publication date: 2023-01-05
Also published as: CN113310647B; CN113310647A

Abstract

A battery pack leakage detection method, comprising: performing feature analysis on battery pack data, and determining, data according to the feature analysis result, whether the battery pack data is fault (S120); in response to determining that the battery pack data is fault data, determining the fault category of the battery pack data, and determining the frequency at which the fault category occurs within a preset diagnosis period (S130); determining an insulation abnormal ratio of the battery pack according to the insulation value of the battery pack in the current diagnosis period and the insulation value of the battery pack in the at least one historical diagnosis period (S140); performing fault evaluation on the battery pack data according to the fault category, the frequency at which the fault category occurs within the preset diagnosis period, and the insulation abnormal ratio value of the battery pack, and determining, according to the fault evaluation result, whether the battery pack is leaking (S150). Further provided are a battery pack leakage detection apparatus, an electronic device, and a storage medium.

Description

Method, device, electronic device and storage medium for battery pack leak detection

This application claims priority to a Chinese patent application with application number 202110734553.X filed with the China Patent Office on June 30, 2021, the entire contents of which are incorporated herein by reference.

technical field

The embodiments of the present application relate to the technical field of automobiles, for example, to a battery pack leakage detection method, device, electronic equipment, and storage medium.

Background technique

The lithium-ion power battery pack is a sealed structure. If the sealed structure of the battery pack is damaged and water vapor enters the battery pack, the metal lithium in the negative electrode will react violently with the water, causing the battery pack to burn extremely fast. Therefore, there is a very large potential safety hazard for the driving of the electric vehicle.

In related technologies, the battery pack mainly relies on the IP67 test during design and the air tightness test during production to detect whether the battery pack leaks water, but it cannot guarantee that the battery pack will not be damaged in the outer shell or the vent valve during use. The resulting ingress of water vapor in the air. In order to solve this problem, the related technology also detects whether the battery pack leaks water by adding a humidity sensor, etc., but this method not only increases the hardware cost, but also makes the controller area network (Controller Area Network, which is already very tense) CAN) communication increases the burden. Therefore, there is an urgent need to design a battery pack leak detection method that can improve the safety of the battery pack without increasing the cost.

Contents of the invention

Embodiments of the present application provide a battery pack leakage detection method, device, electronic device, and storage medium, which can monitor whether a battery pack has a leakage risk in real time and improve the safety of the battery pack.

In the first aspect, the embodiment of the present application provides a method for detecting battery pack leakage, the method comprising:

Obtain the battery pack data of the vehicle;

Perform feature analysis on the battery pack data, and determine whether the battery pack data is fault data according to the feature analysis result;

In response to determining that the battery pack data is fault data, determine a fault category of the battery pack data, and determine a frequency of occurrence of the fault category within a preset diagnostic period;

Determine the insulation abnormality ratio of the battery pack according to the insulation value of the battery pack in the current diagnosis cycle and the insulation value of the battery pack in at least one historical diagnosis cycle;

Perform a fault assessment on the battery pack data according to the fault category, the frequency of the fault category occurring within a preset diagnosis period, and the insulation abnormality ratio of the battery pack, and determine whether the battery pack is based on the fault assessment result Give way.

In the second aspect, the embodiment of the present application provides a battery pack leakage detection device, the device comprising:

The data acquisition module is configured to acquire the battery pack data of the vehicle;

The feature analysis module is configured to perform feature analysis on the battery pack data, and determine whether the battery pack data is fault data according to the feature analysis result;

The first determining module is configured to determine the fault category of the battery pack data in response to determining that the battery pack data is fault data, and determine the frequency of occurrence of the fault type within a preset diagnosis period;

The second determination module is configured to determine the insulation abnormality ratio of the battery pack according to the insulation value of the battery pack in the current diagnosis cycle and the insulation value of the battery pack in at least one historical diagnosis cycle;

The fault determination module is configured to perform fault assessment on the battery pack data according to the fault category, the frequency of the fault category occurring within a preset diagnosis period, and the abnormal insulation ratio of the battery pack, and based on the fault assessment result Determine if the battery pack is leaking.

In a third aspect, an embodiment of the present application provides an electronic device, which includes:

at least one processor;

a storage device configured to store at least one program;

When the at least one program is executed by the at least one processor, the at least one processor is made to implement the battery pack leakage detection method described in any embodiment of the present application.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored, wherein, when the program is executed by a processor, the method for detecting battery pack leakage described in any embodiment of the present application is implemented.

