Battery cell temperature prediction method, device, computer equipment, storage medium and product

Abstract

The present application relates to a method, device, computer equipment, storage medium and product for predicting battery cell temperature. The method comprises: obtaining the surface temperature of a sampling point on the surface of the battery cell to be tested, and the position information of the sampling point on the battery cell to be tested, correcting the surface temperature of the sampling point according to the position information to obtain the corrected temperature of the sampling point, and determining the internal temperature of the battery cell to be tested according to the corrected temperature, position information and battery cell thermal conduction model of the sampling point; the battery cell thermal conduction model characterizes the thermal conduction relationship between the temperature at different positions on the surface of the battery cell and the internal temperature. In the above method, the surface temperature of the sampling point is corrected based on the position information of the sampling point, and the corrected temperature generated by the sampling point being affected by the central heat source point of the battery cell to be tested can be restored, thereby reducing the interference of the external environment, and then the internal temperature of the battery cell to be tested is inferred by using the corrected temperature, which can correspondingly improve the accuracy of the internal temperature of the battery cell to be tested.

Description

Technical Field

[0001] The present application relates to the field of battery cell technology, and in particular to a method, device, computer equipment, storage medium and product for predicting battery cell temperature. Background Art

[0002] The internal temperature of the battery cells is a key parameter for the Battery Management System (BMS) to analyze battery performance.

[0003] In the related art, the surface temperature of a sampling point on the surface of a battery cell is usually sampled to predict the internal temperature of the battery cell based on the surface temperature.

[0004] However, the prediction results of the internal temperature of the battery cell in the related art have the problem of low accuracy. Summary of the invention

[0005] Based on this, it is necessary to provide a battery cell temperature prediction method, device, computer equipment, storage medium and product to address the above technical problems.

[0006] In a first aspect, an embodiment of the present application provides a method for predicting a battery cell temperature, comprising:

[0007] Obtain the surface temperature of the sampling point on the surface of the battery cell to be tested, as well as the position information of the sampling point on the battery cell to be tested;

[0008] The surface temperature of the sampling point is corrected according to the position information to obtain the corrected temperature of the sampling point;

[0009] The internal temperature of the battery cell to be tested is determined based on the corrected temperature and position information of the sampling point and the battery cell thermal conduction model; the battery cell thermal conduction model characterizes the thermal conduction relationship between the temperature at different positions on the surface of the battery cell and the internal temperature.

[0010] In the embodiment of the present application, the surface temperature of the sampling point on the surface of the battery cell to be tested and the position information of the sampling point on the battery cell to be tested are obtained, and the surface temperature of the sampling point is corrected according to the position information to obtain the corrected temperature of the sampling point, thereby determining the internal temperature of the battery cell to be tested according to the corrected temperature, position information and battery cell thermal conduction model of the sampling point; the battery cell thermal conduction model characterizes the thermal conduction relationship between the temperature at different positions on the surface of the battery cell and the internal temperature. In the above method, the surface temperature of the sampling point is corrected based on the position information of the sampling point, and the corrected temperature generated by the sampling point being affected by the central heat source point of the battery cell to be tested can be restored, thereby reducing the interference of the external environment, and then the internal temperature of the battery cell to be tested is inferred by the corrected temperature, which can correspondingly improve the accuracy of the internal temperature of the battery cell to be tested.

[0011] In one embodiment, the surface temperature of the sampling point is corrected according to the position information to obtain the corrected temperature of the sampling point, including:

[0012] Determine the temperature contribution of all hot spots on the surface of the cell to be tested to the sampling point according to the position information; the hot spot is the location of any mechanical part on the surface of the cell to be tested;

[0013] The corrected temperature is obtained by correcting the contributing temperature surface temperature.

[0014] In the embodiment of the present application, the contribution temperature of all hot spots on the surface of the battery cell to be tested to the sampling point is determined based on the position information, and the surface temperature is corrected based on the contribution temperature to obtain the corrected temperature; the hot spot is the location of any mechanical part on the surface of the battery cell to be tested. The surface temperature of the sampling point is the result of the combined action of all hot spots on the surface of the battery cell to be tested and the internal heat source point of the battery cell to be tested. The contribution temperature of the hot spot to the sampling point is first determined, and then the surface temperature is corrected using the contribution temperature to obtain the corrected temperature of the sampling point, which improves the reliability of the sampling point temperature and correspondingly improves the reliability of the prediction result.

[0015] In one embodiment, determining the temperature contribution of all hot spots on the surface of the battery cell to be tested to the sampling point according to the position information includes:

[0016] Determine the drop temperature from each hot spot to the sampling point based on the location information;

[0017] Obtain the heat generation temperature of each hot spot;

[0018] The contribution temperature is determined based on the heat generation temperature and the drop temperature of each hot spot.

[0019] In the embodiment of the present application, the drop temperature from each hot spot to the sampling point is determined according to the position information, the heat generation temperature of each hot spot is obtained, and the contribution temperature is determined according to the heat generation temperature and the drop temperature of each hot spot. In the above method, the contribution temperature for the sampling point is determined based on the drop temperature consumed by the actual heat generation temperature during the propagation process, which simplifies the calculation process, reduces errors, and thus improves the accuracy and reliability of the obtained contribution temperature.

[0020] In one embodiment, determining the drop temperature from each hot spot to the sampling point according to the location information includes:

[0021] For each hotspot, determine the distance from the hotspot to the sampling point based on the location information;

[0022] Determine the thermal resistance coefficient between the hot spot and the sampling point based on the distance;

[0023] Determine the drop temperature based on the thermal resistance coefficient.

[0024] In the embodiment of the present application, for each hot spot, the distance from the hot spot to the sampling point is determined according to the position information, the thermal resistance coefficient between the hot spot and the sampling point is determined according to the distance, and the drop temperature is determined according to the thermal resistance coefficient. In the above method, the drop temperature from each hot spot to the sampling point is determined by using the correlation between the distance from the hot spot to the sampling point, the thermal resistance coefficient, and the drop temperature, which correspondingly improves the accuracy and reliability of the drop temperature obtained.

[0025] In one embodiment, obtaining the heat generation temperature of each heat generation point includes:

[0026] For each hot spot, obtain the thermal resistance heat and environmental exchange heat of the hot spot;

[0027] Obtain the heat difference between the thermal resistance heat and the ambient exchange heat;

[0028] The temperature corresponding to the heat difference is determined according to the corresponding relationship between heat and temperature as the heat generation temperature of the hot spot.

[0029] In the embodiment of the present application, for each hot spot, the thermal resistance heat and the ambient exchange heat of the hot spot are obtained, and the heat difference between the thermal resistance heat and the ambient exchange heat is obtained, so as to determine the temperature corresponding to the heat difference according to the corresponding relationship between heat and temperature, as the heat generation temperature of the hot spot. In the above method, the influence of overcurrent heat generation of mechanical parts and environmental heat dissipation on the heat generation temperature is taken into account, thereby improving the accuracy and reliability of the obtained heat generation temperature.

[0030] In one embodiment, the surface temperature is corrected according to the contribution temperature to obtain the corrected temperature, including:

[0031] Get the temperature difference between the surface temperature and the contribution temperature;

[0032] The correction temperature is determined based on the temperature difference.

[0033] In the embodiment of the present application, the temperature difference between the surface temperature and the contribution temperature is obtained, and the correction temperature is determined according to the temperature difference. In the above method, the correction temperature of the sampling point is determined based on the temperature difference between the surface temperature and the contribution temperature, which simplifies the correction process, improves the correction efficiency, and correspondingly improves the overall prediction efficiency.

[0034] In one embodiment, determining the corrected temperature according to the temperature difference includes:

[0035] Obtain a preset temperature difference range that matches the sampling point;

[0036] Verify the temperature difference according to the preset temperature difference range;

[0037] When the temperature difference passes the verification, the temperature difference is determined as the corrected temperature.

[0038] In the embodiment of the present application, a preset temperature difference range matching the sampling point is obtained, and the temperature difference value is verified according to the preset temperature difference range. If the temperature difference value passes the verification, the temperature difference value is determined as the correction temperature. In the above method, the temperature difference value is verified using the preset temperature difference range matching the sampling point, so that the temperature difference value that passes the verification is determined as the correction temperature, which correspondingly improves the reliability of the obtained correction temperature.

