Evaluation method for short circuit in battery and electronic equipment

Abstract

The embodiment of the invention provides a battery internal short circuit assessment method and electronic equipment, and the method comprises the steps: determining a theoretical terminal voltage of a target battery under a plurality of different battery charging related parameters and a plurality of different internal short circuit resistors based on an equivalent circuit model of a charging circuit; constructing a training set by taking the battery charging related parameters and the theoretical terminal voltage as independent variables and taking the internal short-circuit resistance as a dependent variable; and training a regression model based on the training set, wherein the regression model is used for evaluating the internal short circuit resistance of the battery.

Description

Technical Field

[0001] The present application relates to the field of new energy storage technology, and in particular to a method and electronic device for evaluating short circuits within a battery. Background Art

[0002] With the rapid development of new energy storage field, the safe operation and maintenance of batteries (such as lithium-ion batteries) have attracted much attention.

[0003] Internal short circuit in a battery refers to the short circuit phenomenon in which the positive and negative electrode diaphragms of the battery are punctured in certain scenarios (such as process reasons, mechanical reasons, etc.), resulting in direct contact between the positive and negative electrodes of the battery. The consequences of internal short circuit in a battery are directly related to the size of the internal short circuit resistance. When the internal short circuit phenomenon is relatively mild, the internal short circuit resistance is large, and the internal short circuit branch current is small. At this time, the risk of thermal runaway is controllable, and there is enough time for operation and maintenance protection; when the internal short circuit phenomenon is serious, the internal short circuit resistance is small, and the internal short circuit branch current increases, which will lead to the secondary thermal runaway risk. Therefore, the reasonable evaluation of the internal short circuit resistance is of great practical significance for the safe operation and maintenance of energy storage.

[0004] Related technologies propose to use a regression model to evaluate the internal short-circuit resistance of a battery. However, in actual situations, this method is limited by the scarcity of samples, and it is difficult to obtain a regression model with high evaluation accuracy. For example, for batteries with slight internal short circuits, they are often difficult to monitor and detect in actual operation and maintenance scenarios. For batteries with severe internal short circuits, they may be damaged by fire with a high probability, making it difficult to obtain the internal short-circuit resistance value, and it is also difficult to obtain a sufficient number of internal short-circuit battery samples for training the regression model. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a method and electronic device for evaluating internal short circuits in batteries, which can solve the problem of low evaluation accuracy of the regression model obtained due to a lack of internal short circuit battery samples.

[0006] To solve the above technical problems, the embodiments of the present application are implemented through the following aspects.

[0007] In a first aspect, an embodiment of the present application provides a method for evaluating an internal short circuit of a battery, comprising: determining a theoretical terminal voltage of a target battery under a plurality of different battery charging-related parameters and a plurality of different internal short-circuit resistances based on an equivalent circuit model of a charging circuit; taking the battery charging-related parameters and the theoretical terminal voltage as independent variables, and taking the internal short-circuit resistance as a dependent variable, to construct a training set; and training a regression model based on the training set, wherein the regression model is used to evaluate the internal short-circuit resistance of the battery.

[0008] In a second aspect, an embodiment of the present application provides a method for evaluating an internal short circuit of a battery, comprising: evaluating the internal short circuit resistance of a battery based on a regression model; wherein the regression model is obtained by training based on a training set, and the training set is obtained by taking battery charging-related parameters and a theoretical terminal voltage as independent variables, and taking the internal short circuit resistance as a dependent variable; the theoretical terminal voltage is obtained based on an equivalent circuit model of a charging circuit, multiple different battery charging-related parameters, and multiple different internal short circuit resistances.

[0009] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor, and computer executable instructions stored in the memory and executable on the processor, wherein the computer executable instructions, when executed by the processor, implement the steps of executing the method described in the first aspect or the second aspect.

[0010] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which is used to store computer-executable instructions. When the computer-executable instructions are executed by a processor, the steps of the method described in the first aspect or the second aspect are implemented.

