Battery detection method, device, apparatus, storage medium and computer program product

Abstract

A battery detection method, apparatus (200), device, storage medium, and computer program product are disclosed. The battery detection method includes: acquiring first operating data of a first battery (110); determining a first detection result of the first battery (120) based on the fault detection model corresponding to the first battery and the first operating data, wherein the first detection result includes a first score for the first battery to experience a target fault; and determining a second detection result of the first battery (130) based on the first score and a time influence factor for the first battery to experience the target fault, wherein the time influence factor is determined based on the time information of the first battery experiencing the target fault within a first preset time period. This method can solve the problems of low detection accuracy and high false alarm rate in battery fault detection results.

Description

Technical Field

[0001] This application relates to the field of batteries, and in particular to a battery testing method, apparatus, device, storage medium, and computer program product. Background Technology

[0002] With the continuous development of science and technology, batteries are being used more and more widely in daily production and life, and their safety performance is receiving increasing attention. During battery application, the battery's operating status is often monitored by collecting its electrical parameters to determine if any faults exist.

[0003] However, current commonly used detection methods, such as using the same detection model to detect batteries in different application environments, can only provide a rough judgment on whether a battery is faulty. This results in low detection accuracy and a high false alarm rate, which is not conducive to improving the safety of battery applications. Summary of the Invention

[0004] In view of the above problems, this application provides a battery testing method, apparatus, device, storage medium, and computer program product to solve the problems of low detection accuracy and high false alarm rate of battery fault detection results.

[0005] In a first aspect, this application provides a battery testing method, including:

[0006] Obtain the first operating data of the first battery;

[0007] Based on the fault detection model corresponding to the first battery and the first operating data, the first detection result of the first battery is determined, wherein the first detection result includes the first score of the first battery for the target fault.

[0008] The second detection result of the first battery is determined based on the first score and the time influence factor of the first battery's target failure. The time influence factor is determined based on the time information of the first battery's target failure within a first preset time period.

[0009] In the technical solution of this application embodiment, for the first operating data generated by the first battery, a first detection result of the first battery is determined based on the fault detection model corresponding to the first battery and the first operating data. The first detection result includes a first score for the first battery to experience a target fault, thereby achieving a preliminary detection of the first battery. Next, the preliminary detection result is adjusted by combining the time influence factor of the first battery experiencing a target fault, to obtain a second detection result. Since the time influence factor is determined based on the time information of the first battery experiencing a target fault within a first preset time period, the second detection result can be generated by combining the situation of the first battery experiencing a target fault within the first preset time period, so as to improve the reliability of the detection result.

[0010] In some embodiments, determining a second detection result for the first battery based on a first score and a time-related factor influencing the target failure of the first battery includes:

[0011] Based on the first score and the time-related factor of the first battery's target failure, the second score for the first battery's target failure is determined.

[0012] When the second score is greater than the preset fault threshold, the second detection result includes a warning message that the first battery has a target fault.

[0013] According to the embodiments of this application, after adjusting the first score by the time influence factor to obtain the second score, the detection result is determined based on the second score and the preset fault threshold. This can make the difference between the detection results of whether the target fault has occurred more obvious, and facilitate the issuance of accurate early warning information.

[0014] In some embodiments, before basing the first score and the time-related factor of the first battery's target failure, the method further includes:

[0015] Acquire first time information of the target fault occurring in the first battery within a first preset time period and second time information of the first operating data collected;

[0016] The time influence factor is determined based on the difference between the first and second time information. The magnitude of the time influence factor is negatively correlated with the magnitude of the difference.

[0017] According to the embodiments of this application, by combining the difference between the first time information and the second time information, the magnitude of the time influence factor is determined. This allows for the determination of different time influence factors based on different time spans, enabling adaptive adjustment of the first score to obtain a more reliable second score. This is beneficial for improving the reliability of battery detection results and reducing the false alarm rate of battery failures.

[0018] In some embodiments, determining a time influence factor based on the difference between first time information and second time information includes:

[0019] Based on the preset time decay formula and the difference between the first and second time information, the time influence factor is determined. The preset time decay formula is as follows:

[0020] T = C × e -Δt

[0021] Where T is the time influence factor, Δt is the difference between the first time information and the second time information, and C is a preset constant.

[0022] According to the embodiments of this application, by combining the difference between the first time information and the second time information, as well as the preset time decay formula, it is possible to determine different time influence factors according to different time spans, so as to achieve adaptive adjustment of the first score, which is beneficial to improving the reliability of battery detection results.

[0023] In some embodiments, before determining the first detection result of the first battery based on the fault detection model corresponding to the first battery and the first operating data, the method further includes:

[0024] Acquire multiple second operating data generated by the first battery within a second preset time period, and the second detection result corresponding to each second operating data;

[0025] Based on multiple second operating data and the second detection result corresponding to each second operating data, the initial fault detection model is updated and trained to obtain the fault detection model corresponding to the first battery.

[0026] According to the embodiments of this application, by combining the historical operating data generated by the first battery itself, the initial fault detection model is updated and trained to obtain the fault detection model corresponding to the first battery, which can reduce the training cost; moreover, based on the fault detection model corresponding to the first battery, one-to-one battery fault detection can be achieved, which is beneficial to improving the accuracy of the detection results.

