A short-circuit resistance estimation method and device, computer equipment and medium

Abstract

The application provides a short-circuit resistance value estimation method and device, computer equipment and medium. The short-circuit resistance value estimation method comprises the following steps: obtaining a measured battery power and a model voltage error of a normal battery based on a normal equivalent circuit model of the normal battery; determining a predicted battery power of a short-circuit battery based on the model voltage error and an internal short-circuit equivalent circuit model of the short-circuit battery, wherein the internal short-circuit equivalent circuit model is obtained by connecting an internal short-circuit resistor in parallel with the normal equivalent circuit model; and calculating an internal short-circuit resistance value in the internal short-circuit equivalent circuit model based on the measured battery power of the normal battery and the predicted battery power of the short-circuit battery. Through the application, the internal short-circuit resistance value can be accurately estimated, and the influence of model error on the estimation accuracy of the internal short-circuit resistance value is inhibited.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of battery fault diagnosis, and in particular to a short-circuit resistance estimation method, apparatus, computer equipment and medium. Background Technology

[0002] With the expanding application of lithium-ion batteries, safety accidents caused by thermal runaway of lithium-ion batteries occur frequently. Internal short circuits are a key cause of thermal runaway in lithium-ion batteries. Their short-circuit resistance is unstable and strongly correlated with safety risks. Therefore, it is necessary to conduct accurate online estimation of internal short-circuit resistance and dynamic assessment of safety risks after detecting an internal short circuit, thereby providing the necessary basis for the formulation and intervention of risk management measures.

[0003] Existing methods for estimating internal short-circuit resistance, particularly non-model-driven methods, typically rely on operational data at specific time intervals or under specific operating conditions as input, thus lacking real-time performance. Model-driven methods, on the other hand, have greater potential for application in internal short-circuit resistance estimation due to their real-time advantage. When estimating internal short-circuit resistance, the battery charge of the short-circuited battery is first estimated. The internal short-circuit resistance is then calculated using the estimated battery charge. The accuracy of the internal short-circuit resistance estimation depends on the accuracy of the battery charge estimation, but this estimation is significantly constrained by model errors. When the model accuracy is low, the accuracy of the internal short-circuit resistance is greatly reduced. Summary of the Invention

[0004] To accurately estimate the internal short-circuit resistance and suppress the impact of model errors on the estimation accuracy, this invention proposes a short-circuit resistance estimation method, apparatus, computer equipment, and medium.

[0005] In a first aspect, the present invention provides a method for estimating short-circuit resistance, the method comprising:

[0006] Based on the normal equivalent circuit model of a normal battery, the measured battery capacity and model voltage error of a normal battery are obtained.

[0007] Based on the model voltage error and the internal short-circuit equivalent circuit model of the short-circuit battery, the predicted battery capacity of the short-circuit battery is determined. The internal short-circuit equivalent circuit model is obtained by connecting an internal short-circuit resistor in parallel with the normal equivalent circuit model.

[0008] Based on the measured battery capacity of a normal battery and the predicted battery capacity of a short-circuited battery, the internal short-circuit resistance value in the internal short-circuit equivalent circuit model is calculated.

[0009] By using the above method, when estimating the battery capacity of a short-circuited battery, the terminal voltage in the internal short-circuit equivalent circuit model is compensated and corrected using the model voltage error. The predicted battery capacity of the short-circuited battery is then calculated using the compensated and corrected terminal voltage. This avoids the influence of the model voltage error on the battery capacity estimation of the short-circuited battery, resulting in higher accuracy of the predicted battery capacity and thus improving the accuracy of the internal short-circuit resistance value.

[0010] In conjunction with the first aspect, in the first embodiment of the first aspect, the model voltage error is obtained based on the normal equivalent circuit model of a normal battery, including:

[0011] Based on the normal equivalent circuit model, the predicted value of the terminal voltage of a normal battery is determined.

[0012] Obtain the measured value of the terminal voltage of a normal battery;

[0013] The model voltage error is determined based on the predicted and measured values ​​of the terminal voltage of a normal battery.

