A battery cell temperature prediction method, device, apparatus and storage medium

Abstract

The application provides a kind of electric core temperature prediction method, device, equipment and storage medium, by constructing electric core heat production model and electric core heat transfer model, constructs electric core energy balance formula;The relationship between the change of cooling liquid temperature with time is constructed, the relationship between the import and export temperature of liquid cooling unit and the average temperature of cooling liquid is constructed, based on the two relationships, a cooling liquid temperature prediction model is constructed, so as to predict the cooling liquid temperature at the corresponding position of the highest temperature electric core and the lowest temperature electric core;The electric core energy balance formula, the electric core heat production model, the electric core heat transfer model and the cooling liquid temperature prediction model are coupled to construct the electric core temperature prediction model, the relevant parameters are input into the electric core temperature prediction model, and the predicted temperature of the electric core is output by the electric core temperature prediction model, the prediction of the highest temperature and the lowest temperature electric core temperature in the battery pack is realized, the complexity of solving the electric core temperature is reduced, and the influence of temperature distribution in the liquid cooling unit on the electric core temperature is refined.

Description

Technical Field

[0001] This invention relates to the field of battery thermal management technology, and in particular to a method, apparatus, device, and storage medium for predicting battery cell temperature. Background Technology

[0002] Currently, lithium-ion batteries are widely used as energy storage systems in electric vehicles due to their high energy density and good cycle life. Lithium-ion batteries generate a significant amount of heat during operation; however, their performance and safety are greatly affected by temperature. Therefore, power battery packs often require a thermal management system (hereinafter referred to as a cooling system) to dissipate the heat generated by the battery or heat it to a suitable operating temperature in cold environments. Based on the heat transfer medium, common cooling systems include liquid cooling systems and air cooling systems. Due to its excellent heat dissipation effect, liquid cooling systems are widely used in high-performance electric vehicles. Battery pack liquid cooling systems utilize a circulating liquid working fluid (hereinafter referred to as coolant) to exchange heat with the battery pack, carrying away or transferring heat from the battery pack, thereby achieving the effect of cooling or heating the battery pack.

[0003] In liquid cooling systems, the temperature of the coolant changes continuously over time, and different temperature distributions exist at different locations within the system. For newly manufactured battery packs or those with short service lives, the consistency of individual cells is relatively good, and the heat generation under operating conditions is not significantly different. Therefore, the temperature distribution of the coolant within the liquid cooling plate becomes the primary factor affecting cell temperature differences. Consequently, the impact of coolant temperature on cell temperature must be considered in battery pack temperature estimation and thermal management control logic design. Currently, lumped parameter models or one-dimensional physical models are commonly used to estimate battery temperature. The former uses the Bernardi heat generation model to calculate cell heat generation and then estimates the cell temperature based on coolant temperature data collected by sensors. The latter uses physical simulation software such as Amesim or Simscape to perform one-dimensional physical modeling of the entire battery pack and its liquid cooling system, followed by solution processing.

[0004] In battery pack temperature estimation methods based on the lumped parameter concept, there are two main approaches to handling coolant temperature. One approach treats the temperature at the inlet of the battery pack's liquid cooling plate as the temperature of the coolant within the plate, without considering the temperature distribution of the coolant within the plate. The other approach uses sensors to collect coolant temperature data at corresponding locations. However, this method cannot predict the coolant temperature under future operating conditions, making it difficult to predict the cell temperature.

[0005] While one-dimensional physical models can accurately determine the cell temperature at various locations, their modeling process requires a large number of physical parameters, most of which relate to the properties of component materials, such as the coefficient of friction of the cooling pipe inner walls. In forward system design, one-dimensional physical models can be used for system simulation and optimization. However, in real-world vehicle applications such as Battery Management Systems (BMS), engineers often struggle to obtain all the physical parameters required for one-dimensional simulation, raising the barrier to entry for practical vehicle applications. Furthermore, thermal management design often only considers the highest and lowest temperatures of the cells within the battery pack, without needing to calculate the temperature of each individual cell, resulting in significant redundancy in the method. Summary of the Invention

[0006] This invention provides a cell temperature prediction method, apparatus, device, and storage medium to address the shortcomings of existing battery pack temperature estimation methods based on the lumped parameter concept, which either fail to consider the temperature distribution of the coolant in the liquid cooling plate or lack prediction of the coolant temperature under future operating conditions, thus making it difficult to predict the cell temperature. This invention enables the prediction of the highest and lowest temperature cells in the battery pack, reduces the complexity of solving for the temperature of all cells, and refines the influence of the temperature distribution in the liquid cooling unit on the cell temperature.

[0007] This invention provides a method for predicting battery cell temperature, comprising: Construct a cell heat generation model and a cell heat transfer model; Constructing a battery cell energy balance system; Construct the relationship between coolant temperature and time; The relationship between the inlet and outlet temperatures of the liquid cooling unit and the average temperature of the coolant was established. Based on the relationship between the coolant temperature and time and the relationship between the inlet and outlet temperatures of the liquid cooling unit and the average coolant temperature, a coolant temperature prediction model is constructed. The cell energy balance model, the cell heat generation model, the cell heat transfer model, and the coolant temperature prediction model are coupled together to construct a cell temperature prediction model. Input relevant parameters into the cell temperature prediction model, and calculate and output the predicted cell temperature based on the cell temperature prediction model.

[0008] According to the method for predicting battery cell temperature provided by the present invention, a battery cell heat generation model and a battery cell heat transfer model are constructed, specifically including: Constructing a cell heat generation model: ; in, For the heat generation rate of the battery cell, For current, The internal resistance of the battery cell. For cell temperature, The entropy thermal coefficient of the battery cell; Constructing a cell heat transfer model: Calculate the heat exchange rate between the battery cell and the liquid cooling unit: ; in, The heat exchange rate between the battery cell and the liquid cooling unit. The convective heat transfer coefficient is... This is the equivalent area of ​​the contact surface between the battery cell and the liquid cooling unit. The temperature of the coolant at the corresponding location of the battery cell; Calculate the heat exchange rate of the remaining surfaces of the battery cell: ; in, The heat exchange rate of one side of the battery cell is given. Thermal conductivity, The equivalent thermal conductivity area of ​​the remaining surfaces is given. For thermal conductivity distance, The temperature at which heat is exchanged with the remaining surfaces; Calculate the heat absorption power of the battery cell itself: ; in, This refers to the heat absorption power of the battery cell itself. The specific heat capacity of the battery cell itself. The mass of the battery cell, The temperature change rate of the battery cell is denoted as .