[0039] In one embodiment, determining the internal temperature of the battery cell to be tested according to the corrected temperature and position information of the sampling point and the thermal conduction model of the battery cell includes:

[0040] Determine the vector value of the sampling point in different spatial axes according to the position information; the origin of different spatial axes is the center point of the battery cell to be tested;

[0041] The corrected temperature of the sampling point and the vector value of the sampling point in different spatial axes are input into the battery cell heat conduction model to obtain the internal temperature of the battery cell to be tested.

[0042] In the embodiment of the present application, the vector value of the sampling point in different spatial axes is determined according to the position information, and the corrected temperature of the sampling point and the vector value of the sampling point in different spatial axes are input into the battery cell thermal conduction model to obtain the internal temperature of the battery cell to be tested. Among them, the origin of the different spatial axes is the center point of the battery cell to be tested. In the above method, the position information of the sampling point is decomposed in different spatial axes, which improves the accuracy of the spatial positioning of the sampling point, thereby improving the accuracy of predicting the internal temperature based on the thermal conduction relationship between the corrected temperature of the battery cell at different positions and the internal temperature.

[0043] In one embodiment, determining the vector value of the sampling point on different spatial axes according to the position information includes:

[0044] Determine the heat conduction vector from the center point of the cell to be tested to the sampling point according to the position information;

[0045] The vector values ​​of the sampling points on different spatial axes are determined according to the heat conduction vector.

[0046] In the embodiment of the present application, the heat conduction vector from the center point of the cell to be tested to the sampling point is determined according to the position information of the sampling point on the reference cell, and the vector value of the sampling point on different spatial axes is determined according to the heat conduction vector. In the above method, the position information of the sampling point is decomposed on different spatial axes using the heat conduction vector from the center point to the sampling point, and the vector value of the sampling point on different spatial axes is obtained, which improves the accuracy of the spatial positioning of the sampling point and helps to improve the accuracy and reliability of the prediction results.

[0047] In one embodiment, different spatial axes include mutually perpendicular X-axis, Y-axis and Z-axis; determining the vector value of the sampling point on different spatial axes according to the heat conduction vector includes:

[0048] Project the heat conduction vector onto the X-axis to obtain the vector value of the sampling point on the X-axis;

[0049] Project the heat conduction vector onto the Y axis to obtain the vector value of the sampling point on the Y axis;

[0050] Project the heat conduction vector onto the Z axis to obtain the vector value of the sampling point on the Z axis.

[0051] In the embodiment of the present application, different spatial axes include mutually perpendicular X-axis, Y-axis and Z-axis, and the heat conduction vector is projected to the X-axis to obtain the vector value of the sampling point on the X-axis; the heat conduction vector is projected to the Y-axis to obtain the vector value of the sampling point on the Y-axis; the heat conduction vector is projected to the Z-axis to obtain the vector value of the sampling point on the Z-axis. In the above method, the decomposition of the sampling point in the three-dimensional space is realized, so as to accurately locate the position of the sampling point in the three-dimensional space, and simultaneously improve the accuracy and reliability of the prediction results.

[0052] In a second aspect, an embodiment of the present application further provides a battery core temperature prediction device, comprising:

[0053] A data acquisition module is used to obtain the surface temperature of a sampling point on the surface of the battery cell to be tested, and the position information of the sampling point on the battery cell to be tested;

[0054] A temperature correction module is used to correct the surface temperature of the sampling point according to the position information to obtain the corrected temperature of the sampling point;

[0055] The temperature prediction module is used to determine the internal temperature of the battery cell to be tested based on the corrected temperature and position information of the sampling point and the battery cell thermal conduction model; the battery cell thermal conduction model represents the thermal conduction relationship between the temperature at different positions on the battery cell surface and the internal temperature.

[0056] In a third aspect, an embodiment of the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in the battery cell temperature prediction method provided in any embodiment of the first aspect are implemented.

[0057] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the battery cell temperature prediction method provided in any one of the embodiments in the first aspect above are implemented.

[0058] In a fifth aspect, an embodiment of the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements the steps in the battery cell temperature prediction method provided in any embodiment of the first aspect above.

[0059] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is an internal structure diagram of a computer device in one embodiment;

[0061] Figure 2 Schematic diagram of a flow chart of a method for predicting battery core temperature in one embodiment;

[0062] Figure 3 A schematic diagram of a process for determining a corrected temperature in one embodiment;

[0063] Figure 4 A schematic diagram of the structure of a battery cell to be tested in one embodiment;

[0064] Figure 5 A schematic diagram of a process for determining a contribution temperature in one embodiment;

[0065] Figure 6 is a schematic diagram of the structure of a battery cell to be tested in another embodiment;

[0066] Figure 7 A schematic diagram of a process for determining a drop temperature in one embodiment;

[0067] Figure 8 is a schematic diagram of the structure of a battery cell to be tested in another embodiment;

[0068] Fig. 9 A schematic diagram of a process for determining a heat generation temperature in one embodiment;

[0069] Fig.10 A schematic diagram of a process for determining a corrected temperature in another embodiment;

[0070] Fig.11 A schematic diagram of a process for determining a corrected temperature in another embodiment;

[0071] Fig.12 A schematic diagram of a process for determining the internal temperature in one embodiment;

[0072] Fig.13 A schematic diagram of a process for determining a vector value on a spatial axis in one embodiment;

[0073] Fig.14 is a schematic diagram of the structure of a battery cell to be tested in another embodiment;

[0074] Fig.15 A schematic diagram of a process for determining a vector value on a spatial axis in one embodiment;

[0075] Fig.16 is a schematic flow chart of a method for predicting battery core temperature in another embodiment;

[0076] Fig.17 FIG. 4 is a structural block diagram of a battery cell temperature prediction device in an embodiment. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0078] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the term "including" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0079] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0080] In the description of the embodiments of the present application, the term "and / or" is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. In the description of the embodiments of the present application, the term "plurality" refers to more than two (including two), unless otherwise clearly and specifically defined.

[0081] With the development of new energy technology, batteries have been increasingly used in people's production and life, and battery performance analysis has become a research focus for technicians. The internal temperature of the battery cell is a key parameter for the battery management system (BMS) to perform battery performance analysis.

[0082] In the related art, the surface temperature of a sampling point on the surface of the battery cell is usually directly sampled to predict the internal temperature of the battery cell based on the surface temperature.

[0083] However, the surface temperature of the sampling point obtained by direct sampling in the related art is not only affected by the heat source inside the battery cell, but also by the surrounding environmental factors, such as overcurrent heat generation of mechanical parts on the surface of the battery cell, environmental heat dissipation, etc. The surface temperature of the sampling point obtained by direct sampling cannot accurately reflect the internal temperature of the battery cell, especially the influence of overcurrent heat generation of mechanical parts on the surface temperature of the sampling point will increase with the increase of the battery cell current rate, which will further increase the prediction error.

[0084] Therefore, there is a large error between the internal temperature of the battery cell predicted in the related art and the actual internal temperature of the battery cell, resulting in low accuracy of the prediction result.

[0085] Based on this, the present application provides a method for predicting the temperature of a battery cell, which corrects the surface temperature of a sampling point obtained by direct sampling and uses the corrected temperature to predict the internal temperature of the battery cell, thereby achieving the technical effect of improving the accuracy of the prediction result.

[0086] In one embodiment, a method for predicting battery core temperature is provided. Figure 1 The computing device in is used as an example for explanation. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for predicting the temperature of a battery cell is implemented.

[0087] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a partial structure related to the embodiment of the present application, and does not constitute a limitation on the computer device to which the embodiment of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0088] In one embodiment, the present application provides a method for predicting the temperature of a battery cell, so as to predict the internal temperature of the battery cell by the surface temperature of the battery cell. Figure 2As shown, the method comprises the following steps:

[0089] S210, obtaining the surface temperature of the sampling point on the surface of the battery cell to be tested, and the position information of the sampling point on the battery cell to be tested.

[0090] The cell to be tested is a jellyroll. For example, the cell to be tested may be a rectangular cell, a cylindrical cell or a blade cell. The sampling point is any position point on the surface of the cell to be tested. The surface temperature of the sampling point is the surface temperature of the cell to be tested at the sampling point.

[0091] Optionally, the sampling point may be any position point on the top cover surface of the cell to be tested, any position point on the bottom surface of the cell to be tested, any position point on the side surface (usually with the largest area) of the cell to be tested that contacts the external cooling component, or any position point on other sides of the cell to be tested. Exemplarily, in order to facilitate sampling and reduce interference from the surrounding environment, a position point on the top cover surface may be selected as the sampling point.