[0014] In conjunction with the first embodiment of the first aspect, in the second embodiment of the first aspect, based on the model voltage error and the internal short-circuit equivalent circuit model of the short-circuit battery, the predicted battery capacity of the short-circuit battery is determined, including:

[0015] The measured value of the short-circuited battery terminal voltage is corrected by using the model voltage error, and the corrected value of the short-circuited battery terminal voltage is obtained.

[0016] Based on the correction value of the terminal voltage and the input current of the short-circuited battery in the internal short-circuit equivalent circuit model, the predicted battery capacity of the short-circuited battery is obtained.

[0017] In conjunction with the first aspect or the second embodiment of the first aspect, in the third embodiment of the first aspect, based on the measured battery capacity of a normal battery and the predicted battery capacity of a short-circuit battery, the internal short-circuit resistance value in the internal short-circuit equivalent circuit model is calculated, including:

[0018] Based on the measured battery capacity of the normal battery and the predicted battery capacity of the short-circuited battery, the power consumption of the short-circuited battery per unit time is determined.

[0019] Based on the power consumption, calculate the internal short-circuit resistance in the internal short-circuit equivalent circuit model.

[0020] In conjunction with the second embodiment of the first aspect, in the fourth embodiment of the first aspect, based on the correction value of the terminal voltage of the short-circuited battery and the input current in the internal short-circuit equivalent circuit model, the predicted battery capacity of the short-circuited battery is obtained, including:

[0021] Determine the state-space equations of the internal short-circuit equivalent circuit model;

[0022] The corrected terminal voltage of the short-circuited battery and the input current are input into the state-space equation. The state-space equation is then solved using an extended Kalman filter to obtain the predicted battery capacity of the short-circuited battery.

[0023] In conjunction with the fourth embodiment of the first aspect, in the fifth embodiment of the first aspect, the state-space equation of the internal short-circuit equivalent circuit model is determined, including:

[0024]

[0025] Where, x k Let x be the state variable matrix. k =[U1(k), z j (k)] T U1(k) is the voltage of R1 at time k, z j (k) represents the battery charge at time k during the short circuit; u k For the input variable matrix, I(k) is the input current of the short-circuited battery at time k. y is the correction value for the terminal voltage of the short-circuited battery at time k; k To output the variable matrix, y k =U j (k), U j (k) represents the measured terminal voltage of the short-circuited battery at time k; A k-1 B k-1 C k D k E k Here is the coefficient matrix; Δt is the sampling interval; τ1 is the time constant; R1 is the polarization resistance; R ISC R0 is the internal short-circuit resistance; a1 is the slope coefficient; a2 is the intercept coefficient.

[0026] In conjunction with the first aspect, in the sixth embodiment of the first aspect, the normal equivalent circuit model is a Rint model, a first-order RC model, or a second-order RC model.

[0027] Secondly, the present invention also provides a short-circuit resistance estimation device, the device comprising:

[0028] The module obtains the measured battery capacity and model voltage error of a normal battery based on a normal equivalent circuit model of a normal battery.

[0029] The determination module is used to determine the predicted battery capacity of the short-circuited battery based on the model voltage error and the internal short-circuit equivalent circuit model of the short-circuited battery. The internal short-circuit equivalent circuit model is obtained by connecting an internal short-circuit resistor in parallel with the normal equivalent circuit model.

[0030] The calculation module is used to calculate the internal short-circuit resistance in the internal short-circuit equivalent circuit model based on the measured battery capacity of a normal battery and the predicted battery capacity of a short-circuit battery.

[0031] Thirdly, the present invention also provides a computer device, including a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the steps of the short-circuit resistance estimation method of the first aspect or any embodiment of the first aspect.

[0032] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the short-circuit resistance estimation method of the first aspect or any embodiment of the first aspect. Attached Figure Description

[0033] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0034] Figure 1 This is a flowchart of a short-circuit resistance estimation method proposed according to an exemplary embodiment;

[0035] Figure 2 This is a schematic diagram of the circuit structure of a normal equivalent circuit model in an example;

[0036] Figure 3 This is a schematic diagram of the circuit structure of the internal short-circuit equivalent circuit model in one example;

[0037] Figure 4 This is a schematic diagram of a short-circuit resistance estimation device according to an exemplary embodiment;

[0038] Figure 5 This is a schematic diagram of the hardware structure of a computer device according to an exemplary embodiment. Detailed Implementation

[0039] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Furthermore, 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.

