Battery short circuit fault detection method, device, computer equipment and medium

Abstract

The present invention provides a battery short circuit fault detection method, device, computer equipment and medium. Among them, the battery short circuit fault detection method includes: obtaining the circuit information and temperature information of the battery; calculating the short circuit resistance of the battery based on the circuit information, temperature information and short circuit model, the short circuit model includes an electrical model under the short circuit state and a thermal model under the short circuit state, the electrical model under the short circuit state and the thermal model under the short circuit state are respectively used to characterize the voltage state and temperature state of the battery under the short circuit state; when the short circuit resistance value is less than the preset short circuit resistance value, it is determined that the battery has a short circuit fault. Through the present invention, when diagnosing a battery short circuit fault, both the change of the battery voltage signal and the temperature change of the battery are considered, thereby improving the accuracy of the battery short circuit fault.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of circuits, and in particular to a battery short circuit fault detection method, device, computer equipment and medium. Background Art

[0002] With the development of the new energy industry in recent years, lithium-ion batteries have gradually become the main energy storage components in application scenarios such as electric vehicles and energy storage power stations due to their advantages such as high energy density, long cycle life, and environmental friendliness. However, the safety issues caused by thermal runaway of power batteries have always restricted the rapid development of the industry. Among them, internal short circuit is the key link of battery thermal runaway. Therefore, how to quickly and accurately diagnose short circuit faults inside the battery is of great significance to improving the safety of the battery system.

[0003] In the prior art, the short-circuit diagnosis algorithm only considers the change of the battery voltage signal and ignores the temperature change of the battery when the short-circuit fault occurs, and cannot accurately determine the short-circuit fault information during the diagnosis process. Summary of the invention

[0004] In order to improve the accuracy of battery short circuit fault diagnosis, the present invention proposes a battery short circuit fault detection method, device, computer equipment and medium.

[0005] In a first aspect, the present invention provides a battery short circuit fault detection method, the method comprising:

[0006] Obtain the circuit information and temperature information of the battery;

[0007] Calculating the short-circuit resistance of the battery based on the circuit information, the temperature information and the short-circuit model, wherein the short-circuit model includes an electrical model under a short-circuit state and a thermal model under a short-circuit state, wherein the electrical model under a short-circuit state and the thermal model under a short-circuit state are used to characterize the voltage state and the temperature state of the battery under a short-circuit state, respectively;

[0008] When the short-circuit resistance value is less than the preset short-circuit resistance value, it is determined that the battery has a short-circuit fault.

[0009] Through the above method, when a short circuit fault occurs in the battery, the heat generated by the short-circuit resistor will cause the battery temperature to rise. In the process of battery short-circuit fault diagnosis, temperature information associated with the battery state is added on the basis of voltage information, and the voltage state and temperature state of the battery are respectively characterized by an electrical model and a thermal model. The voltage information and temperature information of the battery are combined to calculate the short-circuit resistance, and the battery short-circuit fault is diagnosed based on the short-circuit resistance, thereby improving the accuracy of battery short-circuit fault diagnosis and enhancing the safety of the battery system.

[0010] In combination with the first aspect, in a first embodiment of the first aspect, calculating the short-circuit resistance of the battery based on the circuit information, the temperature information and the short-circuit model includes:

[0011] The short-circuit resistance and the corresponding estimated temperature of the battery are calculated based on the circuit information, the temperature information and the short-circuit model. The short-circuit resistance minimizes the root mean square error between the corresponding estimated temperature and the measured temperature.

[0012] In combination with the first embodiment of the first aspect, in the second embodiment of the first aspect, the electrical model under the short-circuit state is a first-order RC equivalent circuit model under the short-circuit state, and the first-order RC equivalent circuit model under the short-circuit state is established by the following steps:

[0013] Obtain the circuit information of the battery in normal state under various working conditions;

[0014] Determine a first parameter in a first-order RC equivalent circuit model under a short-circuit state based on circuit information of the battery under normal conditions under various operating conditions;

[0015] A first-order RC equivalent circuit model under the short-circuit state is established based on the first parameter establishment and the circuit structure under the short-circuit state.