[0011] In the embodiment of the present application, based on the equivalent circuit model of the charging circuit, the theoretical terminal voltage of the target battery under multiple different battery charging related parameters and multiple different internal short-circuit resistances is determined; then the battery charging related parameters and the theoretical terminal voltage are used as independent variables, and the internal short-circuit resistance is used as the dependent variable, so as to construct a sufficient number of representative training sets; finally, a regression model is trained based on the training set, and the regression model is used to evaluate the internal short-circuit resistance of the battery. In the embodiment of the present application, a sufficient number of representative training sets can be constructed, which is conducive to improving the evaluation accuracy of the regression model. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0013] Figure 1 A schematic diagram showing a flow chart of a method for evaluating a short circuit in a battery provided in an embodiment of the present application;

[0014] Figure 2 An equivalent circuit diagram of a battery internal short circuit in a battery internal short circuit evaluation method provided in an embodiment of the present application is shown;

[0015] Figure 3 A schematic diagram showing a specific application of the method for evaluating a short circuit in a battery provided in an embodiment of the present application;

[0016] Figure 4 A schematic diagram showing a flow chart of a method for evaluating a short circuit in a battery provided in an embodiment of the present application;

[0017] Figure 5 A schematic diagram of the hardware structure of an electronic device for executing the method for evaluating internal short circuit in a battery provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0019] Figure 1 A schematic flow chart of a method for evaluating a short circuit in a battery provided by an embodiment of the present application is shown. The method can be executed by an electronic device (such as a terminal). In other words, the method can be executed by software or hardware installed in the electronic device. Figure 1 As shown, the method may include the following steps.

[0020] S102: Determine a theoretical terminal voltage of a target battery under a plurality of different battery charging related parameters and a plurality of different internal short-circuit resistances based on an equivalent circuit model of a charging circuit.

[0021] The equivalent circuit model of the charging circuit in this embodiment can be referred to Figure 2 ,exist Figure 2 In the figure, I represents the charging current of the target battery, Ix(t) represents the internal short-circuit current of the target battery, OCV represents the open-circuit voltage of the target battery, Rx represents the internal short-circuit resistance of the target battery, and R0 represents the ohmic internal resistance of the target battery.

[0022] Based on the equivalent circuit model of the charging circuit, this embodiment can calculate the theoretical terminal voltage Ux(t) of the target battery under known battery charging related parameters and known internal short-circuit resistance, where t represents the charging time. The values ​​of multiple battery charging related parameters and multiple different internal short-circuit resistance values ​​in this embodiment can be values ​​set based on experience.

[0023] Optionally, the battery charging related parameters in this embodiment may include at least one of the following parameters of the target battery: battery health (State of Health, SOH), temperature (Temperature, TEMP), initial state of charge (State of Charge, SOC), charging current I, charging time t. Of course, the battery charging related parameters in this embodiment may also include other parameters not listed.

[0024] Optionally, S102 may include the following steps:

[0025] 1) Based on the first mapping relationship, the second mapping relationship and the preset internal short-circuit resistance, determine the internal short-circuit current Ix(t) of the target battery from time t-1 to time t.

[0026] Before executing this embodiment, the following steps may also be included: performing a small current constant current charge and discharge experiment on the target battery with different battery health conditions at multiple different temperatures, and obtaining the first mapping relationship according to the experimental results.

[0027] Before executing this embodiment, the following steps may also be included: performing a hybrid power pulse characteristic (HPPC) experiment on the target battery with different battery health conditions at multiple different temperatures, and obtaining the second mapping relationship according to the experimental results.

[0028] 2) Determine the state of charge of the target battery at time t based on the state of charge at time t-1 and the charging current I, the internal short-circuit current Ix(t) and the preset battery health.

[0029] 3) Based on the first mapping relationship, the state of charge and the charging current I at time t-1, and the second mapping relationship, determine the theoretical terminal voltage of the target battery at sampling time t.

[0030] Among them, t is the charging time, the first mapping relationship includes the mapping relationship between the open circuit voltage of the target battery and the temperature, battery health and charge state; the second mapping relationship includes the mapping relationship between the ohmic internal resistance of the target battery and the temperature, battery health and charge state; the battery charging related parameters include: the battery health, the temperature, the initial charge state, the charging current I, and the charging time t.

[0031] S104: Taking the battery charging related parameters and the theoretical terminal voltage as independent variables, and taking the internal short-circuit resistance as a dependent variable, to construct a training set.

[0032] S106: Training a regression model based on the training set, where the regression model is used to evaluate the internal short-circuit resistance of the battery.

[0033] Optionally, the regression model may be a neural network model or the like.

[0034] After S106 is executed, this embodiment may further include the following steps: inputting actual battery charging parameters and measured terminal voltage into the regression model, and obtaining the internal short-circuit resistance of the battery output by the regression model; the actual battery charging parameters include: temperature, battery health, battery initial charge state, charging current and charging time.

