New energy automobile battery safety detection method and device

Abstract

The invention provides a new energy automobile battery safety detection method and device. The new energy automobile battery safety detection method comprises the steps that at least two pieces of to-be-detected battery data of a target vehicle in a preset time interval are acquired; detecting a vehicle battery of the target vehicle based on the at least two pieces of to-be-detected battery data to obtain a detection result; and determining battery state information of the vehicle battery based on the detection result. By acquiring the to-be-detected battery data of the target vehicle, performing safety detection on the to-be-detected battery data and determining the battery state information of the vehicle battery according to the detection result of the to-be-detected battery data, the safety state of the target vehicle is further determined according to the state information of the vehicle battery, and the safety of the vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a new energy vehicle battery safety detection method, a new energy vehicle battery safety detection device, a computing device, and a computer-readable storage medium. Background Art

[0002] As people's quality of life improves, more and more people rely on cars for travel. However, due to long-term use of cars, problems such as car aging and functional failures will occur, which will have a great impact on people's safety. For example, the sales of new energy vehicles are growing faster and faster, and it is necessary to strengthen the safety monitoring of new energy vehicles. By testing the batteries of new energy vehicles, potential warnings and dangerous problems of new energy vehicles can be discovered.

[0003] Therefore, it is particularly important to monitor the safety of cars and improve their safety. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a new energy vehicle battery safety detection method. The present invention also relates to a new energy vehicle battery safety detection device, a computing device, and a computer-readable storage medium to solve the above problems existing in the prior art.

[0005] According to a first aspect of an embodiment of the present invention, a new energy vehicle battery safety detection method is provided, comprising:

[0006] Acquire at least two battery data to be tested of the target vehicle within a preset time interval;

[0007] Based on the at least two battery data to be detected, detecting the vehicle battery of the target vehicle to obtain a detection result;

[0008] Based on the detection result, battery status information of the vehicle battery is determined.

[0009] According to a second aspect of an embodiment of the present invention, a new energy vehicle battery safety detection device is provided, comprising:

[0010] An acquisition module is configured to acquire at least two battery data to be detected of a target vehicle within a preset time interval;

[0011] An acquisition module is configured to detect the vehicle battery of the target vehicle based on the at least two battery data to be detected, and obtain a detection result;

[0012] The determination module is configured to determine the battery status information of the vehicle battery based on the detection result.

[0013] According to a third aspect of an embodiment of the present invention, a computing device is provided, comprising a memory, a processor, and computer instructions stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned new energy vehicle battery safety detection method when executing the computer instructions.

[0014] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, which stores computer instructions, and when the computer instructions are executed by a processor, the steps of the above-mentioned new energy vehicle battery safety detection method are implemented.

[0015] The new energy vehicle battery safety detection method provided by the present invention includes: obtaining at least two battery data to be detected of a target vehicle within a preset time interval; based on the at least two battery data to be detected, detecting the vehicle battery of the target vehicle to obtain a detection result; based on the detection result, determining the battery status information of the vehicle battery.

[0016] An embodiment of the present invention obtains the battery data to be tested of the target vehicle, performs a safety test on the battery data to be tested, and determines the battery status information of the vehicle battery according to the test results of the battery data to be tested, thereby further determining the safety status of the target vehicle according to the status information of the vehicle battery, thereby improving the safety of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of an application scenario of a new energy vehicle battery safety detection method provided by an embodiment of the present invention;

[0018] Figure 2 This is a flow chart of a new energy vehicle battery safety detection method provided by one embodiment of the present invention;

[0019] Figure 3 A detection flow chart for detecting battery data to be detected provided by an embodiment of the present invention;

[0020] Figure 4a is a schematic diagram of current health information of a vehicle battery provided by an embodiment of the present invention;

[0021] Figure 4b is a schematic diagram of health prediction information of a vehicle battery provided by an embodiment of the present invention;

[0022] Figure 5 This is a processing flow chart of a new energy vehicle battery safety detection method provided by an embodiment of the present invention;

[0023] Figure 6 It is a structural schematic diagram of a new energy vehicle battery safety detection device provided by an embodiment of the present invention;

[0024] Figure 7 It is a structural block diagram of a computing device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] Many specific details are set forth in the following description to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present invention, so the present invention is not limited to the specific implementation disclosed below.

[0026] The terms used in one or more embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of the present invention. The singular forms of "a", "said" and "the" used in one or more embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in one or more embodiments of the present invention refers to and includes any or all possible combinations of one or more associated listed items.

[0027] It should be understood that, although the terms first, second, etc. may be used to describe various information in one or more embodiments of the present invention, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of the present invention, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0028] In the field of new energy vehicles, batteries are an indispensable part of new energy vehicles. In practical applications, due to the long-term use of new energy vehicles, the car battery will be accompanied by the problem of battery aging. Therefore, in order to ensure the safety of the car and the user, the car battery can be tested for safety and health prediction during subsequent use.

[0029] In the present invention, a new energy vehicle battery safety detection method is provided. The present invention also relates to a new energy vehicle battery safety detection device, a computing device, and a computer-readable storage medium, which are described in detail one by one in the following embodiments.