[0016] In combination with the first embodiment of the first aspect or the second embodiment of the first aspect, in a third embodiment of the first aspect, the circuit information includes input current, the thermal model in the short-circuit state is a concentrated mass thermal model in the short-circuit state, and the concentrated mass thermal model in the short-circuit state is established by the following steps:

[0017] Obtain the input current and temperature information of the battery in normal state under various working conditions;

[0018] Determine a second parameter in a lumped mass thermal model under a short circuit state based on input current and temperature information of the battery under normal conditions under various operating conditions;

[0019] A lumped mass thermal model under the short-circuit state is established based on the second parameter establishment and the circuit structure under the short-circuit state.

[0020] In combination with the second embodiment of the first aspect, in a fourth embodiment of the first aspect, a first-order RC equivalent circuit model in a short-circuit state is expressed as follows:

[0021]

[0022]

[0023] Among them, U1 represents polarization voltage, I represents input current, R1 represents polarization resistance, C1 represents polarization capacitance, and U t Represents terminal voltage, R SC Indicates short-circuit resistance, U OCV represents the open circuit voltage, and R0 represents the ohmic internal resistance.

[0024] In combination with the third embodiment of the first aspect, in a fifth embodiment of the first aspect, the concentrated mass thermal model in the short circuit state is expressed as follows:

[0025]

[0026]

[0027] Among them, Q totall represents the total heat generated by the battery, I represents the input current, U t Represents terminal voltage, R SC Indicates short-circuit resistance, R0 indicates ohmic internal resistance, R1 indicates polarization resistance, U t represents the terminal voltage, T represents the battery temperature, T ∞ represents the ambient temperature, m represents the mass, c p represents specific heat capacity, h represents convection heat transfer coefficient, and A represents surface area.

[0028] In combination with the first aspect, in a sixth embodiment of the first aspect, the method further includes:

[0029] The failure degree of the battery is evaluated based on the short-circuit resistance value and the preset short-circuit resistance value.

[0030] Through the above embodiment, a reference is provided for the severity of the battery failure according to the calculated short-circuit resistance value and the preset short-circuit resistance value.

[0031] In a second aspect, the present invention further provides a battery short circuit fault detection device, the device comprising:

[0032] An acquisition module, used to acquire circuit information and temperature information of the battery;

[0033] A calculation module, used to calculate the short-circuit resistance of the battery based on circuit information, temperature information and a short-circuit model, wherein the short-circuit model includes an electrical model under a short-circuit state and a thermal model under a short-circuit state, wherein the electrical model under a short-circuit state and the thermal model under a short-circuit state are used to characterize a voltage state and a temperature state of the battery under a short-circuit state, respectively;

[0034] The determination module is used to determine that a short circuit fault occurs in the battery when the short circuit resistance value is less than a predetermined short circuit resistance value.

[0035] Through the above device, when a short circuit fault occurs in the battery, the heat generated by the short-circuit resistor will cause the battery temperature to rise. In the process of battery short-circuit fault diagnosis, temperature information associated with the battery state is added on the basis of voltage information, and the voltage state and temperature state of the battery are respectively characterized by an electrical model and a thermal model. The voltage information and temperature information of the battery are combined to calculate the short-circuit resistance, and the battery short-circuit fault is diagnosed based on the short-circuit resistance, thereby improving the accuracy of battery short-circuit fault diagnosis and enhancing the safety of the battery system.

[0036] In a third aspect, the present invention further provides a computer device, comprising a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the steps of the battery short circuit fault detection method of the first aspect or any embodiment of the first aspect by executing the computer instructions.

