Battery temperature control method and device, medium and electronic equipment

Abstract

The invention provides a battery temperature control method and device, a medium and electronic equipment. An electrothermal coupling model is constructed based on an off-line resistance and capacitance parameter identification method, a thermoelectric similarity principle, a three-dimensional unsteady state heat transfer theory and a Bernardi heat generation rate formula, the electrothermal coupling model is adopted to process initial data of a battery to obtain a battery temperature field, and the battery temperature field is calculated according to the battery temperature field. The battery overall temperature prediction value at the target time point is determined, so that compared with an existing scheme, a large amount of computing power requirements are omitted for temperature prediction, and finally the first processing mode or the second processing mode is executed according to the size of the battery overall temperature prediction value. Therefore, the problems that in an existing scheme, a large amount of computing power is needed in the process of controlling the TMS to stop the battery temperature to reduce power consumption, and operation on embedded software is difficult are solved.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a battery temperature control method, device, medium and electronic device. Background Art

[0002] The TMS system can effectively control the battery temperature and ensure that the battery operates within a suitable temperature range, thereby improving its safety and service life. However, the energy consumed in the temperature adjustment process is a considerable burden on the battery system. This not only reduces the overall endurance, but also has a significant impact on the market competitiveness of the product.

[0003] When the BMS detects that the temperature of the battery cell is abnormal at the current moment, it sends a temperature control (heating / cooling) instruction to the TMS, and the TMS processes it according to the current state. However, due to the limitations of the NTC layout inside the battery box, most of them are arranged on the surface of the battery cell, and it takes a long time for the battery to generate (absorb) heat and completely diffuse it to the entire battery cell, so its sensor temperature feedback has serious lag. As a result, the battery temperature is overheated or underheated after the TMS system intervenes, wasting a lot of energy and reducing the overall cruising range of the vehicle.

[0004] The existing solution CN117648790A provides a temperature analysis method for a water-cooled battery pack, which includes constructing an electrothermal coupling model and a water-cooling plate model, inputting the battery heat dissipation temperature output by the water-cooling plate model at the previous moment of the current moment into the electrothermal coupling model, the electrothermal coupling model outputs the battery heat generation temperature at the current moment, and the battery heat generation temperature is input into the water-cooling plate model, the water-cooling plate model outputs the battery heat dissipation temperature at the current moment, and the battery heat generation temperature at the current moment is used as the input of the electrothermal coupling model at the next moment, and the cycle is repeated until the heat generation temperature of each battery at multiple moments is obtained, and then the temperature rise simulation curve is obtained.

[0005] Existing solutions require a lot of computing power in the process of controlling the TMS system to stop the battery temperature to reduce power consumption, which is difficult to run on embedded software. Summary of the invention

[0006] The main purpose of the present application is to provide a battery temperature control method, device, medium and electronic device to at least solve the problem that the existing solution requires a lot of computing power in the process of controlling the TMS system to stop the battery temperature to reduce power consumption, and is difficult to run on embedded software.

[0007] In order to achieve the above object, according to one aspect of the present application, a method for controlling battery temperature is provided, the method comprising:

[0008] Acquiring initial battery data, wherein the initial battery data includes voltage, current and temperature of the battery;

[0009] Based on the offline identification method of resistance and capacitance parameters, the thermoelectric similarity principle, the three-dimensional unsteady heat transfer theory and the Bernardi heat generation rate formula, an electrothermal coupling model is constructed.

[0010] The initial battery data is processed using the electrothermal coupling model to obtain a battery temperature field, and a predicted value of the overall battery temperature at a target time point is determined based on the battery temperature field;

[0011] According to the size of the predicted value of the overall battery temperature, a first processing method or a second processing method is executed. The first processing method indicates that it is not necessary to use the TMS system to dissipate heat for the battery and the TMS system is controlled to stop running. The second processing method indicates that it is necessary to use the TMS system to continue to dissipate heat for the battery and the TMS system is kept running.

[0012] Optionally, in the process of constructing the electrothermal coupling model based on the offline identification method of resistance and capacitance parameters, the thermoelectric similarity principle, the three-dimensional unsteady-state heat transfer theory and the Bernardi heat generation rate formula, the method further includes:

[0013] Based on the offline method for identifying resistance and capacitance parameters, a first formula is constructed:

[0014]

[0015] Where, I is the current of the battery in the second-order RC equivalent circuit, E is the terminal voltage of the battery, and U ocv is the open circuit voltage of the battery, t is the time, U1 is the voltage across the first RC module in the second-order RC equivalent circuit, U2 is the voltage across the second RC module in the second-order RC equivalent circuit, C1 is the capacitance of the first RC module, C2 is the capacitance of the second RC module, R1 is the resistance of the first RC module, and R2 is the resistance of the first RC module.

[0016] Optionally, in the process of constructing the electrothermal coupling model based on the offline identification method of resistance and capacitance parameters, the thermoelectric similarity principle, the three-dimensional unsteady-state heat transfer theory and the Bernardi heat generation rate formula, the method further includes:

[0017] Based on the thermoelectric similarity principle, the second formula is constructed:

[0018]

[0019] Wherein, q is the heat generation rate of the battery, c1 is the internal heat capacity of the battery, c2 is the surface heat capacity of the battery, T1 is the center temperature of the battery, T2 is the surface temperature of the battery, T ∞ is the ambient temperature of the battery, r i With r o They respectively represent the internal thermal resistance and external thermal resistance of the battery in three directions, and t is time.