[0009] According to the present invention, a method for predicting battery cell temperature is provided, which constructs a battery cell energy balance equation, specifically including: ; Where n is the number of surfaces of the battery cell other than the contact surface with the liquid cooling unit.

[0010] According to the present invention, a cell temperature prediction method is provided, which constructs the relationship between coolant temperature and time, specifically including: Constructing a heat balance: ; in The heat absorbed by the coolant itself. The heat exchanged between the heat exchanger and the coolant in the liquid cooling system. The total heat exchanged between all battery cells and the coolant through the liquid cooling unit; the liquid cooling system includes a heat exchanger, a water pump, and the liquid cooling unit; The heat absorbed by the coolant itself is as follows: ; Therefore, we get: ; Where c is the specific heat capacity of the coolant, and m is the total mass of the coolant in the liquid cooling system. For the coolant in The average temperature at any given time For the coolant in The average temperature at any given time for Time to The amount of heat absorbed by the coolant itself within a given time period.

[0011] According to the present invention, a cell temperature prediction method is provided, which establishes the relationship between the inlet and outlet temperatures of a liquid cooling unit and the average temperature of the coolant, specifically including: The relationship between the inlet and outlet temperatures of the liquid cooling unit is as follows: ; in, The heat exchange power of the liquid cooling unit is... The mass flow rate of the coolant in the liquid cooling unit. The temperature of the coolant at the outlet of the liquid cooling unit. The temperature of the coolant at the inlet of the liquid cooling unit; and The correspondence is as follows: ; in, This represents the total number of battery cells. Depend on calculate : ; in For time intervals; The average temperature of the coolant in the liquid cooling unit is: ; The average temperature of the coolant in the liquid cooling unit The average temperature of the coolant in the liquid cooling system The relationship is: ; in, This is the conversion factor.

[0012] According to the present invention, a cell temperature prediction method is provided, which constructs a coolant temperature prediction model based on the relationship between the coolant temperature and time and the relationship between the inlet and outlet temperatures of the liquid cooling unit and the average coolant temperature. The method specifically includes: At any given moment The inlet and outlet water temperatures of the liquid cooling unit are as follows: ; ; in, For the coolant in The temperature at the inlet of the liquid cooling unit at any given time. For the coolant in The temperature at the outlet of the liquid cooling unit at any given time. For the liquid cooling unit in Heat exchange power at any given time; The coolant temperatures corresponding to the battery cell with the highest temperature and the battery cell with the lowest temperature are respectively: ; ; in, This refers to the coolant temperature at the location corresponding to the highest temperature of the battery cell. The temperature of the coolant at the location corresponding to the lowest temperature of the battery cell. This is the equivalent distance between the highest temperature battery cell and the inlet of the liquid cooling unit. This is the equivalent distance between the lowest temperature battery cell and the inlet of the liquid cooling unit. The equivalent length of the piping in the liquid cooling unit.

[0013] According to the present invention, a cell temperature prediction method is provided, which inputs relevant parameters into the cell temperature prediction model, calculates and outputs the predicted temperature of the cell based on the cell temperature prediction model, specifically including: (1) Input the corresponding model parameters into the cell heat transfer model, the cell heat generation model and the coolant temperature prediction model; (2) Input the current operating current, the current highest temperature of the battery cell and the current lowest temperature of the battery cell into the battery cell heat generation model, and calculate the current heat generation rate of the battery cell with the highest temperature and the current heat generation rate of the battery cell with the lowest temperature. (3) Input the current highest temperature cell temperature, the current lowest temperature cell temperature, the coolant temperature at the corresponding position of the current highest temperature cell, and the coolant temperature at the corresponding position of the current lowest temperature cell into the cell heat transfer model, and calculate the heat transfer power between the current highest temperature cell and the liquid cooling unit, the heat transfer power between the current lowest temperature cell and the liquid cooling unit, as well as the heat transfer power of the remaining surfaces of the current highest temperature cell and the heat transfer power of the remaining surfaces of the current lowest temperature cell; (4) Input the current coolant temperature at the location corresponding to the highest temperature cell, the current coolant temperature at the location corresponding to the lowest temperature cell, the current highest temperature cell temperature, and the current lowest temperature cell temperature into the coolant temperature prediction model, and calculate the coolant temperature at the location corresponding to the highest temperature cell and the coolant temperature at the location corresponding to the lowest temperature cell after the next time step. (5) Based on the cell energy balance, the cell heat absorption rate of the highest temperature cell is calculated according to the heat exchange power between the highest temperature cell and the liquid cooling unit, the heat exchange power of the remaining surface of the highest temperature cell, and the cell heat generation rate of the highest temperature cell. The cell temperature change rate is calculated according to the cell heat absorption rate of the highest temperature cell. Finally, the highest temperature cell temperature after the next time step is calculated according to the cell temperature change rate and the current highest temperature cell temperature. Based on the cell energy balance formula, the cell heat absorption rate of the lowest temperature cell is calculated according to the heat exchange power between the current lowest temperature cell and the liquid cooling unit, the heat exchange power of the remaining surface of the current lowest temperature cell, and the cell heat generation rate of the current lowest temperature cell. The cell temperature change rate is calculated according to the cell heat absorption rate of the lowest temperature cell. Finally, the lowest temperature cell temperature after the next time step is calculated according to the cell temperature change rate and the current lowest temperature cell temperature. (6) Take the average temperature of the coolant after the next time step as the current average temperature of the coolant; take the highest temperature cell temperature after the next time step as the current highest temperature cell temperature; take the lowest temperature cell temperature after the next time step as the current lowest temperature cell temperature; and proceed to step (2).

[0014] The present invention also provides a cell temperature prediction device, comprising: a model building module, a parameter input module, and a calculation module; The model building module is used to build a cell heat generation model and a cell heat transfer model. Based on the cell heat generation model and the cell heat transfer model, a cell energy balance equation is built, the relationship between coolant temperature and time is built, and the relationship between the inlet and outlet temperatures of the liquid cooling unit and the average temperature of the coolant is built. Based on the relationship between coolant temperature and time and the relationship between the inlet and outlet temperatures of the liquid cooling unit and the average temperature of the coolant, a coolant temperature prediction model is built. The cell energy balance equation, the cell heat generation model, the cell heat transfer model and the coolant temperature prediction model are coupled to build a cell temperature prediction model. The parameter input module is used to input relevant parameters into the cell temperature prediction model; The calculation module is used to calculate and output the predicted temperature of the battery cell using the battery cell temperature prediction model.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the cell temperature prediction method as described above.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the cell temperature prediction method as described above.

