Methods, devices and related equipment for predicting the internal temperature of battery cells

Abstract

This application provides a method, apparatus, and related equipment for predicting the internal temperature of a battery cell. The method includes: acquiring the external temperature of the battery cell; inputting the current external temperature of the battery cell into a dynamic prediction mathematical model; and predicting the current internal temperature of the battery cell using the dynamic prediction mathematical model. The dynamic prediction mathematical model is determined based on physical relationships and is used to predict the internal temperature of the battery cell based on the external temperature. These physical relationships include the relationship between the external temperature of the battery cell and the thermal resistance at various locations within the battery cell, as well as the conversion of chemical energy into heat energy during the charging and discharging process of the battery cell.

Description

Technical Field

[0001] This application relates to the field of battery cell internal temperature prediction technology, and in particular to a method, apparatus and related equipment for predicting the internal temperature of a battery cell. Background Technology

[0002] For power batteries, proper judgment and indication of their operating status are crucial for battery thermal management. Based on this, it is usually necessary to predict the internal temperature of individual battery cells in real time in order to better manage battery thermal.

[0003] Currently, predicting the internal temperature of a battery cell often requires using the temperatures collected from multiple temperature sampling points inside the battery cell as input to predict the temperature of the central region inside the battery cell. However, in practical applications, it is difficult to set up multiple temperature sampling points inside the battery cell, which makes collecting the temperatures from multiple temperature sampling points cumbersome and the prediction process of the internal temperature of the battery cell complicated and difficult. Summary of the Invention

[0004] This application provides a method, apparatus, and related equipment for predicting the internal temperature of a battery cell, in order to solve the technical problem that existing methods for predicting the internal temperature of a battery cell are complex and difficult.

[0005] In a first aspect, embodiments of this application provide a method for predicting the internal temperature of a single battery cell, including:

[0006] Obtain the external temperature of the battery cell.

[0007] The current external temperature of the battery cell is input into the dynamic prediction mathematical model, which then predicts the current internal temperature of the battery cell.

[0008] Among them, the dynamic prediction mathematical model is determined based on physical relationships and is used to predict the internal temperature of the battery cell based on the external temperature of the battery cell. The physical relationships include the relationship between the external temperature of the battery cell and the thermal resistance at various locations inside the battery cell, as well as the conversion of chemical energy into heat energy during the charging and discharging process of the battery cell.

[0009] In this embodiment, by inputting the current external temperature of the battery cell into the dynamic prediction mathematical model, the dynamic prediction mathematical model can output the current internal temperature of the battery cell. That is, the internal temperature of the battery cell can be accurately and in real time calculated using only the temperature of at least one temperature sampling point on the surface of the battery cell as input. The internal temperature of the battery cell is readable, and no additional components are required, making the operation of predicting the internal temperature of the battery cell simple and highly accurate.

[0010] In some embodiments, before inputting the current external temperature of the battery cell into the dynamic prediction mathematical model, and predicting the current internal temperature of the battery cell using the dynamic prediction mathematical model, the method further includes:

[0011] Based on the physical relationships, establish the model formula.

[0012] Collect measured temperature data inside each battery cell.

[0013] Generate a measured curve based on the measured temperature data.

[0014] By fitting the measured curves to the model formula, the model parameters of the model formula are obtained.

[0015] A dynamic prediction mathematical model is established based on the model formula and model parameters.

[0016] In this embodiment, the curve of the measured temperature data inside the battery cell can be fitted with a model formula based on a physical relationship to obtain the model parameters in the model formula, thereby establishing an accurate dynamic prediction mathematical model. This facilitates the accurate and real-time prediction of the internal temperature of the battery cell using the dynamic prediction mathematical model.

[0017] In some embodiments, a model formula is established based on physical relationships, including:

[0018] Based on physical relationships, the factors affecting temperature within a single battery cell are determined. These factors include the temperature of the central region within the battery cell and the thermal energy conversion value of the battery cell's chemical energy.

[0019] A model formula is established based on the external temperature of the battery cell, the temperature of the central region inside the battery cell, and the chemical energy conversion heat value of the battery cell.

[0020] In this embodiment, the model formula can be established based on physical relationships, taking into account factors such as the external temperature of the battery cell, the temperature of the central region inside the battery cell, and the chemical energy conversion heat value of the battery cell, thereby making the model formula more reasonable and facilitating subsequent curve fitting to obtain an accurate dynamic prediction mathematical model.

[0021] In some embodiments, a model formula is established based on physical relationships, including:

[0022] A model formula is established based on physical relationships and the historical temperature inside the battery cell.

[0023] In this embodiment, the model formula also takes into account the historical temperature of the battery cell when calculating the internal temperature of the battery cell, which further improves the rationality of the model formula and thus improves the accuracy of the dynamic prediction mathematical model.

[0024] In some embodiments, a dynamic prediction mathematical model is established based on the model formula and model parameters, including:

[0025] Based on the model formula and model parameters, an initial model is established.

[0026] Collect the external temperature of each battery cell within a preset time period, as well as the real-time internal temperature of each battery cell within a preset time period.

[0027] The external temperature of a single battery cell within a preset time period is input into the initial model to obtain the predicted internal temperature of the single battery cell within the preset time period.