[0020] Optionally, in the process of constructing the electrothermal coupling model based on the offline identification method of resistance and capacitance parameters, the thermoelectric similarity principle, the three-dimensional unsteady-state heat transfer theory and the Bernardi heat generation rate formula, the method further includes:

[0021] Based on the three-dimensional unsteady heat transfer theory, the third formula is constructed:

[0022]

[0023] Wherein, ρ is the density of the battery, c is the specific heat capacity of the battery, h is the convection heat transfer coefficient of the surface of the battery, X, Y, Z are the lengths of the battery along the x, y, z directions, are the rates of change of temperature along the three directions of the battery, T ∞ is the ambient temperature of the battery, T surfx＝X , Tsurfy=Y, Tsurfz=Z are the surface temperatures of the battery along the lengths of the three directions x, y and z respectively, r ix 、r iy 、r iz are the thermal conductivity of the battery along the x, y and z directions respectively, q is the heat generation rate of the battery, is the temperature gradient and t is the time.

[0024] Optionally, in the process of constructing the electrothermal coupling model based on the offline identification method of resistance and capacitance parameters, the thermoelectric similarity principle, the three-dimensional unsteady-state heat transfer theory and the Bernardi heat generation rate formula, the method further includes:

[0025] Based on the Bernardi heat generation rate formula, the fourth formula is constructed:

[0026]

[0027] Wherein, q is the heat generation rate of the battery, I is the current of the battery, E is the terminal voltage of the battery, and U ocv is the open circuit voltage of the battery, T is the average temperature of the battery, V b is the volume of the battery.

[0028] Optionally, the initial battery data is processed using the electrothermal coupling model to obtain a battery temperature field, including:

[0029] The initial data of the battery is processed using the electrothermal coupling model to obtain the surface temperature of the battery along the x, y, and z directions;

[0030] The battery temperature field is constructed based on the surface temperature of the battery along three directions: x, y, and z.

[0031] Optionally, according to the size of the predicted value of the battery overall temperature, executing the first processing method or the second processing method includes:

[0032] When the predicted value of the overall battery temperature is greater than or equal to a temperature threshold, determining that it is not necessary to use the TMS system to dissipate heat for the battery, and controlling the TMS system to stop running;

[0033] When the predicted value of the overall battery temperature is less than the temperature threshold, it is determined that the TMS system needs to be used to continue to dissipate heat for the battery, and the TMS system is kept running.

[0034] According to another aspect of the present application, a battery temperature control device is provided, the device comprising:

[0035] An acquisition unit, used to acquire initial battery data, wherein the initial battery data includes voltage, current and temperature of the battery;

[0036] A construction unit for constructing an electrothermal coupling model based on the offline identification method of resistance and capacitance parameters, the thermoelectric similarity principle, the three-dimensional unsteady heat transfer theory and the Bernardi heat generation rate formula;

[0037] A first processing unit is used to process the initial battery data using the electrothermal coupling model to obtain a battery temperature field, and determine a predicted value of the overall battery temperature at a target time point based on the battery temperature field;

[0038] The second processing unit is used to execute a first processing method or a second processing method according to the size of the predicted value of the overall temperature of the battery. The first processing method indicates that it is currently not necessary to use the TMS system to dissipate heat for the battery, and the TMS system is controlled to stop running. The second processing method indicates that it is currently necessary to use the TMS system to continue to dissipate heat for the battery, and the TMS system is kept running.

[0039] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the control methods.

[0040] According to another aspect of the present application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include a method for executing any one of the control methods described.

[0041] By applying the technical solution of the present application, an electrothermal coupling model is constructed based on an offline resistance and capacitance parameter identification method, a thermoelectric similarity principle, a three-dimensional non-steady-state heat transfer theory and a Bernardi heat generation rate formula, and the electrothermal coupling model is used to process the initial data of the battery to obtain a battery temperature field, and based on the battery temperature field, a predicted value of the overall battery temperature at a target time point is determined, thereby saving a lot of computing power requirements for temperature prediction compared to existing solutions. Finally, based on the size of the predicted value of the overall battery temperature, the first processing method or the second processing method is executed, thereby solving the problem that the existing solution requires a lot of computing power in the process of controlling the TMS system to stop the battery temperature to reduce power consumption, and is difficult to run on embedded software. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0043] Figure 1 A schematic diagram of a flow chart of a battery temperature control method provided according to an embodiment of the present application is shown;

[0044] Figure 2 A schematic diagram of the structure of a second-order RC equivalent circuit provided according to an embodiment of the present application is shown;

[0045] Figure 3 A schematic diagram of the structure of a lumped parameter thermal model provided according to an embodiment of the present application is shown;

[0046] Figure 4 A schematic diagram of a process of executing a first processing method or a second processing method according to the size of the predicted value of the battery overall temperature according to the present application is shown;

[0047] Figure 5 A schematic flow chart of another battery temperature control method provided according to an embodiment of the present application is shown;

[0048] Figure 6 A structural block diagram of a battery temperature control device provided according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0049] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0050] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0051] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0052] For the convenience of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0053] The off-line identification method of resistance and capacitance parameters is to sample and analyze the input and output signals of the circuit, and use mathematical models and signal processing technology to determine the resistance and capacitance parameters in the circuit.

