Battery core temperature determination method and device, vehicle and readable storage medium

Abstract

The invention relates to a battery core temperature determination method and device, a vehicle and a readable storage medium. The method comprises the following steps: acquiring a heat transfer equation satisfied by a battery in a preset heat transfer direction and a Berner heat production equation for quantifying heat; determining an associated parameter associated with the core temperature of the battery, and correcting the open-circuit voltage in the Berner-th heat production equation according to the associated parameter to obtain a corrected Berner-th heat production equation; constructing a battery semi-closed-loop temperature estimation model of the battery according to the heat transfer equation and the corrected Bernath heat production equation; working parameters of the battery under different preset working conditions are obtained, parameter identification is conducted on the battery semi-closed-loop temperature estimation model according to the working parameters, model parameters of the battery semi-closed-loop temperature estimation model are determined, and a temperature estimation model used for determining the core temperature of the battery is obtained. The method can accurately estimate the core temperature of the battery and ensure the safety of the battery.

Description

Technical Field

[0001] The present application relates to the technical field of battery temperature detection, and in particular to a method, device, vehicle and readable storage medium for determining battery core temperature. Background Art

[0002] With the optimization of battery material systems and the advancement of manufacturing processes, the battery charging and discharging performance continues to improve, but it also brings more heat. The heat will cause changes in battery temperature. Temperature is an important indicator that affects the battery's electrochemical performance, life, and safety. Obtaining accurate internal battery temperature has always been an important technology for battery management systems.

[0003] In traditional technology, foreign objects are implanted inside the battery, such as electrode sensors, temperature sensors, etc., to obtain more information about the internal status of the battery. Foreign objects inside the battery greatly increase the risk of thermal runaway. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, vehicle, computer-readable storage medium and computer program product for determining the battery core temperature that can ensure battery safety and core temperature accuracy in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for determining a battery core temperature, comprising: Obtaining a heat transfer equation satisfied by the battery in a preset heat transfer direction and a Bernardi heat generation equation for quantifying the heat; Determine a correlation parameter associated with the battery core temperature, and correct the open circuit voltage in the Bernardi heat generation equation according to the correlation parameter to obtain a corrected Bernardi heat generation equation; Constructing a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the modified Bernardi heat generation equation; The operating parameters of the battery under different preset working conditions are obtained, and parameter identification is performed on the battery semi-closed-loop temperature estimation model according to the operating parameters to determine the model parameters of the battery semi-closed-loop temperature estimation model, so as to obtain a temperature estimation model for determining the battery core temperature.

[0006] In one embodiment, obtaining the heat transfer equation satisfied by the battery in a preset heat transfer direction includes: Obtain a battery thermal convection lumped parameter model of the battery; The battery thermal convection lumped parameter model is converted to obtain a heat transfer equation satisfied by the battery in a preset heat transfer direction.

[0007] In one embodiment, the preset heat transfer direction includes a first heat transfer direction from the battery core to the battery surface and a second heat transfer direction from the battery surface to the battery liquid cooling plate, and the battery thermal convection lumped parameter model is converted to obtain a heat transfer equation satisfied by the battery in the preset heat transfer direction, including: Converting the battery thermal convection lumped parameter model to obtain a first heat transfer equation satisfied by the battery in a first heat transfer direction and a second heat transfer equation satisfied by the battery in a second preset heat transfer direction; Among them, the first heat transfer equation is used to characterize the relationship between the battery surface temperature at the current sampling cycle and the next sampling cycle, the heat input at the current cycle, and the battery ambient temperature; the second heat transfer equation is used to characterize the relationship between the internal temperature of the battery cell collected at the current sampling cycle and the next sampling cycle, the heat input at the current cycle, and the battery surface temperature.

[0008] In one embodiment, determining the associated parameter associated with the battery core temperature, and correcting the open circuit voltage in the Bernardi heat generation equation according to the associated parameter to obtain a corrected Bernardi heat generation equation, includes: Determine all candidate parameters associated with the battery core temperature, and according to the correlation between each candidate parameter and the battery core temperature, determine the candidate parameter corresponding to the maximum correlation as the associated parameter; Determining a correction relationship satisfied by the open circuit voltage according to the associated parameters; The open circuit voltage in the Bernardi heat generation equation is corrected according to the correction relationship to obtain a corrected Bernardi heat generation equation.