[0028] If the comparison between the predicted temperature and the real-time temperature meets the preset error conditions, the initial model is determined as a dynamic prediction mathematical model.

[0029] In this embodiment, after obtaining the model parameters, an initial model can be established first, and then the model can be verified by collecting the external temperature of the battery cell within a preset time period and the real-time temperature of the battery cell within a preset time period, thereby ensuring the accuracy of the dynamic prediction mathematical model and improving the accuracy of the prediction of the internal temperature of the battery cell.

[0030] In some embodiments, the battery cell includes N measurement locations, where N is an integer greater than or equal to 1.

[0031] The current external temperature of the battery cell is input into the dynamic prediction mathematical model, which then predicts the current internal temperature of the battery cell, including:

[0032] The current external temperature of the battery cell is input into the dynamic prediction mathematical model corresponding to the first measurement location, and the temperature at the current time of the first measurement location is predicted by the dynamic prediction mathematical model corresponding to the first measurement location.

[0033] The first measurement position is any one of the N measurement positions.

[0034] In this embodiment, the battery cell may include N measurement locations, and each measurement location may correspond to a different dynamic prediction mathematical model. In this way, the current external temperature of the battery cell can be input into the dynamic prediction mathematical model corresponding to any measurement location, thereby obtaining the temperature of any measurement location inside the battery cell.

[0035] Secondly, embodiments of this application also provide a device for predicting the internal temperature of a single battery cell, comprising:

[0036] The acquisition module is used to acquire the external temperature of individual battery cells.

[0037] The prediction module is used to input the current external temperature of the battery cell into the dynamic prediction mathematical model, and then use the dynamic prediction mathematical model to predict the current internal temperature of the battery cell.

[0038] Among them, the dynamic prediction mathematical model is determined based on physical relationships and is used to predict the internal temperature of the battery cell based on the external temperature of the battery cell. The physical relationships include the relationship between the external temperature of the battery cell and the thermal resistance at various locations inside the battery cell, as well as the conversion of chemical energy into heat energy during the charging and discharging process of the battery cell.

[0039] In this embodiment, by inputting the current external temperature of the battery cell into the dynamic prediction mathematical model, the dynamic prediction mathematical model can output the current internal temperature of the battery cell. That is, the internal temperature of the battery cell can be accurately and in real time calculated using only the temperature of at least one temperature sampling point on the surface of the battery cell as input. The internal temperature of the battery cell is readable, and no additional components are required, making the operation of predicting the internal temperature of the battery cell simple and highly accurate.

[0040] In some embodiments, the battery cell internal temperature prediction device further includes:

[0041] The formula creation module is used to create model formulas based on physical relationships.

[0042] The data acquisition module is used to collect measured temperature data inside individual battery cells.

[0043] The generation module is used to generate measured curves based on measured temperature data.

[0044] The fitting module is used to perform curve fitting between the measured curve and the model formula to obtain the model parameters.

[0045] The model building module is used to build dynamic prediction mathematical models based on model formulas and model parameters.

[0046] In this embodiment, the curve of the measured temperature data inside the battery cell can be fitted with a model formula based on a physical relationship to obtain the model parameters in the model formula, thereby establishing an accurate dynamic prediction mathematical model. This facilitates the accurate and real-time prediction of the internal temperature of the battery cell using the dynamic prediction mathematical model.

[0047] In some embodiments, the formula generation module is further configured to:

[0048] Based on physical relationships, the factors affecting temperature within a single battery cell are determined. These factors include the temperature of the central region within the battery cell and the thermal energy conversion value of the battery cell's chemical energy.

[0049] A model formula is established based on the external temperature of the battery cell, the temperature of the central region inside the battery cell, and the chemical energy conversion heat value of the battery cell.

[0050] In this embodiment, the model formula can be established based on physical relationships, taking into account factors such as the external temperature of the battery cell, the temperature of the central region inside the battery cell, and the chemical energy conversion heat value of the battery cell, thereby making the model formula more reasonable and facilitating subsequent curve fitting to obtain an accurate dynamic prediction mathematical model.

[0051] In some embodiments, the formula generation module is further configured to:

[0052] A model formula is established based on physical relationships and the historical temperature inside the battery cell.

[0053] In this embodiment, the model formula also takes into account the historical temperature of the battery cell when calculating the internal temperature of the battery cell, which further improves the rationality of the model formula and thus improves the accuracy of the dynamic prediction mathematical model.

[0054] In some embodiments, the model building module is further configured to:

[0055] Based on the model formula and model parameters, an initial model is established.

[0056] Collect the external temperature of each battery cell within a preset time period, as well as the real-time internal temperature of each battery cell within a preset time period.

[0057] The external temperature of a single battery cell within a preset time period is input into the initial model to obtain the predicted internal temperature of the single battery cell within the preset time period.

[0058] If the comparison between the predicted temperature and the real-time temperature meets the preset error conditions, the initial model is determined as a dynamic prediction mathematical model.

[0059] In this embodiment, after obtaining the model parameters, an initial model can be established first, and then the model can be verified by collecting the external temperature of the battery cell within a preset time period and the real-time temperature of the battery cell within a preset time period, thereby ensuring the accuracy of the dynamic prediction mathematical model and improving the accuracy of the prediction of the internal temperature of the battery cell.