[0037] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the battery short circuit fault detection method of the first aspect or any embodiment of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 is a flow chart of a battery short circuit fault detection method proposed according to an exemplary embodiment;

[0040] Figure 2 is a schematic diagram of a circuit structure in a normal state in an example;

[0041] Figure 3 is a diagram of parameter identification results of a first-order equivalent circuit model under normal conditions in an example;

[0042] Figure 4 is a schematic diagram of a circuit structure in a short-circuit state in an example;

[0043] Figure 5 is the short-circuit resistance calculated in one example;

[0044] Figure 6 is a structural schematic diagram of a battery short circuit fault detection device proposed according to an exemplary embodiment;

[0045] Figure 7 It is a schematic diagram of the hardware structure of a computer device proposed according to an exemplary embodiment. DETAILED DESCRIPTION

[0046] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0048] In order to improve the accuracy of battery short circuit fault diagnosis, the present invention proposes a battery short circuit fault detection method, device, computer equipment and medium.

[0049] Figure 1 FIG. 1 is a flow chart of a battery short circuit fault detection method according to an exemplary embodiment. Figure 1 As shown, the method includes the following steps S101 to S104.

[0050] Step S101: Obtaining circuit information and temperature information of the battery.

[0051] Specifically, the circuit information includes the input current, terminal voltage and open circuit voltage of the battery, and the temperature information includes the measured temperature and ambient temperature of the battery.

[0052] Step S102: Calculate the short-circuit resistance of the battery based on the circuit information, the temperature information and the short-circuit model, wherein the short-circuit model includes an electrical model under a short-circuit state and a thermal model under a short-circuit state, and the electrical model under a short-circuit state and the thermal model under a short-circuit state are respectively used to characterize the voltage state and the temperature state of the battery under a short-circuit state.

[0053] Specifically, the electrical model includes an equivalent circuit model, which may be a first-order RC model, a second-order RC model, etc., and the thermal model may be a concentrated mass thermal model, a continuous network thermal circuit model, a local network thermal circuit model, etc.

[0054] Step S103: when the short-circuit resistance value is less than the preset short-circuit resistance value, it is determined that a short-circuit fault occurs in the battery.

[0055] Through the above method, when a short circuit fault occurs in the battery, the heat generated by the short-circuit resistor will cause the battery temperature to rise. In the process of battery short-circuit fault diagnosis, temperature information associated with the battery state is added on the basis of voltage information, and the voltage state and temperature state of the battery are respectively characterized by an electrical model and a thermal model. The voltage information and temperature information of the battery are combined to calculate the short-circuit resistance, and the battery short-circuit fault is diagnosed based on the short-circuit resistance, thereby improving the accuracy of battery short-circuit fault diagnosis and enhancing the safety of the battery system.

[0056] In one example, in the above step S102, the electrical model under the short-circuit state is a first-order RC equivalent circuit model under the short-circuit state, and the first-order RC equivalent circuit model under the short-circuit state is established by the following steps:

[0057] First, obtain the circuit information of the battery in normal state under various working conditions.

[0058] Exemplarily, the circuit information under normal conditions can be obtained by conducting battery characteristic experimental tests under various working conditions. Among them, if it is necessary to perform fault detection on the battery used in the vehicle, when a first-order RC equivalent circuit model is established for the battery used in the vehicle, the battery characteristic experimental test includes: battery static capacity test, open circuit voltage (OCV) test, hybrid pulse power characteristic (HPPC) test at different temperatures and state of charge (SOC), dynamic stress test (DST), and actual vehicle working conditions such as the Federal Urban Driving Schedule (FUDS) and the Urban Dynamometer Driving Schedule (UDDS).

[0059] Then, based on the circuit information of the battery in a normal state under various operating conditions, a first parameter in a first-order RC equivalent circuit model in a short-circuit state is determined.

[0060] Specifically, a parameter in a first-order RC equivalent circuit model in a normal state is obtained according to circuit information of the battery in a normal state under various working conditions, and the parameter is used as the first parameter in the first-order RC equivalent circuit model in a short-circuit state.