[0054] The thermoelectric similarity principle means that when two objects have similar thermoelectric properties, there is a similarity relationship between the thermocurrent and thermovoltage between them.

[0055] Three-dimensional non-steady-state heat transfer theory studies the non-steady-state heat conduction process of an object in three-dimensional space, and uses mathematical models and equations to describe the change of the internal temperature distribution of the object over time.

[0056] The Bernardi heat generation rate formula is used to describe the rate at which heat is generated inside an organism and can be used to analyze the distribution and transfer of heat within an organism.

[0057] TMS (Thermal Management System): A thermal management system is a system used to control and manage heat in a device or system. The main purpose of this system is to ensure that the device operates within its optimal operating temperature range to prevent performance degradation, device failure, or even damage caused by overheating.

[0058] BMS (Battery Management System): It is an electronic monitoring system specially designed to monitor and protect the operating status of rechargeable batteries (such as lithium-ion batteries) to ensure that the batteries work under safe, efficient and reliable conditions.

[0059] HPPC test refers to High Power Pulse Charging test, which is a test method used to evaluate battery performance. In the HPPC test, the battery is charged and discharged quickly to simulate the high-power charge and discharge conditions in actual use. Through this test method, important parameters such as battery power density, cycle life and safety performance can be evaluated. HPPC test can help researchers and manufacturers better understand the performance characteristics of batteries, optimize battery design and improve battery performance.

[0060] As introduced in the background technology, when the BMS detects that the temperature of the battery cell is abnormal at the current moment, it sends a temperature control (heating / cooling) instruction to the TMS, and the TMS processes it according to the current state. However, due to the limitations of the NTC layout inside the battery case, most of them are arranged on the surface of the battery cell, and it takes a long time for the battery to generate (absorb) heat and completely diffuse it to the entire battery cell, so its sensor temperature feedback has serious lag. As a result, the battery temperature will be over-temperature or under-temperature after the TMS system intervenes, which wastes a lot of energy and reduces the overall cruising range of the vehicle. In order to solve the problem that the existing solution requires a lot of computing power in the process of controlling the TMS system to stop the battery temperature to reduce power consumption, and it is difficult to run on embedded software, the embodiments of the present application provide a battery temperature control method, device, medium and electronic device.

[0061] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0062] In this embodiment, a method for controlling battery temperature is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0063] Figure 1 FIG. 1 is a flow chart of a method for controlling battery temperature according to an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0064] Step S101, obtaining initial battery data, the initial battery data including the voltage, current and temperature of the battery;

[0065] Step S102, constructing an electrothermal coupling model based on an offline resistance and capacitance parameter identification method, a thermoelectric similarity principle, a three-dimensional unsteady-state heat transfer theory, and a Bernardi heat generation rate formula;

[0066] In one embodiment of the present application, in the process of constructing an electrothermal coupling model based on an offline identification method of resistance and capacitance parameters, a thermoelectric similarity principle, a three-dimensional non-steady-state heat transfer theory and a Bernardi heat generation rate formula, the method further includes:

[0067] Based on the above offline identification method of resistance and capacitance parameters, the first formula is constructed:

[0068]

[0069] Where, I is the current of the battery in the second-order RC equivalent circuit, E is the terminal voltage of the battery, and U ocv is the open circuit voltage of the battery, t is the time, U1 is the voltage across the first RC module in the second-order RC equivalent circuit, U2 is the voltage across the second RC module in the second-order RC equivalent circuit, C1 is the capacitance of the first RC module, C2 is the capacitance of the second RC module, R1 is the resistance of the first RC module, and R2 is the resistance of the first RC module.

[0070] Specifically, the first formula is an electrical characteristic model, which is equivalent to a second-order RC equivalent circuit. The structure of the second-order RC equivalent circuit is as follows: Figure 2 Through the HPPC test at different temperatures and SOCs, the internal resistance of the battery at different temperatures and SOCs is obtained according to the first formula.

[0071] In one embodiment of the present application, in the process of constructing an electrothermal coupling model based on an offline identification method of resistance and capacitance parameters, a thermoelectric similarity principle, a three-dimensional non-steady-state heat transfer theory and a Bernardi heat generation rate formula, the method further includes:

[0072] Based on the above thermoelectric similarity principle, the second formula is constructed:

[0073]

[0074] Wherein, q is the heat generation rate of the battery, c1 is the internal heat capacity of the battery, c2 is the surface heat capacity of the battery, T1 is the center temperature of the battery, T2 is the surface temperature of the battery, T∞ is the ambient temperature of the battery, r i With r o They respectively represent the internal thermal resistance and external thermal resistance of the above battery in three directions, and t is the time.

[0075] Specifically, the second formula is a lumped parameter thermal model, which identifies the thermal characteristic parameters of the battery pack itself offline as the model parameters of the thermal characteristic model. The structure of the lumped parameter thermal model is as follows: Figure 3 shown.