[0017] This invention provides a method, apparatus, device, and storage medium for predicting battery cell temperature. It constructs a battery cell heat generation model and a battery cell heat transfer model to establish a battery cell energy balance equation; it establishes the relationship between coolant temperature and time, and the relationship between the inlet and outlet temperatures of the liquid cooling unit and the average coolant temperature. Based on these relationships, a coolant temperature prediction model is constructed to predict the coolant temperature at the corresponding locations of the highest and lowest temperature battery cells under future operating conditions. The battery cell energy balance equation, the battery cell heat generation model, the battery cell heat transfer model, and the coolant temperature prediction model are coupled to construct a battery cell temperature prediction model. Relevant parameters are input into the battery cell temperature prediction model, and the model outputs the predicted temperature of the battery cells. This achieves the prediction of the highest and lowest temperature battery cells in the battery pack, reducing the complexity of solving for battery cell temperature, refining the impact of temperature distribution in the liquid cooling unit on battery cell temperature, and requiring relatively small computational power and resources. It can be applied to vehicle battery management systems. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is one of the flowcharts of the cell temperature prediction method provided by the present invention; Figure 2 This is a schematic diagram illustrating the relationship between the various models in the embodiments of the present invention; Figure 3 This is a schematic diagram of the operating current in an embodiment of the present invention; Figure 4 This is a schematic diagram of the heating power data of the heat exchanger in an embodiment of the present invention; Figure 5 This is a schematic diagram of the self-heat generation rate data of the battery cell in an embodiment of the present invention; Figure 6 This is a schematic diagram of the average temperature data of the coolant in an embodiment of the present invention; Figure 7 This is a schematic diagram of the heat exchange power data of the liquid cooling plate in an embodiment of the present invention; Figure 8 This is a schematic diagram comparing the predicted temperature data and the measured temperature data of the coolant at the inlet and outlet of the liquid cooling plate in an embodiment of the present invention. Figure 9 This is a schematic diagram comparing the predicted temperature data and the measured temperature data of the highest and lowest temperature battery cells in this embodiment of the invention. Figure 10 This is a schematic diagram of the battery cell temperature prediction device provided by the present invention; Figure 11 This is a schematic diagram of the structure of the electronic device provided by the present invention; Figure 12 This is the second schematic flowchart of the cell temperature prediction method in this embodiment of the invention.

[0020] Figure label: 101: Model building module; 102: Parameter input module; 103: Calculation module. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] The following is combined Figures 1-4 The present invention describes the cell temperature prediction method provided by the present invention.

[0023] like Figure 1 As shown, the present invention provides a method for predicting battery cell temperature, including: S1. Construct a cell heat generation model and a cell heat transfer model; S2, Construct a cell energy balance system; S3. Construct the relationship between coolant temperature and time; S4. Construct the relationship between the inlet and outlet temperatures of the liquid cooling unit and the average temperature of the coolant; S5. Based on the relationship between coolant temperature and time and the relationship between the inlet and outlet temperatures of the liquid cooling unit and the average coolant temperature, a coolant temperature prediction model is constructed. S6. Couple the cell energy balance model, cell heat generation model, cell heat transfer model and coolant temperature prediction model to construct a cell temperature prediction model. S7. Input relevant parameters into the cell temperature prediction model, calculate and output the predicted temperature of the cell based on the cell temperature prediction model.

[0024] like Figure 1 As shown, it should be understood that in a feasible embodiment of the present invention, there is no specific timing relationship between steps S1 to S2 and S3 to S5.

[0025] Specifically, in step S1 above, the cell heat generation model and cell heat transfer model are constructed, including: 1. Construct a cell heat generation model: According to the Bernardi heat generation model, the cell temperature is mainly affected by irreversible heat and reversible heat, and the calculation formula is as follows: ; in For the heat generation rate of the battery cell, The current is given by the operating condition data. The internal resistance of the battery cell can be obtained by testing the Hybrid Pulse Power Characteristic (HPPC) parameters, which can be mapped to the internal resistance map. For the desired cell temperature, The entropy thermal coefficient of the battery cell can be obtained experimentally.

[0026] 2. Construct a heat transfer model for the battery cell. Taking square battery cells as an example: (1) Heat transfer at the contact surface with the liquid cooling unit is convective, as shown in the following formula: ; in This refers to the heat exchange power between the battery cell (a single cell) and the liquid cooling unit. The convective heat transfer coefficient can be obtained experimentally. This is the equivalent area of ​​the contact surface between the battery cell and the liquid cooling unit. This refers to the coolant temperature at the corresponding location of the battery cell.

[0027] (2) All other surfaces undergo heat conduction heat transfer, as shown in the following formula: ; in The heat exchange rate of this surface. Thermal conductivity can be obtained experimentally. This is the equivalent heat conduction area of ​​the remaining surfaces. For thermal conductivity distance, The ambient or shell temperature at which heat is exchanged with the remaining surfaces.

[0028] (3) The heat absorption of the battery cell material itself will cause a temperature change, as shown in the following formula: ; in This refers to the heat absorption power of the battery cell itself. For the specific heat capacity of the battery cell itself, For cell quality, This represents the rate of change of cell temperature.

[0029] It should be understood that, in one feasible embodiment of the invention, the liquid cooling system includes components such as a liquid cooling unit, a heat exchanger, and a water pump, wherein the liquid cooling unit is a liquid cooling plate.

[0030] In step S2 above, the cell energy balance is constructed, specifically including: For a battery cell, its energy flow mainly involves four aspects: the heat generation power of the cell itself, the heat exchange power between the cell and the liquid cooling unit (which can be simplified as convective heat transfer), the heat exchange power of other surfaces of the cell (i.e., the surfaces that exchange heat with other components or the surrounding environment besides the surfaces that exchange heat with the liquid cooling unit, which can be simplified as thermal conduction heat transfer), and the heat absorption power of the cell itself. Its energy balance equation is as follows: ; Where n is the number of faces of the battery cell excluding the face that contacts the liquid cooling unit. For example, for a square battery cell, it has a total of 6 faces. Excluding the face that contacts the liquid cooling unit, there are 5 faces, so n is 5.

[0031] In step S3 above, the relationship between coolant temperature and time is constructed, specifically including: Taking the above liquid cooling system as an example, assuming the system piping is adiabatic and the water pump only provides the pressure required for coolant circulation without heat exchange, the system's heat balance equation is: ; in The heat absorbed by the coolant itself. The heat exchanged between the heat exchanger and the coolant (i.e., the heat exchanger power). This represents the total heat exchanged between all battery cells and the coolant through the liquid cooling unit.

