Battery internal short circuit fault diagnosis method and device, storage medium and electronic equipment

Abstract

The invention discloses a battery internal short circuit fault diagnosis method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring electric power data of a target battery in a constant-current charging stage; a characteristic curve of the target battery is obtained, and the characteristic curve is used for describing the incidence relation between the open-circuit voltage and the state of charge of the target battery; determining a first state of charge and a second state of charge of the target battery based on the power data and the characteristic curve; based on the first charge state and the second charge state, fault diagnosis is carried out on the target battery, a target fault diagnosis result of the target battery is obtained, and the target fault diagnosis result is used for indicating whether the target battery has an internal short circuit fault or not. The technical problem that the diagnosis result of the internal short circuit fault of the battery is not accurate in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of battery safety, in particular to a battery internal short circuit fault diagnosis method and device, a storage medium and an electronic device. BACKGROUND

[0002] With the popularization of new energy applications, batteries as key energy storage units play a core role in electric vehicles, energy storage systems, consumer electronics and other fields. However, the safety problem of batteries, especially internal short circuit fault, has become a key factor restricting the wide application of batteries. Internal short circuit fault not only leads to a sharp decline in battery performance, but also may cause battery thermal runaway, resulting in fire, explosion and other serious safety accidents, which poses a major threat to personnel and property safety. Diagnosing the internal short circuit fault of the battery can effectively prevent potential safety risks and ensure the stability and reliability of the battery system. In related technologies, a model (such as an equivalent circuit model, an electrochemical model, etc.) is established for the battery, and the internal short circuit fault of the battery is diagnosed through the difference between the actual measured value and the model estimated value. However, the accuracy of the model is affected by the complexity of the battery and the change of external conditions, and there is a large uncertainty, which leads to a large error in the obtained fault diagnosis result. Therefore, the related technologies have the technical problem of inaccurate internal short circuit fault diagnosis result of the battery.

[0003] At present, no effective solution has been proposed for the above problems. SUMMARY

[0004] The embodiments of the application provide a battery internal short circuit fault diagnosis method, device, storage medium and electronic device to at least solve the technical problem of inaccurate internal short circuit fault diagnosis result of the battery in related technologies.

[0005] According to an aspect of an embodiment of the application, a battery internal short circuit fault diagnosis method is provided, including: collecting power data of a target battery in a constant current charging phase; obtaining a characteristic curve of the target battery, wherein the characteristic curve is used to describe the correlation between the open circuit voltage and the state of charge of the target battery; determining a first state of charge and a second state of charge of the target battery based on the power data and the characteristic curve; and performing fault diagnosis on the target battery based on the first state of charge and the second state of charge to obtain a target fault diagnosis result of the target battery, wherein the target fault diagnosis result is used to indicate whether the target battery has an internal short circuit fault.

[0006] According to another aspect of the embodiments of this application, a battery internal short-circuit fault diagnosis device is provided, comprising: a data acquisition module for acquiring power data of a target battery during a constant current charging phase; a characteristic curve acquisition module for acquiring a characteristic curve of the target battery, wherein the characteristic curve is used to describe the correlation between the open-circuit voltage and the state of charge of the target battery; a first determination module for determining a first state of charge and a second state of charge of the target battery based on the power data and the characteristic curve; and a fault diagnosis module for performing fault diagnosis on the target battery based on the first state of charge and the second state of charge to obtain a target fault diagnosis result of the target battery, wherein the target fault diagnosis result is used to indicate whether an internal short-circuit fault has occurred in the target battery.

[0007] According to another aspect of the embodiments of this application, a non-volatile storage medium is provided, which stores a plurality of instructions adapted for a battery internal short-circuit fault diagnosis method, any one of which is loaded by a processor.

[0008] According to another aspect of the embodiments of this application, an electronic device is provided, including: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any one of the following battery internal short-circuit fault diagnosis methods.

[0009] According to another aspect of the embodiments of this application, a computer program product is provided, which, when executed on a data processing device, is adapted to perform the steps of a method for diagnosing internal short-circuit faults in a battery.