[0027] In some embodiments, the second detection result includes a warning message indicating that the first battery has experienced a target fault or that the first battery has not experienced a target fault; based on multiple second operating data and the second detection result corresponding to each second operating data, the initial fault detection model is updated and trained to obtain a fault detection model corresponding to the first battery, including:

[0028] Based on multiple first running data and the second detection result corresponding to each second running data, the first training sample of the initial fault detection model is updated to obtain the second training sample;

[0029] The initial fault detection model is updated and trained based on the second training sample to obtain the fault detection model corresponding to the first battery.

[0030] In some embodiments, the second training sample includes multiple second operating data of the first battery and multiple third operating data of the second battery, a second detection result corresponding to each second operating data, and a third detection result corresponding to each third operating data;

[0031] The initial fault detection model is updated and trained based on the second training samples to obtain the fault detection model corresponding to the first battery, including:

[0032] Based on multiple second operating data of the first battery and multiple third operating data of the second battery, a second detection result corresponding to each second operating data, and a third detection result corresponding to each third operating data, a first score for the first battery to experience a target fault is determined, wherein the first score is used to represent the first probability that the first battery experiences a target fault when the first operating data meets the preset fault judgment conditions.

[0033] According to the embodiments of this application, by updating and training the initial fault detection model based on the operating data generated by the first battery itself, a fault detection model corresponding to the first battery can be obtained, which can reduce training costs and improve the accuracy of fault detection.

[0034] Secondly, this application provides a battery testing device, comprising:

[0035] The acquisition module is used to acquire the first operating data of the first battery;

[0036] The processing module is used to determine the first detection result of the first battery based on the fault detection model corresponding to the first battery and the first operating data, wherein the first detection result includes the first score of the first battery having a target fault;

[0037] The processing module is also used to determine the second detection result of the first battery based on the first score and the time influence factor of the first battery's target failure, wherein the time influence factor is determined based on the time information of the first battery's target failure within a first preset time period.

[0038] According to the embodiments of this application, for the first operating data generated by the first battery, a first detection result of the first battery is determined based on the fault detection model corresponding to the first battery and the first operating data. The first detection result includes a first score for the first battery to experience a target fault, thereby achieving a preliminary detection of the first battery. Next, the preliminary detection result is adjusted by combining the time influence factor of the first battery experiencing a target fault, to obtain a second detection result. Since the time influence factor is determined based on the time information of the first battery experiencing a target fault within a first preset time period, the second detection result can be generated by combining the situation of the first battery experiencing a target fault within the first preset time period, so as to improve the reliability of the detection result.

[0039] Thirdly, this application provides a battery testing device, which includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the battery testing method described in the first aspect or any implementable method of the first aspect.

[0040] Fourthly, this application provides a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the battery detection method described in the first aspect or any implementable embodiment of the first aspect.

[0041] Fifthly, embodiments of this application provide a computer program product whose instructions, when executed by a processor of an electronic device, cause the electronic device to perform the battery detection method as described in the first aspect or any implementable embodiment of the first aspect.

[0042] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0043] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0044] Figure 1 This is a schematic flowchart of a battery detection method provided in an embodiment of this application;

[0045] Figure 2 This is a schematic diagram of the structure of a battery detection device provided in an embodiment of this application;

[0046] Figure 3 This is a schematic diagram of the structure of a battery testing device provided in an embodiment of this application. Detailed Implementation

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0048] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0049] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0050] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0051] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0052] In the description of the embodiments of this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0053] With the continuous development of science and technology, batteries are increasingly widely used in daily production and life. Batteries are not only used in energy storage power systems such as hydropower, thermal power, wind power, and solar power plants, but also widely used in electric vehicles such as electric bicycles, electric motorcycles, and electric cars, as well as in military equipment and aerospace. As the application fields of batteries continue to expand, the market demand is also constantly increasing. Battery safety performance is also receiving increasing attention. The battery mentioned in the embodiments of this application refers to a single physical module comprising one or more battery cells to provide higher voltage and capacity. For example, the battery mentioned in this application may include a battery module or battery pack, etc.

[0054] In the application of batteries, the operating status of the battery is often detected by collecting the power consumption parameters generated by the battery in order to determine whether there is a fault in the battery.

[0055] The inventors of this application have noted that current commonly used battery testing methods typically train a unified model using historical data from all batteries. However, due to differences in production, storage, transportation, and usage conditions between different batteries, inconsistencies exist, resulting in varying sensitivities for each battery characteristic. For example, some batteries have frequently experienced excessive temperature fluctuations during historical use, yet still function normally; others, which have never exhibited excessive temperature fluctuations, fail to function properly when such fluctuations suddenly occur. Therefore, using the same detection model to detect faults in batteries across different application environments only yields a rough assessment of whether a battery is faulty. This leads to low detection accuracy and a high false alarm rate, hindering the improvement of battery application safety.