[0092] The position information of the sampling point on the cell to be tested represents the positional relationship of the sampling point relative to the cell to be tested. The position information of the sampling point on the cell to be tested may include the spatial coordinates of the sampling point relative to a preset reference point on the cell to be tested. For example, the preset reference point may be a corner point, a center point of a surface, or a geometric center of the cell to be tested.

[0093] Optionally, the computer device can obtain the surface temperature of any sampling point on the surface of the battery cell to be tested through a temperature sensor, and obtain the spatial coordinates of the sampling point relative to a preset reference point in the battery cell to be tested as the position information of the sampling point on the battery cell to be tested.

[0094] S220 , correcting the surface temperature of the sampling point according to the position information to obtain a corrected temperature of the sampling point.

[0095] Among them, the surface temperature of the sampling point is not only affected by the internal heat source point (such as the internal center point) of the battery cell to be tested, but also by other environmental factors, such as environmental heat dissipation, heat generation of mechanical parts around the sampling point, etc. Therefore, it is necessary to correct the surface temperature of the sampling point to restore the actual temperature of the sampling point only under the influence of the internal heat source point of the battery cell to be tested, that is, the above-mentioned corrected temperature.

[0096] Optionally, after obtaining the surface temperature of the sampling point, the computer device may correct the surface temperature according to the sampling location information to obtain the corrected temperature of the sampling point. Generally, the surface temperature of the sampling point needs to be lowered to obtain the corrected temperature. Exemplarily, the computer device may determine the temperature difference corresponding to the location information of the sampling point according to a preset correspondence between the location and the temperature difference, and subtract the temperature difference from the surface temperature of the sampling point to correct the surface temperature of the sampling point to obtain the corrected temperature of the sampling point.

[0097] S230, determining the internal temperature of the battery cell to be tested according to the corrected temperature and position information of the sampling point and the battery cell thermal conduction model; the battery cell thermal conduction model represents the thermal conduction relationship between the temperature at different positions on the surface of the battery cell and the internal temperature.

[0098] Among them, the battery cell thermal conduction model is an equivalent thermal physics model of battery cell heat transfer, which can usually be constructed based on the battery cell heat generation calculation and Fourier's heat transfer law.

[0099] Optionally, after obtaining the corrected temperature of the sampling point, the computer device can input the corrected temperature and position information of the sampling point into a pre-built battery cell thermal conduction model, and use the thermal conduction relationship between the corrected temperature at different positions on the battery cell represented by the battery cell thermal conduction model and the internal temperature to determine the internal temperature corresponding to the sampling point as the internal temperature of the battery cell to be tested.

[0100] For example, the cell thermal conduction model can characterize the thermal conduction relationship between the corrected temperature and the internal temperature of the cell at different thermal conduction distances. The computer device can determine the thermal conduction distance corresponding to the sampling point according to the location information of the sampling point, and determine the internal temperature corresponding to the corrected temperature of the sampling point at the thermal conduction distance according to the cell thermal conduction model as the internal temperature of the cell to be tested.

[0101] It should be noted that when determining the internal temperature of the battery cell to be tested based on the battery cell thermal conduction model, in addition to inputting the surface temperature and position information of the sampling point, other relevant data can also be input, such as the current, voltage, remaining power (State Of Charge, SOC) of the battery cell to be tested, etc.

[0102] The thermal conduction model of the battery cell is constructed based on the reference battery cell. The reference battery cell is a prefabricated battery cell with a built-in thermocouple. The computer device can obtain the corrected temperatures of multiple reference sampling points on the reference battery cell, and obtain the spatial coordinates of each reference sampling point relative to the central heat source point of the reference battery cell as the position information of the corresponding reference sampling point on the reference battery cell, and at the same time obtain the internal temperature of the reference battery cell measured by the thermocouple inside the reference battery cell. The computer device can use the corrected temperatures, position information of all reference sampling points and the internal temperature of the reference battery cell, and construct a thermal conduction relationship between the corrected temperatures at different positions on the battery cell and the internal temperature based on the heat generation calculation of the battery cell and Fourier's heat transfer law, and use this thermal conduction relationship as the thermal conduction model of the battery cell.

[0103] It should be noted that, in addition to determining the internal temperature of the battery cell to be tested by using the above-mentioned equivalent thermal physical model of battery cell heat transfer, the internal temperature of the battery cell to be tested may also be determined by using a pre-trained network model.

[0104] Optionally, after obtaining the corrected temperature of the sampling point, the computer device may input the corrected temperature and position information of the sampling point into a temperature prediction model, and obtain the internal temperature corresponding to the sampling point through the temperature prediction model as the internal temperature of the battery cell to be tested.

[0105] The temperature prediction model is a pre-trained network model. Exemplarily, the temperature prediction model may be a convolutional neural network (CNN) model. When determining the internal temperature of the battery cell to be tested based on the temperature prediction model, in addition to inputting the corrected temperature and position information of the sampling point, other relevant data may also be input, such as the current, voltage, remaining power (State Of Charge, SOC) of the battery cell to be tested, etc.

[0106] In the embodiment of the present application, the surface temperature of the sampling point on the surface of the battery cell to be tested and the position information of the sampling point on the battery cell to be tested are obtained, and the surface temperature of the sampling point is corrected according to the position information to obtain the corrected temperature of the sampling point, thereby determining the internal temperature of the battery cell to be tested according to the corrected temperature, position information and thermal conduction model of the sampling point; the thermal conduction model of the battery cell characterizes the thermal conduction relationship between the temperature at different positions on the surface of the battery cell and the internal temperature. In the above method, the surface temperature of the sampling point is corrected based on the position information of the sampling point, and the corrected temperature generated by the sampling point being affected by the central heat source point of the battery cell to be tested can be restored, thereby reducing the interference of the external environment, and then the internal temperature of the battery cell to be tested is inferred by the corrected temperature, which can correspondingly improve the accuracy of the internal temperature of the battery cell to be tested. At the same time, the thermal conduction model of the battery cell is an equivalent thermal physics model that characterizes the thermal conduction relationship between the corrected temperature and the internal temperature of the battery cell, which can simulate the thermal conduction process of the battery cell, so inferring the internal temperature based on the thermal conduction relationship also improves the accuracy of the prediction result.

[0107] The mechanical parts on the surface of the battery cell to be tested will generate heat under the action of current, thereby affecting the temperature of the sampling point. Based on this, in one embodiment, Figure 3 As shown, the above S220, correcting the surface temperature of the sampling point according to the position information to obtain the corrected temperature of the sampling point, includes:

[0108] S310, determining the contribution temperature of all hot spots on the surface of the battery cell to be tested to the sampling point according to the position information; the hot spot is the position of any mechanical part on the surface of the battery cell to be tested.

[0109] Among them, the mechanical part on the surface of the battery cell to be tested is a thermal resistor arranged on the surface of the battery cell to be tested, which can generate heat under the action of current. Exemplarily, the mechanical part on the surface of the battery cell to be tested can be an earphone or a tab, and the hot spot is the location of the ear or tab. The temperature contributed by the hot spot to the sampling point is limited by the heat generated by the hot spot (i.e., the heat generated) and the distance between the hot spot and the sampling point.

[0110] Optionally, for each hotspot, the computer device can obtain the heat generation of the hotspot, read the position information of the hotspot on the surface of the battery cell to be tested, determine the distance between the hotspot and the sampling point according to the position information of the hotspot and the position information of the sampling point, determine the total heat loss value from the hotspot to the sampling point based on the distance and the pre-calibrated heat loss value per unit distance, so as to obtain the difference between the heat generation and the total heat loss value, obtain the sum of the differences of all the hotspots, and determine the temperature corresponding to the sum of the differences as the contribution temperature of all the hotspots on the surface of the battery cell to the sampling point.

[0111] For example, Figure 4As shown, the surface of the cell to be tested includes two hot spots A and B, which correspond to the positions of the left and right tabs, respectively. The computer equipment obtains the heat generation QA of the hot spot A, the heat generation QB of the hot spot B, the distance LA between the hot spot A and the sampling point S, the distance LB between the hot spot B and the sampling point S, and the pre-calibrated heat loss value △q per unit distance. The difference QA-LA*△q between the heat generation QA of the corresponding hot spot A and the total heat loss value LA*△q, and the difference QB-LA*△q between the heat generation QB of the corresponding hot spot B and the total heat loss value LB*△q are calculated. According to the corresponding relationship between heat and temperature, the temperature of the sum of the differences between the corresponding hot spots A and B (QA-LA*△q) + (QB-LA*△q) is determined as the contribution temperature T0 of the hot spots A and B to the sampling point S.