[0041] To accurately estimate the internal short-circuit resistance and suppress the impact of model errors on the estimation accuracy, this invention proposes a short-circuit resistance estimation method, apparatus, computer equipment, and medium. Figure 1 This is a flowchart of a short-circuit resistance estimation method proposed according to an exemplary embodiment. For example... Figure 1 As shown, the short-circuit resistance estimation method includes the following steps S101 to S103.

[0042] Step S101: Based on the normal equivalent circuit model of a normal battery, obtain the measured battery capacity and model voltage error of the normal battery.

[0043] In one alternative embodiment, the normal equivalent circuit model is a Rint model, a first-order RC model, or a second-order RC model.

[0044] In an alternative embodiment, the parameters in the normal equivalent circuit model are obtained through offline testing.

[0045] In an optional embodiment, the model voltage error refers to the difference between the predicted value of the terminal voltage obtained from the normal equivalent circuit model and the measured value of the terminal voltage of the normal battery.

[0046] In one alternative embodiment, the model voltage error can be obtained from the normal equivalent circuit model of a normal battery, or from the average voltage error of the normal equivalent circuit models of multiple normal batteries.

[0047] Step S102: Based on the model voltage error and the internal short-circuit equivalent circuit model of the short-circuit battery, determine the predicted battery capacity of the short-circuit battery. The internal short-circuit equivalent circuit model is obtained by connecting an internal short-circuit resistor in parallel with the normal equivalent circuit model.

[0048] In one optional embodiment, the predicted value of the terminal voltage in the internal short-circuit equivalent circuit model is corrected based on the model voltage error, thereby obtaining the predicted battery capacity of the short-circuit battery, where battery capacity refers to the remaining battery capacity (State of Charge, SOC).

[0049] In an optional embodiment, when the difference between the battery capacity of the normal battery and the battery capacity of the short-circuited battery is within a preset range, the model voltage error of the normal equivalent circuit model corresponding to the normal battery is the same as the model voltage error of the internal short-circuit equivalent circuit model corresponding to the short-circuit battery. In this invention, the circuit connection between the normal battery and the short-circuit battery is in series, ensuring that the battery capacity of the normal battery and the short-circuit battery are the same, and that the model voltage error of the normal equivalent circuit model corresponding to the normal battery is the same as the model voltage error of the internal short-circuit equivalent circuit model corresponding to the short-circuit battery.

[0050] Step S103: Based on the measured battery capacity of the normal battery and the predicted battery capacity of the short-circuited battery, calculate the internal short-circuit resistance value in the internal short-circuit equivalent circuit model.

[0051] In an optional embodiment, the difference between the measured battery charge of a normal battery and the predicted battery charge of a short-circuited battery is the battery charge consumed by the internal short-circuit resistor, and the internal short-circuit resistance value can be obtained by the battery charge consumed by the internal short-circuit resistor.

[0052] This method compensates for and corrects the terminal voltage in the internal short-circuit equivalent circuit model by using the model voltage error when estimating the battery capacity of a short-circuit battery. The predicted battery capacity of the short-circuit battery is then calculated using the compensated and corrected terminal voltage. This avoids the influence of the model voltage error on the battery capacity estimation of the short-circuit battery, resulting in higher accuracy of the predicted battery capacity and thus improving the accuracy of the internal short-circuit resistance value.

[0053] Figure 2 This is a schematic diagram of the circuit structure of the normal equivalent circuit model. When m = 0, this model is a Rint model; when m = 1, this model is a first-order RC model; when m = 2, this model is a second-order RC model. I represents the input current, and R1 ~ R m Represents the polarization resistance, C1~C m U represents the polarization capacitor, and U represents the terminal voltage. OC R0 represents the open-circuit voltage, and R0 represents the internal resistance in ohms. Figure 3 This is a schematic diagram of the circuit structure of the internal short-circuit equivalent circuit model. The internal short-circuit equivalent circuit model is obtained by connecting an internal short-circuit resistor in parallel with the normal equivalent circuit model. SC For the current to flow through the internal short-circuit resistor R SC The current, I b The current flowing through the ohmic internal resistance R0 is denoted as .