[0076] In one embodiment of the present application, in the process of constructing an electrothermal coupling model based on an offline identification method of resistance and capacitance parameters, a thermoelectric similarity principle, a three-dimensional non-steady-state heat transfer theory and a Bernardi heat generation rate formula, the method further includes:

[0077] Based on the above three-dimensional unsteady heat transfer theory, the third formula is constructed:

[0078]

[0079] Wherein, ρ is the density of the battery, c is the specific heat capacity of the battery, h is the convection heat transfer coefficient of the surface of the battery, X, Y, Z are the lengths of the battery along the x, y, z directions, are the rates of change of temperature along the three directions of the battery, T ∞ is the ambient temperature of the battery, T surfx＝X , Tsurfy=Y, Tsurfz=Z are the surface temperatures of the battery along the lengths of the three directions x, y and z respectively, r ix 、r iy 、r iz are the thermal conductivity of the battery along the x, y and z directions respectively, q is the heat generation rate of the battery, is the temperature gradient and t is the time.

[0080] Specifically, Figure 3 As shown, with r ix 、r iy 、r iz Similarly, r o It also has three directions of extension, namely r ox 、r oy 、r oz , I will not elaborate on it here. Based on the three-dimensional unsteady heat transfer theory, combined with the parameters obtained by the concentrated parameter thermal model identification, the third formula is constructed. The corresponding temperature field is constructed by obtaining the surface temperature of the above-mentioned battery along the three directions of x, y, and z.

[0081] In one embodiment of the present application, in the process of constructing an electrothermal coupling model based on an offline identification method of resistance and capacitance parameters, a thermoelectric similarity principle, a three-dimensional non-steady-state heat transfer theory and a Bernardi heat generation rate formula, the method further includes:

[0082] Based on the above Bernardi heat generation rate formula, the fourth formula is constructed:

[0083]

[0084] Wherein, q is the heat generation rate of the battery, I is the current of the battery, E is the terminal voltage of the battery, U ocv is the open circuit voltage of the battery, T is the average temperature of the battery, V b is the volume of the above battery.

[0085] Specifically, the obtained q is applied to the second formula and the third formula to obtain the surface temperature of the battery along the three directions of x, y and z.

[0086] Step S103, using the above-mentioned electrothermal coupling model to process the above-mentioned initial battery data to obtain a battery temperature field, and determining a predicted value of the battery overall temperature at a target time point according to the above-mentioned battery temperature field;

[0087] Specifically, the target time point represents a moment in the future relative to the current moment. For example, the current surface temperature is T0, and the temperature T1 inside the battery can be obtained based on the temperature field. Then, based on the temperature field, the heating source is cut off, that is, q=0. According to the three-dimensional unsteady heat transfer theory, the battery surface temperature after a specified time can be predicted.

[0088] In one embodiment of the present application, the above-mentioned initial battery data is processed using the above-mentioned electrothermal coupling model to obtain the battery temperature field, including:

[0089] The initial data of the battery is processed by using the electrothermal coupling model to obtain the surface temperature of the battery along the x, y and z directions;

[0090] The battery temperature field is constructed based on the surface temperature of the battery along the x, y and z directions.

[0091] Specifically, the battery temperature field is constructed by measuring the surface temperature of the battery along the x, y and z directions, so as to facilitate the subsequent determination of the predicted value of the battery overall temperature at the target time point based on the battery temperature field.

[0092] Step S104, according to the size of the predicted value of the overall temperature of the battery, execute the first processing method or the second processing method, the first processing method indicates that it is not necessary to use the TMS system to dissipate heat for the battery, and the TMS system is controlled to stop running, and the second processing method indicates that it is necessary to use the TMS system to continue to dissipate heat for the battery, and the TMS system is kept running.

[0093] In the above steps, an electrothermal coupling model is constructed based on the offline identification method of resistance and capacitance parameters, the thermoelectric similarity principle, the three-dimensional non-steady-state heat transfer theory and the Bernardi heat generation rate formula, and the electrothermal coupling model is used to process the above battery initial data to obtain the battery temperature field, and based on the above battery temperature field, the predicted value of the overall battery temperature at the target time point is determined, thereby saving a lot of computing power requirements for temperature prediction compared with existing solutions. Finally, according to the size of the above battery overall temperature prediction value, the first processing method or the second processing method is executed, thereby solving the problem that the existing solution requires a lot of computing power in the process of controlling the TMS system to stop the battery temperature to reduce power consumption, and is difficult to run on embedded software.

[0094] In one embodiment of the present application, Figure 4 As shown, according to the size of the predicted value of the overall battery temperature, the first processing method or the second processing method is executed, including the following steps:

[0095] Step S401, when the predicted value of the overall battery temperature is greater than or equal to the temperature threshold, determining that the TMS system is not needed to dissipate heat for the battery, and controlling the TMS system to stop running;

[0096] Step S402: when the predicted value of the overall battery temperature is less than the temperature threshold, it is determined that the TMS system needs to be used to continue to dissipate heat for the battery, and the TMS system is kept running.

[0097] Specifically, by setting the temperature threshold required by the corresponding TMS system, when the above-mentioned battery overall temperature prediction value is greater than or equal to the temperature threshold, it is determined that the heat dissipation of the current battery has achieved the expected effect, so there is no need to use the TMS system to assist in heat dissipation of the above-mentioned battery, that is, the TMS system is controlled to stop running, thereby achieving the purpose of reducing power consumption; when the above-mentioned battery overall temperature prediction value is less than the above-mentioned temperature threshold, it is determined that the heat dissipation of the current battery has not achieved the expected effect, and the TMS system is still needed to assist in heat dissipation of the above-mentioned battery.