[0030] Figure 1 The following is a schematic diagram showing an application scenario of a new energy vehicle battery safety detection method provided according to an embodiment of the present invention. Figure 1As shown, if the user has the need to perform a safety test on the battery of a new energy vehicle, the user can select the corresponding model according to the different models of the new energy vehicle to input the battery data that needs to be tested. After the terminal obtains the battery data that needs to be tested, the battery data is sent to the server, and the server performs a safety test on the battery data to be tested, and visualizes the battery status information generated after the test, outputs it to the terminal, and presents it to the user, so that the user can know whether there is any abnormality in the battery status, thereby determining the safety of the car battery.

[0031] like Figure 1 As shown, the visual output data includes not only battery status information, but also battery analysis data, including the vehicle number, the input data corresponding to the vehicle number (i.e., the battery data to be tested), the test results of the battery data to be tested, and the average value data of the battery data to be tested. On this basis, a line chart corresponding to the battery data to be tested is further displayed, which can intuitively show the user the usage of the vehicle battery within a preset time period.

[0032] Among them, users can be R&D personnel or ordinary users who have battery testing needs in actual applications.

[0033] For R&D personnel, they only need to input the battery data that needs to be tested to perform a safety test on the battery, without the need for manual maintenance or multiple attempts to perform battery safety tests. For ordinary users, they can input the battery data to be tested into the terminal to obtain the corresponding battery status information, thereby knowing the battery safety status and improving the safety of the car.

[0034] The new energy vehicle battery safety detection method provided by the present invention can perform safety detection on the vehicle battery by obtaining the battery information to be detected corresponding to the target vehicle, thereby determining the battery status information of the vehicle battery, so that research and development personnel can discover problems with the vehicle battery based on the battery status information of the vehicle battery and make timely adjustments, so that ordinary users can directly obtain the battery status information corresponding to the vehicle battery, thereby increasing the user's awareness of vehicle safety.

[0035] Figure 2 A flow chart of a new energy vehicle battery safety detection method provided according to an embodiment of the present invention is shown, which specifically includes the following steps:

[0036] Step 202: Obtain at least two battery data to be tested of the target vehicle within a preset time interval.

[0037] In actual applications, when people drive cars, they often need to ensure the safety of the car to ensure the safety of the users. Therefore, car safety testing is indispensable. Correspondingly, car safety testing is also an important challenge for R&D personnel.

[0038] The new energy vehicle battery safety detection method provided by the present invention starts with detecting the safety of the vehicle battery. Specifically, the battery data to be detected corresponding to the vehicle is first obtained, and the vehicle battery is safety detected based on the battery data to be detected, so as to obtain the detection result of the vehicle battery, thereby achieving the purpose of knowing the safety of the vehicle based on the detection result of the vehicle battery.

[0039] Among them, the target vehicle refers to the vehicle determined for battery safety detection, which can be a new energy vehicle. The target vehicle can be one car or multiple cars. The specific setting of the number of target vehicles can be set based on actual conditions, that is, the new energy vehicle battery safety detection method provided by the present invention can perform battery safety detection on the batteries of one or more cars. For example, if vehicle A needs to perform battery safety detection, then vehicle A is the target vehicle; if vehicle B and vehicle C need to perform battery safety detection, then vehicle B and vehicle C are both target vehicles.

[0040] The preset time interval refers to a preset time interval during which the data of the battery to be tested can be obtained. For example, the preset time interval can be the past 3 years, the past 5 years, the past 24 months, or the past 12 months.

[0041] The battery data to be tested refers to the vehicle battery data used for battery testing of the target vehicle within a preset time interval, and can be specifically the battery temperature data used for battery temperature testing of the target vehicle within a preset time interval.

[0042] In order to improve the accuracy of new energy vehicle battery safety testing, the preset time interval can be set to the past 24 months or the past 12 months. In this way, it can ensure that the data used for battery testing is data from a period adjacent to the testing time, thereby improving data availability, and it can also improve the richness of the data and avoid the randomness of the test results.

[0043] Take the target vehicle as vehicle A and the preset time interval as the recent 24 months as an example. Obtaining at least two battery data to be tested of the target vehicle within the preset time interval is to obtain the battery data to be tested of vehicle A within the recent 24 months. Specifically, in order to avoid redundant data, the average battery temperature of vehicle A in each month of the 24 months can be calculated in units of months, and then the battery data to be tested of 24 vehicle A can be obtained.

[0044] It should be noted that in the process of obtaining the battery data to be detected, it is not limited to calculating the data to be detected within the preset time interval in units of months. The average value can also be calculated every two months, and the average value of the data every two months can be obtained as the battery data to be detected. Similarly, the battery data to be detected can also be obtained in units of days. The above examples are only for illustrative purposes. The specific acquisition method can be limited according to the actual application situation, and the present invention is not limited here.

[0045] Step 204: Based on the at least two battery data to be detected, detect the vehicle battery of the target vehicle to obtain a detection result.