[0035] Optionally, after obtaining the internal short-circuit resistance of the battery output by the regression model, the method further comprises: issuing an alarm when the internal short-circuit resistance is less than a preset value.

[0036] The battery internal short circuit evaluation method provided in the embodiment of the present application is based on the equivalent circuit model of the charging circuit, and determines the theoretical terminal voltage of the target battery under multiple different battery charging related parameters and multiple different internal short circuit resistances; then the battery charging related parameters and the theoretical terminal voltage are used as independent variables, and the internal short circuit resistance is used as the dependent variable, so as to construct a sufficient number of representative training sets; finally, a regression model is trained based on the training set, and the regression model is used to evaluate the internal short circuit resistance of the battery. The embodiment of the present application can construct a sufficient number of representative training sets, which is conducive to improving the evaluation accuracy of the regression model.

[0037] The battery internal short circuit evaluation method provided in the embodiment of the present application can be applied to the field of new energy storage, and specifically can be applied to different scenarios such as energy storage systems, distributed energy storage, and large-scale energy storage to ensure the safe, stable, and reliable operation of the battery system.

[0038] To explain in detail the method for evaluating a short circuit in a battery provided by the embodiment of the present application, the following will be combined with Figure 3 Provide detailed explanation.

[0039] This embodiment proposes a battery internal short-circuit assessment method that integrates mechanism and data drive. First, the basic parameters and basic mapping relationships of the battery can be determined by conducting basic experiments on the target battery. Then, the battery equivalent circuit model under typical charging conditions and typical internal short-circuit degrees is analyzed and the theoretical terminal voltage value is derived. Finally, the constructed samples are used to train the neural network and capture the mapping relationship between the terminal voltage value of the target battery under specific charging conditions and the internal short-circuit fault degree (i.e., the internal short-circuit resistance), thereby realizing online internal short-circuit degree identification.

[0040] The embodiment of the present application can adopt a technical solution including the following steps for a specific brand or batch of target lithium-ion batteries:

[0041] Step 1: Fit the open circuit voltage mapping relationship.

[0042] At multiple different temperatures (TEMP), small current constant current charge and discharge experiments are carried out for target batteries with different battery health (SOH). The regression model NN1 is trained according to the experimental results to fit the mapping relationship f1 between temperature (TEMP), battery health (SOH), state of charge (SOC) and open circuit voltage (OCV). The specific formula is as follows: Formula 1:

[0043] OCV = f1(TEMP, SOH, SOC) Formula 1

[0044] Step 2: Fit the internal resistance mapping relationship.

[0045] HPPC experiments are conducted on target batteries with different battery health states (SOH) at multiple different temperatures (TEMP). The regression model NN2 is trained based on the experimental results to fit the mapping relationship f2 between TEMP, SOH, SOC and battery ohmic internal resistance (R0), where:

[0046] R0 = f2(TEMP, SOH, SOC) Formula 2

[0047] The above steps 1 and 2 can be found in Figure 3 The mapping relationship in .

[0048] Step 3: Simulate internal short circuit to generate training samples.

[0049] Based on the typical battery health SOH and typical temperature TEMP, typical constant current charging conditions (including different initial charge states SOC_Begin and charging current I), and typical internal short-circuit resistance Rx, the circuit characteristics during internal short circuit are simulated.

[0050] Specifically, the theoretical terminal voltage Ux(t) of the target battery (factory capacity is AH, OCV mapping relationship is f1 and battery internal resistance mapping relationship is f2) at each sampling time t is calculated according to the following algorithm 1 when the sampling interval is dt.

[0051] Algorithm 1 is described in detail as follows:

[0052] At time 0:

[0053] The SOC of the battery at the zero charging moment is SOC_Begin.

[0054] SOC(0) = SOC_Begin Formula 3

[0055] From time t-1 to time t:

[0056] Figure 2 This is the equivalent circuit diagram of a battery with an internal short circuit, including the self-discharge circuit above and the normal charging circuit of the battery.