[0060] In some embodiments, the battery cell includes N measurement locations, where N is an integer greater than or equal to 1. The prediction module is also used for:

[0061] The current external temperature of the battery cell is input into the dynamic prediction mathematical model corresponding to the first measurement location, and the temperature at the current time of the first measurement location is predicted by the dynamic prediction mathematical model corresponding to the first measurement location.

[0062] The first measurement position is any one of the N measurement positions.

[0063] In this embodiment, the battery cell may include N measurement locations, and each measurement location may correspond to a different dynamic prediction mathematical model. In this way, the current external temperature of the battery cell can be input into the dynamic prediction mathematical model corresponding to any measurement location, thereby obtaining the temperature of any measurement location inside the battery cell.

[0064] Thirdly, embodiments of this application provide a temperature prediction device, including:

[0065] Dynamic prediction mathematical model: The dynamic prediction mathematical model is used to predict the internal temperature of a battery cell based on the external temperature of the battery cell.

[0066] Fourthly, embodiments of this application provide a temperature prediction system, including:

[0067] Sensors are used to collect the external temperature of individual battery cells.

[0068] For example, the temperature prediction device in the third aspect is connected to a sensor to obtain the external temperature of the battery cell and input the external temperature of the battery cell into a dynamic prediction mathematical model to predict the internal temperature of the battery cell.

[0069] Fifthly, embodiments of this application provide an electronic device, the device comprising:

[0070] The processor and the memory storing programs or instructions implement the above-described method when the processor executes the programs or instructions.

[0071] Sixthly, embodiments of this application provide a machine-readable storage medium storing a program or instructions that, when executed by a processor, implement the above-described method.

[0072] In a seventh aspect, embodiments of this application provide a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform the above-described method.

[0073] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0074] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0075] Figure 1 A schematic flowchart illustrating the method for predicting the internal temperature of a battery cell provided in an embodiment of this application;

[0076] Figure 2 This is a comparison chart of the predicted temperature and the real-time temperature in the battery cell internal temperature prediction method provided in the embodiments of this application;

[0077] Figure 3 A schematic diagram of the structure of a battery cell internal temperature prediction device provided in another embodiment of this application;

[0078] Figure 4 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application.

[0079] The accompanying drawings are not drawn to scale. Detailed Implementation

[0080] The embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The detailed description of the following embodiments and the accompanying drawings are used to illustrate the principles of this application by way of example, but should not be used to limit the scope of this application, that is, this application is not limited to the described embodiments.

[0081] In the description of this application, it should be noted that, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," and "outer," etc., indicating orientation or positional relationships, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this application. Furthermore, the terms "first," "second," and "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. "Vertical" is not vertical in the strict sense, but within the allowable tolerance range. "Parallel" is not parallel in the strict sense, but within the allowable tolerance range.

[0082] The directional terms used in the following description refer to the directions shown in the figures and are not intended to limit the specific structure of this application. It should also be noted in the description of this application that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0083] In this application, the battery cell may include a lithium-ion secondary battery cell, a lithium-ion primary battery cell, a lithium-sulfur battery cell, a sodium-lithium-ion battery cell, a sodium-ion battery cell, or a magnesium-ion battery cell, etc., and the embodiments of this application are not limited thereto. The battery cell may be cylindrical, flat, cuboid, or other shapes, etc., and the embodiments of this application are not limited thereto. Battery cells are generally classified into three types according to their packaging method: cylindrical battery cells, cuboid / square battery cells, and pouch battery cells, and the embodiments of this application are not limited thereto.

[0084] A single battery cell includes an electrode assembly and an electrolyte. The electrode assembly consists of a positive electrode, a negative electrode, and a separator. The battery cell primarily functions by the movement of metal ions between the positive and negative electrode plates. The electrode assembly can be a wound structure or a stacked structure, and the embodiments of this application are not limited to these.

[0085] This application provides a method, apparatus, and related equipment for predicting the internal temperature of a battery cell, which can improve the accuracy of predicting the internal temperature of a battery cell and simplify operation. The method for predicting the internal temperature of a battery cell provided in this application is described below.

[0086] Please refer to Figure 1 , Figure 1 The following is a flowchart illustrating a method for predicting the internal temperature of a battery cell according to some embodiments of this application. This method may include the following steps:

[0087] Step 101: Obtain the external temperature of the individual battery cells.

[0088] Step 102: Input the current external temperature of the battery cell into the dynamic prediction mathematical model, and use the dynamic prediction mathematical model to predict the current internal temperature of the battery cell.

[0089] Among them, the dynamic prediction mathematical model is determined based on physical relationships and is used to predict the internal temperature of the battery cell based on the external temperature of the battery cell. The physical relationships include the relationship between the external temperature of the battery cell and the thermal resistance at various locations inside the battery cell, as well as the conversion of chemical energy into heat energy during the charging and discharging process of the battery cell.