[0046] After obtaining the battery data to be detected of the target vehicle, the battery of the target vehicle can be tested based on the battery data to be detected, and then the test result corresponding to the vehicle battery can be obtained.

[0047] In a specific embodiment provided by the present invention, based on the at least two battery data to be detected, detecting the vehicle battery of the target vehicle to obtain the detection result includes:

[0048] Obtain preset detection parameter information;

[0049] According to the preset detection parameter information, the at least two battery data to be detected are detected to obtain a detection result.

[0050] Among them, the preset detection parameter information refers to the parameter data information used to detect whether there is an abnormality in the battery data to be detected. Specifically, it includes the upper control limit, the lower control limit, the average value of the upper control limit and the lower control limit, as well as the detection interval and the proportional coefficient. The value range of the detection interval and the value of the proportional coefficient can be changed according to the actual application situation. For example, in the process of conducting new energy vehicle battery safety testing based on the original preset detection parameter information, the research and development personnel found that the accuracy of the test results needs to be improved. The value range of the detection interval or the value of the proportional coefficient can be changed. Correspondingly, the setting of the detection interval can be, for example, (-∞, 0.3], (0.3, 1), [1, +∞); for example, (-∞, 0.3], (0.3, 0.6), [0.6, +∞), etc.

[0051] The upper control limit refers to the upper limit of the vehicle battery data that can be used; the lower control limit refers to the lower limit of the vehicle battery data that can be used. For example, when detecting the vehicle battery temperature, the upper limit of the vehicle battery temperature that can be used is the upper control limit, and the lower limit of the vehicle battery temperature that can be used is the lower control limit.

[0052] The detection interval refers to a data detection interval used to determine whether there is abnormal battery data in the battery data to be detected.

[0053] It should be noted that, for R&D personnel, the preset detection parameter information can be changed online based on different battery data to be tested. During the testing of the battery data to be tested, if the R&D personnel find problems in the testing process, they can solve the corresponding problems by changing the preset detection parameter information. This eliminates the need for R&D personnel to perform manual calculations and coding, greatly reducing the workload of R&D personnel, reducing the workload of improving new energy vehicle battery safety testing, and shortening the time for improving vehicle battery inspections.

[0054] For ordinary users, the server will perform battery testing on the target vehicle's battery based on the preset testing parameter information that has been determined and the battery data to be tested provided by the user, and feedback the test results to the user.

[0055] After obtaining at least two battery data to be detected, it is necessary to further obtain preset detection parameter information so that the at least two battery data to be detected are detected based on the preset detection parameter information and the detection results are obtained. Specifically, detecting at least two battery data to be detected is actually to determine whether at least two battery data to be detected contain abnormal battery data. Therefore, the proportionality coefficient actually refers to the ratio between abnormal battery data and normal battery data in the battery data to be detected. For example, the battery data to be detected obtained is the battery temperature data of the past 24 months, among which there are 16 abnormal battery temperature data and 8 normal battery temperature data, then the proportionality coefficient can be calculated to be 16 / 8*100%=200%.

[0056] Based on this, the implementation method of detecting at least two battery data to be detected is as follows:

[0057] In a specific embodiment provided by the present invention, detecting the at least two battery data to be detected according to the preset detection parameter information to obtain the detection result includes:

[0058] According to the preset detection parameter information, it is determined whether there is abnormal battery data in the at least two battery data to be detected.

[0059] The abnormal battery data refers to abnormal battery data. For example, if the battery data to be detected is battery temperature data, and the battery temperature data in May 2022 shows that the temperature is abnormal, it can be determined that the battery temperature data in May 2022 is abnormal temperature data.

[0060] Specifically, based on preset detection parameter information, judging whether there is abnormal battery data in at least two battery data to be detected can include determining a target detection interval based on an upper control limit, a lower control limit, and an average value, and detecting at least two battery data to be detected based on detection conditions corresponding to the target detection interval. Specifically, it can be determined whether there is abnormal battery data in at least two batteries to be detected by determining whether there is battery data to be detected within the target detection interval.

[0061] Take the setting of the detection interval as (-∞, 0.3], (0.3, 1), [1, +∞) as an example for explanation. For target vehicle A, its corresponding upper control limit is 0.3332, and its lower control limit is 0.0402. It can be determined that its corresponding average value is 0.1867. Based on the average value of 0.1867 and the upper control limit of 0.3332, it can be determined that the target detection intervals corresponding to target vehicle A are (-∞, 0.3] and (0.3, 1). If the battery data to be detected for target vehicle A contains data belonging to the above two target detection intervals, it is determined that there is abnormal battery data in the battery data to be detected for target vehicle A.

[0062] The new energy vehicle battery safety detection method provided by the present invention detects at least two battery data to be detected by setting and obtaining preset detection parameter information, and determines the detection result by determining whether the battery data to be detected exists in the detection interval.

[0063] Step 206: Based on the detection result, determine the battery status information of the vehicle battery.

[0064] Among them, the battery status information refers to the information on whether the vehicle battery has an abnormality determined based on the test results of the battery data to be tested, specifically including normal status and abnormal status.