[0061] In an optional embodiment, the first parameter may be obtained by a parameter identification method. The specific steps may be:

[0062] 1. Based on the circuit structure under normal conditions, a first-order RC equivalent circuit model under normal conditions is established. Figure 2 This is a schematic diagram of the circuit structure under normal conditions. The model established for this circuit structure can be expressed as follows:

[0063]

[0064] U t =U OCV -U1-R0I

[0065] Tao=R1×C1

[0066] Among them, U1 represents polarization voltage, I represents input current, R1 represents polarization resistance, C1 represents polarization capacitance, and U t Indicates terminal voltage, U oCV represents the open circuit voltage, R0 represents the ohmic internal resistance, and Tao represents the time constant.

[0067] 2. Based on the circuit information of the battery in normal state under various working conditions, the first-order RC equivalent circuit model parameters R0, R1, and C1 in normal state are identified by using a nonlinear parameter identification method, and are used as the first parameters in the first-order RC equivalent circuit model in short-circuit state. Figure 3 This is the parameter identification result diagram of the first-order equivalent circuit model under normal conditions.

[0068] Finally, a first-order RC equivalent circuit model under the short-circuit state is established based on the first parameter and the circuit structure under the short-circuit state, that is, the value of the first parameter is substituted into the model established for the circuit structure under the short-circuit state to obtain the first-order RC equivalent circuit model under the short-circuit state.

[0069] Figure 4 It is a schematic diagram of the circuit structure under the short-circuit state. Taking the discharge condition as an example, in the first-order RC equivalent circuit model under the short-circuit state, the short-circuit resistance R SC The voltage across the two ends is the same as the battery terminal voltage, and the actual current I flowing through the short-circuited cell is f Equal to the input current I and the short-circuit current I SC The sum is:

[0070]

[0071] I f =I+I SC

[0072] Combined with the short-circuit current, the first-order circuit model under normal conditions is updated. Compared with normal conditions, the actual current flowing through the short-circuited cell increases, and excessive power consumption causes the SOC to drop faster and the voltage to drop faster. Figure 4 In the short-circuit state, the first-order RC equivalent circuit model can be expressed as follows:

[0073]

[0074]

[0075] Among them, U1 represents polarization voltage, I represents input current, R1 represents polarization resistance, C1 represents polarization capacitance, and U t Represents terminal voltage, R SC Indicates short-circuit resistance, U OCV represents the open circuit voltage, and R0 represents the ohmic internal resistance.

[0076] In one example, in the above step S102, the thermal model in the short-circuit state is a concentrated mass thermal model in the short-circuit state, the circuit information includes the input current, and the concentrated mass thermal model in the short-circuit state is established by the following steps:

[0077] First, the input current and temperature information of the battery in normal state under various working conditions are obtained.

[0078] For example, the input current and temperature information under normal conditions can also be obtained by conducting battery characteristic experimental tests under various working conditions, which will not be described in detail here.

[0079] Then, based on the input current and temperature information of the battery in a normal state under various operating conditions, a second parameter in the lumped mass thermal model in a short-circuit state is determined.

[0080] Specifically, a parameter in the lumped mass thermal model in the normal state is obtained according to the input current and temperature information of the battery in the normal state under various working conditions, and the parameter is used as the second parameter in the lumped mass thermal model in the short-circuit state.

[0081] In an optional embodiment, the second parameter is also obtained by using a parameter identification method. The specific steps are as follows:

[0082] 1. Based on the circuit structure under normal conditions, establish a concentrated mass thermal model under normal conditions. Figure 2 In the circuit structure, the heat generation term mainly comes from the ohmic internal resistance and polarization resistance heat generation. Therefore, the concentrated mass thermal model under normal conditions can be expressed as follows:

[0083] Q totall =I 2 (R0+R1)

[0084]

[0085] Among them, Q totall represents the total heat generated by the battery, I represents the input current, R0 represents the ohmic internal resistance, R1 represents the polarization resistance, m represents the mass, c p represents specific heat capacity, h represents convection heat transfer coefficient, A represents surface area, T represents battery temperature, T ∞ Indicates the ambient temperature.