[0057] Depend on Figure 2 Internal short-circuit loop, the internal short-circuit current from t-1 to t is:

[0058] Ix(t)=OCV(t-1) / (Rx+R0(t-1)))

[0059] Substituting formula 1 (mapping f1) and formula 2 (mapping f2) into the equation, we get:

[0060] Ix(t) = f1(TEMP, SOH, SOC(t-1)) / (Rx + f2(TEMP, SOH, SOC(t-1))) Formula 4

[0061] Depend on Figure 2 Charging circuit, we know that the actual charging current from t-1 to t is I-Ix(t), so the battery SOC at t can be expressed as follows:

[0062] SOC(t) = SOC(t-1) + (I - Ix(t)) * dt / (SOH * AH) Formula 5

[0063] Then the theoretical terminal voltage Ux(t) at time t is:

[0064] Ux(t)=OCV(t)+(I-Ix(t))*R0(t)

[0065] Substituting formula 1 (mapping f1) and formula 2 (mapping f2) into the equation, we get:

[0066] Ux(t) = f1(TEMP, SOH, SOC(t)) + (I - Ix(t))*f2(TEMP, SOH, SOC(t)) Formula 6

[0067] Among them, Ix(t) in Formula 4 represents the internal short-circuit current at time t, SOC(t) in Formula 5 represents the actual state of charge SOC at time t, and Ux(t) in Formula 6 represents the theoretical output voltage at time t.

[0068] Step 4: Construct a training set.

[0069] Construct the training sample vector row, its independent variable part:

[0070] row.x=<SOH,TEMP,SOC_Begin,I,t,Ux(t)>

[0071] Its dependent variable label part:

[0072] row.y= <rx>

[0073] Insert the training sample vector row into the training set matrix TrainDs. The TrainDs form is as follows:

[0074] SOH TEMP SOC_Begin I t Ux(t) Rx 100％ 45℃ 20％ 0.5C 60s 2.932V 1000Ω ... ... ... ... ... ... ... 100％ 25℃ 30％ 1C 120s 3.160V 1000Ω ... ... ... ... ... ... ... 97％ 0℃ 68％ 2C 90s 3.369V 100Ω ... ... ... ... ... ... ... 90％ 15℃ 52％ 0.1C 300s 3.083V 10Ω ... ... ... ... ... ... ...

[0075] Step 5: Train the internal short-circuit resistance value model.

[0076] The regression model NN3 for evaluating the internal short-circuit resistance value of the target battery is trained according to TrainDs.

[0077] Step 6: Internal short circuit resistance evaluation.

[0078] Under the actual constant current charging condition, the vector consisting of the target battery's battery health SOH, the battery's current temperature TEMP, the charging current value I, the battery's initial state of charge SOC_Begin at the start of charging, the charging time t, and the actually measured terminal voltage is input into the trained model NN3 to evaluate the current internal short-circuit resistance value of the battery.

[0079] In a specific application embodiment, the following steps are included:

[0080] The target lithium-ion battery of batch 01 of brand X is denoted as XB1, and the factory capacity is AH. The specific embodiment of identifying the degree of short circuit in battery XB1 of this brand and this batch is as follows.

[0081] Step 1: Constant current charge and discharge experiment, fitting the open circuit voltage mapping relationship.

[0082] Take a battery cell XB1-1 in XB1, and perform a small current constant current charge and discharge experiment on XP1 when the health state (SOH) of XB1-1 is 100% to 70% (attenuated by 1%) and the temperature (TEMP) is 0℃ to 50℃ (attenuated by 5℃). Record the terminal voltage value every time the SOC changes by 1%. At each SOC point, calculate the average value of charge and discharge to obtain 101 pairs of<SOC,OCV> Data as the experimental results in this state.

[0083] The experimental results under all the above conditions constitute the data set<TEMP,SOH,SOC,OCV> , where OCV is used as the label. After constructing the polynomial features, the linear regression model is used to train the regression model NN1 to capture the mapping relationship f1 between the state of charge SOC and the temperature TEMP, the battery health SOH, and the open circuit voltage OCV, that is, OCV = f1 (TEMP, SOH, SOC).

[0084] Step 2: HPPC experiment fitting internal resistance mapping relationship.

[0085] Take a battery cell XB1-2 in XB1, and measure the battery internal resistance by HPPC test (SOC range is 100% to 0%, granularity is 1% SOC) when the health state (SOH) of battery XB1-2 is 100% to 70% (attenuation by 1%) and the temperature (TEMP) is 0℃ to 50℃ (increase by 5℃). The results show that 101 pairs of<SOC,R0> Data is the experimental result under this state. The experimental results under all the above states constitute the data set<TEMP,SOH,SOC,R0> , where R0 is used as the label. After constructing the polynomial features, the linear regression model is used to train the regression model to train the neural network NN2, capturing the mapping relationship f2 between the battery ohmic internal resistance R0 and the temperature TEMP, battery health SOH, and state of charge SOC, that is, R0 = f2 (TEMP, SOH, SOC).