[0010] In this embodiment, power data of the target battery during the constant current charging stage is collected; the characteristic curve of the target battery is obtained, wherein the characteristic curve describes the correlation between the open-circuit voltage and the state of charge of the target battery; based on the power data and the characteristic curve, the first state of charge and the second state of charge of the target battery are determined; based on the first state of charge and the second state of charge, fault diagnosis is performed on the target battery to obtain the target fault diagnosis result of the target battery, wherein the target fault diagnosis result is used to indicate whether the target battery has an internal short-circuit fault. This achieves the technical effect of improving the accuracy of the internal short-circuit fault diagnosis result of the target battery by collecting power data of the target battery during the constant current charging stage, combining it with the characteristic curve, and obtaining the target fault diagnosis result of the target battery by analyzing the first state of charge and the second state of charge. This solves the technical problem of inaccurate internal short-circuit fault diagnosis results of batteries in related technologies. Attached Figure Description

[0011] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0012] Figure 1 is a flow chart of a battery internal short circuit fault diagnosis method according to an embodiment of the application;

[0013] Figure 2 is a flow chart of an optional battery internal short circuit fault diagnosis method according to an embodiment of the application;

[0014] Figure 3 is an optional equivalent circuit model schematic diagram according to an embodiment of the application;

[0015] Figure 4 is a schematic diagram of an optional battery internal short circuit fault diagnosis device according to an embodiment of the application. DETAILED DESCRIPTION

[0016] In order to make the personnel in the technical field better understand the application scheme, the technical scheme in the embodiments of the application will be clearly and completely described below in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are only a part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the application.

[0017] It should be noted that the terms "first", "second", etc. in the specification and claims of the application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0018] According to an embodiment of the application, a method embodiment of a battery internal short circuit fault diagnosis method is provided. It should be noted that the steps shown in the flow chart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flow chart, in some cases, the steps shown or described herein can be executed in a different order than here.

[0019] Figure 1 FIG. 1 is a flowchart of a method for diagnosing an internal short circuit fault of a battery according to an embodiment of the present application. As shown in FIG. 1, the method comprises the following steps: Figure 1

[0020] In step S102, power data of the target battery in the constant current charging phase is collected.

[0021] It can be understood that the data of the target battery is collected when the target battery is in the constant current charging phase, and the power data of the target battery is obtained, such as current data, voltage data and resistance data, etc. By collecting the power data of the target battery in the constant current charging phase, the accuracy of the internal short circuit fault diagnosis result of the target battery can be significantly enhanced.

[0022] Optionally, the constant current charging phase is a period when the state of the target battery is relatively stable. In the constant current charging phase, since the current is constant, any significant change or fluctuation in voltage can be more directly attributed to changes in the internal state of the battery. This means that voltage changes caused by the health of the battery, the state of charge, or any potential faults (such as internal short circuits) will be more clear, easy to identify and analyze. At the same time, in the constant current charging phase, since the charging current remains unchanged, the influence of external interference factors (such as power fluctuations, contact resistance changes) on voltage measurement is relatively small, thereby reducing the noise level. Low-noise measurement data helps to capture the small changes of the battery more accurately, which is particularly important for early fault diagnosis.

[0023] In step S104, a characteristic curve of the target battery is obtained, wherein the characteristic curve is used to describe the correlation between the open circuit voltage and the state of charge of the target battery.

[0024] It can be understood that by obtaining the characteristic curve (i.e. OCV-SOC curve) of the target battery for describing the correlation between the open circuit voltage (OCV) and the state of charge (SOC) of the target battery, the abnormal behavior of the battery can be effectively identified, thereby improving the accuracy of the target fault diagnosis result of the target battery.

[0025] ​Optionally, the OCV-SOC curve of the target battery can be obtained by pulse charging the target battery. First, a series of short pulse charging currents are applied to the target battery, each pulse lasting a short time, usually between a few seconds and tens of seconds. During the pulse charging, the charging current and the terminal voltage of the battery are recorded. Second, after each pulse charging period, the target battery is allowed to stand for a period of time, usually a few minutes to a few hours, so that the internal electrochemical reactions of the target battery reach a balanced state, at which time the measured open circuit voltage is closer to the true value, reducing the voltage deviation under the influence of current. Next, during the target battery standing period, the open circuit voltage is recorded, and to avoid the influence of load current on the voltage, ensure that the measurement is carried out in a no-load state. Then, based on the ampere-hour integration method or the initial state of charge of the target battery and the current and time information during pulse charging, the new state of charge value of the target battery after each pulse charging is calculated, and the open circuit voltage measured after each standing period and the corresponding new state of charge value are recorded. Repeat the above steps until the target battery reaches full charge. Finally, all the open circuit voltage and state of charge data are sorted to form the OCV-SOC curve.

[0026] In step S106, based on the power data and the characteristic curve, the first state of charge and the second state of charge of the target battery are determined.