[0098] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the battery temperature control method of the present application will be described in detail below in conjunction with specific embodiments.

[0099] This embodiment relates to a specific method for controlling battery temperature. Figure 5 As shown, the following steps are included:

[0100] Obtaining initial battery data, including battery voltage, current and temperature;

[0101] Based on the offline identification method of resistance and capacitance parameters, the thermoelectric similarity principle, the three-dimensional unsteady heat transfer theory and the Bernardi heat generation rate formula, an electrothermal coupling model is constructed.

[0102] Specifically, based on the offline identification method of resistance and capacitance parameters, the first formula is constructed:

[0103]

[0104] Where I is the current of the battery in the second-order RC equivalent circuit, E is the terminal voltage of the battery, and U ocv is the open circuit voltage of the battery, t is the time, U1 is the voltage across the first RC module in the second-order RC equivalent circuit, U2 is the voltage across the second RC module in the second-order RC equivalent circuit, C1 is the capacitance of the first RC module, C2 is the capacitance of the second RC module, R1 is the resistance of the first RC module, and R2 is the resistance of the first RC module.

[0105] Based on the thermoelectric similarity principle, the second formula is constructed:

[0106]

[0107] Where q is the heat generation rate of the battery, c1 is the internal heat capacity of the battery, c2 is the surface heat capacity of the battery, T1 is the center temperature of the battery, T2 is the surface temperature of the battery, and T ∞ is the ambient temperature of the battery, r i With r o They represent the internal thermal resistance and external thermal resistance of the battery in three directions respectively, and t is the time.

[0108] Based on the three-dimensional unsteady heat transfer theory, the third formula is constructed:

[0109]

[0110] Wherein, ρ is the density of the battery, c is the specific heat capacity of the battery, h is the convection heat transfer coefficient of the surface of the battery, X, Y, Z are the lengths of the battery along the x, y, z directions, are the rates of change of temperature along the three directions of the battery, T ∞ is the ambient temperature of the battery, T surfx＝X , Tsurfy=Y, Tsurfz=Z are the surface temperatures of the battery along the lengths of the three directions x, y and z respectively, r ix 、r iy 、r iz are the thermal conductivity of the battery along the x, y and z directions respectively, q is the heat generation rate of the battery, is the temperature gradient and t is the time.

[0111] Based on the Bernardi heat generation rate formula, the fourth formula is constructed:

[0112]

[0113] Where, q is the heat generation rate of the battery, I is the current of the battery, E is the terminal voltage of the battery, and U ocv is the open circuit voltage of the battery, T is the temperature of the battery, V b is the volume of the battery.

[0114] The initial data of the battery is processed using an electrothermal coupling model to obtain the surface temperature of the battery along the x, y, and z directions;

[0115] Construct the battery temperature field based on the surface temperature of the battery along the x, y, and z directions;

[0116] According to the battery temperature field, determine the predicted value of the battery overall temperature at the target time point;

[0117] When the predicted value of the overall battery temperature is greater than or equal to the temperature threshold, it is determined that the TMS system is not needed to dissipate heat for the battery, and the TMS system is controlled to stop running;

[0118] When the predicted value of the overall battery temperature is less than the temperature threshold, it is determined that the TMS system needs to be used to continue to dissipate heat for the battery, and the TMS system is kept running.

[0119] An electrothermal coupling model is constructed based on an offline resistance and capacitance parameter identification method, thermoelectric similarity principle, three-dimensional non-steady-state heat transfer theory and Bernardi heat generation rate formula, and the electrothermal coupling model is used to process the initial battery data to obtain the battery temperature field, and based on the battery temperature field, the predicted value of the overall battery temperature at the target time point is determined, thereby saving a lot of computing power requirements for temperature prediction compared with existing solutions. Finally, based on the size of the predicted value of the overall battery temperature, the first processing method or the second processing method is executed, thereby solving the problem that the existing solution requires a lot of computing power in the process of controlling the TMS system to stop the battery temperature to reduce power consumption, and is difficult to run on embedded software.

[0120] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0121] The embodiment of the present application also provides a battery temperature control device. It should be noted that the battery temperature control device of the embodiment of the present application can be used to execute the battery temperature control method provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0122] The following is an introduction to the battery temperature control device provided in the embodiment of the present application.

[0123] Figure 6 is a structural block diagram of a battery temperature control device provided according to an embodiment of the present application. Figure 6 As shown, the device comprises:

[0124] An acquisition unit 61 is used to acquire initial battery data, where the initial battery data includes the voltage, current and temperature of the battery;

[0125] A construction unit 62 is used to construct an electrothermal coupling model based on an offline resistance and capacitance parameter identification method, a thermoelectric similarity principle, a three-dimensional unsteady heat transfer theory and a Bernardi heat generation rate formula;

[0126] A first processing unit 63 is used to process the initial battery data using the electric-thermal coupling model to obtain a battery temperature field, and determine a predicted value of the overall battery temperature at a target time point based on the battery temperature field;

[0127] The second processing unit 64 is used to execute the first processing method or the second processing method according to the size of the above-mentioned battery overall temperature prediction value. The above-mentioned first processing method indicates that it is currently not necessary to use the TMS system to dissipate heat for the above-mentioned battery, and the TMS system is controlled to stop running. The above-mentioned second processing method indicates that it is currently necessary to use the TMS system to continue to dissipate heat for the above-mentioned battery, and keep the TMS system running.