[0032] The coolant is in The average temperature at any given time can be described by the following formula: ; Therefore, we can obtain: ; Where c is the specific heat capacity of the coolant, and m is the total mass of the coolant in the liquid cooling system. , coolant in Time (i.e., the time after the next time step) and The average temperature at that moment (i.e., the current time). for Time to The heat absorbed by the coolant itself within a given time period ( It can be calculated using the heat balance formula of the system described above.

[0033] In step S4 above, the relationship between the inlet and outlet temperatures of the liquid cooling unit and the average temperature of the coolant is established, specifically including: At any given moment, the inlet and outlet temperatures of the liquid cooling unit have the following relationship: ; in This represents the heat exchange power of the entire liquid cooling unit. This refers to the mass flow rate of the coolant in the liquid cooling unit. This refers to the temperature of the coolant at the outlet of the liquid cooling unit. This refers to the temperature of the coolant at the inlet of the liquid cooling unit.

[0034] The heat transfer model of the battery cell can be used to... (This refers to the heat exchange power between a single battery cell and the liquid cooling unit), and the corresponding relationship is as follows: ; in This represents the total number of battery cells, and the total heat exchanged between all cells and the coolant through the liquid cooling unit. can be Calculated ; in For time intervals.

[0035] The temperature of the coolant at the inlet of the liquid cooling unit is The temperature at the water outlet is The average temperature of the coolant in the liquid cooling unit is: ; Average temperature of coolant in liquid cooling unit Average temperature of coolant in liquid cooling system The following correspondence exists between them: ; and The conversion coefficient k between them can be obtained from actual vehicle operating condition data. In the actual vehicle data and liquid cooling system studied in this invention, k is almost a constant value, and when the length of the external pipeline of the liquid cooling unit is much smaller than the length of the internal pipeline of the liquid cooling unit, Almost equal to .

[0036] In step S5 above, a coolant temperature prediction model is constructed based on the relationship between coolant temperature and time, and the relationship between the inlet and outlet temperatures of the liquid cooling unit and the average coolant temperature. Specifically, this includes: in any The inlet and outlet water temperatures of the liquid cooling unit are as follows: ; ; in, For coolant in The temperature at the inlet of the liquid cooling unit at any given time. For coolant in The temperature at the outlet of the liquid cooling unit at any given time. For liquid cooling unit in Heat exchange power at any given time.

[0037] The coolant temperature corresponding to the hottest cell in the battery pack is The coolant temperature corresponding to the lowest temperature cell is Assuming the coolant temperature in the liquid cooling unit exhibits a linear distribution, let the equivalent distance from the highest temperature cell to the liquid cooling unit inlet be denoted as... The equivalent distance between the lowest temperature battery cell and the inlet of the liquid cooling unit is The temperatures of the coolant at the highest and lowest temperature battery cells can be approximated by this geometric positional relationship: ; ; in This is the equivalent length of the liquid cooling unit piping. and This refers to the coolant temperature at the corresponding location required in the cell temperature calculation process. Through extensive operating data or experiments, the locations of the highest and lowest temperature cells in the battery pack can be determined. For example, in thermal management cooling conditions, the cell closest to the liquid cooling unit inlet is often the lowest temperature cell, while the cell closest to the outlet is often the highest temperature cell. In thermal management heating conditions, the opposite is true: the cell closest to the liquid cooling unit inlet is often the highest temperature cell, and the cell closest to the outlet is often the lowest temperature cell.

[0038] According to the working conditions , , Data and average coolant temperature With the initial value, the relationship between the inlet and outlet temperatures of the liquid cooling unit and time can be established, and then the inlet and outlet water temperatures of the liquid cooling unit at any given time can be solved.

[0039] In step S6 above, the cell energy balance model, cell heat generation model, cell heat transfer model, and coolant temperature prediction model are coupled to construct a cell temperature prediction model, which specifically includes: Based on the above cell energy balance formula: ; In this formula, the heat exchange power between the battery cell and the liquid cooling unit is... and the heat exchange power of other sides of the battery cell The heat generation power of the battery cell itself can be calculated using a battery cell heat generation model. It can be calculated using a cell heat generation model, under known conditions. , ,and In this case, the heat absorption power of the battery cell itself can be calculated using the cell energy balance formula. .

[0040] According to the above calculation formula In providing and Once the model parameters are obtained, the cell temperature change rate can be calculated. .

[0041] Given the cell temperature change rate In this case, after obtaining the current temperature of the battery cell, the predicted temperature of the battery cell after the next time step is calculated as: current temperature of the battery cell * ... This allows for the calculation of the predicted temperature of the battery cell at the next time step.

[0042] like Figure 2 As shown, in step S7 above, relevant parameters are input into the cell temperature prediction model, and the cell temperature prediction model outputs the predicted temperature of the cell. Specifically, this includes the following steps: (1) Input the corresponding model parameters into the cell heat transfer model, cell heat generation model and coolant temperature prediction model, and input the operating current into the cell heat generation model.

[0043] Among them, the model parameters input to the cell heat generation model include, but are not limited to, parameters such as cell internal resistance and cell entropy thermal coefficient; The model parameters input to the cell heat transfer model include, but are not limited to, convective heat transfer coefficient, equivalent area of ​​the contact surface between the cell and the liquid cooling unit, thermal conductivity, equivalent heat conduction area of ​​the remaining surfaces, heat conduction distance, ambient or shell temperature at which heat is exchanged with the remaining surfaces, specific heat capacity of the cell itself, cell mass, and other parameters. The model parameters input to the coolant temperature prediction model include, but are not limited to, the specific heat capacity of the coolant, the total mass of the coolant, the mass flow rate of the coolant in the liquid cooling unit, the conversion coefficient k, the equivalent length of the liquid cooling unit pipeline, the equivalent distance between the highest temperature battery cell and the liquid cooling unit inlet, and the equivalent distance between the lowest temperature battery cell and the liquid cooling unit inlet.

[0044] (2) Input the current operating current, the current highest temperature of the battery cell and the current lowest temperature of the battery cell into the battery cell heat generation model, and calculate the battery cell heat generation rate of the current highest temperature battery cell and the battery cell heat generation rate of the current lowest temperature battery cell.

[0045] It should be understood that when the prediction is first executed, an initial value needs to be input into the cell heat generation model. The initial value includes the highest temperature cell temperature and the lowest temperature cell temperature at the initial time. No further initial values ​​are input at other times. The highest temperature cell temperature and the lowest temperature cell temperature at the next time step, calculated by the cell energy balance formula, are used as the input values ​​instead of the initial values.