Description of drawings

The accompanying drawings are used to better understand the solution, and do not constitute a limitation to the application. in:

FIG. 1A is a schematic diagram of a battery pack leakage detection system provided in an embodiment of the present application;

FIG. 1B is a schematic flowchart of a first method for detecting battery pack leaks provided by an embodiment of the present application;

FIG. 2 is a second schematic flowchart of a battery pack leakage detection method provided in an embodiment of the present application;

FIG. 3 is a schematic flowchart of a third method for detecting a battery pack leak provided by an embodiment of the present application;

FIG. 4 is a schematic structural diagram of a battery pack leak detection device provided in an embodiment of the present application;

Fig. 5 is a block diagram of an electronic device used to implement a battery pack leakage detection method according to an embodiment of the present application.

detailed description

The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

FIG. 1A is a schematic diagram of a battery pack leak detection system provided by an embodiment of the present application; FIG. 1B is a schematic flowchart of a first method for detecting a battery pack leak provided by an embodiment of the present application. This embodiment is applicable to detecting whether the battery pack leaks and enters water. A battery pack leak detection method provided in this embodiment can be executed by the battery pack leak detection device provided in the embodiment of the present application, which can be implemented by software and/or hardware, and integrated in the electronic device that executes this method. in the device. The electronic device in the embodiment of the present application is carried by the battery pack leakage detection system.

Referring to FIG. 1A , it is a schematic diagram of a battery pack leakage detection system provided by an embodiment of the present application. As shown in the figure, the battery pack leakage detection system includes: a vehicle-mounted terminal 11 , a cloud server 12 , and a background server 13 . The vehicle is equipped with a vehicle-mounted terminal (such as a vehicle-mounted Telematics-BOX) that can upload vehicle data to a cloud server. The background server can download the data of the vehicle from the cloud server and perform battery pack leakage detection on it. If the monitoring results show that the vehicle has a battery pack leak, a leak information notification will be sent to the vehicle terminal.

Referring to Figure 1B, the method of this embodiment includes but is not limited to the following steps:

S110. Acquire battery pack data of the vehicle.

Wherein, a battery sampler and a battery pack are arranged in the vehicle, and a voltage sensor and a temperature sensor are arranged in the battery pack for monitoring voltage data and temperature data of the battery pack respectively. The battery sampler is used to collect the battery pack data of the battery pack, such as voltage data and temperature data; in addition, the battery pack data also includes the insulation value of the high-voltage circuit of the vehicle, the historical driving data of the vehicle, the charging data of the vehicle, and the voltage data of the battery pack , at least one of the temperature data of the battery pack, the serial number of the vehicle, and the collection time of the battery pack data.

In the embodiment of the present application, the vehicle terminal first uploads and stores the battery pack data of the vehicle to the cloud server. When the background server is performing battery pack leakage detection and analysis on the vehicle, the background server can obtain the battery pack data from the cloud server. For example, the vehicle-mounted terminal can also directly upload the battery pack data of the vehicle to the background server, so that the background server can detect and analyze the battery pack leakage.

For example, the data can be uploaded to the cloud server or background server according to the data upload standard stipulated in the national standard GBT32960 (such as data upload frequency, data field), so the battery pack data uploaded to the cloud server or background server is more universal . Exemplarily, the battery collector in the vehicle collects the temperature data and voltage data of the battery pack every 10 seconds, and the vehicle terminal compares the temperature data and voltage data of the battery pack with the historical driving data of the vehicle, the charging data of the vehicle, and the The voltage data, the temperature data of the battery pack, the serial number of the vehicle and the collection time of the battery pack data are aggregated into the battery pack data and uploaded to the cloud server or background server.

S120. Perform feature analysis on the battery pack data, and determine whether the battery pack data is fault data according to the feature analysis result.

In the embodiment of the present application, after the background server acquires the battery pack data of the vehicle, it performs feature analysis on the battery pack data. For example, if the battery pack data of a vehicle is fault data, it will show various characteristics. Set a preset diagnosis cycle (such as 1 day), and analyze all the battery pack data of the vehicle within the preset diagnosis cycle. If the battery pack data without this feature is normal data; if the battery pack data with this feature is faulty data, in response to determining that the battery pack data is faulty data, it is necessary to further determine whether the battery pack is leaking, and the specific leak determination The procedure will be illustrated in the following examples.

In the embodiment of the present application, the feature analysis is mainly performed from the temperature data of the battery pack, the voltage data of the battery pack, and the insulation value of the pure battery pack state, and whether the battery pack data is fault data is judged according to the feature analysis results.