[0086] Step 3: Internal short circuit simulation.

[0087] The internal short circuit simulation is performed based on the mapping relationships f1 and f2 established in the experiments of step 1 and step 2 for battery XB1. The constant current charging of the battery pack under different working conditions and different internal short circuit degrees is simulated, and the parameter settings are as follows:

[0088]

[0089]

[0090] Algorithm 1:

[0091] 0 time:

[0092] SOC(0) = SOC_Begin Formula 3

[0093] From time t-1 to time t:

[0094] Ix(t) = f1(TEMP, SOH, SOC(t-1)) / (Rx + f2(TEMP, SOH, SOC(t-1))) Formula 4

[0095] SOC(t) = SOC(t-1) + (I - Ix(t)) * dt / (SOH * AH) Formula 5

[0096] Ux(t) = f1(TEMP, SOH, SOC(t)) + (I - Ix(t))*f2(TEMP, SOH, SOC(t)) Formula 6

[0097] According to the above algorithm 1, when the sampling interval dt = 1s, the theoretical terminal voltage Ux(t) of the battery XB1 at each sampling time t during the process from SOC_Begin to 100% is calculated. At each sampling time t, a line<SOH,TEMP,SOC_Begin,I,t,Ux(t),Rx> data.

[0098] Step 4: Construct a training set.

[0099] For all the data at all sampling moments obtained under all parameter combinations in step 3, construct training sample vectors row one by one, and its independent variable part row.x =<SOH,TEMP,SOC_Begin,I,t,Ux(t)> , its dependent variable label part row.y= <rx>, insert the training sample vectors sequentially into the training set matrix TrainDs.

[0100] Step 5: Train the internal short-circuit resistance value evaluation model.

[0101] According to the training set matrix TrainDs, a multilayer perceptron containing three hidden layers is used to train the internal short-circuit resistance value evaluation neural network NN3 of the battery XB1.

[0102] Step 6: Internal short circuit resistance evaluation.

[0103] For the battery cell XB1-x of XB1, under the actual constant current charging condition of the battery, the trained model NN3 is input with the battery health SOH of the battery XB1-x, the current temperature TEMP, the initial state of charge SOC_Begin of the battery at the beginning of charging, the charging current value I, the charging time t, and the actual measured battery terminal voltage Ux(t) in sequence.<SOH,TEMP,SOC_Begin,I,t,Ux(t))> , the NN3 model output obtains the current internal short-circuit resistance value Rx of the battery XB1-x.

[0104] like Figure 3 As shown, the embodiment of the present application combines white box mechanism drive and black box data drive technology. First, the white box mechanism drive technology is used to expand the training samples, and then the internal short circuit resistance value evaluation model with generalization ability is obtained through data-driven method training. The embodiment of the present application uses the white box mechanism to generate a large number of training samples for the interpretability of the internal short circuit, so as to maximize the stability of the black box data driven evaluation. The embodiment of the present application absorbs the advantages of the two technical routes, and at the same time solves the problem that the internal short circuit data of the black box model is difficult to obtain and the problem that the white box mechanism resistance identification is easily disturbed by real-time errors, so it can effectively evaluate the internal short circuit degree of the lithium battery and improve the level of energy storage safety management.

[0105] For the white-box mechanism route, the core idea is to model the equivalent circuit of the lithium battery and solve the internal short-circuit resistance value in the circuit model in real time based on the measured value of the actual working conditions. Since the real-time solution of the circuit model requires high computing power, and the measurement error under actual working conditions has a great impact on the solution accuracy of the internal short-circuit resistance value, the internal short-circuit diagnosis based on the white-box mechanism route has only theoretical advantages.

[0106] For the black box data-driven route, the core idea is to learn the nonlinear mapping relationship between the runtime sampling data of the internal short-circuit battery and the internal short-circuit resistance value. Ideally, this method can give full play to the advantages of big data analysis, learn the universal laws of battery sampling data and internal short-circuit resistance values ​​to suppress the interference of real-time errors, and make the identification of internal short-circuit resistance relatively stable. However, in actual situations, this method has the limitation of scarce samples: for batteries with slight internal short circuits, actual operation and maintenance scenarios are often difficult to monitor and discover. For batteries with severe internal short circuits, there is a high probability that they will be damaged by fire, which makes it difficult to obtain the internal short-circuit resistance value, and it is also difficult to obtain a sufficient scale of internal short-circuit battery samples for model training.