[0009] In one embodiment, the associated parameter is the battery surface temperature, and determining the correction relationship satisfied by the open circuit voltage according to the associated parameter includes: The relationship satisfied by the open circuit voltage of the battery at the current sampling cycle and the next sampling cycle, the battery surface temperature of the battery at the current sampling cycle and the real-time estimated surface temperature of the battery is used as the correction relationship satisfied by the open circuit voltage.

[0010] In one embodiment, the obtaining the operating parameters of the battery under different preset working conditions, performing parameter identification on the battery semi-closed loop temperature estimation model according to the operating parameters, and determining the model parameters of the battery semi-closed loop temperature estimation model include: The operating parameters of the battery at different preset temperatures are collected using pulse operating conditions; the operating parameters include core temperature, battery surface temperature, ambient temperature, voltage and current under charge and discharge conditions; Using the operating parameters as inputs of the battery semi-closed-loop temperature estimation model, and determining the thermophysical parameters of the battery semi-closed-loop temperature estimation model using a least squares method; The preset simulation working parameters are input into a battery semi-closed loop temperature estimation model including the thermophysical parameters, and correction parameters of the battery semi-closed loop temperature estimation model are determined.

[0011] In one embodiment, the method further comprises: Obtain the battery surface temperature, voltage and current of the battery to be tested, as well as the ambient temperature; The battery surface temperature, the voltage, the current and the ambient temperature are input into the temperature estimation model to obtain the core temperature of the battery to be tested.

[0012] In a second aspect, the present application also provides a device for determining a battery core temperature, comprising: A data acquisition module, used to obtain a heat transfer equation satisfied by the battery in a preset heat transfer direction and a Bernardi heat generation equation for quantifying heat; a correction module, configured to determine a correlation parameter associated with the battery core temperature, and to correct the open circuit voltage in the Bernardi heat generation equation according to the correlation parameter to obtain a corrected Bernardi heat generation equation; A model building module, used for building a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the modified Bernardi heat generation equation; A parameter identification module is used to obtain the operating parameters of the battery under different preset working conditions, perform parameter identification on the battery semi-closed loop temperature estimation model according to the operating parameters, determine the model parameters of the battery semi-closed loop temperature estimation model, and obtain a temperature estimation model for determining the battery core temperature.

[0013] In a third aspect, the present application further provides a vehicle, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: Obtaining a heat transfer equation satisfied by the battery in a preset heat transfer direction and a Bernardi heat generation equation for quantifying the heat; Determine a correlation parameter associated with the battery core temperature, and correct the open circuit voltage in the Bernardi heat generation equation according to the correlation parameter to obtain a corrected Bernardi heat generation equation; Constructing a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the modified Bernardi heat generation equation; The operating parameters of the battery under different preset working conditions are obtained, and parameter identification is performed on the battery semi-closed-loop temperature estimation model according to the operating parameters to determine the model parameters of the battery semi-closed-loop temperature estimation model, so as to obtain a temperature estimation model for determining the battery core temperature.

[0014] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented: Obtaining a heat transfer equation satisfied by the battery in a preset heat transfer direction and a Bernardi heat generation equation for quantifying the heat; Determine a correlation parameter associated with the battery core temperature, and correct the open circuit voltage in the Bernardi heat generation equation according to the correlation parameter to obtain a corrected Bernardi heat generation equation; Constructing a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the modified Bernardi heat generation equation; The operating parameters of the battery under different preset working conditions are obtained, and parameter identification is performed on the battery semi-closed-loop temperature estimation model according to the operating parameters to determine the model parameters of the battery semi-closed-loop temperature estimation model, so as to obtain a temperature estimation model for determining the battery core temperature.

[0015] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor: Obtaining a heat transfer equation satisfied by the battery in a preset heat transfer direction and a Bernardi heat generation equation for quantifying the heat; Determine a correlation parameter associated with the battery core temperature, and correct the open circuit voltage in the Bernardi heat generation equation according to the correlation parameter to obtain a corrected Bernardi heat generation equation; Constructing a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the modified Bernardi heat generation equation; The operating parameters of the battery under different preset working conditions are obtained, and parameter identification is performed on the battery semi-closed-loop temperature estimation model according to the operating parameters to determine the model parameters of the battery semi-closed-loop temperature estimation model, so as to obtain a temperature estimation model for determining the battery core temperature.