It should be noted that in the design of electric vehicles in the related art, the insulation value feeds back the insulation resistance value of the entire high-voltage circuit, and the insulation value of the pure battery pack state is needed to judge the leakage of the battery pack. Since the entire circuit in the charging state includes the part of the charging pile, and the driving state includes all high-voltage circuit states such as the battery pack system and the motor control system including the inverter. Therefore, this application regards the state that the vehicle has just been powered on and the relay is not connected to the motor controller as the state of the pure battery pack. That is, the state of the pure battery pack needs to meet three conditions at the same time: the vehicle state is the driving state, the charging state is not charging, and the motor controller voltage of the vehicle does not reach the battery voltage.

The method of determining the insulation value of the pure battery pack state is as follows: the electric vehicle is a system, and the battery pack data stores the insulation value of the high-voltage circuit of the vehicle. When the vehicle is powered on, if the voltage data of the motor controller is much smaller than the voltage data of the battery pack, then the insulation value of the high-voltage circuit of the vehicle can be considered as the insulation value of the pure battery pack state, which can be used for battery pack leakage analysis.

In the embodiment of the present application, due to the leakage of the battery pack, the entry of water vapor will cause abnormalities in the data of the battery pack (that is, fault data), such as abnormal temperature data, abnormal voltage data, and abnormal insulation values inside the vehicle. The fault data identification and characteristics described in Table 1 are specific examples, and the conditions of other battery packs need to be adjusted accordingly according to the specific status. The fault data feature recognition is shown in Table 1 below:

Table 1. Fault data feature recognition table

S130. In response to determining that the battery pack data is fault data, determine a fault type of the battery pack data, and determine a frequency of occurrence of the fault type within a preset diagnosis cycle.

In the embodiment of the present application, after the above steps, after determining that the battery pack data is faulty data, it is necessary to further determine whether the battery pack is leaked through S130-S150. In this step, when the battery pack data is faulty data, specifically analyze the battery pack data within the preset diagnosis cycle to determine the fault category to which it belongs, and determine that the number of frame data corresponding to the fault type accounts for the number of frames in the preset diagnostic cycle. The ratio of the total amount of data, that is, the frequency of occurrence of the fault category within the preset diagnosis period. For example, the fault category can be for a battery cell, for example, for a battery cell, whether only the temperature data is abnormal, only the voltage data is abnormal, or both temperature data and voltage data are abnormal; the fault category can also be for the abnormal condition of the circuit , such as whether the abnormal condition of the circuit in the battery pack is an open circuit or a short circuit.

S140. Determine an insulation abnormality ratio of the battery pack according to the insulation value of the battery pack in the current diagnosis cycle and the insulation value of the battery pack in at least one historical diagnosis cycle.

In the embodiment of the present application, to analyze whether the battery pack leaks, it is necessary to determine the abnormal insulation condition of the battery pack. For example, the abnormal insulation condition of the battery pack may be expressed by using the abnormal insulation ratio of the battery pack. For example, one battery pack data corresponds to one frame of data, so all the battery pack data in the preset diagnosis cycle contains several frames of data, each battery pack data pack has the insulation value of the battery pack, first calculate the battery pack in the current diagnosis cycle The characteristic value of the insulation value (such as the average or median, etc.), and then calculate the characteristic value of the insulation value of the battery pack in the previous diagnosis cycle, and calculate the difference between the two (that is, the difference between the insulation value of the battery pack ), and finally divide the difference of the insulation value of the battery pack by the characteristic value of the insulation value of the battery pack in the current diagnosis cycle to obtain the abnormal insulation ratio of the battery pack.

It should be noted that, when the difference of the insulation value of the battery pack is a negative value, indicating that the insulation value of the battery pack has increased, the difference of the insulation value of the battery pack is recorded as zero. The smaller the characteristic value of the insulation value of the battery pack in the current diagnosis cycle, the greater the insulation abnormality ratio of the battery pack.

S150. Perform fault assessment on the battery pack data according to the fault category, the frequency of the fault category within the preset diagnosis period, and the insulation abnormality ratio of the battery pack, and determine whether the battery pack is leaking according to the fault assessment result.

In the embodiment of the present application, after the above-mentioned steps, the frequency of occurrence of the fault category within the preset diagnosis period and the abnormality ratio of the battery pack insulation are calculated, and then the fault assessment is performed on the data of the battery pack. The method of fault assessment can be: using a pre-trained fault assessment model, using the fault category, the frequency of the fault category within the preset diagnosis period, and the abnormal insulation ratio of the battery pack as the feature value, and inputting it into the fault assessment model to obtain the fault evaluation result. Determine whether the battery pack is leaking based on the fault assessment results.