[0027] It can be understood that the power data and the characteristic curve of the target battery are analyzed, and the first state of charge and the second state of charge of the target battery are calculated. By comparing and analyzing the first state of charge and the second state of charge obtained by using two independent state of charge estimation methods, the reliability and accuracy of the internal short circuit fault diagnosis result can be improved, and early internal short circuit fault warning of the target battery can be realized.

[0028] In an optional embodiment, based on the power data and the characteristic curve, the first state of charge and the second state of charge of the target battery are determined, including: based on the current data in the power data and the capacity of the target battery, the third state of charge is obtained by using the Kalman filtering algorithm; based on the third state of charge and the characteristic curve, the open circuit voltage of the target battery is determined; based on the open circuit voltage, the third state of charge is corrected to obtain the first state of charge; based on the current data and the capacity, the second state of charge is obtained by using the ampere-hour integration method.

[0029] It can be understood that, according to the current data in the power data, in combination with the capacity of the target battery, the third state of charge of the target battery is calculated by using the Kalman filtering algorithm. According to the third state of charge, in combination with the characteristic curve of the target battery, the open circuit voltage of the target battery is determined. According to the open circuit voltage, the third state of charge is corrected to obtain the first state of charge of the target battery. At the same time, according to the current data of the target battery and the capacity of the target battery, the second state of charge of the target battery is obtained by using the ampere-hour integration method. The Kalman filtering algorithm can effectively process system noise and observation noise, and provides a SOC estimation value (i.e. the first state of charge) that is closer to the actual situation. The ampere-hour integration method is based on the direct measurement of current and gives a SOC estimation value (i.e. the second state of charge) based on charge accumulation. By comparing the first state of charge and the second state of charge, the state of charge estimation deviation caused by internal short circuit can be effectively identified, thereby improving the accuracy of internal short circuit fault diagnosis.

[0030] In an optional embodiment, the third state of charge is corrected based on the open circuit voltage to obtain the first state of charge, including: based on the open circuit voltage, the current data and the resistance data in the power data, determining the predicted terminal voltage of the target battery; based on the predicted terminal voltage and the voltage data in the power data, correcting the third state of charge to obtain the first state of charge.

[0031] It can be understood that, according to the open circuit voltage of the target battery, the current data and the resistance data of the target battery, the equivalent circuit model of the target battery is used to determine the predicted terminal voltage of the target battery. According to the difference between the predicted terminal voltage and the voltage data (i.e. the actual terminal voltage of the target battery) actually collected in the power data, the third state of charge is corrected to obtain the first state of charge of the target battery. Through the dynamic correction of the Kalman filtering algorithm, in combination with the OCV-SOC characteristic curve and the equivalent circuit model, the first state of charge of the target battery can be more accurately estimated, thereby improving the accuracy of the target fault diagnosis result.

[0032] Optionally, an equivalent circuit model of the target battery can be established according to the power data of the target battery and the OCV-SOC curve. The equivalent circuit model can be represented in the following way:

[0033]

[0034] Wherein ，U t represents the predicted terminal voltage of the target battery, OCV(SOC) represents the open circuit voltage corresponding to the state of charge SOC of the target battery, which is obtained through the OCV-SOC curve, I represents the current data of the target battery, and R represents the resistance of the target battery.

[0035] Optionally, by extending the Kalman filter to the above equivalent circuit model, the target battery can be estimated using the Kalman filter algorithm. k First state of charge at time 1 .

[0036]

[0037]

[0038]

[0039] in, Indicates the target battery in k The third state of charge at time t, Indicates the target battery in k The first state of charge at time -1 Indicates the target battery in k Current data at time -1 This represents the difference between two adjacent time points. Indicates the capacity of the target battery. express k Process noise at time -1 Indicates the target battery in k Predicted terminal voltage at time [time] Indicates the target battery's state of charge. The corresponding open-circuit voltage under the given conditions is obtained from the OCV-SOC curve. express k Observation noise at any given moment express k Kalman gain at time step Indicates the target battery in k Voltage data at any given time.

[0040] Alternatively, based on the collected power data, the ampere-hour integration method can be used to determine the target battery's performance. k The second state of charge at time t .

[0041]

[0042] in, Indicates the target battery at the initial moment The initial state of charge, Indicates the target battery in k Current data at any given time. The second state of charge is calculated directly from the target battery's current data using the ampere-hour integration method. This ensures that the calculation of the second state of charge is closely related to the actual charging process of the target battery, thus providing actual data on the target battery's state of charge.