[0065] In a specific embodiment provided by the present invention, determining the battery status information of the vehicle battery based on the detection result includes:

[0066] In the case that there is no abnormal battery data in the at least two battery data to be detected, determining that the vehicle battery is in a normal state;

[0067] In the case that abnormal battery data exists in the at least two battery data to be detected, the battery status information of the vehicle battery is determined based on abnormal information of the abnormal battery data.

[0068] Since detecting at least two battery data to be detected and obtaining the detection result is actually a process of detecting whether there is abnormal battery data in the at least two battery data to be processed, after obtaining the detection result for at least two battery data to be processed, the battery status information of the vehicle battery can be further determined by determining whether there is abnormal battery data in the at least two battery data to be detected.

[0069] Specifically, based on the detection results, if it is determined that there is no abnormal battery data in at least two battery data to be detected, it can be directly determined that the battery status of the vehicle battery is normal; if it is determined that there is abnormal battery data in at least two battery data to be detected, in order to ensure the accuracy of the battery status information of the vehicle battery, it is necessary to further determine the battery status information of the vehicle battery based on the abnormal information of the abnormal battery data.

[0070] The abnormal information refers to the data information corresponding to the abnormal battery data.

[0071] In a specific embodiment provided by the present invention, the abnormal information includes the abnormal quantity and abnormal interval;

[0072] The number of abnormalities refers to the number of abnormal battery data in at least two battery data to be detected. For example, if the number of battery data to be detected is 24, and the number of abnormal battery data is 3, then the number of abnormalities is 3.

[0073] The abnormal interval refers to the time interval in the preset time interval that the abnormal battery data belongs to. Using the above example, the above three abnormal battery data are data from March 2022, April 2022 and September 2022, respectively. It can be determined that the abnormal intervals to which the above three abnormal battery data belong are March 2022, April 2022 and September 2022, respectively.

[0074] Determining battery status information of the vehicle battery based on abnormal information of the abnormal battery data includes:

[0075] Based on the abnormal number and abnormal interval of the abnormal battery data, battery state information of the vehicle battery is determined.

[0076] Specifically, after determining that there is abnormal battery data in at least two battery data to be detected, in order to avoid the randomness of the determined battery status information and improve the accuracy of the battery status information, abnormal information corresponding to the abnormal battery data can be further obtained, that is, the abnormal number and abnormal interval of the abnormal battery data can be obtained, and then, based on the abnormal number and abnormal interval of the abnormal battery data, the battery status information of the vehicle battery can be determined.

[0077] Further, in a specific embodiment provided by the present invention, determining the battery status information of the vehicle battery based on the abnormal quantity and abnormal interval of the abnormal battery data includes:

[0078] When the number of abnormalities is one, determining that the vehicle battery is in a normal state;

[0079] When the number of abnormalities is greater than one, battery state information of the vehicle battery is determined based on the abnormal interval.

[0080] In actual applications, different numbers of abnormal battery data obtained may have different effects on the determination of the battery status information of the vehicle battery. For example, if the number of abnormal battery data obtained is small, it may be an accidental situation that occurs during long-term use. For example, if the abnormal battery data is scattered within a preset time interval, the accuracy of the determined battery status information may be low. Therefore, in the process of determining the battery status information of the vehicle battery based on abnormal battery data, and when the number of abnormal battery data is greater than one, the abnormal interval of the abnormal battery data can be combined for determination to improve the accuracy of the battery status information.

[0081] Specifically, after obtaining the abnormal battery data, the number of abnormal battery data is counted to determine the abnormal number of abnormal battery data. If the abnormal number of abnormal battery data is one, the battery status information of the vehicle battery is determined to be normal; if the abnormal number of abnormal battery data is greater than one, further judgment needs to be made based on the abnormal interval of the abnormal battery data.

[0082] Furthermore, in a specific embodiment provided by the present invention, determining the battery status information of the vehicle battery based on the abnormal interval includes:

[0083] Based on the abnormal interval, determining whether there is continuous abnormal battery data in the abnormal battery data;

[0084] In the case where there is continuous abnormal battery data among the abnormal battery data, it is determined that the vehicle battery is in an abnormal state.

[0085] Among them, continuous abnormal battery data refers to abnormal battery data whose abnormal interval is a continuous abnormal interval. For example, the abnormal intervals of abnormal battery data 1, abnormal battery data 2, and abnormal battery data 3 are March 2022, April 2022, and September 2022, respectively. Since March 2022 and April 2022 are continuous abnormal intervals, it can be determined that abnormal battery data 1 and abnormal battery data 2 are continuous abnormal battery data.

[0086] Specifically, according to the abnormal interval of the abnormal battery data, it is determined whether there is continuous abnormal battery data in the abnormal battery data, and if there is continuous abnormal battery data, the battery status information of the vehicle battery is determined to be in an abnormal state. The number of continuous abnormal battery data is greater than or equal to two.

[0087] Furthermore, in order to further improve the accuracy of the battery status information, further judgment can be made based on the different detection intervals. Different detection intervals correspond to different vehicle battery stabilities, and due to the differences in detection intervals, different detection intervals correspond to different detection rules.