[0112] Optionally, the heat generation of the hotspot is mainly affected by the heat generation of the mechanical parts process. Therefore, the thermal resistance heat generated by the hotspot can be calculated based on the working current of the battery cell to be tested and the thermal resistance of the mechanical parts corresponding to the hotspot, so as to determine the heat generation of the sampling point based on the thermal resistance heat. If the thermal resistance heat is directly used as the heat generation, the thermal resistance heat can also be further processed to obtain the heat generation.

[0113] S320, correcting the surface temperature according to the contribution temperature to obtain a corrected temperature.

[0114] The surface temperature of the sampling point is the result of the combined effect of all the hot spots on the surface of the cell to be tested and the internal heat source points of the cell to be tested. The hot spots on the surface of the cell to be tested act on the sampling point to produce the contribution temperature, and the internal heat source points of the cell to be tested act on the sampling point to produce the correction temperature.

[0115] Optionally, after the computer device obtains the contribution temperature of the hot spot to the sampling point, it can determine the correction amount according to the contribution temperature, and obtain the surface temperature of the sampling point minus the correction amount to obtain the corrected temperature of the sampling point. The contribution temperature is positively correlated with the correction amount. Exemplarily, the ratio of the preset weight W% to the contribution temperature T0 is obtained as the correction amount, and the corrected temperature T'=T- W%*T0 of the sampling point.

[0116] In the embodiment of the present application, the contribution temperature of all hot spots on the surface of the battery cell to be tested to the sampling point is determined based on the position information, and the surface temperature is corrected based on the contribution temperature to obtain the corrected temperature; the hot spot is the location of any mechanical part on the surface of the battery cell to be tested. In the above method, the surface temperature of the sampling point is the result of the combined action of all hot spots on the surface of the battery cell to be tested and the internal heat source point of the battery cell to be tested. The contribution temperature of the hot spot to the sampling point is first determined, and then the surface temperature is corrected using the contribution temperature to obtain the corrected temperature of the sampling point, which improves the reliability of the sampling point temperature and correspondingly improves the reliability of the prediction result.

[0117] To improve the accuracy of the obtained contribution temperature, in one embodiment, as Figure 5 As shown, the above S310, determining the contribution temperature of the hot spot on the surface of the battery cell to be tested to the sampling point according to the position information, includes:

[0118] S510, determining the drop temperature from each hot spot to the sampling point according to the location information.

[0119] The drop temperature refers to the temperature lost when the temperature of the hot spot is transmitted along the shell of the battery cell to be tested to the sampling point.

[0120] Optionally, for each hot spot, the computer device can read the position information of the hot spot on the surface of the battery cell to be tested, determine the distance between the hot spot and the sampling point according to the position information of the hot spot and the position information of the sampling point, and determine the temperature loss value from the hot spot to the sampling point based on the distance and the temperature loss value per unit distance obtained in advance, as the temperature drop from the hot spot to the sampling point.

[0121] For example, Figure 6 As shown, the surface of the cell to be tested includes two hot spots A and B, which correspond to the positions of the left and right tabs. The computer equipment obtains the distance LA between the hot spot A and the sampling point S, the distance LB between the hot spot B and the sampling point S, and the pre-calibrated temperature loss value per unit distance △t, the temperature loss value from the hot spot A to the sampling point S △T1=LA*△t, the heat loss value from the hot spot B to the sampling point S △T2=LB*△t, and the computer equipment obtains the temperature drop △T1 from the hot spot A to the sampling point S, and the temperature drop △T2 from the hot spot B to the sampling point S.

[0122] S520, obtaining the heat generation temperature of each heat generation point.

[0123] The heat generation temperature of the hot spot is the surface temperature of the hot spot.

[0124] Optionally, for each hot spot, the computer device can obtain the surface temperature of the hot spot on the surface of the cell to be tested through a temperature sensor as the heat generation temperature of each hot spot, or can calculate the heat generation temperature of each hot spot in real time according to relevant parameters. Among them, the overcurrent heat generation of the mechanical parts corresponding to the hot spot is the main factor affecting the heat generation temperature.

[0125] For example, when only the overcurrent heat generation of the mechanical part affects the heat generation temperature, for each heat generation point, the computer device can obtain the thermal resistance heat of the heat generation point to determine the heat generation temperature of the heat generation point based on the thermal resistance heat. Continuing with the above example, the working current I of the battery cell to be tested, the heat generation point A corresponds to the thermal resistance R1 of the mechanical part, and the heat generation point B corresponds to the thermal resistance R2 of the mechanical part. The thermal resistance heat of the heat generation point A is calculated to be Q1=I 2R1, corresponding to the heat generation temperature TA, the thermal resistance heat of the hot spot B is Q2=I 2 R2, corresponding to the heat generation temperature TB.

[0126] S530, determining the contribution temperature according to the heat generation temperature and the drop temperature of each heat generation point.

[0127] Optionally, after the computer device obtains the heat generation temperature and the drop temperature of each hot spot, it obtains the temperature difference between the heat generation temperature and the drop temperature for each hot spot, and obtains the sum of the temperature differences of all hot spots as the contribution temperature of all hot spots to the sampling point. Exemplarily, continuing the above example, the temperature difference TA-△T1 is calculated for the hot spot A, and the temperature difference TB-△T2 is calculated for the hot spot B, and the contribution temperature T0=(TA-△T1)+(TB-△T2) of the hot spots A and B to the sampling point S is obtained accordingly.

[0128] In the embodiment of the present application, the drop temperature from each hot spot to the sampling point is determined according to the position information, the heat generation temperature of each hot spot is obtained, and the contribution temperature is determined according to the heat generation temperature and the drop temperature of each hot spot. In the above method, the contribution temperature for the sampling point is determined based on the drop temperature consumed by the actual heat generation temperature during the propagation process, which simplifies the calculation process, reduces errors, and thus improves the accuracy and reliability of the obtained contribution temperature.

[0129] The temperature drop from the hot spot to the sampling point can be determined based on the thermal resistance coefficient between the hot spot and the sampling point. Figure 7 As shown, the above S510, determining the drop temperature from each hot spot to the sampling point according to the location information, includes:

[0130] S710. For each hotspot, determine the distance from the hotspot to the sampling point according to the location information.

[0131] The distance from the hotspot to the sampling point is the straight-line distance between the hotspot and the sampling point.

[0132] Optionally, for each hot spot, the computer device may read the position information of the hot spot on the surface of the battery cell to be tested, and determine the distance between the hot spot and the sampling point according to the position information of the hot spot and the position information of the sampling point.

[0133] S720, determining the thermal resistance coefficient between the hot spot and the sampling point according to the distance.

[0134] Among them, the thermal resistance coefficient is also called thermal resistance, which is the ability of the thermal conductive material to hinder the conduction of heat flow. The larger the thermal resistance coefficient of the thermal conductive material, the stronger its ability to hinder heat conduction.

[0135] Optionally, for each hot spot, the computer device may input the distance between the hot spot and the sampling point into the heat conduction model, and the heat conduction model outputs the thermal resistance coefficient between the hot spot and the sampling point. The heat conduction model is a data model that characterizes the thermal resistance coefficient calculation process, and specifically satisfies the following formula:

[0136] Rth=L / λ*S

[0137] Rth represents the thermal resistance coefficient between two points, L is the distance between the two points (i.e., the heat conduction distance), λ is the thermal conductivity coefficient, S is the cross-sectional area of ​​the heat conduction medium, and λ and S can be preset. λ depends on the material of the heat conduction medium. The heat conduction medium in the embodiment of the present application is the battery cell housing to be tested. For example, Figure 8 As shown, the computer device can obtain the thermal resistance coefficient K1 from the hot spot A to the sampling point S, and the thermal resistance coefficient K2 from the hot spot B to the sampling point S accordingly.

[0138] S730, determining the drop temperature according to the thermal resistance coefficient.

[0139] Optionally, after obtaining the thermal resistance coefficient of each hot spot relative to the sampling point, the computer device may determine the drop temperature corresponding to each thermal resistance coefficient based on the corresponding relationship between the thermal resistance coefficient and the temperature.