[0090] In this embodiment, by inputting the current external temperature of the battery cell into the dynamic prediction mathematical model, the dynamic prediction mathematical model can output the current internal temperature of the battery cell. That is, the internal temperature of the battery cell can be accurately and in real time calculated using only the temperature of at least one temperature sampling point on the surface of the battery cell as input. The internal temperature of the battery cell is readable, and no additional components are required, making the operation of predicting the internal temperature of the battery cell simple and highly accurate.

[0091] In step 101, the external temperature of the battery cell can refer to the temperature of at least one sampling point on the surface of each battery cell, such as the bus sampling temperature of each battery cell. The external temperature of the battery cell can be obtained by arranging a temperature sensor on the bus of each battery cell and collecting the external temperature of the battery cell in real time through the temperature sensor.

[0092] In step 102, the current time can be any time at which the internal temperature of the battery cell is to be predicted; no specific limitation is made here. The dynamic prediction mathematical model can be a pre-established model, which can be established based on the relationship between the external temperature of the battery cell and the thermal resistance at various locations inside the battery cell, as well as the physical relationships such as the conversion of chemical energy into heat energy during the charging and discharging process of the battery cell. This dynamic prediction mathematical model can calculate the internal temperature of the battery cell from the external temperature of the battery cell.

[0093] For example, a well-established dynamic prediction mathematical model can be embedded in a computing device to obtain the external temperature of a battery cell in real time. The current external temperature of the battery cell is input into the computing device, and the computing device uses the dynamic prediction mathematical model to calculate and output the current internal temperature of the battery cell in real time.

[0094] In this embodiment, for multiple battery cells in the battery pack, their internal temperatures can be predicted based on their external temperatures, and then the relative temperature difference between the internal temperatures of each battery cell can be calculated.

[0095] In some embodiments, prior to step 102, the method for predicting the internal temperature of a single battery cell may further include the following steps:

[0096] Based on the physical relationships, establish the model formula.

[0097] Collect measured temperature data inside each battery cell.

[0098] Generate a measured curve based on the measured temperature data.

[0099] By fitting the measured curves to the model formula, the model parameters of the model formula are obtained.

[0100] A dynamic prediction mathematical model is established based on the model formula and model parameters.

[0101] In this embodiment, measured temperature data inside the battery cell can be collected. It is understood that temperature sampling points can be arranged inside the battery cell, and measured temperature data can be collected by temperature sensors.

[0102] Based on the collected measured temperature data, a measured curve can be generated. In some examples, the measured temperature data can be preprocessed first, for example, by filtering out some obviously erroneous data, before generating the measured curve.

[0103] The model formula can be a pre-defined function based on physical relationships, or a function determined based on measured curves. In this function, the external temperature of the battery cell can be used as the independent variable, the internal temperature of the battery cell can be used as the dependent variable, and model parameters are also included. The model parameters can be obtained by curve fitting between the measured curves and the model formula.

[0104] For example, before curve fitting, outlier data can be removed, the curve fitting interval can be determined, and the data can be dimensionless. Then, based on the general trend of the actual measurements, the functional form of the model formula can be initially selected, and the relevant coefficients (i.e., model parameters) in the functional expression can be determined. Curve fitting can be performed using methods such as linear regression analysis. For instance, the measured temperature data can be transformed into a linear function through variable substitution, and then converted to obtain linear data. Then, multiple regression analysis methods such as least squares can be used to find the relationship between the variables in the functional expression in order to obtain the model parameters of the model formula.

[0105] In some examples, the measured curves and model formulas can also be input into existing software for curve fitting, the residual range can be set, and the values ​​of the model parameters can be obtained.

[0106] A dynamic prediction mathematical model can be established based on the model formula and model parameters. In other words, the dynamic prediction mathematical model can be a model formula that determines the model parameters. Subsequently, the external temperature of the battery cell can be substituted into the model formula to calculate the internal temperature of the battery cell.

[0107] In this embodiment, the curve of the measured temperature data inside the battery cell can be fitted with a model formula based on a physical relationship to obtain the model parameters in the model formula, thereby establishing an accurate dynamic prediction mathematical model. This facilitates the accurate and real-time prediction of the internal temperature of the battery cell using the dynamic prediction mathematical model.

[0108] In some embodiments, the above-mentioned establishment of model formulas based on physical relationships may further include the following steps:

[0109] Based on physical relationships, the factors affecting temperature within a single battery cell are determined. These factors include the temperature of the central region within the battery cell and the thermal energy conversion value of the battery cell's chemical energy.

[0110] A model formula is established based on the external temperature of the battery cell, the temperature of the central region inside the battery cell, and the chemical energy conversion heat value of the battery cell.

[0111] In this embodiment, the model formula can be based on physical relationships, taking into account factors including the external temperature of the battery cell, the temperature of the central region inside the battery cell, and the chemical energy conversion heat energy value of the battery cell. The external temperature of the battery cell can be a measured value, the temperature of the central region inside the battery cell can be a value calculated based on the external temperature, and the chemical energy conversion heat energy value can be obtained using the Bernadi formula for simulating heat generation in lithium batteries.

[0112] For example, the model formula can be as shown in formula (1):

[0113] S i =a*A i +b*B i +c*C (1)

[0114] Among them, S i A represents the current internal temperature of a single battery cell. i B represents the current external temperature of the battery cell. i denoted as , where is the temperature of the central region inside the battery cell at the current moment, C is the chemical energy conversion heat energy value of the battery cell, and a, b, and c are constant factors (i.e., model parameters).