[0128] In the above-mentioned device, an electrothermal coupling model is constructed based on an offline method for identifying resistance and capacitance parameters, thermoelectric similarity principle, three-dimensional non-steady-state heat transfer theory and Bernardi heat generation rate formula, and the above-mentioned electrothermal coupling model is used to process the above-mentioned battery initial data to obtain the battery temperature field, and based on the above-mentioned battery temperature field, the predicted value of the overall battery temperature at the target time point is determined, thereby saving a lot of computing power requirements for temperature prediction compared with existing solutions. Finally, according to the size of the above-mentioned overall battery temperature prediction value, the first processing method or the second processing method is executed, thereby solving the problem that the existing solution requires a lot of computing power in the process of controlling the TMS system to stop the battery temperature to reduce power consumption, and is difficult to run on embedded software.

[0129] In one embodiment of the present application, the construction unit includes a first construction module, in the process of constructing an electrothermal coupling model based on an offline identification method of resistance and capacitance parameters, a thermoelectric similarity principle, a three-dimensional non-steady-state heat transfer theory and a Bernardi heat generation rate formula,

[0130] The first building block is used to build a first formula based on the above-mentioned offline method for identifying resistance and capacitance parameters:

[0131]

[0132] Where, I is the current of the battery in the second-order RC equivalent circuit, E is the terminal voltage of the battery, and U ocv is the open circuit voltage of the battery, t is the time, U1 is the voltage across the first RC module in the second-order RC equivalent circuit, U2 is the voltage across the second RC module in the second-order RC equivalent circuit, C1 is the capacitance of the first RC module, C2 is the capacitance of the second RC module, R1 is the resistance of the first RC module, and R2 is the resistance of the first RC module.

[0133] In one embodiment of the present application, the construction unit includes a second construction module, in the process of constructing the electrothermal coupling model based on the offline identification method of resistance and capacitance parameters, the thermoelectric similarity principle, the three-dimensional non-steady-state heat transfer theory and the Bernardi heat generation rate formula,

[0134] The second building block is used to construct the second formula based on the above thermoelectric similarity principle:

[0135]

[0136] Wherein, q is the heat generation rate of the battery, c1 is the internal heat capacity of the battery, c2 is the surface heat capacity of the battery, T1 is the center temperature of the battery, T2 is the surface temperature of the battery, T ∞ is the ambient temperature of the battery, r i With r o They respectively represent the internal thermal resistance and external thermal resistance of the above battery in three directions, and t is the time.

[0137] In one embodiment of the present application, the construction unit includes a third construction module. In the process of constructing the electrothermal coupling model based on the offline identification method of resistance and capacitance parameters, the thermoelectric similarity principle, the three-dimensional non-steady-state heat transfer theory and the Bernardi heat generation rate formula,

[0138] The third building block is used to construct the third formula based on the above three-dimensional unsteady heat transfer theory:

[0139]

[0140] Wherein, ρ is the density of the battery, c is the specific heat capacity of the battery, h is the convection heat transfer coefficient of the surface of the battery, X, Y, Z are the lengths of the battery along the x, y, z directions, are the rates of change of temperature along the three directions of the battery, T ∞ is the ambient temperature of the battery, T surfx＝X , Tsurfy=Y, Tsurfz=Z are the surface temperatures of the battery along the lengths of the three directions x, y and z respectively, r ix 、r iy 、r iz are the thermal conductivity of the battery along the x, y and z directions respectively, q is the heat generation rate of the battery, is the temperature gradient and t is the time.

[0141] In one embodiment of the present application, the construction unit includes a fourth construction module, in the process of constructing the electrothermal coupling model based on the offline identification method of resistance and capacitance parameters, the thermoelectric similarity principle, the three-dimensional non-steady-state heat transfer theory and the Bernardi heat generation rate formula,

[0142] The fourth building block is used to construct a fourth formula based on the above Bernardi heat generation rate formula:

[0143]

[0144] Wherein, q is the heat generation rate of the battery, I is the current of the battery, E is the terminal voltage of the battery, U ocv is the open circuit voltage of the battery, T is the average temperature of the battery, V b is the volume of the above battery.

[0145] In one embodiment of the present application, the first processing unit includes a first processing module and a second processing module.

[0146] The first processing module is used to process the initial data of the battery using the electrothermal coupling model to obtain the surface temperature of the battery along the x, y and z directions;

[0147] The second processing module is used to construct the battery temperature field based on the surface temperature of the battery along three directions of x, y and z.

[0148] In one embodiment of the present application, the second processing unit includes a first determining module and a second determining module.

[0149] The first determination module is used to determine that it is not necessary to use the TMS system to dissipate heat for the battery at present, and control the TMS system to stop running when the predicted value of the overall temperature of the battery is greater than or equal to the temperature threshold;

[0150] The second determination module is used for determining that the TMS system needs to be used to continue to dissipate heat for the battery and keep the TMS system running when the predicted value of the overall battery temperature is less than the temperature threshold.