[0140] In the embodiment of the present application, for each hot spot, the distance from the hot spot to the sampling point is determined according to the position information, the thermal resistance coefficient between the hot spot and the sampling point is determined according to the distance, and the drop temperature is determined according to the thermal resistance coefficient. In the above method, the drop temperature from each hot spot to the sampling point is determined by using the correlation between the distance from the hot spot to the sampling point, the thermal resistance coefficient, and the drop temperature, which correspondingly improves the accuracy and reliability of the drop temperature obtained.

[0141] In practical applications, the heat generation temperature of the hot spot is not only affected by the heat generation of the mechanical parts due to overcurrent, but also by the heat dissipation of the environment. Fig. 9 As shown, the above S520, obtaining the heat generation temperature of each hot spot, includes:

[0142] S910. For each hot spot, obtain the thermal resistance heat and the ambient exchange heat of the hot spot.

[0143] Among them, the environmental exchange heat is used to characterize the exchange heat between the mechanical parts corresponding to the hot spot and the environment. Since the mechanical parts generate heat and the temperature rises, the environmental exchange heat here can be understood as the heat dissipated to the environment, that is, the heat dissipation.

[0144] Optionally, for each hot spot, the computer device can calculate the thermal resistance heat of the hot spot according to the working current of the battery cell to be tested and the thermal resistance of the mechanical part corresponding to the hot spot, and determine the heat transfer coefficient corresponding to the hot spot according to the position information of the hot spot on the surface of the battery cell to be tested, and obtain the temperature change value of the hot spot at the current moment, so as to bring the heat transfer coefficient and temperature change value of the sampling point into the correlation relationship between the heat transfer coefficient, temperature change value and environmental exchange heat, and determine the environmental exchange heat corresponding to the hot spot. For example, Figure 8 As shown, the computer device can obtain the heat transfer coefficient K3 of the hot spot A, corresponding to the environmental exchange heat Q3, and the heat transfer coefficient K4 of the hot spot B, corresponding to the environmental exchange heat Q4.

[0145] S920: Obtain the heat difference between the heat of the thermal resistance and the heat exchanged with the environment.

[0146] Optionally, after the computer device obtains the thermal resistance heat and the ambient exchange heat of each hot spot, the heat difference between the two can be obtained. For example, continuing the above example, the thermal resistance heat Q1 and the ambient exchange heat Q3 of hot spot A, the thermal resistance heat Q2 and the ambient exchange heat Q4 of hot spot B, the computer device obtains the heat difference Q1-Q3 of hot spot A, and the heat difference Q2-Q4 of hot spot B.

[0147] S930, determining the temperature corresponding to the heat difference according to the corresponding relationship between heat and temperature, as the heat generation temperature of the hot spot.

[0148] Optionally, the computer device may read the pre-stored correspondence between heat and temperature to determine the temperature corresponding to the heat difference of the hot spot as the heat generation temperature of the hot spot, thereby obtaining the heat generation temperatures of all hot spots.

[0149] In the embodiment of the present application, for each hot spot, the thermal resistance heat and the ambient exchange heat of the hot spot are obtained, and the heat difference between the thermal resistance heat and the ambient exchange heat is obtained, so as to determine the temperature corresponding to the heat difference according to the corresponding relationship between heat and temperature, as the heat generation temperature of the hot spot. In the above method, the influence of overcurrent heat generation of mechanical parts and environmental heat dissipation on the heat generation temperature is taken into account, thereby improving the accuracy and reliability of the obtained heat generation temperature.

[0150] To simplify the correction process, in one embodiment, Fig.10 As shown, the above S320, correcting the surface temperature according to the contribution temperature to obtain the corrected temperature, includes:

[0151] S1010, obtaining a temperature difference between the surface temperature and the contribution temperature.

[0152] Optionally, after the computer device obtains the contribution temperature of all hot spots to the sampling point, the difference between the surface temperature of the sampling point and the contribution temperature can be obtained. For example, the surface temperature T of the sampling point S, the contribution temperature T0, the difference T-T0 between the surface temperature and the contribution temperature is calculated.

[0153] S1020: Determine a correction temperature according to the temperature difference.

[0154] Optionally, the computer device may directly use the temperature difference as the corrected temperature of the sampling point, or may further process the temperature difference to obtain the corrected temperature of the sampling point.

[0155] In the embodiment of the present application, the temperature difference between the surface temperature and the contribution temperature is obtained, and the correction temperature is determined according to the temperature difference. In the above method, the correction temperature of the sampling point is determined based on the temperature difference between the surface temperature and the contribution temperature, which simplifies the correction process, improves the correction efficiency, and correspondingly improves the overall prediction efficiency.

[0156] To improve the reliability of the obtained corrected temperature, in one embodiment, as Fig.11 As shown, the above S1020, determining the corrected temperature according to the temperature difference, includes:

[0157] S1110. Obtain a preset temperature difference range that matches the sampling point.

[0158] The preset temperature difference range is the temperature difference range between the surface temperature of the sampling point and the corrected temperature obtained based on a large amount of measured data. The preset temperature difference range that matches the sampling point is the preset temperature difference range that corresponds to the same surface of the sampling point on the cell to be tested, the type of cell to be tested, and the operating parameters (such as current) as the sampling point.

[0159] For example, taking the top cover as an example, the preset temperature difference ranges corresponding to the cell types and currents of different cells to be tested are shown in the following table:

[0160]

[0161] Optionally, the computer device may first determine the surface to which the sampling point belongs on the battery cell to be tested, as well as the battery cell type and operating parameters of the battery cell to be tested, to obtain a preset temperature difference range corresponding to the surface, battery cell type and operating parameters as the preset temperature difference range matching the sampling point.

[0162] S1120, verifying the temperature difference according to a preset temperature difference range.

[0163] Optionally, the computer device may obtain the difference between the surface temperature of the sampling point and the temperature difference, and compare the difference with a preset temperature difference range to verify the temperature difference obtained by the difference. Exemplarily, when the difference falls within the preset temperature difference range, the computer device determines that the temperature difference corresponding to the difference has passed the verification; when the difference does not fall within the preset temperature difference range, the computer device determines that the temperature difference corresponding to the difference has not passed the verification.

[0164] S1130. When the temperature difference passes the verification, determine the temperature difference as the corrected temperature.

[0165] Optionally, if the temperature difference passes verification, the computer device may directly determine the temperature difference as the corrected temperature. If the temperature difference fails verification, the computer device may further process the temperature difference, or re-determine the temperature difference until the temperature difference passes verification.

[0166] In the embodiment of the present application, a preset temperature difference range matching the sampling point is obtained, and the temperature difference value is verified according to the preset temperature difference range. If the temperature difference value passes the verification, the temperature difference value is determined as the correction temperature. In the above method, the temperature difference value is verified using the preset temperature difference range matching the sampling point, so that the temperature difference value that passes the verification is determined as the correction temperature, which correspondingly improves the reliability of the obtained correction temperature.

[0167] The battery core heat conduction model actually simulates the heat conduction process from the central heat source point in the battery core to the surface of the battery core. Based on this, in one embodiment, Fig.12 As shown, the internal temperature of the battery cell to be tested is determined based on the corrected temperature of the sampling point, the position information and the battery cell heat conduction model, including:

[0168] S1210. Determine the vector value of the sampling point on different spatial axes according to the position information; the origin of the different spatial axes is the center point of the battery cell to be tested.

[0169] The center point of the cell to be tested is the center heat source point of the cell to be tested.

[0170] Optionally, after obtaining the position information of the sampling point, the computer device may perform spatial transformation on the position information to obtain the vector value of the sampling point on each spatial axis with the central heat source point as the origin. Exemplarily, when the position information of the sampling point is the spatial coordinate of the sampling point relative to the central heat source point, the computer device may project the sampling point to each spatial axis according to the spatial coordinate of the sampling point, and use the coordinate value of the sampling point on each spatial axis as the vector value of the sampling point on the corresponding spatial vector; when the position information of the sampling point is the spatial coordinate of the sampling point relative to a non-central heat source point, the computer device may first convert the spatial coordinate of the sampling point into the spatial coordinate relative to the central heat source point, and then use the above process to determine the vector value of the sampling point on each spatial axis.

[0171] S1220, input the corrected temperature of the sampling point and the vector value of the sampling point in different spatial axes into the battery cell heat conduction model to obtain the internal temperature of the battery cell to be tested.