[0043] In step S108, the target battery is diagnosed based on the first state of charge and the second state of charge to obtain a target fault diagnosis result of the target battery, where the target fault diagnosis result is used to indicate whether the target battery has an internal short circuit fault.

[0044] It can be understood that the fault diagnosis of the target battery is achieved by analyzing the difference between the first state of charge and the second state of charge of the target battery, and the target fault diagnosis result of the target battery is obtained, and then it is determined whether the target battery has an internal short circuit fault. The high precision of the Kalman filter algorithm plus the stability and intuitiveness of the ampere-hour integral method make the fault diagnosis of the target battery not only fast in response, but also accurate in determining whether the target battery has an internal short circuit fault, greatly reducing the misdiagnosis rate and missed diagnosis rate.

[0045] In an optional embodiment, the target battery is diagnosed based on the first state of charge and the second state of charge to obtain a target fault diagnosis result of the target battery, including: determining a difference value threshold of the target battery; performing difference value processing based on the first state of charge and the second state of charge to obtain a target state of charge difference value of the target battery; and determining the target fault diagnosis result based on the difference value threshold and the target state of charge difference value.

[0046] It can be understood that the first state of charge and the second state of charge are subtracted to obtain a target state of charge difference value of the target battery, the target state of charge difference value is compared with the difference value threshold of the target battery, and the target fault diagnosis result of the target battery is obtained according to the comparison result. By calculating the target state of charge difference value between the first state of charge and the second state of charge and comparing it with the difference value threshold, it can be accurately determined whether the target battery has an internal short circuit fault, and then an early warning is given, improving the timeliness and accuracy of the internal short circuit fault diagnosis.

[0047] In an optional embodiment, the difference value threshold of the target battery is determined, including: collecting a historical state of charge difference value of a test battery, where the test battery is a battery of the same type as the target battery, and the historical state of charge difference value includes a first state of charge difference value of the test battery in a normal state and a second state of charge difference value of the test battery in an internal short circuit fault state; performing statistical analysis on the first state of charge difference value to obtain a first statistical feature; performing statistical analysis on the second state of charge difference value to obtain a second statistical feature; and determining the difference value threshold based on the first statistical feature and the second statistical feature.

[0048] It can be understood that the difference threshold of the target battery is determined in the following manner. The historical state-of-charge difference of a test battery of the same type as the target battery is collected, including a first state-of-charge difference of the test battery in a normal state and a second state-of-charge difference of the test battery in an internal short circuit fault state. Statistical analysis is respectively performed on the first state-of-charge difference and the second state-of-charge difference to obtain first statistical characteristics of the first state-of-charge difference, such as mean, variance and standard deviation, and second statistical characteristics of the second state-of-charge difference, such as mean, variance and standard deviation. The difference threshold of the target battery is determined according to the first statistical characteristics and the second statistical characteristics obtained above. By analyzing the statistical characteristics of the state-of-charge difference in the normal state and the internal short circuit fault state, the purpose of dynamically adjusting the difference threshold according to the change of the operating condition of the target battery can be achieved, and the adaptability and stability of the internal short circuit fault diagnosis result are ensured.

[0049] In an optional embodiment, based on the difference threshold and the target state-of-charge difference, the target fault diagnosis result is determined, including: in the case that the target state-of-charge difference is less than the difference threshold, determining that the target fault diagnosis result is that the target battery does not have an internal short circuit fault; or in the case that the target state-of-charge difference is greater than or equal to the difference threshold, determining that the target fault diagnosis result is that the target battery has an internal short circuit fault.

[0050] It can be understood that the target state-of-charge difference of the target battery and the difference threshold are compared, if the target state-of-charge difference is less than the difference threshold, the target fault diagnosis result is that the target battery does not have an internal short circuit fault; otherwise, if the target state-of-charge difference is greater than or equal to the difference threshold, the target fault diagnosis result is that the target battery has an internal short circuit fault. Based on the difference threshold and the target state-of-charge difference, the target fault diagnosis result is determined, which not only can improve the accuracy of the internal short circuit fault diagnosis, but also, by dynamically adjusting the difference threshold, can adapt to different environmental conditions and enhance the universality and robustness of the internal short circuit fault diagnosis.