[0086] 2. Based on the input current and temperature information of the battery under normal conditions, the nonlinear parameter identification method is used to identify the lumped mass thermal model parameters m and c under normal conditions. p , h, A, which is used as the second parameter in the lumped mass thermal model under short-circuit conditions.

[0087] Finally, a concentrated mass thermal model under the short-circuit state is established based on the second parameter establishment and the circuit structure under the short-circuit state.

[0088] Taking the discharge condition as an example, Figure 4 As shown in the figure, the total heat generated by the battery consists of the heat generated by the internal resistance of the battery cell itself and the heat generated by the short-circuit resistance. Moreover, since the actual current flowing through the battery increases, the heat generated by the internal resistance of the battery cell itself will also increase compared to a normal battery. Therefore, the concentrated mass thermal model under the short-circuit state can be expressed as follows:

[0089]

[0090]

[0091] Among them, Q totall represents the total heat generated by the battery, I represents the input current, U t Represents terminal voltage, R SC Indicates short-circuit resistance, R0 indicates ohmic internal resistance, R1 indicates polarization resistance, U t represents the terminal voltage, T represents the battery temperature, T ∞ represents the ambient temperature, m represents the mass, c p represents specific heat capacity, h represents convection heat transfer coefficient, and A represents surface area.

[0092] In the common working range of the battery, although R0 and R1 have different parameters at different ambient temperatures and SOCs, the parameter differences are not large and are basically in the same order of magnitude. The sum of the average values ​​of R0 and R1 is regarded as the equivalent internal resistance R of the battery. e , which can further reduce the amount of calculation for short-circuit resistance estimation and make it easier to apply to actual battery management systems.

[0093] In one example, the short-circuit resistance and the corresponding estimated temperature of the battery are calculated based on the circuit information, temperature information and short-circuit model of the battery, and the short-circuit resistance minimizes the root mean square error between the corresponding estimated temperature and the measured temperature.

[0094] For example, a nonlinear parameter identification algorithm can be used to solve the short-circuit resistance of the battery and the corresponding estimated temperature. In a specific embodiment, a particle swarm optimization algorithm is used, with the short-circuit resistance as the optimization variable, so that the root mean square error between the corresponding estimated temperature and the measured temperature is minimized. The optimization objective function is expressed as follows:

[0095]

[0096] Among them, θ T is the optimization variable, N T is the data length of the temperature sequence, T t,k is the measured temperature of the battery at time k, is the estimated temperature of the battery at time k.

[0097] Figure 5 is the short-circuit resistance calculated according to the battery short-circuit fault detection method provided in the above embodiment. Figure 5 It can be seen that the actual short-circuit resistance of the battery is 1Ω. The short-circuit resistance calculated in this embodiment is maintained near 1Ω, with an average value of 1.094Ω, which is less than the actual short-circuit resistance. The short-circuit resistance calculated by the battery short-circuit fault detection method provided in the above embodiment has high accuracy. At the same time, this method can capture abnormal weak signals through voltage information and temperature information, and detect faults in the early stage of battery short circuit, that is, when the calculated short-circuit resistance value is different from the preset short-circuit resistance value, which is helpful for early warning of battery thermal runaway.

[0098] In one example, the preset short-circuit resistance value in the above step S103 can be set according to the specified battery capacity standard. The national standard GB / T 31486-2015 stipulates that the capacity of the battery after 28 days of standing should not be less than 85%. The short-circuited battery self-discharge consumes additional power. Therefore, the self-discharge equivalent short-circuit resistance of a specific battery can be calculated as:

[0099]

[0100] in, is the equivalent short-circuit resistance of self-discharge, U0 is the nominal voltage of the battery, and C0 is the nominal capacity of the battery. As the preset short-circuit resistance value, when the short-circuit resistance calculated according to the electrical model and the thermal model is less than the preset short-circuit resistance value, it is determined that a short-circuit fault occurs in the battery.