For example, the battery pack data of the vehicle may also include a maintenance log of the battery pack, and the maintenance log includes the maintenance situation and time of the battery pack. Therefore, the present application can also determine the latest time of the vehicle's battery maintenance according to the vehicle's battery pack data; Maintenance reminder information.

The technical solution provided in this embodiment obtains the battery pack data of the vehicle; performs feature analysis on the battery pack data, and determines whether the battery pack data is fault data according to the feature analysis results; and determines whether the battery pack data is fault data in response to determining that the battery pack data is fault data The fault category of the packet data, and determine the frequency of the fault category within the preset diagnosis period; determine the insulation abnormality ratio of the battery pack according to the insulation value of the battery pack in the current diagnosis cycle and the insulation value of the battery pack in at least one historical diagnosis cycle ; According to the fault category, the frequency of the fault category within the preset diagnosis period, and the abnormal insulation ratio of the battery pack, perform fault assessment on the battery pack data, and determine whether the battery pack is leaking according to the fault assessment result. This application determines whether it is faulty data by analyzing the characteristics of the battery pack data. If it is faulty data, continue to analyze whether the battery pack data leaks. This application can monitor whether the battery pack has a leakage risk in real time without adding hardware. , which greatly improves the safety of the battery pack and protects the safety of the driver's life and property.

FIG. 2 is a second schematic flow chart of a method for detecting battery pack leakage provided by an embodiment of the present application. The embodiment of the present application is refined on the basis of the above-mentioned embodiments, and a detailed explanation of the judging process of battery pack leakage is added.

Referring to Fig. 2, the method of the present embodiment includes but not limited to the following steps:

S210. Acquire battery pack data of the vehicle.

S220. Perform feature analysis on the battery pack data, and determine whether the battery pack data is fault data according to the feature analysis result.

S230. In response to determining that the battery pack data is fault data, determine a fault category of the battery pack data, and determine a frequency of occurrence of the fault category within a preset diagnosis cycle.

S240. Determine the insulation abnormality ratio of the battery pack according to the insulation value of the battery pack in the current diagnosis cycle and the insulation value of the battery pack in at least one historical diagnosis cycle.

S250. Carry out cumulative summation according to the fault category, the frequency of the fault category within the preset diagnosis period, and the number of battery cells corresponding to the fault category, to determine a fault value corresponding to the battery pack.

Wherein, the battery pack includes at least one battery cell.

In the embodiment of the present application, after the above steps are performed to determine the fault type of the battery pack data, the frequency of the fault type within the preset diagnosis period, and the insulation abnormality ratio of the battery pack, the fault value corresponding to the battery pack is then determined based on these. For example, taking the case where the fault category is for a single battery cell, that is, the fault category includes three cases where only temperature data is abnormal, only voltage data is abnormal, and both temperature data and voltage data are abnormal. The fault category is calculated separately as the product of the frequency of the fault category in the three cases within the preset diagnosis cycle and the number of battery cells corresponding to the fault category to obtain three fault sub-values; then the three fault sub-values are compared Add to get the fault value corresponding to the battery pack. The formula for determining the fault value is as follows:

Among them, P represents the fault value corresponding to the battery pack; C represents the weight corresponding to the fault category, and the value of the weight is 1 or 0. The value of 1 means that the battery pack data has this fault type in the preset diagnosis cycle, and the value is 0 means that the battery pack data does not have this type of fault in the preset diagnosis period; N means the number of battery cells corresponding to the fault type; f means the frequency of the fault type in the preset diagnosis cycle; n means the number of types of the fault type, Correspondingly, i is a natural integer between 1 and n.

For example, the weight C corresponding to the fault category can also take a value between 0 and 1 according to the actual situation, which is adjustable.

S260. Summing the fault value and the insulation abnormality ratio of the battery pack, and then normalizing the summation result to obtain a fault evaluation result.

In the embodiment of the present application, the fault value and the insulation abnormality ratio of the battery pack obtained in the above steps are summed, and then normalized to obtain the fault evaluation result.

For example, the failure assessment result is determined by the following risk assessment formula:

Among them, score represents the fault evaluation result; P represents the fault value corresponding to the battery pack; D _median represents the difference between the insulation value of the battery pack in the current diagnosis cycle and the insulation value of the battery pack in at least one historical diagnosis cycle; A _median represents the current diagnosis The insulation value of the cycled battery pack; k is the adjustment factor, which is used to adjust the abnormal insulation ratio of the battery pack; M is the theoretical fault value corresponding to the failure of all battery cells in the battery pack; Q is a constant; 100 on the right side of the equal sign means The higher the score, the better the evaluation value is the risk assessment scheme.