[0107] In summary, the solution of the internal short-circuit resistance value of lithium batteries under the white-box mechanism route only uses the measurement point data at a single moment, which is easily disturbed by real-time observation errors; the black-box data-driven route can in principle make full use of the internal laws of the big data of the sample to achieve a relatively stable evaluation effect, but faces the practical difficulty of collecting dependent variable labels. As a result, the current quantitative solution for internal short-circuit evaluation has stagnated and urgently needs to be improved.

[0108] The embodiment of the present application integrates white box mechanism drive and black box data drive technology, integrating mechanism drive with data drive, and can use the sampling data of the charging condition to evaluate the internal short circuit degree of the lithium battery.

[0109] The battery internal short circuit evaluation method provided in the embodiment of the present application has the convenience of detecting the internal short circuit fault of the energy storage battery. Specifically, by modeling the typical charging conditions and typical internal short circuit degree of the target battery under different SOH health conditions, constructing a sufficiently representative training data set and training the evaluation model, the internal short circuit fault warning of the target battery under the key charging conditions is realized. There is no need to identify the battery resistance and capacitance in real time under the traditional white box mechanism driving scheme, and it can make full use of big data to resist interference.

[0110] The battery internal short circuit evaluation method provided in the embodiment of the present application is conducive to promoting the development of lithium-ion battery technology: specifically, it can improve the safety and reliability of lithium-ion battery packs and promote the development of lithium-ion battery technology.

[0111] Combination of the above Figure 1 The method for evaluating a short circuit in a battery according to an embodiment of the present application is described in detail. Figure 4 A method for evaluating a short circuit in a battery according to another embodiment of the present application is described in detail. It can be understood that part of the execution process of this embodiment is similar to that of Figure 1 The descriptions in the methods shown are the same or corresponding, and to avoid repetition, the relevant descriptions are appropriately omitted.

[0112] Figure 4 The figure is a schematic diagram of the implementation flow of the method for evaluating the short circuit in the battery according to the embodiment of the present application, which can be applied to electronic devices. Figure 4 As shown, the method 400 includes the following steps.

[0113] S402: Evaluate the internal short-circuit resistance of the battery based on a regression model; wherein the regression model is obtained based on a training set, the training set is obtained by taking battery charging-related parameters and a theoretical terminal voltage as independent variables, and taking the internal short-circuit resistance as a dependent variable; the theoretical terminal voltage is obtained based on an equivalent circuit model of a charging circuit, a plurality of different battery charging-related parameters, and a plurality of different internal short-circuit resistances.

[0114] In the battery internal short circuit evaluation method provided in the embodiment of the present application, the regression model is obtained by training with a sufficient number of representative training sets, which is conducive to improving the evaluation accuracy of the regression model.

[0115] Optionally, the evaluation of the internal short-circuit resistance of the battery based on the regression model includes: inputting actual battery charging parameters and measured terminal voltage into the regression model, and obtaining the internal short-circuit resistance of the battery output by the regression model; the actual battery charging parameters include: temperature, battery health, battery initial charge state, charging current and charging time.

[0116] Optionally, the battery charging related parameters include: battery health, temperature, initial charge state, charging current I, and charging time t.

[0117] Figure 5 A schematic diagram of the hardware structure of an electronic device that implements the embodiment of the present application is shown. Referring to the figure, at the hardware level, the electronic device includes a processor, and optionally, an internal bus, a network interface, and a memory. Among them, the memory may include a memory, such as a high-speed random access memory (Random-Access Memory, RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage, etc. Of course, the electronic device may also include hardware required for other services.

[0118] The processor, the network interface and the memory may be interconnected through an internal bus, which may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one bidirectional arrow is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0119] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0120] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a device for locating the target user at the logical level. The processor executes the program stored in the memory and is specifically used to execute: Figure 1-4 The method disclosed in the illustrated embodiment realizes the functions and beneficial effects of each method described in the foregoing method embodiments, which will not be described in detail here.

[0121] The above application Figure 1-4 The method disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0122] The electronic device can also execute the methods described in the foregoing method embodiments, and realize the functions and beneficial effects of the methods described in the foregoing method embodiments, which will not be repeated here.