[0172] Optionally, after obtaining the corrected temperature of the sampling point and the vector value of the sampling point on each spatial axis, the computer can input the corrected temperature of the sampling point and each vector value into the battery cell thermal conduction model, and use the thermal conduction relationship between the corrected temperature at different positions on the battery cell represented by the battery cell thermal conduction model and the internal temperature to determine the internal temperature corresponding to the sampling point as the internal temperature of the battery cell to be tested.

[0173] It should be noted that there is more than one internal temperature of the battery cell to be tested. In one embodiment, the internal temperature of the battery cell to be tested includes an internal maximum temperature and an internal minimum temperature. The battery cell thermal conduction model characterizes the thermal conduction relationship between the corrected temperature at different positions on the battery cell and the internal maximum temperature and the internal minimum temperature of the battery cell.

[0174] Affected by the external environment of the battery cell (such as placement and connection relationship), the internal temperature of the battery cell is also unevenly distributed, and there is a corresponding maximum internal temperature and a minimum internal temperature. Usually, the maximum internal temperature of the battery cell is the temperature at the location of the central heat source. The minimum internal temperature of the battery cell is the temperature at the location closest to the external cooling component in the battery cell. Exemplarily, the external cooling component can be a cooling plate in contact with the surface (such as a large surface) of the battery cell.

[0175] In the embodiment of the present application, the vector value of the sampling point in different spatial axes is determined according to the position information, and the corrected temperature of the sampling point and the vector value of the sampling point in different spatial axes are input into the battery cell thermal conduction model to obtain the internal temperature of the battery cell to be tested. Among them, the origin of the different spatial axes is the center point of the battery cell to be tested. In the above method, the position information of the sampling point is decomposed in different spatial axes, which improves the accuracy of the spatial positioning of the sampling point, thereby improving the accuracy of predicting the internal temperature based on the thermal conduction relationship between the corrected temperature of the battery cell at different positions and the internal temperature.

[0176] The vector values ​​of the sampling points on different spatial axes can be determined based on the heat conduction vector from the center point of the tested battery cell to the sampling point. Fig.13 As shown, in the above S1210, the vector values ​​of the sampling points on different spatial axes are determined according to the position information, including:

[0177] S1310 , determining a heat conduction vector from the center point of the cell to be tested to the sampling point according to the position information.

[0178] Optionally, the computer device may determine the sampling point in the cell to be tested according to the position information of the sampling point on the cell to be tested, and then connect the center point in the cell to be tested with the sampling point to form a heat conduction vector pointing from the center point to the sampling point. Fig.14 As shown in the figure, O is the center point of the cell to be tested, and S is the sampling point on the surface of the cell to be tested. The center point O and the sampling point S are connected to form a heat conduction vector .

[0179] S1320. Determine the vector values ​​of the sampling points on different spatial axes according to the heat conduction vector.

[0180] Optionally, after obtaining the heat conduction vector of the sampling point, the computer device may project the heat conduction vector of the sampling point onto the corresponding spatial axis to obtain the coordinate values ​​of the sampling point on different spatial axes as the vector value of the sampling point on the corresponding spatial axis.

[0181] In the embodiment of the present application, the heat conduction vector from the center point of the cell to be tested to the sampling point is determined according to the position information of the sampling point on the reference cell, and the vector value of the sampling point on different spatial axes is determined according to the heat conduction vector. In the above method, the position information of the sampling point is decomposed on different spatial axes using the heat conduction vector from the center point to the sampling point, and the vector value of the sampling point on different spatial axes is obtained, which improves the accuracy of the spatial positioning of the sampling point and helps to improve the accuracy and reliability of the prediction results.

[0182] In the case where different spatial axes include mutually perpendicular X-axis, Y-axis and Z-axis, in one embodiment, as Fig.15As shown, the above S1320, determining the vector value of the sampling point on different spatial axes according to the heat conduction vector, includes:

[0183] S1510, project the heat conduction vector onto the X-axis to obtain the vector value of the sampling point on the X-axis.

[0184] Among them, the X-axis, Y-axis and Z-axis are the three coordinate axes of the space coordinate system formed with the center point of the cell to be tested as the origin. The heat conduction vector is a line segment vector with the center point as the starting point and the sampling point as the end point. Therefore, the heat conduction vector can be directly projected to the X-axis, Y-axis and Z-axis respectively to obtain the vector values ​​of the sampling point on the X-axis, Y-axis and Z-axis respectively.

[0185] Optionally, the computer device projects the heat conduction vector onto the X-axis to obtain a coordinate value of the sampling point on the X-axis as a vector value of the sampling point on the X-axis.

[0186] S1520, project the heat conduction vector onto the Y axis to obtain the vector value of the sampling point on the Y axis.

[0187] Optionally, along with the X-axis, the computer device projects the heat conduction vector onto the Y-axis to obtain the coordinate value of the sampling point on the Y-axis as the vector value of the sampling point on the Y-axis.

[0188] S1530, project the heat conduction vector onto the Z axis to obtain the vector value of the sampling point on the Z axis.

[0189] Optionally, as with the X-axis and the Y-axis, the computer device projects the heat conduction vector onto the Z-axis to obtain the coordinate value of the sampling point on the Z-axis as the vector value of the sampling point on the Z-axis.

[0190] In the embodiment of the present application, different spatial axes include mutually perpendicular X-axis, Y-axis and Z-axis, and the heat conduction vector is projected to the X-axis to obtain the vector value of the sampling point on the X-axis; the heat conduction vector is projected to the Y-axis to obtain the vector value of the sampling point on the Y-axis; the heat conduction vector is projected to the Z-axis to obtain the vector value of the sampling point on the Z-axis. In the above method, the decomposition of the sampling point in the three-dimensional space is realized, so as to accurately locate the position of the sampling point in the three-dimensional space, and simultaneously improve the accuracy and reliability of the prediction results.

[0191] In order to facilitate the understanding of those skilled in the art, the battery core temperature prediction method provided in this application is described in detail below. Fig.16 As shown, the method may include:

[0192] S1601, obtaining the surface temperature of the sampling point on the surface of the battery cell to be tested, and the position information of the sampling point on the battery cell to be tested;

[0193] S1602, determining the distance from each hot spot on the surface of the cell to be tested to the sampling point according to the position information of the sampling point; the hot spot is the position of any mechanical part on the surface of the cell to be tested;

[0194] S1603, determining the thermal resistance coefficient between each hot spot and the sampling point according to the distance;

[0195] S1604, determining the drop temperature from each hot spot to the sampling point according to the thermal resistance coefficient;

[0196] S1605, for each hot spot, obtaining the thermal resistance heat and the ambient exchange heat of the hot spot, and obtaining the heat difference between the thermal resistance heat and the ambient exchange heat;

[0197] S1606, determining the temperature corresponding to the heat difference according to the corresponding relationship between heat and temperature, as the heat generation temperature of the hot spot;

[0198] S1607, determining the contribution temperature of all hot spots to the sampling point according to the heat generation temperature and drop temperature of each hot spot;

[0199] S1608, obtaining the temperature difference between the surface temperature of the sampling point and the contribution temperature;

[0200] S1609, obtaining a preset temperature difference range that matches the sampling point, and verifying the temperature difference value according to the preset temperature difference range;

[0201] S1610, when the temperature difference passes the verification, determining the temperature difference as the corrected temperature;

[0202] S1611, determining a heat conduction vector from the center point of the cell to be tested to the sampling point according to the position information;

[0203] S1612, projecting the heat conduction vector onto the X-axis to obtain the vector value of the sampling point on the X-axis; projecting the heat conduction vector onto the Y-axis to obtain the vector value of the sampling point on the Y-axis; projecting the heat conduction vector onto the Z-axis to obtain the vector value of the sampling point on the Z-axis; the X-axis, the Y-axis and the Z-axis are perpendicular to each other;

[0204] S1613. Input the corrected temperature of the sampling point and the vector value of the sampling point in different spatial axes into the battery cell thermal conduction model to obtain the internal temperature of the battery cell to be tested; the battery cell thermal conduction model represents the thermal conduction relationship between the temperature at different positions on the surface of the battery cell and the internal temperature.

[0205] It should be noted that for the description in the above S1601-S1613, reference can be made to the relevant description in the above embodiment, and the effects are similar, so this embodiment will not be repeated here.