[0054] In one example, step S101 above is implemented in the following manner, including:

[0055] First, based on the normal equivalent circuit model, the predicted value of the terminal voltage of the normal battery is determined.

[0056] Then, obtain the measured value of the terminal voltage of a normal battery.

[0057] Finally, the model voltage error is determined based on the predicted and measured values ​​of the terminal voltage of a normal battery.

[0058] To ensure greater accuracy of the model voltage error, the average model voltage error of multiple normal batteries is used as the model voltage error. In this embodiment, n-1 normal batteries and one short-circuited battery constitute a battery pack numbered i = 1, 2, ..., n, with the batteries connected in series. The battery pack capacity is obtained through standard constant current charge-discharge testing in an offline environment, and the open-circuit voltage U of each individual battery in the battery pack is obtained through pulse charge-discharge testing. OC 1. Ohmic resistor R0, polarization resistor R1 and polarization capacitor C1.

[0059] Assuming battery j in the battery pack is a short-circuited battery, during the operation of the battery pack, the terminal voltages of all normal batteries except battery j are synchronously predicted by the normal equivalent circuit model. At any time k during the operation of the battery pack, the average model error of multiple normal equivalent circuit models is taken as the model voltage error. The average model error of the normal equivalent circuit model is determined according to the following formula:

[0060]

[0061] in, U represents the mean model error of a normal battery at time k. i (k) represents the measured terminal voltage of a normal battery at time k. This is the predicted value of the terminal voltage of a normal battery at time k.

[0062] In one example, step S102 above is implemented as follows:

[0063] First, the measured value of the short-circuited battery's terminal voltage is corrected using the model voltage error, resulting in the corrected value of the short-circuited battery's terminal voltage.

[0064] For example, the correction value for the terminal voltage of the short-circuited battery is calculated according to the following formula:

[0065]

[0066] in, U is the correction value for the terminal voltage of the short-circuited battery at time k. j (k) represents the measured value of the terminal voltage of the short-circuited battery at time k. Let be the mean of the model error of multiple normal batteries at time k.

[0067] Then, based on the corrected terminal voltage and input current of the short-circuited battery in the internal short-circuit equivalent circuit model, the predicted battery capacity of the short-circuited battery is obtained. For example, the predicted battery capacity of the short-circuited battery can be obtained using filter techniques, such as an Extended Kalman Filter (EKF), an Adaptive Extended Kalman Filter (AEKF), or an Unscented Kalman Filter (UKF).

[0068] In an optional embodiment, an extended Kalman filter is used to obtain the predicted battery capacity of the short-circuited battery, and the specific steps include:

[0069] First, determine the state-space equations of the internal short-circuit equivalent circuit model:

[0070]

[0071] Where, x k Let x be the state variable matrix. k =[U1(k), z j (k)] T U1(k) is the voltage of R1 at time k, z j (k) represents the battery charge at time k during the short circuit; u k For the input variable matrix, I(k) is the input current of the short-circuited battery at time k. y is the correction value for the terminal voltage of the short-circuited battery at time k; k To output the variable matrix, y k =U j (k), U j (k) represents the measured terminal voltage of the short-circuited battery at time k; A k-1 B k-1 C k D k E k Here is the coefficient matrix; Δt is the sampling interval; τ1 is the time constant; R1 is the polarization resistance; R ISC R0 is the internal short-circuit resistance; a1 is the slope coefficient; a2 is the intercept coefficient.

[0072] Then, the corrected terminal voltage of the short-circuited battery and the input current are input into the state-space equation, and the state-space equation is solved by an extended Kalman filter to obtain the predicted battery capacity of the short-circuited battery.

[0073] In this embodiment of the invention, the open circuit voltage (OCV) of the battery is calculated using linear interpolation with respect to a typical state of charge (SOC), i.e., U oc [z j (k)]=a1z j (k)+a2, thus satisfying the observer's requirement for local linearization of the OCV-SOC curve.

[0074] Based on EKF, the SOC of the short-circuited battery is estimated to obtain the predicted battery capacity. The working process of EKF is as follows, mainly including six steps: state space estimation, covariance estimation, system innovation calculation, Kalman gain calculation, state space estimation result calculation, and error covariance matrix calculation.