[0101] In a specific embodiment, when the nominal capacity and nominal voltage of the battery are 3.66V and 174Ah respectively, it can be calculated that is 94.23Ω. If the short-circuit resistance value calculated based on the electrical model and thermal model is less than When , it means that the battery has a short circuit fault and the short circuit resistor consumes extra power.

[0102] Of course, you can also adjust the preset short-circuit resistance value according to actual needs to reduce the false alarm rate. By balancing the detection sensitivity and false alarm rate, a reasonable preset short-circuit resistance value is set.

[0103] In one example, the fault degree of the battery is evaluated based on the short-circuit resistance value and a preset short-circuit resistance value.

[0104] Exemplarily, different fault levels are set according to the difference between the short-circuit resistance value and the preset short-circuit resistance value. When the difference between the short-circuit resistance value calculated according to the electrical model and the thermal model and the preset short-circuit resistance value is larger, it indicates that the battery fault is more serious and the fault level is higher. Of course, the fault level can also be evaluated according to the ratio of the short-circuit resistance value to the preset short-circuit resistance value.

[0105] Based on the same inventive concept, the embodiment of the present invention also provides a channel parameter prediction device, such as Figure 6 As shown, the device comprises:

[0106] The acquisition module 601 is used to acquire the circuit information and temperature information of the battery. For details, please refer to the description of step S101 in the above embodiment, which will not be repeated here.

[0107] The calculation module 602 is used to calculate the short-circuit resistance of the battery based on the circuit information, the temperature information and the short-circuit model, wherein the short-circuit model includes an electrical model under a short-circuit state and a thermal model under a short-circuit state, wherein the electrical model under a short-circuit state and the thermal model under a short-circuit state are used to characterize the voltage state and the temperature state of the battery under a short-circuit state, respectively. For details, please refer to the description of step S102 in the above embodiment, which will not be repeated here.

[0108] The determination module 603 is used to determine that a short circuit fault occurs in the battery when the short circuit resistance value is less than a predetermined short circuit resistance value. For details, please refer to the description of step S103 in the above embodiment, which will not be repeated here.

[0109] In one example, in the calculation module 602, the module is used to calculate the short-circuit resistance and the corresponding estimated temperature of the battery based on the circuit information, the temperature information and the short-circuit model, and the short-circuit resistance minimizes the root mean square error between the corresponding estimated temperature and the measured temperature. For details, please refer to the description in the above embodiment, which will not be repeated here.

[0110] In another example, in the calculation module 602, the electrical model in the short-circuit state is a first-order RC equivalent circuit model in the short-circuit state, and the calculation module 602 includes:

[0111] The first acquisition submodule is used to acquire the circuit information of the battery in a normal state under various working conditions. For details, please refer to the description in the above embodiment, which will not be repeated here.

[0112] The first determination submodule is used to determine the first parameter in the first-order RC equivalent circuit model under the short-circuit state based on the circuit information of the battery under normal state under various working conditions. For details, please refer to the description in the above embodiment, which will not be repeated here.

[0113] The first establishment submodule is used to establish a first-order RC equivalent circuit model under the short-circuit state based on the first parameter establishment and the circuit structure under the short-circuit state. For details, please refer to the description in the above embodiment, which will not be repeated here.

[0114] In one example, in the calculation module 602, the circuit information includes the input current, the thermal model in the short-circuit state is a concentrated mass thermal model in the short-circuit state, and the calculation module 602 further includes:

[0115] The second acquisition module is used to acquire the input current and temperature information of the battery in a normal state under various working conditions. For details, please refer to the description in the above embodiment, which will not be repeated here.