[0206] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0207] In one embodiment, Fig.17 As shown, a battery core temperature prediction device is provided, including: a data acquisition module 1701, a temperature correction module 1702 and a temperature prediction module 1703; wherein:

[0208] The data acquisition module 1701 is used to obtain the surface temperature of the sampling point on the surface of the battery cell to be tested, and the position information of the sampling point on the battery cell to be tested;

[0209] The temperature correction module 1702 is used to correct the surface temperature of the sampling point according to the position information to obtain the corrected temperature of the sampling point;

[0210] The temperature prediction module 1703 is used to determine the internal temperature of the cell to be tested based on the corrected temperature and position information of the sampling point and the cell thermal conduction model; the cell thermal conduction model represents the thermal conduction relationship between the temperature at different positions on the cell surface and the internal temperature.

[0211] In one embodiment, the temperature correction module 1702 includes:

[0212] The contribution submodule is used to determine the contribution temperature of all hot spots on the surface of the battery cell to be tested to the sampling point according to the position information; the hot spot is the location of any mechanical part on the surface of the battery cell to be tested;

[0213] The correction submodule is used to perform correction according to the contributing temperature surface temperature to obtain the corrected temperature.

[0214] In one embodiment, the contribution submodule includes:

[0215] A drop determination unit, used to determine the drop temperature from each hot spot to the sampling point according to the position information;

[0216] A heat generation acquisition unit, used to obtain the heat generation temperature of each heat generation point;

[0217] The contribution determination unit is used to determine the contribution temperature according to the heat generation temperature and the drop temperature of each heat generation point.

[0218] In one embodiment, the drop determination unit includes:

[0219] The distance subunit is used to determine the distance from each hotspot to the sampling point according to the location information;

[0220] The thermal resistance subunit is used to determine the thermal resistance coefficient between the hot spot and the sampling point according to the distance;

[0221] The temperature subunit is used to determine the drop temperature based on the thermal resistance coefficient.

[0222] In one embodiment, the heat generating unit comprises:

[0223] The heat subunit is used to obtain the thermal resistance heat and environmental exchange heat of each hot spot;

[0224] A difference subunit, used to obtain the heat difference between the heat of the thermal resistance and the heat exchanged with the environment;

[0225] The heat generating subunit is used to determine the temperature corresponding to the heat difference according to the corresponding relationship between heat and temperature, as the heat generating temperature of the heat generating point.

[0226] In one embodiment, the correction submodule includes:

[0227] A difference acquisition unit, used for acquiring the temperature difference between the surface temperature and the contribution temperature;

[0228] The correction determination unit is used to determine the correction temperature according to the temperature difference.

[0229] In one embodiment, the correction determination unit includes:

[0230] The temperature difference range subunit is used to obtain a preset temperature difference range that matches the sampling point;

[0231] A difference verification subunit, used to verify the temperature difference according to a preset temperature difference range;

[0232] The temperature determination subunit is used to determine the temperature difference as the correction temperature when the temperature difference passes the verification.

[0233] In one embodiment, the temperature prediction module 1703 includes:

[0234] The vector submodule is used to determine the vector value of the sampling point on different spatial axes according to the position information; the origin of different spatial axes is the center point of the battery cell to be tested;

[0235] The prediction submodule is used to input the corrected temperature of the sampling point and the vector value of the sampling point in different spatial axes into the battery cell thermal conduction model to obtain the internal temperature of the battery cell to be tested.

[0236] In one embodiment, the vector submodule includes:

[0237] A pointing unit, used to determine a heat conduction vector pointing from the center point of the cell to be tested to the sampling point according to the position information;

[0238] The numerical unit is used to determine the vector value of the sampling point in different spatial axes according to the heat conduction vector.

[0239] In one embodiment, the different spatial axes include an X-axis, a Y-axis, and a Z-axis that are perpendicular to each other; and the numerical unit is used for:

[0240] A first projection subunit is used to project the heat conduction vector onto the X-axis to obtain a vector value of the sampling point on the X-axis;

[0241] A second projection subunit is used to project the heat conduction vector onto the Y axis to obtain a vector value of the sampling point on the Y axis;

[0242] The third projection subunit is used to project the heat conduction vector onto the Z axis to obtain the vector value of the sampling point on the Z axis.

[0243] Each module in the above-mentioned battery core temperature prediction device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0244] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0245] The surface temperature of the sampling point on the surface of the battery cell to be tested and the position information of the sampling point on the battery cell to be tested are obtained; the surface temperature of the sampling point is corrected according to the position information to obtain the corrected temperature of the sampling point; the internal temperature of the battery cell to be tested is determined according to the corrected temperature and position information of the sampling point and the battery cell thermal conduction model; the battery cell thermal conduction model characterizes the thermal conduction relationship between the temperature at different positions on the surface of the battery cell and the internal temperature.

[0246] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0247] Determine the contribution temperature of all hot spots on the surface of the cell to be tested to the sampling point based on the position information; the hot spot is the location of any mechanical part on the surface of the cell to be tested; and make corrections based on the contribution temperature surface temperature to obtain the corrected temperature.

[0248] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0249] Determine the drop temperature from each hot spot to the sampling point according to the location information; obtain the heat generation temperature of each hot spot; and determine the contribution temperature according to the heat generation temperature and drop temperature of each hot spot.

[0250] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0251] For each hot spot, the distance from the hot spot to the sampling point is determined based on the location information; the thermal resistance coefficient between the hot spot and the sampling point is determined based on the distance; and the drop temperature is determined based on the thermal resistance coefficient.

[0252] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0253] For each hot spot, the thermal resistance heat and the ambient exchange heat of the hot spot are obtained; the heat difference between the thermal resistance heat and the ambient exchange heat is obtained; and the temperature corresponding to the heat difference is determined according to the corresponding relationship between heat and temperature as the heat generation temperature of the hot spot.

[0254] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0255] The temperature difference between the surface temperature and the contribution temperature is obtained; and the correction temperature is determined according to the temperature difference.

[0256] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0257] A preset temperature difference range matching the sampling point is obtained; the temperature difference value is verified according to the preset temperature difference range; when the temperature difference value passes the verification, the temperature difference value is determined as the correction temperature.

[0258] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0259] The vector values ​​of the sampling points on different spatial axes are determined according to the position information; the origins of the different spatial axes are the center points of the battery cell to be tested; the corrected temperatures of the sampling points and the vector values ​​of the sampling points on different spatial axes are input into the battery cell thermal conduction model to obtain the internal temperature of the battery cell to be tested.

[0260] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0261] The heat conduction vector pointing from the center point of the cell to be tested to the sampling point is determined according to the position information; and the vector value of the sampling point on different spatial axes is determined according to the heat conduction vector.

[0262] In one embodiment, the different spatial axes include an X-axis, a Y-axis, and a Z-axis that are perpendicular to each other; when the processor executes the computer program, the following steps are also implemented:

[0263] Project the heat conduction vector to the X-axis to obtain the vector value of the sampling point on the X-axis; project the heat conduction vector to the Y-axis to obtain the vector value of the sampling point on the Y-axis; project the heat conduction vector to the Z-axis to obtain the vector value of the sampling point on the Z-axis.

[0264] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0265] The surface temperature of the sampling point on the surface of the battery cell to be tested and the position information of the sampling point on the battery cell to be tested are obtained; the surface temperature of the sampling point is corrected according to the position information to obtain the corrected temperature of the sampling point; the internal temperature of the battery cell to be tested is determined according to the corrected temperature and position information of the sampling point and the battery cell thermal conduction model; the battery cell thermal conduction model characterizes the thermal conduction relationship between the temperature at different positions on the surface of the battery cell and the internal temperature.

[0266] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0267] Determine the contribution temperature of all hot spots on the surface of the cell to be tested to the sampling point based on the position information; the hot spot is the location of any mechanical part on the surface of the cell to be tested; and make corrections based on the contribution temperature surface temperature to obtain the corrected temperature.

[0268] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0269] Determine the drop temperature from each hot spot to the sampling point according to the location information; obtain the heat generation temperature of each hot spot; and determine the contribution temperature according to the heat generation temperature and drop temperature of each hot spot.

[0270] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0271] For each hot spot, the distance from the hot spot to the sampling point is determined based on the location information; the thermal resistance coefficient between the hot spot and the sampling point is determined based on the distance; and the drop temperature is determined based on the thermal resistance coefficient.

[0272] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0273] For each hot spot, the thermal resistance heat and the ambient exchange heat of the hot spot are obtained; the heat difference between the thermal resistance heat and the ambient exchange heat is obtained; and the temperature corresponding to the heat difference is determined according to the corresponding relationship between heat and temperature as the heat generation temperature of the hot spot.

[0274] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0275] The temperature difference between the surface temperature and the contribution temperature is obtained; and the correction temperature is determined according to the temperature difference.