[0123] Of course, in addition to software implementation methods, the electronic device of the present application does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0124] The embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable medium stores one or more programs, and when the one or more programs are executed by an electronic device including multiple application programs, the electronic device executes Figure 1-4 The method disclosed in the illustrated embodiment realizes the functions and beneficial effects of each method described in the foregoing method embodiments, which will not be described in detail here.

[0125] The computer-readable storage medium includes a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0126] Furthermore, an embodiment of the present application also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, and when the program instructions are executed by a computer, the following process is implemented: Figure 1-4 The method disclosed in the illustrated embodiment realizes the functions and beneficial effects of each method described in the foregoing method embodiments, which will not be described in detail here.

[0127] In short, the above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0128] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0129] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be 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 erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0130] It should also be noted that the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the existence of other identical elements in the process, method, commodity or device including the element.

[0131] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.< / rx> < / rx>

Claims

1. A method for evaluating short circuit in a battery, include: Based on an equivalent circuit model of the charging circuit, determining a theoretical terminal voltage of a target battery under a plurality of different battery charging related parameters and a plurality of different internal short-circuit resistances; Taking the battery charging related parameters and the theoretical terminal voltage as independent variables and the internal short-circuit resistance as a dependent variable to construct a training set; A regression model is trained based on the training set, and the regression model is used to evaluate the internal short-circuit resistance of a battery.

2. The method according to claim 1, in, The determining of the theoretical terminal voltage of the target battery under multiple different charging-related parameters and multiple different internal short-circuit resistances based on the equivalent circuit model of the charging circuit includes: Determine the internal short-circuit current Ix(t) of the target battery from time t-1 to time t based on the first mapping relationship, the second mapping relationship and a preset internal short-circuit resistance; Determine the state of charge of the target battery at time t based on the state of charge at time t-1 and the charging current I, the internal short-circuit current Ix(t) and a preset battery health; Determine the theoretical terminal voltage of the target battery at sampling time t based on the first mapping relationship, the power state and the charging current I at time t-1, and the second mapping relationship; Among them, t is the charging time, the first mapping relationship includes the mapping relationship between the open circuit voltage of the target battery and the temperature, battery health and charge state; the second mapping relationship includes the mapping relationship between the ohmic internal resistance of the target battery and the temperature, battery health and charge state; the battery charging related parameters include: the battery health, the temperature, the initial charge state, the charging current I, and the charging time t.

3. The method according to claim 2, in, The method further comprises: At a plurality of different temperatures, a small current constant current charge and discharge experiment is performed on the target battery with different battery health conditions, and the first mapping relationship is obtained according to the experimental results.

4. The method according to claim 2, in, The method further comprises: At a plurality of different temperatures, a hybrid power pulse characteristic HPPC experiment is performed on the target battery with different battery health conditions, and the second mapping relationship is obtained according to the experimental results.

5. The method according to claim 1 or 2, in, The method further comprises: Inputting actual battery charging parameters and measured terminal voltage into the regression model, and obtaining the internal short-circuit resistance of the battery output by the regression model; The actual battery charging parameters include: temperature, battery health, battery initial charge state, charging current and charging time.

6. A method for evaluating short circuit in a battery, include: Evaluate the internal short-circuit resistance of the battery based on a regression model; The regression model is obtained based on a training set, wherein the training set is obtained by taking battery charging related parameters and theoretical terminal voltage as independent variables and internal short-circuit resistance as dependent variable; the theoretical terminal voltage is obtained based on an equivalent circuit model of a charging circuit, multiple different battery charging related parameters and multiple different internal short-circuit resistances.

7. The method according to claim 6, in, The method of evaluating the internal short-circuit resistance of the battery based on the regression model includes: Inputting actual battery charging parameters and measured terminal voltage into the regression model, and obtaining the internal short-circuit resistance of the battery output by the regression model; The actual battery charging parameters include: temperature, battery health, battery initial charge state, charging current and charging time.

8. The method according to claim 6, in, The battery charging related parameters include: battery health, temperature, initial charge state, charging current I, and charging time t.

9. An electronic device, include: processor; as well as A memory arranged to store computer executable instructions which, when executed, cause the processor to perform the steps of the method of any one of claims 1 to 8.

10. A computer-readable medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, causes the electronic device to execute the steps of the method according to any one of claims 1 to 8.

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