[0088] Based on the different stabilities of vehicle batteries, the stability of vehicle batteries can be divided into three detection intervals (the number of detection intervals is not limited), namely the first detection interval, the second detection interval and the third detection interval. The stability of the three detection intervals is sorted from high to low, namely the first detection interval, the second detection interval and the third detection interval. The value ranges of the three detection intervals can be set to (-∞, 0.3], (0.3, 1), [1, +∞) respectively.

[0089] Since the battery stability in the first detection interval is the highest, the detection rule of the first detection interval is set to whether there are 4 consecutive abnormal battery data; the detection rule of the second detection interval can be set to whether there are 2 consecutive abnormal battery data; since the battery stability in the third detection interval is relatively low, the detection rule of the third detection interval can be set to whether the value of the proportional coefficient is greater than the preset threshold. Among them, the preset threshold can be set to 150%, 140%, etc., which can be determined according to the actual application situation, and the present invention is not limited here.

[0090] In different detection intervals, the battery status information of the vehicle battery is determined based on the number of continuous abnormal battery data, which can further improve the accuracy of the battery status information and avoid data contingency. In the third detection interval with lower battery stability, the battery status information of the vehicle battery is no longer determined by the number of continuous abnormal batteries, but is determined based on the proportional coefficient in the battery data to be detected, taking into account the stability of the vehicle battery itself, effectively improving the accuracy of the battery status information.

[0091] It should be noted that the setting of the detection interval and the value range corresponding to the detection interval are not limited to the above contents, and the above examples are used for explanation.

[0092] The following combination Figure 3 , explain the detection process of the battery data to be detected, Figure 3A detection flow chart for detecting battery data to be detected provided by an embodiment of the present invention is shown. First, the preset detection parameter information is obtained, and the target detection interval is determined based on the control upper limit and the average value. If the target detection interval includes the first detection interval, then in the first detection interval, it is determined whether there are 4 consecutive abnormal battery data. If so, it is determined that the battery status information of the vehicle battery is in an abnormal state; if the target detection interval includes the second detection interval, then in the second detection interval, it is determined whether there are 2 consecutive abnormal battery data. If so, it is determined that the battery status information of the vehicle battery is in an abnormal state; if the target detection interval includes the third detection interval, then in the third detection interval, based on the number of abnormal battery data and the number of normal battery data, a proportional coefficient is determined, and it is determined whether the proportional coefficient is greater than a preset threshold value. If so, it is determined that the battery status information of the vehicle battery is in an abnormal state.

[0093] The new energy vehicle battery safety detection method provided by the present invention determines whether there is abnormal battery data in the battery data to be detected, and further determines the battery status information of the vehicle battery based on the abnormal information of the abnormal battery data, thereby providing accuracy of the battery status information.

[0094] In order to enable R&D personnel or ordinary users to intuitively obtain the battery status information of the vehicle battery, after determining the battery status information of the vehicle battery, the battery status information can be visualized and output and displayed to the target terminal.

[0095] In a specific embodiment provided by the present invention, after determining the battery status information of the vehicle battery based on the detection result, the method further includes:

[0096] The battery status information is output and displayed to a target terminal.

[0097] In order to improve the user experience and enable the user to gain a deeper understanding of the use of vehicle batteries, the new energy vehicle battery safety detection method provided by the present invention can also predict the health status of the vehicle battery.

[0098] In a specific embodiment provided by the present invention, the method further comprises:

[0099] Obtaining battery data to be predicted of a target vehicle battery in at least one prediction dimension;

[0100] Inputting the battery data to be predicted into a battery prediction model to obtain health prediction information of the target vehicle battery;

[0101] The health prediction information is fed back to the target terminal.

[0102] The target vehicle battery is the vehicle battery that needs to be predicted for health. The prediction dimension refers to the dimension that can be used to predict the health of the target vehicle battery, such as chip, temperature, current, charge increment, trip increment, etc. The prediction dimension can be determined based on different prediction requirements of users.

[0103] The battery data to be predicted refers to the battery data used to predict the health status of the battery of the target vehicle. The battery data to be predicted can be provided by the user independently, and can be obtained from a preset database based on the information of the target vehicle.

[0104] The battery prediction model refers to a model that predicts the health status of the target vehicle's battery. Specifically, it can predict the battery health status in a future time period, for example, three months, five months, three years, five years, etc.

[0105] Specifically, the battery data to be predicted of the target vehicle battery in different dimensions is obtained, and the battery data to be predicted is input into the battery prediction model to obtain the health prediction information of the target vehicle battery, and then the health prediction information of the target vehicle battery obtained based on the battery prediction model is fed back to the target terminal, so that the target terminal obtains the health prediction information of the target vehicle battery.