[0056] Based on the above considerations, and to address the problems of low detection accuracy and high false alarm rate in battery fault detection results, the inventors, through in-depth research, have provided a battery detection method, apparatus, device, storage medium, and computer program product. During the battery detection process, for the first operating data generated by the first battery, a first detection result is determined based on the corresponding fault detection model and the first operating data. This first detection result includes a first score indicating the occurrence of a target fault in the first battery, thus achieving preliminary detection of the first battery. Next, the preliminary detection result is adjusted by incorporating a time-related factor influencing the occurrence of the target fault, resulting in a second detection result. Since the time-related factor is determined based on the time information of the target fault occurring within a first preset time period, the second detection result can be generated by combining the occurrence of the target fault within the first preset time period of the first battery itself, thereby improving the reliability of the detection results.

[0057] The technical solutions described in this application are applicable to batteries and electrical devices that use batteries. These electrical devices can be vehicles, mobile phones, portable devices, laptops, ships, spacecraft, electric toys, and power tools, etc. This application does not impose any special limitations on the aforementioned electrical devices.

[0058] The battery detection method provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0059] Please see Figure 1 , Figure 1 This is a schematic flowchart of a battery detection method provided in an embodiment of this application, as shown below. Figure 1 As shown, the above method may include the following steps 110 to 130.

[0060] Step 110: Obtain the first operating data of the first battery.

[0061] Step 120: Determine the first detection result of the first battery based on the fault detection model corresponding to the first battery and the first operating data.

[0062] The first detection result includes the first score for the first battery to have a target fault;

[0063] Step 130: Determine the second detection result of the first battery based on the first score and the time influence factor of the first battery's target failure.

[0064] The time-related factor is determined based on the time information of the target failure occurring in the first battery within a first preset time period.

[0065] In step 110 above, the first battery can refer to a single physical module comprising one or more battery cells to provide higher voltage and capacity. For example, the battery mentioned in this application may include a battery module or battery pack. A battery generally includes a housing for encapsulating one or more battery cells. The battery cells may include lithium-ion secondary battery cells, lithium-ion primary battery cells, lithium-sulfur battery cells, sodium-lithium-ion battery cells, sodium-ion battery cells, or magnesium-ion battery cells, etc., and this application embodiment is not limited to these.

[0066] The first set of operational data may include, for example, the temperature and voltage of the first battery during application, as well as temperature and voltage changes, etc., which will not be listed here. For instance, battery operational data such as temperature and voltage can be collected according to a preset sampling frequency. Taking temperature as an example, the temperature of the first battery can be collected using a temperature sensor at a preset sampling frequency. Optionally, the temperature sensor can be a contact temperature sensor or a non-contact temperature sensor; there is no specific limitation here. For each collected temperature, the current collected temperature can be compared with the previously collected temperature to obtain the temperature change of the first battery.

[0067] After obtaining the first operating data of the first battery, step 120 of the embodiment of this application can be executed.

[0068] In step 120 above, the fault detection model corresponding to the first battery is a trained model for detecting battery faults. Optionally, the training samples of the fault detection model may include operational data generated by the first battery during application or testing, and may also include third operational data generated by the second battery during application or testing, to increase the amount of training data in the training samples, thereby improving the accuracy of fault detection. Optionally, the second battery may be the same type of battery as the first battery. The second battery may, for example, be in the same application or testing environment as the first battery, or it may be in a different application or testing environment than the first battery; this is not specifically limited here.

[0069] Based on the initial operational data and the corresponding fault detection model for the first battery, a preliminary detection result regarding whether the first battery has malfunctioned can be obtained, i.e., the first detection result. The first detection result may include a first score indicating that the first battery has experienced a target fault.

[0070] For example, the target fault may include a battery overheating fault, and a first score output by the fault detection model can be used to represent the risk of the first battery experiencing an overheating fault. For instance, the higher the first score, the greater the risk of the first battery experiencing an overheating fault.

[0071] To improve the accuracy of first battery fault detection, step 130 of the embodiment of this application can be performed next.

[0072] In step 130 above, the first preset time period can be the time period between the first battery generating the first operating data. Optionally, the length of the first preset time period can be from the time the first battery is first used to the time the first battery generates the first operating data. If a target fault is determined to have occurred in the first battery within the first preset time period, the time when the target fault occurred can be recorded, generating the time information of the target fault. Optionally, the time information of the target fault can be stored in a preset database, without specific limitations.

[0073] Based on the time information of the target failure of the first battery within a first preset time period, the time influence factor of the target failure of the first battery can be determined. For example, the longer the span between the time information of the target failure of the first battery within the first preset time period and the time when the first battery generates the first operating data, the smaller the influence factor; the smaller the span between the time information of the target failure of the first battery within the first preset time period and the time when the first battery generates the first operating data, the greater the influence factor.

[0074] Therefore, the first score can be adjusted using the time influence factor to improve the accuracy of fault detection for the first battery. When generating the second detection result for the first battery, the adjusted first score can be used to determine the second detection result for the first battery.