[0046] (3) Input the current highest temperature battery cell temperature, the current lowest temperature battery cell temperature, the coolant temperature at the corresponding position of the current highest temperature battery cell, and the coolant temperature at the corresponding position of the current lowest temperature battery cell into the battery cell heat transfer model, and calculate the heat transfer power between the current highest temperature battery cell and the liquid cooling unit, the heat transfer power between the current lowest temperature battery cell and the liquid cooling unit, as well as the heat transfer power of the remaining surfaces of the current highest temperature battery cell and the heat transfer power of the remaining surfaces of the current lowest temperature battery cell.

[0047] It should be understood that when the prediction is first executed, initial values ​​need to be input into the cell heat transfer model. The initial values ​​include: the highest temperature cell temperature at the initial moment, the lowest temperature cell temperature at the initial moment, the coolant temperature at the location corresponding to the highest temperature cell at the initial moment, and the coolant temperature at the location corresponding to the lowest temperature cell at the initial moment. No further initial values ​​are input at other times. The coolant temperatures at the locations corresponding to the highest temperature cell at the next time step and the lowest temperature cell at the next time step calculated by the coolant temperature prediction model, as well as the highest temperature cell temperature at the next time step and the lowest temperature cell at the next time step calculated by the cell energy balance formula, are used as input values ​​instead of the initial values.

[0048] (4) Input the current coolant temperature at the location corresponding to the highest temperature cell and the current coolant temperature at the location corresponding to the lowest temperature cell into the coolant temperature prediction model, as well as the current heat exchanger power, and calculate the coolant temperature at the location corresponding to the highest temperature cell and the coolant temperature at the location corresponding to the lowest temperature cell after the next time step.

[0049] It should be understood that when the prediction is first executed, initial values ​​need to be input into the coolant temperature prediction model. The initial values ​​include: the highest temperature cell temperature at the initial moment, the lowest temperature cell temperature at the initial moment, the coolant temperature at the location corresponding to the highest temperature cell at the initial moment, and the coolant temperature at the location corresponding to the lowest temperature cell at the initial moment. No further initial values ​​are input at other times. The coolant temperatures at the locations corresponding to the highest temperature cell at the next time step and the lowest temperature cell at the next time step calculated by the coolant temperature prediction model, as well as the highest temperature cell temperature at the next time step and the lowest temperature cell at the next time step calculated by the cell energy balance formula, replace the initial values ​​as input values.

[0050] (5) Based on the cell energy balance, the cell heat absorption rate of the highest temperature cell is calculated according to the heat exchange power between the highest temperature cell and the liquid cooling unit, the heat exchange power of the remaining surface of the highest temperature cell, and the current cell heat generation rate. The cell temperature change rate is calculated according to the cell heat absorption rate of the highest temperature cell. Finally, the highest temperature cell temperature after the next time step is calculated and output according to the cell temperature change rate and the current highest temperature cell temperature. Based on the cell energy balance model, the cell heat absorption rate of the lowest temperature cell is calculated according to the heat exchange power between the current lowest temperature cell and the liquid cooling unit, the heat exchange power of the remaining surfaces of the current lowest temperature cell, and the current cell heat generation rate. The cell temperature change rate is then calculated based on the cell heat absorption rate of the lowest temperature cell. Finally, the lowest temperature cell temperature after the next time step is calculated and output based on the cell temperature change rate and the current lowest temperature cell temperature.

[0051] (6) Take the coolant temperature at the location corresponding to the highest temperature cell after the next time step as the current coolant temperature at the location corresponding to the highest temperature cell, and take the coolant temperature at the location corresponding to the lowest temperature cell after the next time step as the current coolant temperature at the location corresponding to the lowest temperature cell; take the highest temperature cell after the next time step as the current highest temperature cell, and take the lowest temperature cell after the next time step as the current lowest temperature cell, and proceed to step (2).

[0052] The following is based on Figure 12 Taking an example, the execution process of the cell temperature prediction method provided by this invention will be described in detail: like Figure 12 As shown, it is necessary to first set the model parameters in the cell heat generation model, cell heat transfer model, and coolant temperature prediction model, and then input the total prediction time length N. t(i) represents the time, for example, t(100) represents the time of the 100th second within the total prediction time length N. Set the initial value of i to 0, and then proceed to the calculation stage: (1) Input the time step k for the next prediction. It should be understood that the time step is not a fixed value. For example, the time step k can be set to 1 second, 2 seconds, 5 seconds, etc. The time step can be set manually or automatically by the program. This invention does not limit this.

[0053] (2) Input the operating current at time t(i) into the cell heat generation model and input the heat exchanger power at time t(i) into the coolant temperature prediction model.

[0054] (3) Using Tliquid_max(i), Tliquid_min(i), Tmax(i), and Tmin(i) as input values, the heat transfer power Pmax(i) and Pmin(i) between the cell and the liquid cooling plate at time T(i) are calculated using the cell heat transfer model, and Hmax(i) and Hmin(i) are also calculated.

[0055] Wherein, Tliquid_max(i) is the temperature of the coolant at the location corresponding to the highest temperature cell in the liquid cooling unit at time t(i), Tliquid_min(i) is the temperature of the coolant at the location corresponding to the lowest temperature cell in the liquid cooling unit at time t(i), Tmax(i) is the temperature of the highest temperature cell at time t(i), Tmin(i) is the temperature of the lowest temperature cell at time t(i), Pmax(i) is the heat exchange power between the highest temperature cell and the liquid cooling unit at time t(i), Pmin(i) is the heat exchange power between the lowest temperature cell and the liquid cooling unit at time t(i), Hmax(i) is the heat exchange power between the highest temperature cell and other surfaces besides the heat exchange surface of the liquid cooling unit at time t(i), and Hmin(i) is the heat exchange power between the lowest temperature cell and other surfaces besides the heat exchange surface of the liquid cooling unit at time t(i). It should be understood that when i is 0, that is, at the initial moment, initial values ​​need to be input into the cell heat generation model, cell heat transfer model and coolant temperature model. At this time, Tliquid_max(i), Tliquid_min(i), Tmax(i), Tmin(i), Pmax(i), Pmin(i), Hmax(i), and Hmin(i) respectively represent the temperature of the coolant at the position corresponding to the highest temperature cell in the liquid cooling unit at the initial state, the temperature of the coolant at the position corresponding to the lowest temperature cell in the liquid cooling unit at the initial state, the temperature of the highest temperature cell at the initial state, the temperature of the lowest temperature cell at the initial state, the heat transfer power between the highest temperature cell and the liquid cooling unit at the initial state, the heat transfer power between the highest and lowest temperature cells and the liquid cooling unit at the initial state, the heat transfer power between the highest temperature cell and other surfaces except the heat transfer surface of the liquid cooling unit at the initial state, and the heat transfer power between the lowest temperature cell and other surfaces except the heat transfer surface of the liquid cooling unit at the initial state.