[0051] In an optional embodiment, in the case that the target fault diagnosis result indicates that the target battery has an internal short circuit fault, the method further includes: determining a first state-of-charge difference of the target battery at a fault moment and a second state-of-charge difference of the target battery at a next moment, wherein the fault moment refers to the moment when the target battery is diagnosed to have an internal short circuit fault, and the next moment is a moment after the fault moment; based on the first state-of-charge difference and the second state-of-charge difference, determining a difference change rate of the target battery; based on the difference change rate, determining the short circuit current and the short circuit resistance of the target battery by using a lookup table method.

[0052] It can be understood that in the case where the determined target fault diagnosis result indicates that the target battery has an internal short circuit fault, the short circuit current and the short circuit resistance of the target battery can also be calculated in the following manner. First, the first state of charge difference value of the target battery at the fault moment and the second state of charge difference value at the next moment are determined. Second, according to the first state of charge difference value and the second state of charge difference value, the difference rate of change of the state of charge difference value of the target battery in the time interval determined at the fault moment and the next moment is calculated. Finally, the difference rate of change is used as an index value to look up the pre-established electrical parameter table to obtain the short circuit current and the short circuit resistance of the target battery. By calculating the difference rate of change and combining the table lookup method to estimate the short circuit current and the short circuit resistance of the target battery, the severity of the internal short circuit fault of the target battery can be quantified, which helps to develop more accurate fault response measures to prevent the spread of the fault and the occurrence of battery thermal runaway.

[0053] Optionally, the first state of charge difference value and the second state of charge difference value can be determined in the manner of determining the target state of charge difference value of the target battery.

[0054] Optionally, the electrical parameter table can be obtained by calibrating a large amount of data of the test battery under different internal short circuit conditions. First, a test battery with the same type or similar characteristics as the target battery is selected to ensure the comparability of the test conditions. Second, the state of charge difference value between the first state of charge and the second state of charge calculated by the Kalman filter algorithm and the ampere-hour integration method before and after the internal short circuit of the test battery, the rate of change of the state of charge difference value, and the short circuit current and the short circuit resistance of the test battery are recorded. Next, the above data is preprocessed, and the mathematical relationship between the rate of change of the state of charge difference value and the short circuit current and the short circuit resistance is determined through statistical analysis or fitting method. Finally, the above data is grouped according to the range of the rate of change to ensure that each group of data corresponds to one or a group of short circuit parameters (including the short circuit current and the short circuit resistance), and finally the electrical parameter table is obtained. For the range of the rate of change data that is not directly tested, the interpolation method is used to estimate the short circuit parameters in combination with the mathematical relationship between the rate of change and the short circuit current and the short circuit resistance; for the rate of change beyond the test range, the existing data trend is extrapolated to cover a wider range of fault conditions.

[0055] Optionally, after judging that the target battery has an internal short circuit fault, the short circuit current and the short circuit resistance of the target battery can be determined according to the rate of change of the difference between the differences in state of charge and the relationship between the short circuit current and the short circuit resistance. The difference in state of charge is caused by the short circuit current not being accurately counted in the process of integrating ampere-hours. Therefore, after the short circuit is triggered, the accumulation speed of the difference (i.e., the rate of change of the difference) should be proportional to the size of the short circuit current. According to the first difference in state of charge at the fault time T1 recorded above and the second difference in state of charge at the next time T2 (T1 to T2 is a time window after the internal short circuit occurs), the size of the rate of change of the difference is calculated. The rate of change of the difference is used as an index to look up the electrical parameter table to determine the short circuit current and the short circuit resistance of the target battery.

[0056] Through the above steps S102 to S108, the purpose of determining the first state of charge and the second state of charge of the target battery by collecting the power data of the target battery in the constant current charging phase, combining the characteristic curve, and analyzing the first state of charge and the second state of charge to obtain the target fault diagnosis result of the target battery can be achieved. The technical effect of improving the accuracy of the internal short circuit fault diagnosis result of the target battery is realized, and the technical problem of inaccurate internal short circuit fault diagnosis result of the battery in the related art is solved.

[0057] Based on the above embodiments and optional embodiments, the present application provides an optional implementation of a battery internal short circuit fault diagnosis method. This implementation can be understood as a battery internal short circuit diagnosis method for the charging phase.

[0058] Figure 2 is a flowchart of an optional battery internal short circuit fault diagnosis method provided by an embodiment of the present application, as shown in Figure 2 The steps of the battery internal short circuit diagnosis method for the charging phase include:

[0059] Step S1, collecting power data of a target battery in a constant current charging phase, including voltage data, current data, and resistance data.