[0075] State-space estimation:

[0076] Covariance estimation:

[0077] System information calculation:

[0078] Kalman gain calculation:

[0079] Calculation of state-space estimation results:

[0080] Error covariance matrix calculation:

[0081] in, Let be the prior values ​​of the state space at time k. Let e ​​be the prior value of the covariance at time k. k For the system update at time k, K k The Kalman gain at time k, For state space estimation at time k, Let be the error covariance matrix at time k; R be the measurement noise; and Q be the process noise.

[0082] In one example, step S103 above is implemented as follows:

[0083] First, based on the measured battery capacity of the normal battery and the predicted battery capacity of the short-circuited battery, the power consumption of the short-circuited battery per unit time is determined.

[0084] The power consumption of the short-circuited battery is:

[0085] in, Let z(k) be the charge consumption of the short-circuited battery at time k, and z(k) be the charge of the normal battery at time k, which can be obtained directly through measurement. This is the predicted battery charge at time k for the short-circuited battery.

[0086] The amount of electricity consumed by the short-circuited battery per unit time:

[0087] in, This represents the amount of electricity consumed by the short-circuited battery per unit time. Let be the amount of charge consumed by the short-circuited battery at time k. This represents the amount of electricity consumed by the short-circuited battery at time k-1.

[0088] Then, based on the power consumption, the internal short-circuit resistance in the internal short-circuit equivalent circuit model is calculated, using the following formula:

[0089]

[0090] in, Let k be the short-circuit resistance of the battery at time k. Q is the correction value for the terminal voltage of the short-circuited battery at time k. max This is the maximum capacity of the short-circuited battery.

[0091] Based on the same inventive concept, embodiments of the present invention also provide a short-circuit resistance estimation device, such as... Figure 4 As shown, the device includes:

[0092] The module 401 obtains the measured battery capacity and model voltage error of the normal battery based on the normal equivalent circuit model of the normal battery; for details, please refer to the description of step S101 in the above embodiment, which will not be repeated here.

[0093] The determination module 402 is used to determine the predicted battery capacity of the short-circuited battery based on the model voltage error and the internal short-circuit equivalent circuit model of the short-circuited battery. The internal short-circuit equivalent circuit model is obtained by connecting an internal short-circuit resistor in parallel with the normal equivalent circuit model. For details, please refer to the description of step S102 in the above embodiment, which will not be repeated here.

[0094] The calculation module 403 is used to calculate the internal short-circuit resistance value in the internal short-circuit equivalent circuit model based on the measured battery capacity of the normal battery and the predicted battery capacity of the short-circuit battery. For details, please refer to the description of step S103 in the above embodiments, which will not be repeated here.

[0095] In one example, the normal equivalent circuit model obtained in module 401 is a Rint model, a first-order RC model, or a second-order RC model. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0096] In one example, obtaining module 401 includes:

[0097] The first determining submodule is used to determine the predicted value of the terminal voltage of a normal battery based on the normal equivalent circuit model; for details, please refer to the description in the above embodiments, which will not be repeated here.

[0098] The first acquisition submodule is used to acquire the measured value of the terminal voltage of a normal battery; for details, please refer to the description in the above embodiments, which will not be repeated here.

[0099] The second determination submodule is used to determine the model voltage error based on the predicted value and the measured value of the terminal voltage of a normal battery. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0100] In one example, module 402 includes:

[0101] The third determination submodule is used to correct the measured value of the short-circuited battery terminal voltage using the model voltage error, and obtain the corrected value of the short-circuited battery terminal voltage; for details, please refer to the description in the above embodiments, and will not be repeated here.

[0102] The fourth determination submodule is used to obtain the predicted battery capacity of the short-circuited battery based on the correction value of the terminal voltage of the short-circuited battery in the internal short-circuit equivalent circuit model and the input current. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0103] In an optional embodiment, the fourth determining submodule includes:

[0104] The fifth determining unit is used to determine the state-space equation of the internal short-circuit equivalent circuit model; for details, please refer to the description in the above embodiments, which will not be repeated here.