[0075] According to the embodiments of this application, for the first operating data generated by the first battery, the first detection result of the first battery is determined based on the fault detection model corresponding to the first battery and the first operating data, thereby realizing the preliminary detection of the first battery. Next, the preliminary detection result is adjusted in combination with the time influence factor of the first battery's target fault occurrence to obtain the second detection result. Since the time influence factor is determined based on the time information of the first battery's target fault occurrence within a first preset time period, the second detection result can be generated by combining the situation of the first battery's own target fault occurrence within the first preset time period, so as to improve the reliability of the detection result.

[0076] In some embodiments of this application, step 130 may specifically include: determining a second score for the first battery to experience a target fault based on a first score and a time-related factor influencing the first battery's target fault; and when the second score is greater than a preset fault threshold, the second detection result includes a warning message that the first battery has experienced a target fault.

[0077] For example, when the influence of the time factor is greater, the reliability of the first detection result of the first battery is higher. Thus, the first score can be increased by using the time factor to obtain the second score. When the influence of the time factor is smaller, the reliability of the first detection result of the first battery is lower. Thus, the first score can be decreased by using the time factor to obtain the second score.

[0078] The preset fault threshold can be set according to actual needs. Optionally, when the second score is greater than the preset fault threshold, the risk of the first battery experiencing a target fault can be considered high, and a warning message can be issued. Therefore, the second detection result may include the warning message that the first battery has experienced a target fault. When the second score is less than or equal to the preset fault threshold, the risk of the first battery experiencing a target fault can be considered low or non-existent. Therefore, the second detection result may not include the warning message.

[0079] According to the embodiments of this application, after adjusting the first score by the time influence factor to obtain the second score, the detection result is determined based on the second score and the preset fault threshold. This can make the difference between the detection results of whether the target fault has occurred more obvious, and facilitate the issuance of accurate early warning information.

[0080] In some embodiments of this application, the time influence factor is determined specifically by the following steps: First, first time information of the first battery experiencing a target fault within a first preset time period and second time information of the first operating data being collected are obtained; next, the time influence factor is determined based on the difference between the first time information and the second time information, wherein the magnitude of the time influence factor is negatively correlated with the magnitude of the difference.

[0081] In some embodiments, the first time information may be the time of the most recent occurrence of the target fault before the first battery generates the first operating data, and the second time information may be the time information corresponding to the first battery generating the first operating data.

[0082] In this way, the magnitude of the time influence factor can be determined based on the difference between the first and second time information. The magnitude of the time influence factor is negatively correlated with the magnitude of the difference. That is, the longer the time between the most recent occurrence of the target fault before the first battery generates the first operating data and the second time information, the smaller the time influence factor; conversely, the shorter the time between the most recent occurrence of the target fault before the first battery generates the first operating data and the second time information, the larger the time influence factor.

[0083] Optionally, when determining the time influence factor based on the difference between the first and second time information, it can be determined according to a preset mapping relationship. For example, it can be done by looking up a table, pre-setting multiple difference ranges, with different difference ranges corresponding to different time influence factors; or it can be calculated according to a preset function, where the difference is input into the preset function and the time influence factor is obtained through calculation. The above examples are merely illustrations and no specific limitations are imposed here.

[0084] According to the embodiments of this application, by combining the difference between the first time information and the second time information, the magnitude of the time influence factor is determined. This allows for the determination of different time influence factors based on different time spans, enabling adaptive adjustment of the first score to obtain a more reliable second score. This is beneficial for improving the reliability of battery detection results and reducing the false alarm rate of battery failures.

[0085] In some embodiments of this application, the time influence factor is determined, specifically based on a preset time decay formula and the difference between the first time information and the second time information. The preset time decay formula can be as shown in formula (1).

[0086] T = C × e -Δt (1)

[0087] Where T is the time influence factor, Δt is the difference between the first time information and the second time information, and C is a preset constant.

[0088] As a concrete example, let's say the first operational data is acquired at time t1. This first operational data includes the temperature difference corresponding to t1. Based on this temperature difference and the fault detection model corresponding to the first battery, a score S1 is obtained for the first battery experiencing a target fault. Next, combining this with the first time information of the first battery's last occurrence of the target fault, which includes the time t2 of the last occurrence of the target fault, the difference Δt between t1 and t2 can be obtained. Then, using a preset time decay formula, the time influence factor T can be calculated. Optionally, the product of the time influence factor and the first score can be used as the second score.

[0089] According to the embodiments of this application, by combining the difference between the first time information and the second time information, as well as the preset time decay formula, it is possible to determine different time influence factors according to different time spans, so as to achieve adaptive adjustment of the first score, which is beneficial to improving the reliability of battery detection results.

[0090] In some embodiments of this application, in order to improve the accuracy of the first detection result, the embodiments of this application may further include the following steps before determining the first detection result of the first battery:

[0091] Step 201: Obtain multiple second operating data generated by the first battery within a second preset time period, and the second detection result corresponding to each second operating data;

[0092] Step 202: Based on multiple second running data and the second detection result corresponding to each second running data, update and train the initial fault detection model to obtain the fault detection model corresponding to the first battery.

[0093] Specifically, the second preset time period can be the time period between the generation of the first operating data by the first battery. The first preset time periods can be the same or different, and there is no specific restriction here.