[0056] (4) Based on Tliquid_max(i), Tliquid_min(i), Tmax(i), Tmin(i), Pmax(i), Pmin(i), Hmax(i) and Hmin(i), calculate Tmax(i+k) and Tmin(i+k) at time t(i+k) using the cell temperature prediction model; calculate Tliquid_max(i+k) and Tliquid_min(i+k) at time t(i+k) using the coolant temperature prediction model.

[0057] In this step, the coolant temperature prediction model is used to calculate the coolant temperature at the location corresponding to the highest temperature cell and the lowest temperature cell in the liquid cooling unit after the next time step k. The cell temperature prediction model is used to calculate the temperature of the highest temperature cell and the lowest temperature cell after the next time step k.

[0058] (5) Output the prediction results Tmax(i+k) and Tmin(i+k).

[0059] (6) Determine whether the time point after the next time step exceeds the set total time length, that is, determine whether i+k<N holds: When i+k<N holds, it indicates that the time point after the next time step is still within the total time length required for prediction, the prediction step still needs to be continued, and the value of i is increased by k before proceeding to step (1). That is, the predicted maximum / minimum temperature cell temperature at time t(i+k) and the coolant temperature at the positions corresponding to the maximum temperature cell and the minimum temperature cell at time t(i+k) are taken as initial values, and the prediction process is continued from step (1).

[0060] When i+k<N does not hold, it means that the time point after the next time step has exceeded the set total time length, no further prediction is required, and the process ends.

[0061] In an optional embodiment provided by the present invention, for different liquid cooling system structures, the above cell temperature prediction method can be applied to cooling systems of different configurations only by adding or subtracting the heat exchange amount between the corresponding component and the system in the heat balance equation of the liquid cooling system.

[0062] In addition, in an optional embodiment of the present invention, the above cell temperature prediction method can be implemented in MATLAB / Simulink. Its solution does not involve repeated iteration of a certain parameter, and can directly solve for the maximum / minimum temperature cell temperature, without requiring a large amount of memory to store calculation results. The required computing power and occupied resources are relatively small, and it can be directly applied to the on-board BMS of a real vehicle.

[0063] In the following, how to output the predicted temperature of the cell through the cell temperature prediction model will be described with a specific example: Taking the liquid cooling system of the battery pack of a certain electric vehicle as an example, a low-temperature heating working condition is selected, the working condition current is used as the input of the above cell heat generation model, and the heating power of the heat exchanger is used as the input of the above coolant temperature prediction model. The current data is as Figure 3 shown, Figure 3 where the horizontal axis represents time in seconds, and the vertical axis represents current in amperes; the heating power data of the heat exchanger is as Figure 4 shown, Figure 4 where the horizontal axis represents time in seconds, and the vertical axis represents power in watts. Then input the initial values, set the initial temperatures of the maximum temperature cell and the minimum temperature cell in the cell temperature prediction model to -17 degrees and -20 degrees respectively, set the coolant temperatures at the water inlet and outlet of the liquid cooling plate in the coolant temperature prediction model to both -21 degrees, and input other model parameters (such as the thermophysical parameters of the coolant).

[0064] Using relevant software (MATLAB / Simulink) and based on operating current data, the cell heat generation rate of the highest temperature cell and the lowest temperature cell were calculated according to the cell heat generation model. For example... Figure 5 This demonstrates the self-heating rate data of a high-temperature battery cell. Figure 5 The horizontal axis represents time in seconds, and the vertical axis represents power in watts.

[0065] Using relevant software, based on the heating power data of the heat exchanger and according to the coolant temperature prediction model, the following can be calculated: Figure 6 The average coolant temperature data shown is as follows: Figure 6 The horizontal axis represents time in seconds, and the vertical axis represents temperature in degrees Celsius.

[0066] Using relevant software and a cell heat transfer model, the heat transfer power between the highest temperature cell and the liquid cooling plate, as well as the heat transfer power between the lowest temperature cell and the liquid cooling plate (taking a square cell as an example, i.e., the convective heat transfer between the bottom surface of the square cell and the liquid cooling plate), are calculated. Figure 7 This demonstrates the heat exchange power between a high-temperature battery cell and a liquid cooling plate. Figure 7 The horizontal axis represents time, in seconds, and the vertical axis represents power, in watts. Figure 7 The negative value in the value indicates that the coolant is releasing heat to the battery cell.

[0067] The heat exchange power of the liquid cooling plate is the main reason for the temperature difference of the coolant at the inlet and outlet of the liquid cooling plate. Based on steps S4 and S5 above, the inlet and outlet temperatures of the liquid cooling plate can be calculated (wherein, experimental testing shows that this type of battery pack...). and The conversion coefficient between them is k=1), and the inlet and outlet temperatures of the liquid cooling plate are as follows: Figure 8 As shown, Figure 8 The horizontal axis represents time in seconds, and the vertical axis represents temperature in degrees Celsius. Figure 8 In the diagram, taking the horizontal axis at 500 as an example, from bottom to top, the curves are Predicted_tempoutlet, temp_outlet, Predicted_tempinlet, and temp_inlet. The Predicted_tempoutlet curve represents the predicted outlet temperature, the Predicted_tempinlet curve represents the predicted inlet temperature, the temp_inlet curve represents the measured inlet temperature, and the temp_outlet curve represents the measured outlet temperature.

[0068] To predict the temperatures of the hottest and coldest cells in a battery pack under heating conditions, steps S1 and S2 indicate that the coolant temperatures at the corresponding locations of these cells are required. Experiments show that under heating conditions, the hottest cell is located at the liquid cooling plate inlet, and the coldest cell is located at the liquid cooling plate outlet. Therefore, based on the coolant inlet and outlet temperatures obtained above, the temperatures of the hottest and coldest cells can be predicted. The prediction results are as follows: Figure 9 As shown, Figure 9 The horizontal axis represents time in seconds, and the vertical axis represents temperature in degrees Celsius. Figure 9 In the example with the horizontal axis at 1500, the four curves from bottom to top are the predicted lowest temperature cell temperature, the measured lowest temperature cell temperature, the predicted highest temperature cell temperature, and the measured highest temperature cell temperature, respectively.

[0069] Depend on Figure 9 It can be seen that the temperatures of the highest and lowest temperature cells predicted by the cell temperature prediction method provided by the present invention are very close to the actual measured temperatures of the highest and lowest temperature cells, and can predict the temperatures of the highest and lowest temperature cells relatively accurately.