[0016] The above-mentioned method, device, vehicle, computer-readable storage medium and computer program product for determining the core temperature of the battery obtain the heat transfer equation satisfied by the battery in the preset heat transfer direction and the Bernardi heat generation equation for quantifying the heat, and use the associated parameters associated with the core temperature of the battery to correct the open circuit voltage in the Bernardi heat generation equation. According to the heat transfer equation and the modified Bernardi heat generation equation, a battery semi-closed-loop temperature estimation model of the battery is constructed. This model avoids the situation that the battery core temperature and the associated parameters may change with the operating conditions or battery aging, the errors will continue to accumulate, and the model estimation accuracy cannot be converged. Furthermore, according to the working parameters under different preset operating conditions, the constructed battery semi-closed-loop temperature estimation model is parameter identified to determine the temperature estimation model for determining the core temperature of the battery, that is, the heat is corrected by using the associated parameters, and the battery core temperature is calculated again by the updated heat generation. There is no need to use the input battery state of charge, and the battery core temperature is determined by using an equivalent circuit model to simulate the battery heat transfer process, thereby improving the accuracy of the battery core temperature estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 is an application environment diagram of a method for determining a battery core temperature in one embodiment; Figure 2 is a flow chart of a method for determining a battery core temperature in one embodiment; Figure 3 is a schematic diagram of battery temperature distribution under charge and discharge conditions in one embodiment; Figure 4 is a schematic flow chart of step 208 in one embodiment; Figure 5 is a schematic diagram of the surface and core temperatures of a battery under charge and discharge conditions in one embodiment; Figure 6 is a flow chart of a method for determining model parameters in one embodiment; Figure 7 is a flow chart of a method for determining a battery core temperature in another embodiment; Figure 8 is a flow chart of a method for determining a battery core temperature in another embodiment; Fig. 9 is a structural block diagram of a device for determining a battery core temperature in one embodiment; Fig.10 FIG. 1 is a diagram showing the internal structure of a vehicle in one embodiment. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0020] SOC: (‌SOC (State of Charge), for example, can be understood as the percentage of power in a mobile phone; OCV: (Open Circuit Voltage) is strongly related to the battery SOC and is an important parameter for calculating heat generation. It cannot be directly obtained during the battery charging and discharging process. Entropy heat: The heat absorption and release characteristics exhibited by the battery during charging and discharging, with different coefficients used to reflect its intensity in different SOC segments.

[0021] Temperature is an important indicator that affects the electrochemical performance, life and safety of batteries. Obtaining accurate internal battery temperature has always been an important technology for battery management systems. Take lithium-ion batteries as an example, for example, iron-lithium system positive electrode batteries, and lithium-ion batteries can be lithium-ion battery modules. When measuring the internal temperature of lithium-ion batteries, since lithium-ion batteries are black box systems, they can only obtain internal battery information by externally collecting signals such as voltage, current, and temperature. Currently, more internal battery status information can be obtained by implanting electrode sensors, temperature sensors, and other technologies, but foreign matter inside the battery greatly increases the risk of thermal runaway. Therefore, it is necessary to establish a relevant battery thermal model to simulate the changing trend of internal temperature under various working conditions in real time.

[0022] The existing battery heat generation model mainly has two calculation methods: external characteristics and mechanism. The heat transfer model can be divided into one-dimensional model and three-dimensional model. Among them, the mechanism heat generation model and the three-dimensional heat transfer model are mainly used in finite element analysis during product design and optimization. The process contains a large number of parameters and partial differential equation calculations, which are not suitable for online application of real vehicles. When using the external characteristics heat generation model and the one-dimensional heat transfer model, the heat generation inside the battery needs to be regarded as a particle, and the heat transfer rate in the same direction is uniform, so that the battery core temperature can be estimated online.

[0023] In other words, the heat generation model studied by the existing technology needs to ensure the absolute accuracy of the SOC input, and cannot achieve high-precision calculation of the heat generation in the iron-lithium positive electrode system battery. In addition, the simulation accuracy of the equivalent circuit lumped parameter heat transfer model under complex working conditions such as pulses is low. The above two problems lead to the inability of the existing technology to guarantee error convergence during actual vehicle application.

[0024] In view of the inaccurate estimation of battery core temperature in related technologies, a method for determining the battery core temperature is proposed. The open circuit voltage in the Bernardi heat generation equation is corrected by using the associated parameters associated with the battery core temperature, so that the constructed battery semi-closed-loop temperature estimation model is closed, thereby realizing the estimation of the battery core temperature.

[0025] The method for determining the battery core temperature disclosed in the embodiment of the present application can be applied to, but not limited to, electric devices such as vehicles, ships or aircraft. The embodiment of the present application provides an electric device using a lithium-ion battery as a power source, and the electric device can be, but not limited to, vehicles, power tools and laptop computers.