[0115] The calculation model for the temperature in the central region inside a single battery cell can be shown in formula (2):

[0116] B i =k1*A i +k2*B i-1 +k3*C (2)

[0117] Among them, B i Let A be the temperature of the central region inside the battery cell at the current moment. i B represents the current external temperature of the battery cell. i-1 is the temperature of the central region inside the battery cell at the previous moment, C is the chemical energy conversion heat energy value of the battery cell, and k1, k2, and k3 are constant factors for calculating the temperature of the central region inside the battery cell.

[0118] The measured curve can be fitted with the above formula (1) to obtain the values ​​of a, b, and c, thereby obtaining the dynamic prediction mathematical model.

[0119] In this embodiment, the model formula can be established based on physical relationships, taking into account factors such as the external temperature of the battery cell, the temperature of the central region inside the battery cell, and the chemical energy conversion heat value of the battery cell, thereby making the model formula more reasonable and facilitating subsequent curve fitting to obtain an accurate dynamic prediction mathematical model.

[0120] In some embodiments, the above-mentioned establishment of model formulas based on physical relationships may further include the following steps:

[0121] A model formula is established based on physical relationships and the historical temperature inside the battery cell.

[0122] In this embodiment, the model formula can also take into account the temperature at historical moments inside the battery cell. For example, the model formula can be as shown in formula (3):

[0123] S i =a*A i +b*B i +c*C+d*S i-1 (3)

[0124] Among them, S i S represents the current internal temperature of a single battery cell. i-1 A represents the temperature inside the battery cell at the previous moment. i B represents the current external temperature of the battery cell. i The temperature of the central region inside the battery cell at the current moment is calculated using the formula (2) above, which will not be elaborated here. C is the chemical energy conversion heat value of the battery cell, and a, b, c, and d are constant factors (i.e. model parameters).

[0125] The measured curve can be fitted with the above formula (3) to obtain the values ​​of a, b, c, and d, thereby obtaining the dynamic prediction mathematical model.

[0126] In this embodiment, the model formula also takes into account the historical temperature of the battery cell when calculating the internal temperature of the battery cell, which further improves the rationality of the model formula and thus improves the accuracy of the dynamic prediction mathematical model.

[0127] In some embodiments, establishing a dynamic prediction mathematical model based on model formulas and model parameters may include the following steps:

[0128] Based on the model formula and model parameters, an initial model is established.

[0129] Collect the external temperature of each battery cell within a preset time period, as well as the real-time internal temperature of each battery cell within a preset time period.

[0130] The external temperature of a single battery cell within a preset time period is input into the initial model to obtain the predicted internal temperature of the single battery cell within the preset time period.

[0131] If the comparison between the predicted temperature and the real-time temperature meets the preset error conditions, the initial model is determined as a dynamic prediction mathematical model.

[0132] In this embodiment, an initial model can be established first based on the model formula and model parameters. At this time, the external temperature of the battery cell within a preset time period, as well as the real-time internal temperature of the battery cell within a preset time period, can be collected. Both the external temperature and the real-time internal temperature of the battery cell within the preset time period can be measured values ​​obtained from temperature sensors.

[0133] The external temperature of a battery cell within a preset time period can be input into the initial model. The initial model can then be used to calculate the predicted internal temperature of the battery cell within the preset time period. In other words, the predicted temperature is the internal temperature value of the battery cell calculated by the model.

[0134] The predicted temperature within a preset time period can be compared with the real-time temperature within the same preset time period. If the comparison results meet the preset error conditions, the initial model is considered to have high accuracy and can be used as the final dynamic prediction mathematical model. If the comparison results do not meet the preset error conditions, the model parameters need to be adjusted until the preset error conditions are met.

[0135] It is understandable that preset error conditions can be set according to actual needs, and no specific limitations are made here. For example Figure 2 As shown, for example, if the difference between the predicted temperature and the real-time temperature at any given time is less than the error threshold, it can be considered that the preset error condition is met. The range of the error threshold can be set based on empirical values ​​and is not specifically limited here.

[0136] In this embodiment, after obtaining the model parameters, an initial model can be established first, and then the model can be verified by collecting the external temperature of the battery cell within a preset time period and the real-time temperature of the battery cell within a preset time period, thereby ensuring the accuracy of the dynamic prediction mathematical model and improving the accuracy of the prediction of the internal temperature of the battery cell.

[0137] In some embodiments, the interior of a battery cell may include N measurement locations, where N is an integer greater than or equal to 1. The above-mentioned inputting the current external temperature of the battery cell into a dynamic prediction mathematical model, and predicting the current internal temperature of the battery cell using the dynamic prediction mathematical model, may include the following steps:

[0138] The current external temperature of the battery cell is input into the dynamic prediction mathematical model corresponding to the first measurement location, and the temperature at the current time of the first measurement location is predicted by the dynamic prediction mathematical model corresponding to the first measurement location.

[0139] The first measurement position is any one of the N measurement positions.