[0094] The second set of operational data may include, for example, the temperature and voltage of the first battery during application, as well as temperature and voltage changes, etc., which will not be listed here. For instance, battery operational data such as temperature and voltage of the first battery can be collected according to a preset sampling frequency. Taking temperature as an example, the temperature of the first battery can be collected by a temperature sensor according to a preset sampling frequency. For each collected temperature, the current collected temperature can be compared with the previously collected temperature to obtain the temperature change of the first battery.

[0095] The second detection result is the result obtained after fault detection of the first battery. For example, the second detection result may include whether the first battery has experienced a target fault.

[0096] The initial fault detection model can be a pre-trained detection model. In order to improve the accuracy of the fault detection results, the initial fault detection model is updated and trained using multiple second running data and the second detection results corresponding to each second running data, resulting in the fault detection model corresponding to the first battery.

[0097] According to the embodiments of this application, by combining the historical operating data generated by the first battery itself, the initial fault detection model is updated and trained to obtain the fault detection model corresponding to the first battery, which can reduce the training cost; moreover, based on the fault detection model corresponding to the first battery, one-to-one battery fault detection can be achieved, which is beneficial to improving the accuracy of the detection results.

[0098] In some embodiments of this application, the second detection result includes a warning message indicating that the first battery has experienced a target fault or that the first battery has not experienced a target fault; step 202 can specifically be based on the following steps:

[0099] Based on multiple first running data and the second detection result corresponding to each second running data, the first training sample of the initial fault detection model is updated to obtain the second training sample;

[0100] The initial fault detection model is updated and trained based on the second training sample to obtain the fault detection model corresponding to the first battery.

[0101] Specifically, the first training sample for the initial fault may include third operational data generated during the application or testing of the second battery. The second battery may have the same or different application or testing environment as the first battery; this is not specifically limited. The first training sample also includes battery fault detection information corresponding to each piece of third operational data.

[0102] The first training sample is updated based on multiple first running data and the second detection result corresponding to each second running data, which can effectively expand the sample size of the first training sample.

[0103] For example, the second training sample may include multiple second operating data corresponding to the first battery and a second detection result corresponding to each second operating data, as well as multiple third operating data generated by the second battery and battery fault detection information corresponding to each third operating data.

[0104] According to the embodiments of this application, by updating and training the initial fault detection model based on the operating data generated by the first battery itself, a fault detection model corresponding to the first battery can be obtained, which can reduce training costs and improve the accuracy of fault detection.

[0105] In some embodiments of this application, the second training sample includes multiple second operating data of the first battery and multiple third operating data of the second battery, a second detection result corresponding to each second operating data, and a third detection result corresponding to each third operating data. For example, updating and training the initial fault detection model can specifically be: determining a first score for the first battery to experience a target fault based on the multiple second operating data of the first battery and the multiple third operating data of the second battery, the second detection result corresponding to each second operating data, and the third detection result corresponding to each third operating data, wherein the first score is used to represent the first probability that the first battery experiences a target fault when the first operating data meets the preset fault judgment conditions.

[0106] Specifically, during operation or testing, the second battery can generate multiple third operating data. Each third operating data corresponds to the operating information of the second battery, such as whether the second battery is faulty when it generates the third operating data.

[0107] The preset fault judgment conditions can be set according to the characteristic variables included in the first operating data. The characteristic variables are such as temperature, voltage, temperature change, voltage change, frequency of temperature exceeding the preset temperature, etc., and are not specifically limited here.

[0108] For example, taking a scenario where both the second and third operating data include temperature changes, the preset fault judgment condition is whether the temperature change exceeds a preset temperature threshold. If the temperature change exceeds the preset temperature threshold, it indicates that the battery has experienced an excessive temperature difference event. Based on all operating data from the second training sample, statistical analysis can be performed to obtain the amount of operating data in the entire training sample where the temperature change exceeds the preset temperature threshold, the amount of operating data corresponding to the battery experiencing a target fault, and the amount of operating data corresponding to both the temperature change exceeding the preset temperature threshold and the battery experiencing a target fault. In this way, the probability P(excessive temperature difference), the probability P(target fault), and the probability P(excessive temperature difference|target fault) of both the target fault and the excessive temperature difference can be obtained.

[0109] Next, based on the preset Bayesian probability model, the first probability P(target fault|excessive temperature difference) of the first battery causing the target fault can be calculated. For example, the first probability can be obtained by the following calculation method: P(target fault|excessive temperature difference) = [P(excessive temperature difference|target fault) × P(target fault)] / P(excessive temperature difference).

[0110] After obtaining the first probability, the first score can be determined based on the first probability. To simplify the calculation process, the first probability can be directly assigned to the first score.

[0111] According to the embodiments of this application, by combining multiple second operating data of the first battery to update the initial fault detection model, the reliability of the first score output by the fault detection model can be improved, thereby reducing the false alarm rate.

[0112] To better understand the battery detection method provided in the embodiments of this application, embodiments of the above battery detection method in practical applications are provided here for illustration.

[0113] Step 301: Obtain the initial fault detection model.