[0106] like Figure 4a and Figure 4b As shown, Figure 4a A schematic diagram showing current health information of a vehicle battery provided by an embodiment of the present invention is shown. Figure 4b A schematic diagram of health prediction information of a vehicle battery provided by an embodiment of the present invention is shown. The horizontal axis is used to represent the health status of the target vehicle battery, and the vertical axis is used to represent the vehicle ratio. Figure 4a Taking the midpoint P as an example, the health status of the vehicle battery is 98%, and the vehicle proportion is 25%. The vehicle proportion of 25% means that among the vehicles for which the vehicle battery health prediction is performed, the health status of 25% of the vehicles is 98%.

[0107] Furthermore, in order to improve the prediction accuracy of the battery prediction model, the battery prediction model is trained by the following method:

[0108] Acquire sample battery data of at least one prediction dimension and sample health information corresponding to the sample battery data;

[0109] Inputting the sample battery data into the battery prediction model to obtain predicted health information corresponding to the sample battery data;

[0110] Calculating a model loss value of the battery prediction model according to the sample health information and the predicted health information;

[0111] The model parameters of the battery prediction model are adjusted according to the model loss value, and the battery prediction model is continuously trained until a training stop condition is reached.

[0112] Among them, sample battery data refers to vehicle battery data obtained in the sample battery data set, which is the training sample of the battery prediction model; the sample battery data set refers to a set of vehicle battery data obtained by collecting vehicle battery data of automobiles; sample health information refers to the actual health information corresponding to the sample battery data; predicted health information refers to the health information output by inputting the sample battery data into the battery prediction model; the model loss value refers to the difference between the sample health information and the predicted health information, which is used to measure the difference between the sample health information and the predicted health information.

[0113] Specifically, sample battery data is obtained through the above-mentioned method of obtaining the battery data to be tested, and the sample battery data is input into the battery prediction model. The battery prediction model is used to identify the health information of the sample battery data. At this time, the battery prediction model is a model that has not been trained yet. There will be a deviation between the identified predicted health information and the actual sample health information. The model parameters of the battery prediction model need to be adjusted accordingly. Specifically, the model loss value of the battery prediction model is calculated according to the output predicted health information and the sample health information. The loss function for calculating the model loss value can be a 0-1 loss function, an absolute value loss function, a square loss function, a cross entropy loss function, etc. in actual applications. In the present invention, preferably, the cross entropy function is selected as the loss function for calculating the model loss value, and the model parameters of the battery prediction model are adjusted according to the model loss value. The battery prediction model is continued to be trained based on the adjusted model parameters for the next batch of sample battery data until the stop condition of the model training is reached.

[0114] Specifically, the model training stopping conditions include that the model loss value is less than a preset threshold and / or the training rounds reach a preset round.

[0115] In a specific implementation manner provided by the present invention, taking the training stop condition of the model loss value being less than a preset threshold as an example, the preset threshold is 0.3. When the model loss value is less than 0.3, it is considered that the battery prediction model training is completed.

[0116] In another specific embodiment provided by the present invention, taking the preset training rounds as the training stop condition as an example, the preset training rounds are 30 rounds. When the training rounds of the sample battery data reach 30 rounds, it is considered that the battery prediction model training is completed.

[0117] In another specific embodiment provided by the present invention, two training stop conditions, namely a preset threshold and a preset training round, are set, and the model loss value and the training rounds are monitored at the same time. When either the model loss value or the training rounds meets the training stop condition, the battery prediction model training is considered to be completed.

[0118] The new energy vehicle battery safety detection method provided by the present invention includes: obtaining at least two battery data to be detected of a target vehicle within a preset time interval; based on the at least two battery data to be detected, detecting the vehicle battery of the target vehicle to obtain a detection result; based on the detection result, determining the battery status information of the vehicle battery.

[0119] An embodiment of the present invention obtains the battery data to be tested of the target vehicle, performs a safety test on the battery data to be tested, and determines the battery status information of the vehicle battery according to the safety test result of the battery data to be tested, thereby further determining the safety status of the target vehicle according to the status information of the vehicle battery, thereby improving the safety of the vehicle.

[0120] The following combination Figure 5 , the new energy vehicle battery safety detection method is further described. Among them, Figure 5 A processing flow chart of a new energy vehicle battery safety detection method provided by an embodiment of the present invention is shown, which specifically includes the following steps:

[0121] Step 502: Obtain at least two battery data to be tested of the target vehicle within a preset time interval.

[0122] Step 504: Obtain preset detection parameter information.

[0123] Step 506: According to the preset detection parameter information, determine whether there is abnormal battery data in the at least two battery data to be detected.

[0124] Step 508: When there is no abnormal battery data in the at least two battery data to be detected, determine that the vehicle battery of the target vehicle is in a normal state.

[0125] Step 510: When there is abnormal battery data in the at least two battery data to be detected, and the number of abnormalities in the abnormal battery data is one, determine that the vehicle battery is in a normal state.

[0126] Step 512: When the number of abnormalities is greater than one, determine whether there is continuous abnormal battery data in the abnormal battery data based on the abnormal interval of the abnormal battery data.

[0127] Step 514: When there is continuous abnormal battery data in the abnormal battery data, determine that the vehicle battery is in an abnormal state.

[0128] Step 516: Output and display the battery status information to the target terminal.