[0140] In this embodiment, the temperature at any location inside a battery cell can be predicted using a dynamic prediction mathematical model. For example, N measurement locations with predicted temperatures can be arranged inside the battery cell. For each measurement location, a different dynamic prediction mathematical model can be established. It is understood that these dynamic prediction mathematical models share the same formula, and the measured curves are generated based on the measured temperature data collected at each measurement location. Therefore, the measured curves differ, resulting in differences in the model parameters obtained from curve fitting. The specific method for establishing the dynamic prediction mathematical model corresponding to each measurement location is the same as described above and will not be repeated here.

[0141] The current external temperature of a battery cell can be input into the dynamic prediction mathematical model corresponding to the first measurement location. This model then predicts the current temperature at that first measurement location. It's understood that the first measurement location can be any one of N measurement locations. In other words, based on the current external temperature of the battery cell and the dynamic prediction mathematical models corresponding to each measurement location, the current temperature at each measurement location inside the battery cell can be calculated.

[0142] In this way, the temperature at any location inside a battery cell can be accurately and in real time calculated using only the temperature of at least one temperature sampling point on the surface of the battery cell as input, which further simplifies the operation process of predicting the internal temperature of the battery cell and improves the temperature prediction efficiency.

[0143] In this embodiment, the battery cell may include N measurement locations, and each measurement location may correspond to a different dynamic prediction mathematical model. In this way, the current external temperature of the battery cell can be input into the dynamic prediction mathematical model corresponding to any measurement location, thereby obtaining the temperature of any measurement location inside the battery cell.

[0144] Based on the battery cell internal temperature prediction method provided in the above embodiments, this application also provides an embodiment of a battery cell internal temperature prediction device.

[0145] Figure 3 A schematic diagram of the internal temperature prediction device of a battery cell provided in another embodiment of this application is shown. For ease of explanation, only the parts related to the embodiment of this application are shown.

[0146] Reference Figure 3 The battery cell internal temperature prediction device 300 may include:

[0147] The acquisition module 301 is used to acquire the external temperature of the battery cell.

[0148] The prediction module 302 is used to input the current external temperature of the battery cell into the dynamic prediction mathematical model, and predict the current internal temperature of the battery cell through the dynamic prediction mathematical model.

[0149] Among them, the dynamic prediction mathematical model is determined based on physical relationships and is used to predict the internal temperature of the battery cell based on the external temperature of the battery cell. The physical relationships include the relationship between the external temperature of the battery cell and the thermal resistance at various locations inside the battery cell, as well as the conversion of chemical energy into heat energy during the charging and discharging process of the battery cell.

[0150] In this embodiment, by inputting the current external temperature of the battery cell into the dynamic prediction mathematical model, the dynamic prediction mathematical model can output the current internal temperature of the battery cell. That is, the internal temperature of the battery cell can be accurately and in real time calculated using only the temperature of at least one temperature sampling point on the surface of the battery cell as input. The internal temperature of the battery cell is readable, and no additional components are required, making the operation of predicting the internal temperature of the battery cell simple and highly accurate.

[0151] In some embodiments, the battery cell internal temperature prediction device 300 may further include:

[0152] The formula creation module is used to create model formulas based on physical relationships.

[0153] The data acquisition module is used to collect measured temperature data inside individual battery cells.

[0154] The generation module is used to generate measured curves based on measured temperature data.

[0155] The fitting module is used to perform curve fitting between the measured curve and the model formula to obtain the model parameters.

[0156] The model building module is used to build dynamic prediction mathematical models based on model formulas and model parameters.

[0157] In this embodiment, the curve of the measured temperature data inside the battery cell can be fitted with a model formula based on a physical relationship to obtain the model parameters in the model formula, thereby establishing an accurate dynamic prediction mathematical model. This facilitates the accurate and real-time prediction of the internal temperature of the battery cell using the dynamic prediction mathematical model.

[0158] In some embodiments, the formula generation module can also be used for:

[0159] Based on physical relationships, the factors affecting temperature within a single battery cell are determined. These factors include the temperature of the central region within the battery cell and the thermal energy conversion value of the battery cell's chemical energy.

[0160] A model formula is established based on the external temperature of the battery cell, the temperature of the central region inside the battery cell, and the chemical energy conversion heat value of the battery cell.

[0161] In this embodiment, the model formula can be established based on physical relationships, taking into account factors such as the external temperature of the battery cell, the temperature of the central region inside the battery cell, and the chemical energy conversion heat value of the battery cell, thereby making the model formula more reasonable and facilitating subsequent curve fitting to obtain an accurate dynamic prediction mathematical model.

[0162] In some embodiments, the formula generation module can also be used for:

[0163] A model formula is established based on physical relationships and the historical temperature inside the battery cell.

[0164] In this embodiment, the model formula also takes into account the historical temperature of the battery cell when calculating the internal temperature of the battery cell, which further improves the rationality of the model formula and thus improves the accuracy of the dynamic prediction mathematical model.

[0165] In some embodiments, the model building module can also be used for:

[0166] Based on the model formula and model parameters, an initial model is established.

[0167] Collect the external temperature of each battery cell within a preset time period, as well as the real-time internal temperature of each battery cell within a preset time period.