[0114] Specifically, based on the first training samples, the constructed battery fault detection model is trained to obtain a trained initial fault detection model, which includes the third operating data of the second battery and the second battery operating information corresponding to each third operating data. Specifically, the second battery operating information includes, for example, whether the second battery has experienced a target fault. The target fault, for example, is an overheating fault caused by excessive temperature difference in the second battery. The constructed battery fault detection model can be a Bayesian probabilistic model.

[0115] Step 302: Obtain multiple second operating data of the first battery and the operating information of the first battery corresponding to each second operating data.

[0116] Specifically, the second operating data refers to the battery operating data generated by the first battery during application or testing. After obtaining the second operating data, it is also necessary to obtain the operating information of the first battery when the second operating data was generated, specifically, the operating information of the first battery. For example, whether the first battery experienced an overheating fault due to an excessive temperature difference.

[0117] Step 303: Update the first training sample based on multiple second operating data of the first battery and the operating information of the first battery corresponding to each second operating data to obtain the second training sample.

[0118] Step 304: Update and train the initial fault detection model based on the second training sample to obtain the fault detection model corresponding to the first battery.

[0119] After obtaining the fault detection model corresponding to the first battery, fault detection can be performed on the operating data generated by the first battery based on the fault detection model corresponding to the first battery.

[0120] When updating and training the initial fault detection model, the first probability P(target fault|excessive temperature difference) of the first battery occurring when the temperature difference is too large can be calculated, thus obtaining the fault detection model corresponding to the first battery, that is, the Bayesian probability model. For example, the first probability can be obtained according to the following calculation method: P(target fault|excessive temperature difference) = [P(excessive temperature difference|target fault) × P(target fault)] / P(excessive temperature difference).

[0121] Step 305: Obtain the first operating data of the first battery.

[0122] Step 306: Determine the first detection result of the first battery based on the fault detection model corresponding to the first battery and the first operating data.

[0123] The first operational data could include, for example, the temperature change of the first battery during operation. Based on the temperature change and the fault detection model, a first score can be determined for the first battery to indicate the risk of an overheating fault. For instance, a higher first score indicates a greater risk of an overheating fault.

[0124] Step 307: Obtain the time-related factors of the first battery's target failure.

[0125] Specifically, the first time information can be obtained, including the time t2 when the first battery last experienced a target fault and the time t1 when the first operating data was obtained. Then, the time influence factor can be determined based on the preset time decay formula and the difference between the first time information and the second time information. The preset time decay formula can be as shown in formula (1).

[0126] Step 308: Determine the second detection result of the first battery based on the time influence factor.

[0127] Specifically, a second score for the first battery to experience a target fault can be determined based on the first score and the time-related factor of the first battery experiencing a target fault; when the second score is greater than a preset fault threshold, the second detection result includes a warning message that the first battery has experienced a target fault.

[0128] In the technical solution of this application embodiment, for the first operating data generated by the first battery, a first detection result of the first battery is determined based on the fault detection model corresponding to the first battery and the first operating data. The first detection result includes a first score for the first battery to experience a target fault, thereby achieving preliminary detection of the first battery. Next, the preliminary detection result is adjusted by combining the time influence factor of the first battery experiencing a target fault, resulting in a second detection result. Since the time influence factor is determined based on the time information of the first battery experiencing a target fault within a first preset time period, the second detection result can be generated by combining the situation of the first battery experiencing a target fault within the first preset time period, so as to improve the reliability of the detection result. In addition, after adjusting the first score by the time influence factor to obtain the second score, the detection result is determined based on the second score and the preset fault threshold, which can make the difference between the detection results of whether a target fault has occurred more obvious, and facilitate the issuance of accurate early warning information.

[0129] Figure 2 This is a schematic diagram of the structure of a battery detection device provided in an embodiment of this application, as shown below. Figure 3 As shown, the battery detection device 200 may include an acquisition module 210 and a processing module 220.

[0130] The acquisition module 210 is used to acquire the first operating data of the first battery;

[0131] The processing module 220 is used to determine the first detection result of the first battery based on the fault detection model corresponding to the first battery and the first operating data, wherein the first detection result includes the first score of the first battery having a target fault;

[0132] The processing module 220 is further configured to determine the second detection result of the first battery based on the first score and the time influence factor of the first battery's target fault, wherein the time influence factor is determined based on the time information of the first battery's target fault occurring within a first preset time period.

[0133] According to the embodiments of this application, for the first operating data generated by the first battery, a first detection result of the first battery is determined based on the fault detection model corresponding to the first battery and the first operating data. The first detection result includes a first score for the first battery to experience a target fault, thereby achieving a preliminary detection of the first battery. Next, the preliminary detection result is adjusted by combining the time influence factor of the first battery experiencing a target fault, to obtain a second detection result. Since the time influence factor is determined based on the time information of the first battery experiencing a target fault within a first preset time period, the second detection result can be generated by combining the situation of the first battery experiencing a target fault within the first preset time period, so as to improve the reliability of the detection result.

[0134] In some embodiments, the processing module 220 is further configured to determine a second score for the first battery to experience a target fault based on the first score and the time influence factor of the first battery experiencing a target fault; when the second score is greater than a preset fault threshold, the second detection result includes a warning message that the first battery has experienced a target fault.