[0151] The battery temperature control device includes a processor and a memory. The acquisition unit, the construction unit, the first processing unit, the second processing unit, etc. are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0152] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the kernel parameters are adjusted to solve the problem that the existing solution requires a lot of computing power in the process of controlling the TMS system to stop the battery temperature to reduce power consumption, and is difficult to run on embedded software.

[0153] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0154] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the battery temperature control method.

[0155] An embodiment of the present invention provides a processor, and the processor is used to run a program, wherein the battery temperature control method is executed when the program is run.

[0156] The embodiment of the present invention provides a device, the device includes a processor, a memory and a program stored in the memory and can be run on the processor, and the processor implements at least the following steps when executing the program: obtaining the initial data of the battery, the initial data of the battery including the voltage, current and temperature of the battery; constructing an electrothermal coupling model based on the offline identification method of resistance and capacitance parameters, the thermoelectric similarity principle, the three-dimensional non-steady-state heat transfer theory and the Bernardi heat generation rate formula; using the electrothermal coupling model to process the initial data of the battery to obtain the battery temperature field, and according to the battery temperature field, determining the predicted value of the overall temperature of the battery at the target time point; according to the size of the predicted value of the overall temperature of the battery, executing the first processing method or the second processing method, the first processing method indicates that it is not necessary to use the TMS system to dissipate heat for the battery at present, and controls the TMS system to stop running, and the second processing method indicates that it is necessary to use the TMS system to continue to dissipate heat for the battery at present, and keeps the TMS system running. The device in this article can be a server, a PC, a PAD, a mobile phone, etc.

[0157] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that is initialized with at least the following method steps: obtaining initial battery data, the initial battery data including the voltage, current and temperature of the battery; constructing an electrothermal coupling model based on an offline resistance and capacitance parameter identification method, a thermoelectric similarity principle, a three-dimensional non-steady-state heat transfer theory and a Bernardi heat generation rate formula; using the electrothermal coupling model to process the initial battery data to obtain a battery temperature field, and determining a predicted value of the overall battery temperature at a target time point based on the battery temperature field; executing a first processing method or a second processing method based on the size of the predicted value of the overall battery temperature, the first processing method indicating that it is not currently necessary to use the TMS system to dissipate heat for the battery, and the TMS system is controlled to stop running, and the second processing method indicating that it is currently necessary to use the TMS system to continue to dissipate heat for the battery, and the TMS system is kept running.

[0158] The present application also provides an electronic device, which includes: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any of the above control methods. Based on the offline identification of resistance and capacitance parameters, the thermoelectric similarity principle, the three-dimensional non-steady-state heat transfer theory and the Bernardi heat generation rate formula, an electrothermal coupling model is constructed, and the electrothermal coupling model is used to process the initial data of the battery to obtain the battery temperature field, and the overall temperature prediction value of the battery at the target time point is determined according to the battery temperature field, so that the temperature prediction saves a lot of computing power requirements compared with the existing scheme, and finally according to the size of the overall temperature prediction value of the battery, the first processing method or the second processing method is executed, thereby solving the problem that the existing scheme requires a lot of computing power in the process of controlling the TMS system to stop the battery temperature to reduce power consumption, and is difficult to run on embedded software.

[0159] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0160] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0161] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0162] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0163] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0164] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0165] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0166] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0167] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0168] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0169] 1) The battery temperature control method of the present application constructs an electrothermal coupling model based on an offline resistance and capacitance parameter identification method, a thermoelectric similarity principle, a three-dimensional non-steady-state heat transfer theory and a Bernardi heat generation rate formula, and uses the electrothermal coupling model to process the initial battery data to obtain a battery temperature field, and determines the predicted value of the overall battery temperature at a target time point based on the battery temperature field, thereby saving a lot of computing power requirements for temperature prediction compared to existing solutions. Finally, according to the size of the predicted value of the overall battery temperature, the first processing method or the second processing method is executed, thereby solving the problem that the existing solution requires a lot of computing power in the process of controlling the TMS system to stop the battery temperature to reduce power consumption, and is difficult to run on embedded software.

[0170] 2) The battery temperature control device of the present application constructs an electrothermal coupling model based on an offline resistance and capacitance parameter identification method, a thermoelectric similarity principle, a three-dimensional non-steady-state heat transfer theory and a Bernardi heat generation rate formula, and uses the electrothermal coupling model to process the initial battery data to obtain a battery temperature field, and determines the predicted value of the overall battery temperature at a target time point based on the battery temperature field, thereby saving a lot of computing power requirements for temperature prediction compared to existing solutions. Finally, according to the size of the predicted value of the overall battery temperature, the first processing method or the second processing method is executed, thereby solving the problem that the existing solution requires a lot of computing power in the process of controlling the TMS system to stop the battery temperature to reduce power consumption, and is difficult to run on embedded software.

[0171] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for controlling battery temperature, characterized in that: include: Acquiring initial battery data, wherein the initial battery data includes voltage, current and temperature of the battery; Based on the offline identification method of resistance and capacitance parameters, the thermoelectric similarity principle, the three-dimensional unsteady heat transfer theory and the Bernardi heat generation rate formula, an electrothermal coupling model is constructed. The initial battery data is processed using the electrothermal coupling model to obtain a battery temperature field, and a predicted value of the overall battery temperature at a target time point is determined based on the battery temperature field; According to the size of the predicted value of the overall battery temperature, a first processing method or a second processing method is executed. The first processing method indicates that it is not necessary to use the TMS system to dissipate heat for the battery and the TMS system is controlled to stop running. The second processing method indicates that it is necessary to use the TMS system to continue to dissipate heat for the battery and the TMS system is kept running.