[0070] In summary, the cell temperature prediction method provided by this invention constructs a cell heat generation model and a cell heat transfer model. Based on these models, it establishes a cell energy balance equation; it constructs the relationship between coolant temperature and time, and the relationship between the inlet and outlet temperatures of the liquid cooling unit and the average coolant temperature. Based on these relationships, it constructs a coolant temperature prediction model to predict the inlet and outlet temperatures of the liquid cooling unit under future operating conditions. The cell energy balance equation and the coolant temperature prediction model are coupled to construct a cell temperature prediction model. Relevant parameters are input into this model, and it outputs the predicted cell temperature. This method predicts the highest and lowest cell temperatures in the battery pack, reducing the complexity of solving for cell temperatures, refining the impact of temperature distribution in the liquid cooling unit on cell temperature, and requiring relatively little computational power and resources. It can be applied to vehicle battery management systems.

[0071] Based on the same inventive concept, the present invention also provides a cell temperature prediction device. The cell temperature prediction device provided by the present invention is described below. The cell temperature prediction device described below and the cell temperature prediction method described above can be referred to each other.

[0072] like Figure 10 As shown, the present invention provides a cell temperature prediction device, including a model building module 101, a parameter input module 102, and a calculation module 103.

[0073] Among them, the model building module 101 is used to build a cell heat generation model and a cell heat transfer model, build a cell energy balance model, build a coolant temperature change relationship over time, build a relationship between the inlet and outlet temperatures of the liquid cooling unit and the average coolant temperature, build a coolant temperature prediction model based on the coolant temperature change relationship over time and the relationship between the inlet and outlet temperatures of the liquid cooling unit and the average coolant temperature, and couple the cell energy balance model, the cell heat generation model, the cell heat transfer model and the coolant temperature prediction model to build a cell temperature prediction model.

[0074] The parameter input module 102 is used to input relevant parameters into the cell temperature prediction model, including but not limited to operating current, heat exchanger heating power, model parameters, system initial values, etc.

[0075] The calculation module 103 is used to calculate and output the predicted temperature of the battery cell through the battery cell temperature prediction model.

[0076] Figure 11 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 11 As shown, the electronic device may include: a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140. The processor 1110 can call logic instructions in the memory 1130 to execute a cell temperature prediction method, which includes: Construct a cell heat generation model and a cell heat transfer model; Constructing a battery cell energy balance system; Construct the relationship between coolant temperature and time; The relationship between the inlet and outlet temperatures of the liquid cooling unit and the average temperature of the coolant was established. Based on the relationship between the coolant temperature and time and the relationship between the inlet and outlet temperatures of the liquid cooling unit and the average coolant temperature, a coolant temperature prediction model is constructed. The cell energy balance model, the cell heat generation model, the cell heat transfer model, and the coolant temperature prediction model are coupled together to construct a cell temperature prediction model. Input relevant parameters into the cell temperature prediction model, and calculate and output the predicted temperature of the cell based on the cell temperature prediction model.

[0077] Furthermore, the logical instructions in the aforementioned memory 1130 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0078] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program that can be stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer is able to execute the cell temperature prediction method provided by the above methods, the method comprising: Construct a cell heat generation model and a cell heat transfer model; Constructing a battery cell energy balance system; Construct the relationship between coolant temperature and time; The relationship between the inlet and outlet temperatures of the liquid cooling unit and the average temperature of the coolant was established. Based on the relationship between the coolant temperature and time and the relationship between the inlet and outlet temperatures of the liquid cooling unit and the average coolant temperature, a coolant temperature prediction model is constructed. The cell energy balance model, the cell heat generation model, the cell heat transfer model, and the coolant temperature prediction model are coupled together to construct a cell temperature prediction model. Input relevant parameters into the cell temperature prediction model, and calculate and output the predicted temperature of the cell based on the cell temperature prediction model.

[0079] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the cell temperature prediction method provided by the methods described above, the method comprising: Construct a cell heat generation model and a cell heat transfer model; Constructing a battery cell energy balance system; Construct the relationship between coolant temperature and time; The relationship between the inlet and outlet temperatures of the liquid cooling unit and the average temperature of the coolant was established. Based on the relationship between the coolant temperature and time and the relationship between the inlet and outlet temperatures of the liquid cooling unit and the average coolant temperature, a coolant temperature prediction model is constructed. The cell energy balance model, the cell heat generation model, the cell heat transfer model, and the coolant temperature prediction model are coupled together to construct a cell temperature prediction model. Input relevant parameters into the cell temperature prediction model, and calculate and output the predicted temperature of the cell based on the cell temperature prediction model.

[0080] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0081] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cell temperature prediction method for predicting the highest and lowest temperature cells in a battery pack under low computing power scenarios of an automotive BMS, characterized in that, Includes the following steps: S1. Construct a cell heat generation model and a cell heat transfer model; wherein, the cell heat generation model adopts the Bernardi heat generation formula and simultaneously calculates the irreversible Joule heat generation and reversible entropy heat of the cell; the cell heat transfer model constructs the convective heat transfer power equation between the cell and the liquid cooling unit, the conduction heat transfer power equation of the other surfaces of the cell, and the heat absorption power equation of the cell's own heat capacity. S2. Based on the cell heat generation model and cell heat transfer model, construct the cell energy balance equation; the cell energy balance equation is a closed-loop equation that conserves the cell heat generation power, the cell heat exchange power with the liquid cooling unit, the total heat conduction and heat exchange power of the other surfaces of the cell, and the cell heat absorption power, and is used to solve the cell temperature change rate. S3. Construct the relationship between coolant temperature and time. Based on the heat exchanger heat transfer of the liquid cooling system and the total heat transfer of all cells, calculate the average temperature of the coolant system at the next moment step by step. S4. Construct the relationship between the inlet and outlet temperatures of the liquid cooling unit and the average temperature of the coolant system, and establish the linear temperature gradient relationship of the coolant along the pipeline inside the liquid cooling unit. S5. Combining the time-series recursive relationship of coolant temperature in step S3 and the temperature transformation and linear gradient relationship of liquid cooling unit in step S4, a coolant temperature prediction model is constructed. The coolant temperature prediction model can calculate the temperature of the inlet and outlet of the liquid cooling unit at any time, and based on the equivalent distance between the cell and the inlet, solve the local coolant temperature at the corresponding positions of the highest temperature cell and the lowest temperature cell. S6. Couple the cell energy balance model from step S2, the cell heat generation model and cell heat transfer model from step S1, and the coolant temperature prediction model from step S5 in a step-by-step closed-loop time-series iterative process to construct a cell temperature prediction model. The coupled iterative logic is as follows: calculate cell heat generation at the current moment → calculate heat transfer power of each surface of the cell → update the overall coolant temperature at the next moment → solve for the highest temperature cell and the lowest temperature cell at the next moment; and only iterate the calculation of the two extreme cells, the highest temperature cell and the lowest temperature cell, without solving for all cells in the battery pack, thus achieving a reduction in computing power. S7. Input the corresponding operating condition parameters, model property parameters and initial temperature values ​​into the cell temperature prediction model, and perform time-step-by-time calculations based on closed-loop time-series iterative logic to continuously output the predicted temperatures of the highest and lowest temperature cells in the battery pack at future times.