[0168] The external temperature of a single battery cell within a preset time period is input into the initial model to obtain the predicted internal temperature of the single battery cell within the preset time period.

[0169] If the comparison between the predicted temperature and the real-time temperature meets the preset error conditions, the initial model is determined as a dynamic prediction mathematical model.

[0170] In this embodiment, after obtaining the model parameters, an initial model can be established first, and then the model can be verified by collecting the external temperature of the battery cell within a preset time period and the real-time temperature of the battery cell within a preset time period, thereby ensuring the accuracy of the dynamic prediction mathematical model and improving the accuracy of the prediction of the internal temperature of the battery cell.

[0171] In some embodiments, the battery cell may include N measurement locations, where N is an integer greater than or equal to 1. The prediction module may also be used for:

[0172] The current external temperature of the battery cell is input into the dynamic prediction mathematical model corresponding to the first measurement location, and the temperature at the current time of the first measurement location is predicted by the dynamic prediction mathematical model corresponding to the first measurement location.

[0173] The first measurement position is any one of the N measurement positions.

[0174] In this embodiment, the battery cell may include N measurement locations, and each measurement location may correspond to a different dynamic prediction mathematical model. In this way, the current external temperature of the battery cell can be input into the dynamic prediction mathematical model corresponding to any measurement location, thereby obtaining the temperature of any measurement location inside the battery cell.

[0175] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. They are devices corresponding to the above-mentioned battery electrode alignment detection method. All implementation methods in the above-mentioned method embodiments are applicable to the embodiments of this device. For details on its specific functions and the resulting technical effects, please refer to the method embodiments section, which will not be repeated here.

[0176] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0177] Figure 4 A schematic diagram of the hardware structure of an electronic device provided in yet another embodiment of this application is shown.

[0178] The electronic device may include a processor 401 and a memory 402 storing programs or instructions. When the processor 401 executes the program, it implements the steps in any of the above method embodiments.

[0179] For example, the program can be divided into one or more modules / units, one or more of which are stored in memory 402 and executed by processor 401 to complete this application. The one or more modules / units can be a series of program instruction segments capable of performing a specific function, which describe the execution process of the program in the device.

[0180] Specifically, the processor 401 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0181] Memory 402 may include mass storage for data or instructions. For example, and not limitingly, memory 402 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 402 is non-volatile solid-state memory.

[0182] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) machine-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methods according to one aspect of this disclosure.

[0183] The processor 401 implements any of the methods described above by reading and executing programs or instructions stored in the memory 402.

[0184] In one example, the electronic device may also include a communication interface 403 and a bus 404. The processor 401, memory 402, and communication interface 403 are connected via the bus 404 and communicate with each other.

[0185] The communication interface 403 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0186] Bus 404 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 404 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0187] This application also provides a temperature prediction device, including the above-mentioned dynamic prediction mathematical model, which is used to predict the internal temperature of a battery cell based on the external temperature of the battery cell.

[0188] This application embodiment also provides a temperature prediction system, including: a sensor and the above-mentioned temperature prediction device. The sensor is used to collect the external temperature of the battery cell, and the temperature prediction device is connected to the sensor to obtain the external temperature of the battery cell and input the external temperature of the battery cell into a dynamic prediction mathematical model to predict the internal temperature of the battery cell.

[0189] Furthermore, in conjunction with the methods in the above embodiments, this application embodiment can provide a machine-readable storage medium for implementation. This machine-readable storage medium stores a program or instructions; when executed by a processor, the program or instructions implement any of the methods in the above embodiments. This machine-readable storage medium can be read by a machine such as a computer.

[0190] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above method embodiments and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0191] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0192] This application provides a computer program product stored in a machine-readable storage medium. The program product is executed by at least one processor to implement the various processes of the above method embodiments and achieve the same technical effects. To avoid repetition, it will not be described again here.

[0193] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0194] The functional modules shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer grids such as the Internet, intranets, etc.

[0195] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0196] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by a computer program or instructions. These programs or instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0197] Although this application has been described with reference to preferred embodiments, various modifications can be made thereto and components can be replaced with equivalents without departing from the scope of this application. In particular, the technical features mentioned in the various embodiments can be combined in any manner, provided there is no structural conflict. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for predicting the internal temperature of a single battery cell, comprising: Based on physical relationships, a model formula is established. These physical relationships include the relationship between the external temperature of the battery cell and the thermal resistance at various locations within the battery cell, as well as the conversion of chemical energy into heat energy during the charging and discharging process of the battery cell. Collect measured temperature data inside each battery cell. A measured curve is generated based on the measured temperature data. By performing curve fitting between the measured curve and the model formula, the model parameters of the model formula are obtained. Based on the model formula and the model parameters, a dynamic prediction mathematical model is established. Obtain the external temperature of the battery cell. The current external temperature of the battery cell is input into the dynamic prediction mathematical model, which then predicts the current internal temperature of the battery cell. The dynamic prediction mathematical model is used to predict the internal temperature of a battery cell based on the external temperature of the battery cell.