Determine the battery pack leakage risk level of the vehicle according to the risk assessment formula. If score=100, it means that the battery pack has no leakage risk at all; if score=0, it means that the battery pack has the highest leakage risk. For example, 3 risk levels can be set, such as low level, medium level or high level. If score>=80, the risk level insurance is low level; if 80>score>=60, the risk level insurance is medium level; if score<60, the risk level insurance is high level.

S270. Determine whether the battery pack is leaking according to the fault evaluation result.

In the embodiment of the present application, a leakage risk standard is set, such as score=60 of the risk assessment formula. If the score of the risk assessment formula is less than 60, it indicates that there is leakage in the battery pack, and a leak warning is issued; if the score of the risk assessment formula is >60, it indicates that there is no leakage in the battery pack.

For example, the risk level in S260 and the leakage risk standard in S270 may be revised according to the actual situation. The corrected solution may be: when the driver returns the faulty vehicle to the factory for repair and maintenance, disassemble the battery pack, analyze whether there is a phenomenon corresponding to the fault category in the above embodiment, and correct the risk in S260 according to the fault phenomenon Level and the risk early warning standard in S270; it can also be: make a fault data sample, and use a machine learning model to correct the assessment of the risk level corresponding to the fault category.

The technical solution provided in this embodiment obtains the battery pack data of the vehicle; performs feature analysis on the battery pack data, and determines whether the battery pack data is fault data according to the feature analysis results; and determines whether the battery pack data is fault data in response to determining that the battery pack data is fault data The fault category of the packet data, and determine the frequency of the fault category within the preset diagnosis period; determine the insulation abnormality ratio of the battery pack according to the insulation value of the battery pack in the current diagnosis cycle and the insulation value of the battery pack in at least one historical diagnosis cycle ;According to the fault category, the frequency of the fault category within the preset diagnosis cycle, and the number of battery cells corresponding to the fault category, the cumulative summation is carried out to determine the corresponding fault value of the battery pack; the fault value and the insulation abnormality ratio of the battery pack are calculated and, and then normalize it to obtain the fault evaluation result; determine whether the battery pack is leaking according to the fault evaluation result. This application analyzes whether the battery pack is leaking through the fault assessment of the battery pack data. This application can monitor whether the battery pack is leaking in real time without adding hardware, which greatly improves the safety of the battery pack and protects the driving. safety of life and property of members.

FIG. 3 is a schematic flowchart of a third method for detecting a battery pack leakage provided by an embodiment of the present application. The embodiment of the present application is refined on the basis of the above-mentioned embodiments, and a detailed explanation of the process of judging the accuracy of the battery pack data and the process of analyzing the cause of the leakage is added.

Referring to Fig. 3, the method of the present embodiment includes but not limited to the following steps:

S310. Acquire battery pack data of the vehicle.

S320. Determine whether the battery pack data is accurate according to a preset judgment rule, and delete the battery pack data if the battery pack data is inaccurate.

In the embodiment of the present application, after receiving the battery pack data, the background server can analyze the accuracy of the battery pack data according to the judgment rules for the accuracy of the battery pack data, and delete false positive data at the same time. Exemplarily, taking a battery cell as an example, the voltage range of the battery pack that the battery sampler can collect is 0-5V. When the voltage value is 5.3V, the voltage value must be inaccurate due to signal transmission. The battery pack data should be deleted.

Table 2 below shows the battery pack data accuracy judgment rules in this embodiment. The battery pack data accuracy judgment in this embodiment is a specific example, and other battery pack data accuracy judgment methods are also within the scope of protection.

Table 2. Rules for judging the accuracy of battery pack data

S330. Perform feature analysis on the battery pack data, and determine whether the battery pack data is fault data according to the feature analysis result.

S340. In response to determining that the battery pack data is fault data, determine the fault category of the battery pack data, and determine the frequency of occurrence of the fault category within a preset diagnosis cycle;

S350. Determine the insulation abnormality ratio of the battery pack according to the insulation value of the battery pack in the current diagnosis cycle and the insulation value of the battery pack in at least one historical diagnosis cycle;

S360. Perform fault assessment on the battery pack data according to the fault category, the frequency of the fault category within the preset diagnosis period, and the insulation abnormality ratio of the battery pack, and determine whether the battery pack is leaking according to the fault assessment result.