[0135] According to the embodiments of this application, after adjusting the first score by the time influence factor to obtain the second score, the detection result is determined based on the second score and the preset fault threshold. This can make the difference between the detection results of whether the target fault has occurred more obvious, and facilitate the issuance of accurate early warning information.

[0136] In some embodiments, the acquisition module 210 is further configured to acquire first time information of the first battery experiencing a target fault within a first preset time period and second time information of the first operating data being collected.

[0137] The processing module 220 is also used to determine the time influence factor based on the difference between the first time information and the second time information, wherein the magnitude of the time influence factor is negatively correlated with the magnitude of the difference.

[0138] According to the embodiments of this application, by combining the difference between the first time information and the second time information, the magnitude of the time influence factor is determined. This allows for the determination of different time influence factors based on different time spans, enabling adaptive adjustment of the first score to obtain a more reliable second score. This is beneficial for improving the reliability of battery detection results and reducing the false alarm rate of battery failures.

[0139] In some embodiments, the processing module 220 is further configured to determine a time influence factor based on a preset time decay formula and the difference between the first time information and the second time information, wherein the preset time decay formula is:

[0140] T = C × e -Δt

[0141] Where T is the time influence factor, Δt is the difference between the first time information and the second time information, and C is a preset constant.

[0142] According to the embodiments of this application, by combining the difference between the first time information and the second time information, as well as the preset time decay formula, it is possible to determine different time influence factors according to different time spans, so as to achieve adaptive adjustment of the first score, which is beneficial to improving the reliability of battery detection results.

[0143] In some embodiments, the acquisition module 210 is further configured to acquire multiple second operating data generated by the first battery within a second preset time period, and a second detection result corresponding to each second operating data.

[0144] The processing module 220 is also used to update and train the initial fault detection model based on multiple second running data and the second detection result corresponding to each second running data, so as to obtain the fault detection model corresponding to the first battery.

[0145] According to the embodiments of this application, by combining the historical operating data generated by the first battery itself, the initial fault detection model is updated and trained to obtain the fault detection model corresponding to the first battery, which can reduce the training cost; moreover, based on the fault detection model corresponding to the first battery, one-to-one battery fault detection can be achieved, which is beneficial to improving the accuracy of the detection results.

[0146] In some embodiments, the second detection result includes a warning message indicating that the first battery has experienced a target fault or that the first battery has not experienced a target fault;

[0147] The processing module 220 is also used to update the first training sample of the initial fault detection model based on multiple first running data and the second detection result corresponding to each second running data, so as to obtain the second training sample;

[0148] The processing module 220 is also used to update and train the initial fault detection model based on the second training sample to obtain the fault detection model corresponding to the first battery.

[0149] According to the embodiments of this application, by updating and training the initial fault detection model based on the operating data generated by the first battery itself, a fault detection model corresponding to the first battery can be obtained, which can reduce training costs and improve the accuracy of fault detection.

[0150] In some embodiments, the second training sample includes multiple second operating data of the first battery and multiple third operating data of the second battery, a second detection result corresponding to each second operating data, and a third detection result corresponding to each third operating data;

[0151] The processing module 220 is further configured to determine a first score for the first battery to experience a target fault based on multiple second operating data of the first battery and multiple third operating data of the second battery, a second detection result corresponding to each second operating data, and a third detection result corresponding to each third operating data, wherein the first score is used to represent the first probability that the first battery experiences a target fault when the first operating data meets the preset fault judgment conditions.

[0152] According to the embodiments of this application, by combining multiple second operating data of the first battery to update the initial fault detection model, the reliability of the first score output by the fault detection model can be improved, thereby reducing the false alarm rate.

[0153] It is understood that the battery testing device 200 in this application embodiment can correspond to the execution subject of the battery testing method provided in this application embodiment. For specific details of the operation and / or function of each module / unit of the battery testing device 200, please refer to the description of the corresponding part of the battery testing method provided in the above application embodiment. For the sake of brevity, it will not be repeated here.

[0154] Figure 3 A schematic diagram of the structure of a battery testing device according to an embodiment of this application is shown. Figure 3 As shown, the device may include a processor 301 and a memory 302 storing computer program instructions.

[0155] Specifically, the processor 301 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0156] Memory 302 may include mass storage for information or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. In one instance, memory 302 may include removable or non-removable (or fixed) media, or memory 302 may be non-volatile solid-state memory. Memory 302 may be internal or external to the battery detection device.

[0157] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methods according to one aspect of this disclosure.

[0158] The processor 301 reads and executes the computer program instructions stored in the memory 302 to implement the method described in the embodiments of this application and achieve the corresponding technical effects achieved by executing the method in the embodiments of this application. For the sake of brevity, it will not be described in detail here.

[0159] In one example, the battery testing device may also include a communication interface 303 and a bus 310. Wherein, as... Figure 3 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 310 and complete communication with each other.

[0160] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0161] Bus 310 includes hardware, software, or both, that couples components of an online information flow metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 310 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0162] The battery testing equipment can execute the battery testing method of the embodiments of this application, thereby achieving the corresponding technical effects of the battery testing method described in the embodiments of this application.