[0129] An embodiment of the present invention obtains the battery data to be tested of the target vehicle, performs a safety test on the battery data to be tested, and determines the battery status information of the vehicle battery according to the safety test result of the battery data to be tested, thereby further determining the safety status of the target vehicle according to the status information of the vehicle battery, thereby improving the safety of the vehicle.

[0130] Corresponding to the above method embodiment, the present invention also provides an embodiment of a new energy vehicle battery safety detection device, Figure 6 FIG. 1 is a schematic diagram showing the structure of a new energy vehicle battery safety detection device provided by an embodiment of the present invention. Figure 6 As shown, the device comprises:

[0131] The acquisition module 602 is configured to acquire at least two battery data to be detected of the target vehicle within a preset time interval;

[0132] The obtaining module 604 is configured to detect the vehicle battery of the target vehicle based on the at least two battery data to be detected, and obtain a detection result;

[0133] The determination module 606 is configured to determine the battery status information of the vehicle battery based on the detection result.

[0134] Optionally, the obtaining module 604 is further configured to:

[0135] Obtain preset detection parameter information;

[0136] According to the preset detection parameter information, the at least two battery data to be detected are detected to obtain a detection result.

[0137] Optionally, the obtaining module 604 is further configured to:

[0138] According to the preset detection parameter information, it is determined whether there is abnormal battery data in the at least two battery data to be detected.

[0139] Optionally, the determining module 606 is further configured to:

[0140] In the case that there is no abnormal battery data in the at least two battery data to be detected, determining that the vehicle battery is in a normal state;

[0141] In the case that abnormal battery data exists in the at least two battery data to be detected, the battery status information of the vehicle battery is determined based on abnormal information of the abnormal battery data.

[0142] Optionally, the abnormal information includes the abnormal quantity and abnormal interval;

[0143] The determining module 606 is further configured to:

[0144] Based on the abnormal number and abnormal interval of the abnormal battery data, battery state information of the vehicle battery is determined.

[0145] Optionally, the determining module 606 is further configured to:

[0146] When the number of abnormalities is one, determining that the vehicle battery is in a normal state;

[0147] When the number of abnormalities is greater than one, battery state information of the vehicle battery is determined based on the abnormal interval.

[0148] Optionally, the determining module 606 is further configured to:

[0149] Based on the abnormal interval, determining whether there is continuous abnormal battery data in the abnormal battery data;

[0150] In the case where there is continuous abnormal battery data among the abnormal battery data, it is determined that the vehicle battery is in an abnormal state.

[0151] Optionally, the device further comprises:

[0152] The output module is configured to output and display the battery status information to a target terminal.

[0153] Optionally, the device further comprises:

[0154] A data acquisition module is configured to acquire battery data to be predicted of a battery of a target vehicle in at least one prediction dimension;

[0155] A data input module is configured to input the battery data to be predicted into a battery prediction model to obtain health prediction information of the target vehicle battery;

[0156] The information feedback module is configured to feed back the health prediction information to the target terminal.

[0157] Optionally, the device further comprises a training module configured to:

[0158] Acquire sample battery data of at least one prediction dimension and sample health information corresponding to the sample battery data;

[0159] Inputting the sample battery data into the battery prediction model to obtain predicted health information corresponding to the sample battery data;

[0160] Calculating a model loss value of the battery prediction model according to the sample health information and the predicted health information;

[0161] The model parameters of the battery prediction model are adjusted according to the model loss value, and the battery prediction model is continuously trained until a training stop condition is reached.

[0162] The new energy vehicle battery safety detection device provided by the present invention includes: an acquisition module, configured to obtain at least two battery data to be detected of a target vehicle within a preset time interval; an acquisition module, configured to detect the vehicle battery of the target vehicle based on the at least two battery data to be detected, and obtain a detection result; a determination module, configured to determine the battery status information of the vehicle battery based on the detection result.

[0163] An embodiment of the present invention obtains the battery data to be tested of the target vehicle, performs a safety test on the battery data to be tested, and determines the battery status information of the vehicle battery according to the safety test result of the battery data to be tested, thereby further determining the safety status of the target vehicle according to the status information of the vehicle battery, thereby improving the safety of the vehicle.

[0164] The above is a schematic scheme of a new energy vehicle battery safety detection device of this embodiment. It should be noted that the technical scheme of the new energy vehicle battery safety detection device and the technical scheme of the new energy vehicle battery safety detection method mentioned above belong to the same concept, and the details of the technical scheme of the new energy vehicle battery safety detection device that are not described in detail can all be referred to the description of the technical scheme of the new energy vehicle battery safety detection method mentioned above.

[0165] Figure 7 The block diagram of a computing device 700 provided according to an embodiment of the present invention is shown. The components of the computing device 700 include but are not limited to a memory 710 and a processor 720. The processor 720 is connected to the memory 710 via a bus 730, and the database 750 is used to store data.

[0166] The computing device 700 also includes an access device 740 that enables the computing device 700 to communicate via one or more networks 760. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 740 may include one or more of any type of network interface (e.g., a network interface card (NIC)) of wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, a near field communication (NFC) interface, and the like.