[0060] Step S2, pulse charging the target battery to obtain an OCV-SOC curve (i.e., a characteristic curve) of the target battery.

[0061] Step S3, establishing an equivalent circuit model for the target battery according to the power data of the target battery and the OCV-SOC curve. Figure 3 is an optional equivalent circuit model schematic diagram provided by an embodiment of the present application, as shown in Figure 3 The equivalent circuit model is characterized in the following manner:

[0062]

[0063] wherein，U t The predicted terminal voltage of the target battery is represented by OCV(SOC), which represents the open-circuit voltage of the target battery under the state of charge (SOC) condition and is obtained through the OCV-SOC curve. I represents the current data of the target battery, and R represents the resistance of the target battery.

[0064] Step S4, for the above equivalent circuit model, extend the Kalman filter to estimate the target battery's performance using the Kalman filter algorithm. k First state of charge at time 1 .

[0065]

[0066]

[0067]

[0068] in, Indicates the target battery in k The third state of charge at time t, Indicates the target battery in k The first state of charge at time -1 Indicates the target battery in k Current data at time -1 This represents the difference between two adjacent time points. Indicates the capacity of the target battery. express k Process noise at time -1 Indicates the target battery in k Predicted terminal voltage at time [time] Indicates the target battery's state of charge. The corresponding open-circuit voltage under the given conditions is obtained from the OCV-SOC curve. express k Observation noise at any given moment express k Kalman gain at time step Indicates the target battery in k Voltage data at any given time.

[0069] Step S5: Based on the collected power data, determine the target battery's current usage using the ampere-hour integration method. k The second state of charge at time t .

[0070]

[0071] in, Indicates the target battery at the initial moment The initial state of charge, representing the current data of the target battery at k the time point.

[0072] Step S6, comparing the difference between the first state of charge estimated by Kalman filter algorithm and the second state of charge calculated by ampere-hour integral method (i.e. target state of charge difference), according to the difference and the preset threshold, determining whether the target battery has internal short circuit, and recording the time point T1 (i.e. failure time) in the case of determining that the target battery has internal short circuit failure.

[0073] Comparing the target state of charge difference of the target battery with the difference threshold value, if the target state of charge difference is less than the difference threshold value, the target fault diagnosis result is that the target battery has no internal short circuit failure; otherwise, if the target state of charge difference is greater than or equal to the difference threshold value, the target fault diagnosis result is that the target battery has internal short circuit failure.

[0074] After determining that the target battery has internal short circuit failure, according to the difference rate of the difference of state of charge and the relationship between short circuit current and short circuit resistance, the short circuit current and short circuit resistance of the target battery are determined. The source of the difference of state of charge is that the short circuit current cannot be accurately counted in the process of ampere-hour integral. Therefore, after the short circuit is triggered, the accumulation speed of the difference (i.e. difference rate) should be proportional to the size of the short circuit current. According to the first state of charge difference of the failure time T1 recorded above, and the second state of charge difference of the next time T2 (T1 to T2 is a time window after the internal short circuit occurs), the size of the difference rate is calculated. The difference rate is used as an index to look up the electrical parameter table to determine the short circuit current and short circuit resistance of the target battery.

[0075] The above-mentioned optional implementation at least realizes the following effects: the high precision characteristics of Kalman filter algorithm plus the stability and intuitiveness of ampere-hour integral method make the fault diagnosis of the target battery not only be able to respond quickly, but also be able to accurately determine whether the target battery has internal short circuit failure, greatly reducing the misdiagnosis rate and missed diagnosis rate; determining the target fault diagnosis result based on the difference threshold value and the target state of charge difference not only can improve the accuracy of internal short circuit failure diagnosis, but also can adapt to different environmental conditions through dynamic adjustment of the difference threshold value, enhancing the universality and robustness of internal short circuit failure diagnosis; by calculating the difference rate and estimating the short circuit current and short circuit resistance of the target battery by table lookup method, the severity of the internal short circuit failure of the target battery can be quantified, which helps to develop more accurate fault response measures to prevent the spread of failure and the occurrence of battery thermal runaway.

[0076] It is noted that the steps illustrated in the flowcharts of the figures can be executed by a computer system, such as a computer system comprising a set of computer executable instructions, and that although the steps are presented in the order shown, the steps can be executed in a different order than those shown or described.

[0077] In the embodiment, a battery internal short circuit fault diagnosis apparatus is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be described again. As used below, the term "module" "apparatus" can be a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, implementation of hardware, or a combination of software and hardware, is also possible and contemplated.