[0116] The second determination submodule is used to determine the second parameter in the concentrated mass thermal model under the short circuit state based on the input current and temperature information of the battery under normal conditions under various working conditions. For details, please refer to the description in the above embodiment, which will not be repeated here.

[0117] The second establishment submodule is used to establish a concentrated mass thermal model under the short-circuit state based on the second parameter establishment and the circuit structure under the short-circuit state. For details, please refer to the description in the above embodiment, which will not be repeated here.

[0118] In an optional embodiment, in the first establishment submodule, the first-order RC equivalent circuit model in the short-circuit state is expressed as follows:

[0119]

[0120]

[0121] Among them, U1 represents polarization voltage, I represents input current, R1 represents polarization resistance, C1 represents polarization capacitance, and U t Represents terminal voltage, R SC Indicates short-circuit resistance, U OCV The details are shown in the above embodiments, which will not be described here again.

[0122] In an optional embodiment, in the second establishment submodule, the lumped mass thermal model under the short circuit state is expressed as follows:

[0123]

[0124]

[0125] Among them, Q totall represents the total heat generated by the battery, I represents the input current, U t Represents terminal voltage, R SCIndicates short-circuit resistance, R0 indicates ohmic internal resistance, R1 indicates polarization resistance, U t represents the terminal voltage, T represents the battery temperature, T ∞ represents the ambient temperature, m represents the mass, c p represents specific heat capacity, h represents convection heat transfer coefficient, and A represents surface area. For details, please refer to the description in the above embodiment, which will not be repeated here.

[0126] In one example, the apparatus further comprises:

[0127] The evaluation module is used to evaluate the fault degree of the battery based on the short-circuit resistance value and the preset short-circuit resistance value. For details, please refer to the description in the above embodiment, which will not be repeated here.

[0128] The specific limitations and beneficial effects of the above-mentioned device can be found in the above-mentioned limitations on the battery short circuit fault detection method, which will not be repeated here. The above-mentioned modules can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0129] Figure 7 FIG. 1 is a schematic diagram of a hardware structure of a computer device according to an exemplary embodiment. Figure 7 As shown, the device includes one or more processors 710 and a memory 720, and the memory 720 includes a persistent memory, a volatile memory, and a hard disk. Figure 7 A processor 710 is taken as an example. The device may also include: an input device 730 and an output device 740.

[0130] The processor 710, the memory 720, the input device 730 and the output device 740 may be connected via a bus or other means. Figure 7 The example of connecting through bus is taken in the following.

[0131] The processor 710 may be a central processing unit (CPU). The processor 710 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips. A general-purpose processor may be a microprocessor or the processor may be any conventional processor.

[0132] The memory 720 is a non-transient computer-readable storage medium, including persistent memory, volatile memory and hard disk, which can be used to store non-transient software programs, non-transient computer executable programs and modules, such as program instructions / modules corresponding to the battery short circuit fault detection method in the embodiment of the present application. The processor 710 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 720, that is, any of the above-mentioned battery short circuit fault detection methods is implemented.

[0133] The memory 720 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required by at least one function; the data storage area may store data required for use, etc. In addition, the memory 720 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 720 may optionally include a memory remotely arranged relative to the processor 710, and these remote memories may be connected to the data processing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0134] The input device 730 can receive input digital or character information and generate signal input related to user settings and function control. The output device 740 can include display devices such as display screens.

[0135] One or more modules are stored in the memory 720, and when executed by one or more processors 710, the execution is as follows: Figure 1 The method shown.

[0136] The above-mentioned product can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to Figure 1 Related description of the illustrated embodiment.

[0137] The embodiment of the present invention further provides a non-transitory computer storage medium, which stores computer executable instructions, and the computer executable instructions can execute the detection method in any of the above method embodiments. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memory.

[0138] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0139] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features applied herein.