2. The method according to claim 1, wherein, The establishment of model formulas based on physical relationships includes: Based on physical relationships, the factors affecting temperature within a single battery cell are determined. These factors include the temperature of the central region within the battery cell and the thermal energy conversion value of the battery cell. A model formula is established based on the external temperature of the battery cell, the internal central region temperature of the battery cell, and the chemical energy conversion heat value of the battery cell.

3. The method according to claim 1, wherein, The establishment of model formulas based on physical relationships includes: A model formula is established based on physical relationships and the historical temperature inside the battery cell.

4. The method according to any one of claims 1 to 3, wherein, The process of establishing a dynamic prediction mathematical model based on the model formula and the model parameters includes: Based on the model formula and the model parameters, an initial model is established. Collect the external temperature of each battery cell within a preset time period, and the real-time internal temperature of each battery cell within a preset time period. The external temperature of the battery cell within the preset time period is input into the initial model to obtain the predicted internal temperature of the battery cell within the preset time period. If the comparison between the predicted temperature and the real-time temperature meets the preset error conditions, the initial model is determined as a dynamic prediction mathematical model.

5. The method according to claim 1, wherein, The battery cell contains N measurement locations, where N is an integer greater than or equal to 1. The step of inputting the current external temperature of the battery cell into the dynamic prediction mathematical model, and predicting the current internal temperature of the battery cell using the dynamic prediction mathematical model, includes: The current external temperature of the battery cell is input into the dynamic prediction mathematical model corresponding to the first measurement location, and the temperature at the current moment of the first measurement location is predicted by the dynamic prediction mathematical model corresponding to the first measurement location. Wherein, the first measurement position is any one of the N measurement positions.

6. A device for predicting the internal temperature of a battery cell, comprising: The formula generation module is used to establish model formulas based on physical relationships, including the relationship between the external temperature of the battery cell and the thermal resistance at various locations inside the battery cell, as well as the conversion of chemical energy into heat energy during the charging and discharging process of the battery cell. The data acquisition module is used to collect measured temperature data inside individual battery cells. The generation module is used to generate a measured curve based on the measured temperature data. The fitting module is used to perform curve fitting between the measured curve and the model formula to obtain the model parameters of the model formula. The model building module is used to build a dynamic prediction mathematical model based on the model formula and the model parameters. The acquisition module is used to acquire the external temperature of individual battery cells. The prediction module is used to input the current external temperature of the battery cell into the dynamic prediction mathematical model, and then use the dynamic prediction mathematical model to predict the current internal temperature of the battery cell. The dynamic prediction mathematical model is used to predict the internal temperature of a battery cell based on the external temperature of the battery cell.

7. The apparatus according to claim 6, wherein, The formula generation module is also used for: Based on physical relationships, the factors affecting temperature within a single battery cell are determined. These factors include the temperature of the central region within the battery cell and the thermal energy conversion value of the battery cell. A model formula is established based on the external temperature of the battery cell, the internal central region temperature of the battery cell, and the chemical energy conversion heat value of the battery cell.

8. The apparatus according to claim 6, wherein, The formula generation module is also used for: A model formula is established based on physical relationships and the historical temperature inside the battery cell.

9. The apparatus according to any one of claims 6 to 8, wherein, The model building module is also used for: Based on the model formula and the model parameters, an initial model is established. Collect the external temperature of each battery cell within a preset time period, and the real-time internal temperature of each battery cell within a preset time period. The external temperature of the battery cell within the preset time period is input into the initial model to obtain the predicted internal temperature of the battery cell within the preset time period. If the comparison between the predicted temperature and the real-time temperature meets the preset error conditions, the initial model is determined as a dynamic prediction mathematical model.

10. The apparatus according to claim 6, wherein, The battery cell includes N measurement locations, where N is an integer greater than or equal to 1. The prediction module is also used for: The current external temperature of the battery cell is input into the dynamic prediction mathematical model corresponding to the first measurement location, and the temperature at the current moment of the first measurement location is predicted by the dynamic prediction mathematical model corresponding to the first measurement location. Wherein, the first measurement position is any one of the N measurement positions.

11. A temperature prediction device, comprising: A dynamic prediction mathematical model is used to predict the internal temperature of a battery cell based on the external temperature of the battery cell. The dynamic prediction mathematical model is established based on the model formula and the model parameters of the model formula. The model formula is established based on physical relationships, including the relationship between the external temperature of the battery cell and the thermal resistance at various locations inside the battery cell, as well as the conversion of chemical energy into heat energy during the charging and discharging process of the battery cell. The model parameters are obtained by generating measured curves based on the measured temperature data inside the battery cell, and by curve fitting the measured curves with the model formula.

12. A temperature prediction system, comprising: Sensors are used to collect the external temperature of individual battery cells. The temperature prediction device as described in claim 11 is connected to the sensor and is used to acquire the external temperature of the battery cell and input the external temperature of the battery cell into a dynamic prediction mathematical model to predict the internal temperature of the battery cell.

13. An electronic device, comprising: A processor and a memory storing a program or instructions, wherein the processor, when executing the program or instructions, implements the method as described in any one of claims 1-5.

14. A machine-readable storage medium having a program or instructions stored thereon, which, when executed by a processor, implement the method as described in any one of claims 1-5.

15. A computer program product, wherein instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the method as described in any one of claims 1-5.

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