[0105] The sixth determining unit is used to input the corrected terminal voltage of the short-circuited battery and the input current into the state-space equation, and solve the state-space equation through an extended Kalman filter to obtain the predicted battery capacity of the short-circuited battery. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0106] In an optional embodiment, the fifth determining unit determines the state-space equation of the internal short-circuit equivalent circuit model using the following formula:

[0107]

[0108] Where, x k Let x be the state variable matrix. k =[U1(k), z j (k)] T U1(k) is the voltage of R1 at time k, zj (k) represents the battery charge at time k during the short circuit; u k For the input variable matrix, I(k) is the input current of the short-circuited battery at time k. y is the correction value for the terminal voltage of the short-circuited battery at time k; k To output the variable matrix, y k =U j (k), U j (k) represents the measured terminal voltage of the short-circuited battery at time k; A k-1 B k-1 C k D k E k Here is the coefficient matrix; Δt is the sampling interval; τ1 is the time constant; R1 is the polarization resistance; R ISC R is the internal short-circuit resistance; R0 is the ohmic internal resistance; a1 is the slope coefficient; a2 is the intercept coefficient. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0109] In one example, the calculation module 403 includes:

[0110] The fifth determination submodule is used to determine the power consumption of the short-circuited battery per unit time based on the measured battery power of the normal battery and the predicted battery power of the short-circuited battery; for details, please refer to the description in the above embodiments, and will not be repeated here.

[0111] The calculation submodule is used to calculate the internal short-circuit resistance value in the internal short-circuit equivalent circuit model based on power consumption. For details, please refer to the description in the above embodiments, which will not be repeated here.

[0112] The specific limitations and beneficial effects of the above-mentioned device can be found in the limitations of the short-circuit resistance estimation method described above, and will not be repeated here. Each of the above modules can be implemented entirely or partially through software, hardware, or a combination thereof. Each of the above modules can be embedded in hardware or independently of the processor in a computer device, or stored in software in the memory of a computer device, so that the processor can call and execute the operations corresponding to each of the above modules.

[0113] Figure 5 This is a schematic diagram of the hardware structure of a computer device according to an exemplary embodiment. For example... Figure 5 As shown, the device includes one or more processors 510 and memory 520, which includes persistent memory, volatile memory, and a hard disk. Figure 5 Taking a processor 510 as an example, the device may also include an input device 530 and an output device 540.

[0114] The processor 510, memory 520, input device 530, and output device 540 can be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0115] Processor 510 can be a Central Processing Unit (CPU). Processor 510 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof. The general-purpose processor can be a microprocessor or any conventional processor.

[0116] The memory 520, as a non-transitory computer-readable storage medium, includes persistent memory, volatile memory, and a hard disk. It can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the short-circuit resistance estimation method in this embodiment. The processor 510 executes various server functions and data processing by running the non-transitory software programs, instructions, and modules stored in the memory 520, thereby implementing any of the above-mentioned short-circuit resistance estimation methods.

[0117] The memory 520 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data that is needed and required. Furthermore, the memory 520 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 520 may optionally include memory remotely located relative to the processor 510, and these remote memories can be connected to the data processing device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0118] Input device 530 can receive input digital or character information, and generate signal inputs related to user settings and function control. Output device 540 may include display devices such as a display screen.

[0119] One or more modules are stored in memory 520, and when executed by one or more processors 510, they perform actions such as... Figure 1 The method shown.

[0120] The above-described product can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in [reference 1]. Figure 1 The relevant descriptions in the illustrated embodiments.

[0121] This invention also provides a non-transitory computer storage medium storing computer-executable instructions that can execute the estimation method in any of the above-described method embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium may also include combinations of the above types of memory.