[0078] According to the embodiments of the present application, a device embodiment for implementing the battery internal short circuit fault diagnosis method is also provided, Figure 4 is a schematic diagram of a battery internal short circuit fault diagnosis apparatus according to an embodiment of the present application, as Figure 4 shown, the above-mentioned battery internal short circuit fault diagnosis apparatus comprises a data acquisition module 402, a characteristic curve acquisition module 404, a first determination module 406, a fault diagnosis module 408, and the apparatus will be described below.

[0079] The data acquisition module 402 is used to acquire the power data of the target battery in the constant current charging phase;

[0080] The characteristic curve acquisition module 404 is connected with the data acquisition module 402, and is used to acquire the characteristic curve of the target battery, wherein the characteristic curve is used to describe the correlation between the open circuit voltage and the state of charge of the target battery;

[0081] The first determination module 406 is connected with the characteristic curve acquisition module 404, and is used to determine the first state of charge and the second state of charge of the target battery based on the power data and the characteristic curve;

[0082] The fault diagnosis module 408 is connected with the first determination module 406, and is used to perform fault diagnosis on the target battery based on the first state of charge and the second state of charge, to obtain a target fault diagnosis result of the target battery, wherein the target fault diagnosis result is used to indicate whether the target battery has an internal short circuit fault.

[0083] The application embodiment provides a battery internal short circuit fault diagnosis device.

[0084] It should be noted that the above modules can be implemented by software or hardware. For the latter, the above modules can be located in the same processor, or the above modules can be located in different processors in any combination.

[0085] It should be noted that the above data acquisition module 402, characteristic curve acquisition module 404, first determination module 406, and fault diagnosis module 408 correspond to steps S102 to S108 in the embodiment, and the above modules have the same instances and application scenarios as the corresponding steps, but are not limited to the above disclosed contents. It should be noted that the above modules can run in a computer terminal as a part of the device.

[0086] It should be noted that the optional or preferred embodiments of the present embodiment can refer to the related description in the embodiment, which will not be repeated here.

[0087] The battery internal short circuit fault diagnosis device can further include a processor and a memory, and the data acquisition module 402, the characteristic curve acquisition module 404, the first determination module 406, and the fault diagnosis module 408 are stored in the memory as program units, and the processor executes the above program units stored in the memory to realize the corresponding functions.

[0088] The processor includes a core, and the core retrieves the corresponding program unit from the memory. The core can be provided with one or more. The memory can include a non-permanent memory in a computer readable medium, a random access memory (RAM) and / or a non-volatile memory such as a read-only memory (ROM) or a flash memory (flash RAM), and the memory includes at least one memory chip.

[0089] The application embodiment provides a non-volatile storage medium having a program stored thereon, and the program is executed by a processor to realize the battery internal short circuit fault diagnosis method.

[0090] An electronic device is provided, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: collecting power data of a target battery in a constant current charging phase; obtaining a characteristic curve of the target battery, wherein the characteristic curve is used to describe an association between an open circuit voltage and a state of charge of the target battery; determining a first state of charge and a second state of charge of the target battery based on the power data and the characteristic curve; and performing fault diagnosis on the target battery based on the first state of charge and the second state of charge to obtain a target fault diagnosis result of the target battery, wherein the target fault diagnosis result is used to indicate whether an internal short circuit fault occurs in the target battery. The device herein can be a server, a PC, etc.

[0091] The application further provides a computer program product adapted to execute a program that initializes the following method steps when executed on a data processing device: collecting power data of a target battery in a constant current charging phase; obtaining a characteristic curve of the target battery, wherein the characteristic curve is used to describe an association between an open circuit voltage and a state of charge of the target battery; determining a first state of charge and a second state of charge of the target battery based on the power data and the characteristic curve; and performing fault diagnosis on the target battery based on the first state of charge and the second state of charge to obtain a target fault diagnosis result of the target battery, wherein the target fault diagnosis result is used to indicate whether an internal short circuit fault occurs in the target battery.

[0092] Those skilled in the art should understand that embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0093] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts 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, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0094] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0096] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0097] The memory can include non-persistent memory and / or volatile memory, such as a random access memory (RAM) including a cache area for the temporary storage of data. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, or a combination of non-volatile memories in different forms. The memory is an example of computer readable storage media.

[0098] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0099] It is also to be noted that the terms "comprising", "including", and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a... " does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0100] Those skilled in the art will appreciate that embodiments of the present application can be devised for a method, a system, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer-readable program code.