[0276] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0277] A preset temperature difference range matching the sampling point is obtained; the temperature difference value is verified according to the preset temperature difference range; when the temperature difference value passes the verification, the temperature difference value is determined as the correction temperature.

[0278] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0279] The vector values ​​of the sampling points on different spatial axes are determined according to the position information; the origins of the different spatial axes are the center points of the battery cell to be tested; the corrected temperatures of the sampling points and the vector values ​​of the sampling points on different spatial axes are input into the battery cell thermal conduction model to obtain the internal temperature of the battery cell to be tested.

[0280] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0281] The heat conduction vector pointing from the center point of the cell to be tested to the sampling point is determined according to the position information; and the vector value of the sampling point on different spatial axes is determined according to the heat conduction vector.

[0282] In one embodiment, the different spatial axes include mutually perpendicular X-axis, Y-axis and Z-axis; when the computer program is executed by the processor, the following steps are also implemented:

[0283] Project the heat conduction vector to the X-axis to obtain the vector value of the sampling point on the X-axis; project the heat conduction vector to the Y-axis to obtain the vector value of the sampling point on the Y-axis; project the heat conduction vector to the Z-axis to obtain the vector value of the sampling point on the Z-axis.

[0284] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0285] The surface temperature of the sampling point on the surface of the battery cell to be tested and the position information of the sampling point on the battery cell to be tested are obtained; the surface temperature of the sampling point is corrected according to the position information to obtain the corrected temperature of the sampling point; the internal temperature of the battery cell to be tested is determined according to the corrected temperature and position information of the sampling point and the battery cell thermal conduction model; the battery cell thermal conduction model characterizes the thermal conduction relationship between the temperature at different positions on the surface of the battery cell and the internal temperature.

[0286] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0287] Determine the contribution temperature of all hot spots on the surface of the cell to be tested to the sampling point based on the position information; the hot spot is the location of any mechanical part on the surface of the cell to be tested; and make corrections based on the contribution temperature surface temperature to obtain the corrected temperature.

[0288] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0289] Determine the drop temperature from each hot spot to the sampling point according to the location information; obtain the heat generation temperature of each hot spot; and determine the contribution temperature according to the heat generation temperature and drop temperature of each hot spot.

[0290] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0291] For each hot spot, the distance from the hot spot to the sampling point is determined based on the location information; the thermal resistance coefficient between the hot spot and the sampling point is determined based on the distance; and the drop temperature is determined based on the thermal resistance coefficient.

[0292] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0293] For each hot spot, the thermal resistance heat and the ambient exchange heat of the hot spot are obtained; the heat difference between the thermal resistance heat and the ambient exchange heat is obtained; and the temperature corresponding to the heat difference is determined according to the corresponding relationship between heat and temperature as the heat generation temperature of the hot spot.

[0294] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0295] The temperature difference between the surface temperature and the contribution temperature is obtained; and the correction temperature is determined according to the temperature difference.

[0296] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0297] A preset temperature difference range matching the sampling point is obtained; the temperature difference value is verified according to the preset temperature difference range; when the temperature difference value passes the verification, the temperature difference value is determined as the correction temperature.

[0298] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0299] The vector values ​​of the sampling points on different spatial axes are determined according to the position information; the origins of the different spatial axes are the center points of the battery cell to be tested; the corrected temperatures of the sampling points and the vector values ​​of the sampling points on different spatial axes are input into the battery cell thermal conduction model to obtain the internal temperature of the battery cell to be tested.

[0300] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0301] The heat conduction vector pointing from the center point of the cell to be tested to the sampling point is determined according to the position information; and the vector value of the sampling point on different spatial axes is determined according to the heat conduction vector.

[0302] In one embodiment, the different spatial axes include mutually perpendicular X-axis, Y-axis and Z-axis; when the computer program is executed by the processor, the following steps are also implemented:

[0303] Project the heat conduction vector to the X-axis to obtain the vector value of the sampling point on the X-axis; project the heat conduction vector to the Y-axis to obtain the vector value of the sampling point on the Y-axis; project the heat conduction vector to the Z-axis to obtain the vector value of the sampling point on the Z-axis.

[0304] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0305] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0306] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for predicting battery core temperature, characterized in that: The method comprises: Acquire the surface temperature of a sampling point on the surface of the battery cell to be tested, and the position information of the sampling point on the battery cell to be tested; Determine the contribution temperature of all hot spots on the surface of the battery cell to be tested to the sampling point according to the position information; the hot spot is the location of any mechanical part on the surface of the battery cell to be tested; Obtaining a temperature difference between the surface temperature and the contribution temperature, and obtaining a preset temperature difference range matching the sampling point; Verifying the temperature difference value according to the preset temperature difference range; In the case where the temperature difference passes the verification, determining the temperature difference as the corrected temperature of the sampling point; The internal temperature of the battery cell to be tested is determined according to the corrected temperature of the sampling point, the position information and the battery cell thermal conduction model; the battery cell thermal conduction model characterizes the thermal conduction relationship between the temperature at different positions on the surface of the battery cell and the internal temperature.

2. The method according to claim 1, characterized in that Determining the temperature contributed by all hot spots on the surface of the battery cell to be tested to the sampling point according to the position information includes: Determine the drop temperature from each of the hot spots to the sampling point according to the position information; Obtaining the heat generation temperature of each of the heat generation points; The contribution temperature is determined according to the heat generation temperature and the drop temperature of each of the heat generation points.

3. The method according to claim 2, characterized in that The step of determining the drop temperature from each of the hot spots to the sampling point according to the position information includes: For each of the hot spots, determining the distance from the hot spot to the sampling point according to the position information; Determine the thermal resistance coefficient between the hot spot and the sampling point according to the distance; The drop temperature is determined according to the thermal resistance coefficient.

4. The method according to claim 2, characterized in that: The obtaining of the heat generation temperature of each of the heat generation points comprises: For each of the hot spots, obtaining the thermal resistance heat and the ambient exchange heat of the hot spot; Obtaining a heat difference between the heat of the thermal resistance and the heat exchanged with the environment; The temperature corresponding to the heat difference is determined according to the corresponding relationship between heat and temperature as the heat generation temperature of the hot spot.

5. The method according to any one of claims 1 to 4, characterized in that The step of determining the internal temperature of the battery cell to be tested according to the corrected temperature of the sampling point, the position information and the battery cell heat conduction model includes: Determine the vector value of the sampling point on different spatial axes according to the position information; the origin of the different spatial axes is the center point of the battery cell to be tested; The corrected temperature of the sampling point and the vector value of the sampling point in different spatial axes are input into the battery cell heat conduction model to obtain the internal temperature of the battery cell to be measured.

6. The method according to claim 5, characterized in that The determining, according to the position information, the vector value of the sampling point on different spatial axes includes: Determine a heat conduction vector from the center point of the battery cell to be tested to the sampling point according to the position information; The vector values ​​of the sampling points in different spatial axes are determined according to the heat conduction vector.

7. The method according to claim 6, characterized in that The different spatial axes include an X-axis, a Y-axis, and a Z-axis that are perpendicular to each other; and determining the vector value of the sampling point on the different spatial axes according to the heat conduction vector includes: Projecting the heat conduction vector onto the X-axis to obtain a vector value of the sampling point on the X-axis; Projecting the heat conduction vector onto the Y axis to obtain a vector value of the sampling point on the Y axis; The heat conduction vector is projected onto the Z axis to obtain a vector value of the sampling point on the Z axis.

8. A battery core temperature prediction device, characterized in that: The device comprises: A data acquisition module, used to acquire the surface temperature of a sampling point on the surface of the battery cell to be tested, and the position information of the sampling point on the battery cell to be tested; A temperature correction module, used to determine the contribution temperature of all hot spots on the surface of the battery cell to be tested to the sampling point according to the position information, and obtain the temperature difference between the surface temperature and the contribution temperature, and obtain a preset temperature difference range matching the sampling point; verify the temperature difference according to the preset temperature difference range; if the temperature difference passes the verification, determine the temperature difference as the correction temperature of the sampling point; the hot spot is the location of any mechanical part on the surface of the battery cell to be tested; The temperature prediction module is used to determine the internal temperature of the battery cell to be tested according to the corrected temperature of the sampling point, the position information and the battery cell thermal conduction model; the battery cell thermal conduction model characterizes the thermal conduction relationship between the temperature at different positions on the surface of the battery cell and the internal temperature.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images