[0122] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0123] The above are merely specific embodiments of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily 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 invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for estimating short-circuit resistance, characterized in that, The method includes: Based on the normal equivalent circuit model of a normal battery, the measured battery capacity and model voltage error of the normal battery are obtained. Based on the model voltage error and the internal short-circuit equivalent circuit model of the short-circuit battery, the predicted battery capacity of the short-circuit battery is determined. The internal short-circuit equivalent circuit model is obtained by connecting an internal short-circuit resistor in parallel with the normal equivalent circuit model. Based on the measured battery capacity of a normal battery and the predicted battery capacity of a short-circuited battery, the internal short-circuit resistance value in the internal short-circuit equivalent circuit model is calculated. Based on the measured battery capacity of a normal battery and the predicted battery capacity of a short-circuited battery, the internal short-circuit resistance value in the internal short-circuit equivalent circuit model is calculated, including: Based on the measured battery capacity of the normal battery and the predicted battery capacity of the short-circuited battery, the power consumption of the short-circuited battery per unit time is determined. Based on the power consumption, the internal short-circuit resistance value in the internal short-circuit equivalent circuit model is calculated.

2. The method according to claim 1, characterized in that, Based on the normal equivalent circuit model of a normal battery, the voltage error of the model is obtained, including: Based on the normal equivalent circuit model, the predicted value of the terminal voltage of the normal battery is determined. Obtain the measured value of the terminal voltage of a normal battery; The voltage error of the model is determined based on the predicted and measured values ​​of the terminal voltage of a normal battery.

3. The method according to claim 2, characterized in that, Based on the model voltage error and the internal short-circuit equivalent circuit model of the short-circuit battery, the predicted battery capacity of the short-circuit battery is determined, including: The measured value of the short-circuited battery's terminal voltage is corrected using the model voltage error to obtain the corrected value of the short-circuited battery's terminal voltage. Based on the correction value of the terminal voltage of the short-circuited battery and the input current in the internal short-circuit equivalent circuit model, the predicted battery capacity of the short-circuited battery is obtained.

4. The method according to claim 3, characterized in that, Based on the corrected terminal voltage and input current of the short-circuit battery in the internal short-circuit equivalent circuit model, the predicted battery capacity of the short-circuit battery is obtained, including: Determine the state-space equations of the internal short-circuit equivalent circuit model; The corrected terminal voltage of the short-circuited battery and the input current are input into the state-space equation. The state-space equation is then solved using an extended Kalman filter to obtain the predicted battery capacity of the short-circuited battery.

5. The method according to claim 4, characterized in that, Determining the state-space equations of the internal short-circuit equivalent circuit model includes: in, For the state variable matrix, , for The voltage at time k, Let K be the battery charge of the short-circuited battery at time k. For the input variable matrix, , Let be the input current of the short-circuited battery at time k. This is the correction value for the terminal voltage of the short-circuited battery at time k; To output the variable matrix, , This is the measured value of the terminal voltage of the short-circuited battery at time k; , , , , Here is the coefficient matrix; Δt is the sampling interval; It is a time constant; Polarization resistor; Internal short-circuit resistance; The internal resistance is ohmic; The slope coefficient; This is the intercept coefficient; This is the maximum capacity of the short-circuited battery.

6. The method according to claim 1, characterized in that, The normal equivalent circuit model is a Rint model, a first-order RC model, or a second-order RC model.

7. A short-circuit resistance estimation device, characterized in that, The device includes: The module obtains the measured battery capacity and model voltage error of the normal battery based on the normal equivalent circuit model of the normal battery. The determination module is used to determine the predicted battery capacity of the short-circuited battery based on the model voltage error and the internal short-circuit equivalent circuit model of the short-circuited battery. The internal short-circuit equivalent circuit model is obtained by connecting an internal short-circuit resistor in parallel with the normal equivalent circuit model. The calculation module is used to calculate the internal short-circuit resistance value in the internal short-circuit equivalent circuit model based on the measured battery capacity of a normal battery and the predicted battery capacity of a short-circuit battery. Based on the measured battery capacity of a normal battery and the predicted battery capacity of a short-circuited battery, the internal short-circuit resistance value in the internal short-circuit equivalent circuit model is calculated, including: Based on the measured battery capacity of the normal battery and the predicted battery capacity of the short-circuited battery, the power consumption of the short-circuited battery per unit time is determined. Based on the power consumption, the internal short-circuit resistance value in the internal short-circuit equivalent circuit model is calculated.

8. A computer device, characterized in that, The device includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the steps of the short-circuit resistance estimation method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the short-circuit resistance estimation method as described in any one of claims 1-6.

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