[0163] Furthermore, in conjunction with the battery detection methods described in the above embodiments, this application embodiment can provide a readable storage medium for implementation. This readable storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the battery detection methods described in the above embodiments. Examples of readable storage media can be non-transitory machine-readable media, such as electronic circuits, semiconductor memory devices, read-only memory (ROM), floppy disks, compact disc read-only memory (CD-ROM), optical discs, hard disks, etc.

[0164] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0165] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, read-only memory (ROM), flash memory, erasable read-only memory (EROM), floppy disks, compact disc read-only memory (CD-ROM), optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0166] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0167] Furthermore, in conjunction with the battery detection method, apparatus, and readable storage medium described in the above embodiments, this application can provide a computer program product for implementation. When the instructions in the computer program product are executed by the processor of an electronic device, the electronic device causes the electronic device to perform any of the battery detection methods described in the above embodiments.

[0168] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0169] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A battery testing method, characterized in that, include: Obtain the first operating data of the first battery; Based on the fault detection model corresponding to the first battery and the first operating data, a first detection result of the first battery is determined, wherein the first detection result includes a first score for the first battery to have a target fault. Based on the first score and the time influence factor of the first battery's occurrence of the target fault, a second detection result of the first battery is determined, wherein the time influence factor is determined based on the time information of the first battery's occurrence of the target fault within a first preset time period; Before determining the first detection result of the first battery based on the fault detection model corresponding to the first battery and the first operating data, the method further includes: Acquire multiple second operating data generated by the first battery within a second preset time period, and a second detection result corresponding to each second operating data; Based on the multiple second operating data and the second detection result corresponding to each second operating data, the initial fault detection model is updated and trained to obtain the fault detection model corresponding to the first battery.

2. The method according to claim 1, characterized in that, The step of determining the second detection result of the first battery based on the first score and the time-related factor of the first battery experiencing the target fault includes: Based on the first score and the time-related factor of the first battery experiencing the target fault, a second score for the first battery experiencing the target fault is determined. When the second score is greater than the preset fault threshold, the second detection result includes a warning message that the first battery has experienced the target fault.

3. The method according to claim 1, characterized in that, Before determining the time-related factors of the target failure occurring based on the first score and the first battery, the method further includes: Acquire first time information of the first battery experiencing the target fault within a first preset time period and second time information of the first operational data collected; The time influence factor is determined based on the difference between the first time information and the second time information, wherein the magnitude of the time influence factor is negatively correlated with the magnitude of the difference.

4. The method according to claim 3, characterized in that, The step of determining the time influence factor based on the difference between the first time information and the second time information includes: The time influence factor is determined based on a preset time decay formula and the difference between the first time information and the second time information, wherein the preset time decay formula is: in, The time-related factor, The difference between the first time information and the second time information is given. This is a preset constant.

5. The method according to claim 1, characterized in that, The second detection result includes a warning message indicating that the first battery has experienced the target fault or that the first battery has not experienced the target fault; the step of updating and training the initial fault detection model based on the plurality of second operating data and the second detection result corresponding to each second operating data to obtain the fault detection model corresponding to the first battery includes: Based on the plurality of second operating data and the second detection result corresponding to each second operating data, the first training sample of the initial fault detection model is updated to obtain the second training sample; the first training sample includes third operating data generated during the second battery application process or test and battery fault detection information corresponding to each third operating data; The initial fault detection model is updated and trained based on the second training sample to obtain the fault detection model corresponding to the first battery.

6. The method according to claim 5, characterized in that, The second training sample includes multiple second operating data of the first battery and multiple third operating data of the second battery, a second detection result corresponding to each second operating data, and a third detection result corresponding to each third operating data; The step of updating and training the initial fault detection model based on the second training samples to obtain the fault detection model corresponding to the first battery includes: Based on multiple second operating data of the first battery and multiple third operating data of the second battery, a second detection result corresponding to each second operating data, and a third detection result corresponding to each third operating data, a first score is determined for the first battery to experience the target fault, wherein the first score is used to represent the first probability that the first battery experiences the target fault when the first operating data meets the preset fault judgment conditions.

7. A battery testing device, characterized in that, include: The acquisition module is used to acquire the first operating data of the first battery; The processing module is used to determine the first detection result of the first battery based on the fault detection model corresponding to the first battery and the first operating data, wherein the first detection result includes a first score of the first battery having a target fault; The processing module is further configured to determine a second detection result of the first battery based on the first score and the time influence factor of the first battery experiencing the target fault, wherein the time influence factor is determined based on the time information of the first battery experiencing the target fault within a first preset time period; The acquisition module is further configured to acquire multiple second operating data generated by the first battery within a second preset time period, and a second detection result corresponding to each second operating data. The processing module is further configured to update and train the initial fault detection model based on the plurality of second operating data and the second detection result corresponding to each of the second operating data, so as to obtain the fault detection model corresponding to the first battery.

8. A battery testing device, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the battery detection method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the battery detection method as described in any one of claims 1-6.

10. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device performs the battery detection method as described in any one of claims 1-6.

Images