2. The cell temperature prediction method according to claim 1, characterized in that, The construction of cell heat generation and cell heat transfer models includes: Constructing a cell heat generation model: ； in, For the heat generation rate of the battery cell, For current, The internal resistance of the battery cell. For cell temperature, The entropy thermal coefficient of the battery cell; Constructing a cell heat transfer model: Calculate the heat exchange rate between the battery cell and the liquid cooling unit: ； in, The heat exchange rate between the battery cell and the liquid cooling unit. The convective heat transfer coefficient is... This is the equivalent area of ​​the contact surface between the battery cell and the liquid cooling unit. The temperature of the coolant at the corresponding location of the battery cell; Calculate the heat exchange rate of the remaining surfaces of the battery cell: ； in, The heat exchange rate of one side of the battery cell is given. Thermal conductivity, The equivalent thermal conductivity area of ​​the remaining surfaces is given. For thermal conductivity distance, The temperature at which heat is exchanged with the remaining surfaces; Calculate the heat absorption power of the battery cell itself: ； in, This refers to the heat absorption power of the battery cell itself. The specific heat capacity of the battery cell itself. The mass of the battery cell, The temperature change rate of the battery cell is denoted as .

3. The cell temperature prediction method according to claim 2, characterized in that, Constructing a cell energy balance system specifically includes: ； Where n is the number of surfaces of the battery cell other than the contact surface with the liquid cooling unit.

4. The cell temperature prediction method according to claim 1, characterized in that, The relationship between coolant temperature and time was constructed, specifically including: Constructing a heat balance: ； in The heat absorbed by the coolant itself. The heat exchanged between the heat exchanger and the coolant in the liquid cooling system. The total heat exchanged between all battery cells and the coolant through the liquid cooling unit; the liquid cooling system includes a heat exchanger, a water pump, and the liquid cooling unit; The heat absorbed by the coolant itself is as follows: ； Therefore, we get: ； Where c is the specific heat capacity of the coolant, and m is the total mass of the coolant in the liquid cooling system. For the coolant in The average temperature at any given time For the coolant in The average temperature at any given time for Time to The amount of heat absorbed by the coolant itself within a given time period.

5. The cell temperature prediction method according to claim 4, characterized in that, The relationship between the inlet and outlet temperatures of the liquid cooling unit and the average temperature of the coolant is established, specifically including: The relationship between the inlet and outlet temperatures of the liquid cooling unit is as follows: ； in, The heat exchange power of the liquid cooling unit is... The mass flow rate of the coolant in the liquid cooling unit. The temperature of the coolant at the outlet of the liquid cooling unit. The temperature of the coolant at the inlet of the liquid cooling unit; and The correspondence is as follows: ； in, This represents the total number of battery cells. Depend on calculate : ； in For time intervals; The average temperature of the coolant in the liquid cooling unit is: ； The average temperature of the coolant in the liquid cooling unit The average temperature of the coolant in the liquid cooling system The relationship is: ； Where k is the conversion coefficient.

6. The cell temperature prediction method according to claim 5, characterized in that, When constructing the coolant temperature prediction model in step S5, the calculation formulas for the inlet and outlet temperatures of the liquid cooling unit and the coolant temperature corresponding to the extreme value cell are as follows: t n Inlet water temperature at all times: ; t n Constant water outlet temperature: ; The highest temperature cell corresponds to the coolant temperature: ; Lowest cell temperature corresponding to coolant temperature: ; In the formula, This is the equivalent length of the liquid cooling unit piping. This is the equivalent distance between the highest temperature battery cell and the water inlet. This is the equivalent distance between the lowest temperature battery cell and the water inlet.

7. The cell temperature prediction method according to claim 6, characterized in that, Step S7, the closed-loop timing iterative calculation steps, include: (1) Input the physical property parameters corresponding to the prediction models of cell heat generation, heat transfer, and coolant, and input the operating current, heat exchanger power, initial highest temperature cell temperature, lowest temperature cell temperature, and initial coolant temperature. (2) Based on the current operating current and the current extreme cell temperature, calculate the heat generation rate of the highest temperature cell and the lowest temperature cell using the cell heat generation model; (3) Based on the current extreme cell temperature and the corresponding coolant temperature, calculate the heat exchange power between the two types of extreme cells and the liquid cooling unit, and the total heat conduction power of the remaining surfaces through the cell heat transfer model. (4) Input the current cell heat exchange power and heat exchanger power into the coolant temperature prediction model to calculate the coolant temperature at the corresponding position of the highest temperature cell and the lowest temperature cell at the next moment. (5) Substitute the cell heat generation power, liquid cooling heat exchange power and other surface heat conduction power into the cell energy balance equation to solve the cell heat absorption power and temperature change rate. Combine the current cell temperature to calculate the cell temperature at the next moment, including the cell temperature at the highest and lowest temperatures. (6) Update the coolant temperature and extreme cell temperature obtained in steps (4) and (5) to the current iteration input value, and repeat steps (2) to (5) until the preset prediction time is reached, and output the extreme cell prediction temperature at each time step.

8. A cell temperature prediction device for use in an on-board BMS controller, characterized in that, include: The system comprises a model building module, a parameter input module, and a calculation module. The model building module executes steps S1 to S6 of any one of claims 1 to 6 to build a cell heat generation model, a cell heat transfer model, a cell energy balance model, and a coolant temperature prediction model, and completes step-by-step closed-loop time-series iterative coupling to generate a cell temperature prediction model that is only calculated for the highest and lowest temperature cells. The parameter input module is used to input operating current, heat exchanger power, physical property parameters, and initial temperature values ​​into the cell temperature prediction model. The calculation module is used to calculate step-by-step according to the closed-loop time-series iterative logic of claim 7 and output the predicted temperatures of the highest and lowest temperature cells in the battery pack.

9. An in-vehicle BMS electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the cell temperature prediction method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the vehicle-mounted BMS processor, it implements the cell temperature prediction method as described in any one of claims 1 to 7.

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