[0167] In one embodiment of the present invention, the above components of the computing device 700 and Figure 7 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Figure 7 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of the present invention. Those skilled in the art may add or replace other components as needed.

[0168] The computing device 700 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 700 may also be a mobile or stationary server.

[0169] Among them, when the processor 720 executes the computer instructions, the steps of the new energy vehicle battery safety detection method are implemented.

[0170] The above is a schematic scheme of a computing device of this embodiment. It should be noted that the technical scheme of the computing device and the technical scheme of the above-mentioned new energy vehicle battery safety detection method belong to the same concept, and the details not described in detail in the technical scheme of the computing device can be referred to the description of the technical scheme of the above-mentioned new energy vehicle battery safety detection method.

[0171] An embodiment of the present invention further provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the steps of the new energy vehicle battery safety detection method as described above.

[0172] The above is a schematic scheme of a computer-readable storage medium of this embodiment. It should be noted that the technical scheme of the storage medium and the technical scheme of the above-mentioned new energy vehicle battery safety detection method belong to the same concept, and the details not described in detail in the technical scheme of the storage medium can be referred to the description of the technical scheme of the above-mentioned new energy vehicle battery safety detection method.

[0173] The above describes specific embodiments of the present invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0174] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0175] It should be noted that, for the convenience of description, the aforementioned method embodiments are all described as a series of action combinations, but those skilled in the art should be aware that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0176] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0177] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The optional embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the present invention. The present invention selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A new energy vehicle battery safety detection method, characterized in that: include: Acquire at least two battery data to be tested of the target vehicle within a preset time interval; Based on the at least two battery data to be detected, detecting the vehicle battery of the target vehicle to obtain a detection result; Based on the detection result, battery status information of the vehicle battery is determined.

2. The method according to claim 1, characterized in that Based on the at least two battery data to be detected, detecting the vehicle battery of the target vehicle to obtain a detection result includes: Obtain preset detection parameter information; According to the preset detection parameter information, the at least two battery data to be detected are detected to obtain a detection result.

3. The method according to claim 2, characterized in that According to the preset detection parameter information, detecting the at least two battery data to be detected to obtain a detection result, including: According to the preset detection parameter information, it is determined whether there is abnormal battery data in the at least two battery data to be detected.

4. The method according to claim 3, characterized in that Based on the detection result, determining the battery status information of the vehicle battery includes: In the case that there is no abnormal battery data in the at least two battery data to be detected, determining that the vehicle battery is in a normal state; In the case that abnormal battery data exists in the at least two battery data to be detected, the battery status information of the vehicle battery is determined based on abnormal information of the abnormal battery data.

5. The method according to claim 4, characterized in that The abnormal information includes the abnormal quantity and abnormal interval; Determining battery status information of the vehicle battery based on abnormal information of the abnormal battery data includes: Based on the abnormal number and abnormal interval of the abnormal battery data, battery state information of the vehicle battery is determined.

6. The method according to claim 5, characterized in that Determining battery status information of the vehicle battery based on the abnormal number and abnormal interval of the abnormal battery data includes: When the number of abnormalities is one, determining that the vehicle battery is in a normal state; When the number of abnormalities is greater than one, battery state information of the vehicle battery is determined based on the abnormal interval.

7. The method according to claim 6, characterized in that Determining battery status information of the vehicle battery based on the abnormal interval includes: Based on the abnormal interval, determining whether there is continuous abnormal battery data in the abnormal battery data; In the case where there is continuous abnormal battery data among the abnormal battery data, it is determined that the vehicle battery is in an abnormal state.

8. The method according to claim 1, characterized in that After determining the battery status information of the vehicle battery based on the detection result, the method further includes: The battery status information is output and displayed to a target terminal.

9. The method according to claim 1, characterized in that The method further comprises: Obtaining battery data to be predicted of a target vehicle battery in at least one prediction dimension; Inputting the battery data to be predicted into a battery prediction model to obtain health prediction information of the target vehicle battery; The health prediction information is fed back to the target terminal.

10. The method according to claim 9, characterized in that The battery prediction model is trained by the following method: Acquire sample battery data of at least one prediction dimension and sample health information corresponding to the sample battery data; Inputting the sample battery data into the battery prediction model to obtain predicted health information corresponding to the sample battery data; Calculating a model loss value of the battery prediction model according to the sample health information and the predicted health information; The model parameters of the battery prediction model are adjusted according to the model loss value, and the battery prediction model is continuously trained until a training stop condition is reached.

11. A new energy vehicle battery safety detection device, characterized in that: include: An acquisition module is configured to acquire at least two battery data to be detected of a target vehicle within a preset time interval; An acquisition module is configured to detect the vehicle battery of the target vehicle based on the at least two battery data to be detected, and obtain a detection result; The determination module is configured to determine the battery status information of the vehicle battery based on the detection result.

12. A computing device comprising a memory, a processor, and computer instructions stored in the memory and executable on the processor, characterized in that: When the processor executes the computer instructions, the steps of the method according to any one of claims 1 to 10 are implemented.

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