[0101] The embodiments of the present application are only illustrative and are not intended to limit the present application. Various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for diagnosing internal short-circuit faults in a battery, characterized in that, include: Collect power data of the target battery during the constant current charging phase; Obtain the characteristic curve of the target battery, wherein the characteristic curve is used to describe the correlation between the open-circuit voltage and the state of charge of the target battery; Based on the power data and the characteristic curve, the first state of charge and the second state of charge of the target battery are determined; Based on the first state of charge and the second state of charge, fault diagnosis is performed on the target battery to obtain the target fault diagnosis result of the target battery, wherein the target fault diagnosis result is used to indicate whether the target battery has an internal short circuit fault.

2. The method according to claim 1, characterized in that, Determining the first state of charge and the second state of charge of the target battery based on the power data and the characteristic curve includes: Based on the current data in the power data and the capacity of the target battery, the third state of charge is obtained by using the Kalman filter algorithm; Based on the third state of charge and the characteristic curve, the open-circuit voltage of the target battery is determined; Based on the open-circuit voltage, the third state of charge is corrected to obtain the first state of charge; Based on the current data and the capacity, the second state of charge is obtained using the ampere-hour integration method.

3. The method according to claim 2, characterized in that, The step of correcting the third state of charge based on the open-circuit voltage to obtain the first state of charge includes: Based on the open-circuit voltage, the current data, and the resistance data in the power data, the predicted terminal voltage of the target battery is determined; Based on the predicted terminal voltage and the voltage data in the power data, the third state of charge is corrected to obtain the first state of charge.

4. The method according to claim 1, characterized in that, The step of performing fault diagnosis on the target battery based on the first state of charge and the second state of charge to obtain the target fault diagnosis result of the target battery includes: Determine the difference threshold of the target battery; The target state of charge difference of the target battery is obtained by performing difference processing based on the first state of charge and the second state of charge. The target fault diagnosis result is determined based on the difference threshold and the target state of charge difference.

5. The method according to claim 4, characterized in that, Determining the difference threshold of the target battery includes: Collect historical state of charge (SOC) differences of the test battery, wherein the test battery is a battery of the same type as the target battery, and the historical SOC differences include a first SOC difference of the test battery under normal conditions and a second SOC difference of the test battery under internal short-circuit fault conditions. Statistical analysis is performed on the first state of charge difference to obtain the first statistical characteristic; Statistical analysis was performed on the second state of charge difference to obtain the second statistical characteristic; The difference threshold is determined based on the first statistical feature and the second statistical feature.

6. The method according to claim 4, characterized in that, The step of determining the target fault diagnosis result based on the difference threshold and the target state of charge difference includes: If the target state of charge difference is less than the difference threshold, the target fault diagnosis result is determined to be that the target battery has not experienced an internal short circuit fault; or, If the difference in the target state of charge is greater than or equal to the difference threshold, the target fault diagnosis result is determined to be an internal short circuit fault in the target battery.

7. The method according to any one of claims 1 to 6, characterized in that, If the target fault diagnosis result indicates that the target battery has an internal short-circuit fault, the method further includes: Determine the first state of charge difference of the target battery at the time of the fault, and the second state of charge difference of the target battery at the next time, wherein the time of the fault refers to the time when the internal short circuit fault of the target battery is diagnosed, and the next time is a time after the time of the fault. Based on the first state-of-charge difference and the second state-of-charge difference, the rate of change of the difference of the target battery is determined; Based on the rate of change of the difference, the short-circuit current and short-circuit resistance of the target battery are determined by a lookup table method.

8. A battery internal short-circuit fault diagnosis device, characterized in that, include: The data acquisition module is used to collect the power data of the target battery during the constant current charging phase. The characteristic curve acquisition module is used to acquire the characteristic curve of the target battery, wherein the characteristic curve is used to describe the correlation between the open circuit voltage and the state of charge of the target battery. The first determining module is used to determine the first state of charge and the second state of charge of the target battery based on the power data and the characteristic curve. The fault diagnosis module is used to perform fault diagnosis on the target battery based on the first state of charge and the second state of charge, and obtain the target fault diagnosis result of the target battery, wherein the target fault diagnosis result is used to indicate whether the target battery has an internal short circuit fault.

9. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions adapted for loading and execution by a processor of the battery internal short-circuit fault diagnosis method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, include: One or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the battery internal short-circuit fault diagnosis method according to any one of claims 1 to 7.

Images