S370. In response to determining the leakage of the battery pack, determine the cause of the leakage of the battery pack according to the historical driving data of the vehicle; and send the failure evaluation result of the vehicle and the cause of the leakage to the vehicle-mounted terminal of the vehicle.

Wherein, the historical driving data of the vehicle includes vehicle driving trajectory, vehicle speed information, weather information and so on.

In the embodiment of the present application, the battery pack leakage detection system is equipped with a leakage cause analysis module. When the battery pack is determined to be leaking and a leak warning is issued at S360, the leakage cause analysis module will be activated to analyze the cause of the battery pack leakage. After determining the cause of the leakage of the battery pack, the application sends the vehicle's fault assessment result and the cause of the leakage to the vehicle's on-board terminal to warn the driver that the current vehicle is at a high risk. Among them, the execution process of the leakage cause analysis module is as follows: first, the leakage cause analysis module obtains the time when the leakage occurs, and according to the time when the leakage occurs, searches for the vehicle driving track route, vehicle speed information, and weather information; then, by drawing the vehicle driving track route , combined with the vehicle speed information, to analyze the driver's driving habits, such as driving on the highway or driving on low-lying urban roads; finally, correlating with the weather information, analyzing the local humidity and rainfall, analyzing whether the vehicle is wading, and analyzing the cause of leakage.

The technical solution provided in this embodiment obtains the battery pack data of the vehicle; judges whether the battery pack data is accurate according to the preset judgment rules; if the battery pack data is inaccurate, deletes the battery pack data; characterizes the battery pack data Analyze, and determine whether the battery pack data is faulty data according to the characteristic analysis result; in response to determining that the battery pack data is faulty data, determine the fault category of the battery pack data, and determine the frequency of the fault category occurring within a preset diagnosis period; according to the current The insulation value of the battery pack in the diagnosis cycle and the insulation value of the battery pack in at least one historical diagnosis cycle determine the insulation abnormality ratio of the battery pack; according to the fault category, the frequency of the fault category occurring within the preset diagnosis cycle and the insulation abnormality of the battery pack Ratio, perform fault assessment on the battery pack data, and determine whether the battery pack leaks according to the fault assessment result; in response to determining the battery pack leak, determine the cause of the battery pack leak according to the historical driving data of the vehicle; and compare the vehicle fault assessment result and The leak reason is sent to the vehicle's on-board terminal. This application judges the accuracy of the battery pack data and analyzes the leakage, analyzes the cause of the leak after the battery pack is determined to be leaking, and sends the cause of the leak to the driver, which can monitor whether the battery pack of the vehicle is leaking and analyze the leaks in the battery pack. Risk warning in case of leakage.

Fig. 4 is a schematic structural diagram of a battery pack leak detection device provided in an embodiment of the present application. As shown in Fig. 4, the device 400 may include:

The data acquisition module 410 is configured to acquire battery pack data of the vehicle.

The feature analysis module 420 is configured to perform feature analysis on the battery pack data, and determine whether the battery pack data is fault data according to the feature analysis result.

The first determining module 430 is configured to, in response to determining that the battery pack data is faulty data, determine a fault type of the battery pack data, and determine a frequency of occurrence of the fault type within a preset diagnosis period.

The second determination module 440 is configured to determine the insulation abnormality ratio of the battery pack according to the insulation value of the battery pack in the current diagnosis cycle and the insulation value of the battery pack in at least one historical diagnosis cycle.

The fault determination module 450 is configured to perform fault assessment on the battery pack data according to the fault category, the frequency of the fault category occurring within a preset diagnosis period, and the insulation abnormality ratio of the battery pack, and perform fault assessment on the battery pack data according to the fault assessment As a result, it was determined whether the battery pack was leaking.

For example, the above-mentioned fault determination module 450 is configured to: perform cumulative summation according to the fault category, the frequency of occurrence of the fault category within a preset diagnosis period, and the number of battery cells corresponding to the fault category, to determine that the battery The fault value corresponding to the battery pack; wherein, the battery pack includes at least one battery cell; the fault value and the abnormal insulation ratio of the battery pack are summed, and then normalized to obtain a fault evaluation result.

For example, the failure evaluation result is determined by the following formula:

Among them, score represents the failure evaluation result; C represents the weight corresponding to the fault category; N represents the number of battery cells corresponding to the fault category; f represents the frequency of the fault category occurring within the preset diagnosis period; n represents the fault The number of categories, correspondingly, the value of i is a natural integer between 1 and n; D _median represents the difference between the insulation value of the battery pack in the current diagnosis cycle and the insulation value of the battery pack in at least one historical diagnosis cycle; A _median represents the insulation value of the battery pack in the current diagnosis cycle; k is an adjustment factor used to adjust the abnormal insulation ratio of the battery pack; M is the theoretical fault value corresponding to the failure of all battery cells in the battery pack; Q is a constant.