2. The control method according to claim 1, characterized in that: In the process of constructing an electrothermal coupling model based on an off-line identification method of resistance and capacitance parameters, a thermoelectric similarity principle, a three-dimensional unsteady-state heat transfer theory and a Bernardi heat generation rate formula, the method further includes: Based on the offline method for identifying resistance and capacitance parameters, a first formula is constructed: Where, I is the current of the battery in the second-order RC equivalent circuit, E is the terminal voltage of the battery, and U ocv is the open circuit voltage of the battery, t is the time, U1 is the voltage across the first RC module in the second-order RC equivalent circuit, U2 is the voltage across the second RC module in the second-order RC equivalent circuit, C1 is the capacitance of the first RC module, C2 is the capacitance of the second RC module, R1 is the resistance of the first RC module, and R2 is the resistance of the first RC module.

3. The control method according to claim 1, characterized in that: In the process of constructing an electrothermal coupling model based on an off-line identification method of resistance and capacitance parameters, a thermoelectric similarity principle, a three-dimensional unsteady-state heat transfer theory and a Bernardi heat generation rate formula, the method further includes: Based on the thermoelectric similarity principle, the second formula is constructed: Wherein, q is the heat generation rate of the battery, c1 is the internal heat capacity of the battery, c2 is the surface heat capacity of the battery, T1 is the center temperature of the battery, T2 is the surface temperature of the battery, T ∞ is the ambient temperature of the battery, r i With r o They respectively represent the internal thermal resistance and external thermal resistance of the battery in three directions, and t is time.

4. The control method according to claim 1, characterized in that: In the process of constructing an electrothermal coupling model based on an off-line identification method of resistance and capacitance parameters, a thermoelectric similarity principle, a three-dimensional unsteady-state heat transfer theory and a Bernardi heat generation rate formula, the method further includes: Based on the three-dimensional unsteady heat transfer theory, the third formula is constructed: Wherein, ρ is the density of the battery, c is the specific heat capacity of the battery, h is the convection heat transfer coefficient of the surface of the battery, X, Y, Z are the lengths of the battery along the x, y, z directions, are the rates of change of temperature along the three directions of the battery, T ∞ is the ambient temperature of the battery; T surf|x＝X , T surf|y＝Y , T surf|z＝Z are the surface temperatures of the battery along the x, y, and z directions, respectively, ix 、r iy 、r iz are the thermal conductivity of the battery along the x, y and z directions respectively, q is the heat generation rate of the battery, is the temperature gradient and t is the time.

5. The control method according to claim 1, characterized in that: In the process of constructing an electrothermal coupling model based on an off-line identification method of resistance and capacitance parameters, a thermoelectric similarity principle, a three-dimensional unsteady-state heat transfer theory and a Bernardi heat generation rate formula, the method further includes: Based on the Bernardi heat generation rate formula, the fourth formula is constructed: Wherein, q is the heat generation rate of the battery, I is the current of the battery, E is the terminal voltage of the battery, and U ocv is the open circuit voltage of the battery, T is the average temperature of the battery, V b is the volume of the battery.

6. The control method according to claim 1, characterized in that: The initial battery data is processed using the electrothermal coupling model to obtain a battery temperature field, including: The initial data of the battery is processed using the electrothermal coupling model to obtain the surface temperature of the battery along the x, y, and z directions; The battery temperature field is constructed based on the surface temperature of the battery along three directions: x, y, and z.

7. The control method according to any one of claims 1 to 6, characterized in that: According to the value of the predicted value of the battery overall temperature, a first processing method or a second processing method is executed, including: When the predicted value of the overall battery temperature is greater than or equal to a temperature threshold, determining that it is not necessary to use the TMS system to dissipate heat for the battery, and controlling the TMS system to stop running; When the predicted value of the overall battery temperature is less than the temperature threshold, it is determined that the TMS system needs to be used to continue to dissipate heat for the battery, and the TMS system is kept running.

8. A battery temperature control device, characterized in that: include: An acquisition unit, used to acquire initial battery data, wherein the initial battery data includes voltage, current and temperature of the battery; A construction unit for constructing an electrothermal coupling model based on the offline identification method of resistance and capacitance parameters, the thermoelectric similarity principle, the three-dimensional unsteady heat transfer theory and the Bernardi heat generation rate formula; A first processing unit is used to process the initial battery data using the electrothermal coupling model to obtain a battery temperature field, and determine a predicted value of the overall battery temperature at a target time point based on the battery temperature field; The second processing unit is used to execute a first processing method or a second processing method according to the size of the predicted value of the overall temperature of the battery. The first processing method indicates that it is currently not necessary to use the TMS system to dissipate heat for the battery, and the TMS system is controlled to stop running. The second processing method indicates that it is currently necessary to use the TMS system to continue to dissipate heat for the battery, and the TMS system is kept running.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the control method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a control method for executing any one of claims 1 to 7.

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