Claims

1. A battery short circuit fault detection method, characterized in that: The method comprises: Obtain the circuit information and temperature information of the battery; Calculating the short-circuit resistance of the battery based on the circuit information, the temperature information and a short-circuit model, wherein the short-circuit model includes an electrical model under a short-circuit state and a thermal model under a short-circuit state, wherein the electrical model under a short-circuit state and the thermal model under a short-circuit state are respectively used to characterize a voltage state and a temperature state of the battery under a short-circuit state; When the short-circuit resistance value is less than a preset short-circuit resistance value, it is determined that the battery has a short-circuit fault; The electrical model under the short-circuit state is a first-order RC equivalent circuit model under the short-circuit state. The first-order RC equivalent circuit model under the short-circuit state is expressed as follows: in, represents the polarization voltage, represents the input current, represents the polarization resistance, represents polarized capacitance, Indicates the terminal voltage, Represents the short-circuit resistance, represents the open circuit voltage, Shows ohmic internal resistance; The circuit information includes input current, and the thermal model in the short-circuit state is a concentrated mass thermal model in the short-circuit state. The concentrated mass thermal model in the short-circuit state is expressed as follows: in, Indicates the total heat generated by the battery. Indicates the input current, Indicates the terminal voltage, Represents the short-circuit resistance, Shows ohmic internal resistance, represents the polarization resistance, Indicates the battery temperature. Indicates the ambient temperature, Indicates quality, represents the specific heat capacity, represents the convective heat transfer coefficient, represents surface area; The calculating the short-circuit resistance of the battery based on the circuit information, the temperature information and the short-circuit model includes: A short-circuit resistance and a corresponding estimated temperature of the battery are calculated based on the circuit information, the temperature information and the short-circuit model, wherein the short-circuit resistance minimizes a root mean square error between the corresponding estimated temperature and the measured temperature.

2. The method according to claim 1, characterized in that The first-order RC equivalent circuit model under short-circuit condition is established by the following steps: Obtain the circuit information of the battery in normal state under various working conditions; Based on the circuit information of the battery in a normal state under each working condition, a first parameter in a first-order RC equivalent circuit model in a short-circuit state is determined, wherein the first parameter includes a first-order RC equivalent circuit model parameter , , ; A first-order RC equivalent circuit model under the short-circuit state is established based on the first parameter and the circuit structure under the short-circuit state.

3. The method according to claim 1 or 2, characterized in that: The lumped mass thermal model under short circuit condition is established by the following steps: Obtain the input current and temperature information of the battery in normal state under various working conditions; Based on the input current and temperature information of the battery in a normal state under various working conditions, a second parameter in the lumped mass thermal model in the short-circuit state is determined, where the second parameter is a lumped mass thermal model parameter , , , ; A lumped mass thermal model under the short-circuit state is established based on the second parameter and the circuit structure under the short-circuit state.

4. The method according to claim 1, characterized in that: The method further comprises: The failure degree of the battery is evaluated based on the short-circuit resistance value and the preset short-circuit resistance value.

5. A battery short circuit fault detection device, characterized in that: For executing the method according to claim 1, the device comprises: An acquisition module, used to acquire circuit information and temperature information of the battery; a calculation module, used for calculating the short-circuit resistance of the battery based on the circuit information, the temperature information and a short-circuit model, wherein the short-circuit model includes an electrical model under a short-circuit state and a thermal model under a short-circuit state, wherein the electrical model under a short-circuit state and the thermal model under a short-circuit state are used for characterizing a voltage state and a temperature state of the battery under a short-circuit state, respectively; The determination module is used to determine that a short circuit fault occurs in the battery when the short circuit resistance value is less than a predetermined short circuit resistance value.

6. A computer device, characterized in that: The invention comprises a memory and a processor, wherein the memory and the processor are connected to communicate with each other, the memory stores computer instructions, and the processor executes the steps of the battery short circuit fault detection method according to any one of claims 1 to 4 by executing the computer instructions.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the battery short circuit fault detection method according to any one of claims 1 to 4 are implemented.

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