For example, the above-mentioned device for battery pack leakage detection may also include: a failure notification module;

The failure notification module is configured to, in response to determining the leakage of the battery pack, determine the cause of the leakage of the battery pack according to the historical driving data of the vehicle; send the failure evaluation result of the vehicle and the cause of the leakage to The vehicle-mounted terminal of the vehicle.

For example, the above-mentioned battery pack leak detection device may also include: a maintenance reminder module;

The maintenance reminder module is configured to determine the latest time of battery maintenance of the vehicle according to the battery pack data of the vehicle; if the time interval between the latest time of battery maintenance and the current time is greater than the preset time interval, send The vehicle-mounted terminal of the vehicle sends reminder information for battery maintenance.

For example, the above-mentioned device for battery pack leakage detection may further include: a data judgment module;

The data judging module is configured to judge whether the battery pack data is accurate according to a preset judgment rule before performing feature analysis on the battery pack data, and delete the battery pack if the battery pack data is inaccurate data.

For example, the battery pack data further includes at least one of vehicle historical driving data, vehicle charging data, battery pack voltage data, battery pack temperature data, vehicle serial number, and battery pack data collection time.

The battery pack leak detection device provided in this embodiment can be applied to the battery pack leak detection method provided in any of the above embodiments, and has corresponding functions and beneficial effects.

Fig. 5 is a block diagram of an electronic device used to implement a battery pack leakage detection method according to an embodiment of the present application, and Fig. 5 shows a block diagram of an exemplary electronic device suitable for implementing the implementation of the embodiment of the present application. The electronic device shown in FIG. 5 is only an example, and should not limit the functions and scope of use of this embodiment of the present application. Typically, the electronic device may be a smart phone, a tablet computer, a notebook computer, a vehicle terminal, a wearable device, and the like.

As shown in FIG. 5, electronic device 500 takes the form of a general-purpose computing device. Components of the electronic device 500 may include, but are not limited to: one or more processors or processing units 516, a memory 528, and a bus 518 connecting different system components (including the memory 528 and the processing unit 516).

Bus 518 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus structures. These architectures include, by way of example, but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect ( PCI) bus.

Electronic device 500 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 500 and include both volatile and nonvolatile media, removable and non-removable media.

Memory 528 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 530 and/or cache memory 532 . The electronic device 500 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 534 may be used to read and write to non-removable, non-volatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard drive"). Although not shown in FIG. 5, a disk drive for reading and writing to removable nonvolatile disks (e.g., "floppy disks") may be provided, as well as for removable nonvolatile optical disks (e.g., CD-ROM, DVD-ROM or other optical media) CD-ROM drive. In these cases, each drive may be connected to bus 518 through one or more data media interfaces. The memory 528 may include at least one program product, and the program product has a group (for example, at least one) of program modules configured to execute the functions of the various embodiments of the embodiments of the present application.

Program/utility 540 may be stored, for example, in memory 528 as a set (at least one) of program modules 542 including, but not limited to, an operating system, one or more application programs, other program modules, and program data , each or some combination of these examples may include implementations of network environments. The program module 542 generally executes the functions and/or methods in the embodiments described in the embodiments of this application.

The electronic device 500 may also communicate with one or more external devices 514 (such as a keyboard, pointing device, display 524, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 500, and/or communicate with Any device (eg, network card, modem, etc.) that enables the electronic device 500 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interface 522 . Moreover, the electronic device 500 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and/or a public network such as the Internet) through the network adapter 520 . As shown in FIG. 5 , the network adapter 520 communicates with other modules of the electronic device 500 through the bus 518 . It should be appreciated that although not shown in FIG. 5, other hardware and/or software modules may be used in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape Drives and data backup storage systems, etc.

The processing unit 516 executes various functional applications and data processing by running the programs stored in the memory 528 , such as implementing the battery pack leakage detection method provided in any embodiment of the present application.

The embodiment of the present application also provides a computer-readable storage medium, on which a computer program (or called computer-executable instructions) is stored. When the program is executed by a processor, it can be used to perform the operation provided by any of the above-mentioned embodiments of the present application. A method for battery pack leak detection.

The computer storage medium in the embodiments of the present application may use any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more leads, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), Erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a data signal carrying computer readable program code in baseband or as part of a carrier wave. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. .

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including - but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program codes for performing the operations of the embodiments of the present application may be written in one or more programming languages or combinations thereof, the programming languages including object-oriented programming languages—such as Java, Smalltalk, C++, including A conventional procedural programming language - such as the "C" language or a similar programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as through an Internet service provider). Internet connection).

Claims

A method for battery pack leak detection, comprising:

Obtain the battery pack data of the vehicle;

Perform feature analysis on the battery pack data, and determine whether the battery pack data is fault data according to the feature analysis result;

In response to determining that the battery pack data is fault data, determine a fault category of the battery pack data, and determine a frequency of occurrence of the fault category within a preset diagnostic period;

Determine the insulation abnormality ratio of the battery pack according to the insulation value of the battery pack in the current diagnosis cycle and the insulation value of the battery pack in at least one historical diagnosis cycle;

Perform a fault assessment on the battery pack data according to the fault category, the frequency of the fault category occurring within a preset diagnosis period, and the insulation abnormality ratio of the battery pack, and determine whether the battery pack is based on the fault assessment result Give way.
The method according to claim 1, wherein the fault is performed on the battery pack data according to the fault category, the frequency of the fault category occurring within a preset diagnosis period, and the insulation abnormality ratio of the battery pack assessment, including:

According to the cumulative summation of the fault category, the frequency of occurrence of the fault category within a preset diagnosis period, and the number of battery cells corresponding to the fault category, the fault value corresponding to the battery pack is determined; wherein, the battery the pack includes at least one battery cell;

Summing the fault value and the abnormal insulation ratio of the battery pack, and normalizing the summation result to obtain a fault evaluation result.
The method according to claim 2, wherein the fault assessment result is determined by the following formula:

Among them, score represents the failure evaluation result; C represents the weight corresponding to the fault category; N represents the number of battery cells corresponding to the fault category; f represents the frequency of the fault category occurring within the preset diagnosis period; n represents the fault The number of categories, i is a natural integer between 1 and n; D median represents the difference between the insulation value of the battery pack in the current diagnosis cycle and the insulation value of the battery pack in at least one historical diagnosis cycle; A median represents the current The insulation value of the battery pack in the diagnosis period; k is an adjustment factor used to adjust the abnormal insulation ratio of the battery pack; M is the theoretical fault value corresponding to the failure of all battery cells in the battery pack; Q is a constant.
The method according to claim 1, after determining whether the battery pack leaks according to the fault assessment result, further comprising:

In response to determining that the battery pack is leaking, according to historical driving data of the vehicle, determine a cause of the battery pack leak;

Sending the failure evaluation result of the vehicle and the leakage cause to the vehicle-mounted terminal of the vehicle.
The method according to claim 1, further comprising:

Determining the latest time for battery maintenance of the vehicle according to the battery pack data of the vehicle;

In response to determining that the time interval between the latest time of battery maintenance and the current time is greater than a preset time interval, a reminder message for battery maintenance is sent to the on-board terminal of the vehicle.
The method according to claim 1, before performing feature analysis on the battery pack data, further comprising:

Judging whether the battery pack data is accurate according to a preset judging rule, and deleting the battery pack data based on a judging result that the battery pack data is inaccurate.
The method according to claim 1, wherein the battery pack data further includes the historical driving data of the vehicle, the charging data of the vehicle, the voltage data of the battery pack, the temperature data of the battery pack, the serial number of the vehicle and the collection of battery pack data At least one of the time.
A device for detecting battery pack leaks, comprising:

The data acquisition module is configured to acquire the battery pack data of the vehicle;

The feature analysis module is configured to perform feature analysis on the battery pack data, and determine whether the battery pack data is fault data according to the feature analysis result;

The first determining module is configured to determine the fault category of the battery pack data in response to determining that the battery pack data is fault data, and determine the frequency of occurrence of the fault type within a preset diagnosis period;

The second determination module is configured to determine the insulation abnormality ratio of the battery pack according to the insulation value of the battery pack in the current diagnosis cycle and the insulation value of the battery pack in at least one historical diagnosis cycle;

The fault determination module is configured to perform fault assessment on the battery pack data according to the fault category, the frequency of the fault category occurring within a preset diagnosis period, and the abnormal insulation ratio of the battery pack, and based on the fault assessment result Determine if the battery pack is leaking.
An electronic device comprising:

at least one processor;

a storage device configured to store at least one program;

When the at least one program is executed by the at least one processor, the at least one processor is made to implement the battery pack leakage detection method according to any one of claims 1-7.
A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the battery pack leakage detection method according to any one of claims 1-7 is realized.