[0026] For the convenience of explanation, the following embodiments are described by taking a vehicle 102 as an example of an electric device in an embodiment of the present application. The vehicle 102 can be different types of vehicles, which are not limited here. A battery 104 is arranged inside the vehicle 102, and the location of the battery 104 can be set according to actual needs, for example, it can be set at the bottom or head of the vehicle 102. The battery 104 is used to power the vehicle 102. The vehicle 102 also includes a controller 106, which obtains the heat transfer equation satisfied by the battery in the preset heat transfer direction and the Bernardi heat generation equation for quantifying heat; determines the associated parameters associated with the battery core temperature, and corrects the open circuit voltage in the Bernardi heat generation equation according to the associated parameters to obtain the corrected Bernardi heat generation equation; constructs a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the corrected Bernardi heat generation equation; obtains the working parameters of the battery under different preset working conditions, performs parameter identification on the battery semi-closed loop temperature estimation model according to the working parameters, determines the model parameters of the battery semi-closed loop temperature estimation model, and obtains a temperature estimation model for determining the battery core temperature.

[0027] In an exemplary embodiment, Figure 2 As shown, a method for determining the core temperature of a battery is provided, and the method is applied to Figure 1 Taking the vehicle in the example as an example, the method includes the following steps 202 to 208. Among them: Step 202 , obtaining a heat transfer equation satisfied by the battery in a preset heat transfer direction and a Bernardi heat generation equation for quantifying heat.

[0028] Among them, the number of preset heat transfer directions can be determined according to actual needs, and the preset heat transfer directions can include a first heat transfer direction from the battery core to the battery surface and a second heat transfer direction from the battery surface to the battery liquid cooling plate. The heat transfer equation is determined by discretizing the battery thermal convection lumped parameter model of the battery. The battery thermal convection lumped parameter model can also be called the battery thermal convection lumped parameter heat transfer model. The battery thermal convection lumped parameter model is determined based on the heat transfer characteristics between fluids and solids. It can be used to characterize the relationship between the physical characteristic parameters of the battery, the temperature change rate at the current moment, and the temperature distribution of the battery from the inside to the outside along a certain direction. The physical characteristic parameters include but are not limited to the mass, specific heat capacity, convection heat transfer coefficient and surface area of ​​the battery. The Bernardi heat generation equation can be used to characterize the heat generation of the battery at the current moment, the terminal voltage and current collected in real time during the operation of the battery, the open circuit voltage inside the battery, and the heat generation relationship between the current temperature of the battery and the entropy thermal coefficient of the battery.

[0029] It is understandable that the online temperature estimation of the battery can be completed based on the heat transfer equation and the Bernardi heat generation equation. However, considering that the open circuit voltage inside the battery is strongly coupled with the SOC, the SOC estimation error will directly affect the accuracy of the calorific value calculation. Therefore, it is necessary to use the associated parameters associated with the battery core temperature to recursively correct the open circuit voltage.

[0030] Step 204 , determining correlation parameters associated with the battery core temperature, and correcting the open circuit voltage in the Bernardi heat generation equation according to the correlation parameters to obtain a corrected Bernardi heat generation equation.

[0031] The associated parameter associated with the battery core temperature may be the battery surface temperature.

[0032] Exemplarily, correlation parameters associated with the battery core temperature are determined, and a correction relationship satisfied by the open circuit voltage is determined based on the correlation parameters; the open circuit voltage in the Bernardi heat generation equation is corrected based on the correction relationship to obtain a corrected Bernardi heat generation equation.

[0033] Step 206 , constructing a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the modified Bernardi heat generation equation.

[0034] It should be noted that, considering that the relationship between the battery surface temperature and the core temperature may change with operating conditions or battery aging, if an open-loop model is used to estimate the core temperature, errors will continue to accumulate, and the model estimation accuracy cannot be converged, and the battery core temperature cannot be estimated.

[0035] Exemplarily, based on the heat transfer equation and the modified Bernardi heat generation equation, a battery semi-closed-loop temperature estimation model of the battery is constructed, including the Bernardi heat generation equation corresponding to the heat generation input at the previous sampling cycle and the current sampling cycle, the modified relationship satisfied by the open circuit voltage at the current sampling cycle, the first heat transfer equation satisfied by the battery surface temperature at the current sampling cycle, and the second heat transfer equation satisfied by the battery core temperature at the next sampling cycle.

[0036] Among them, the Bernardi heat production equation corresponding to the calorific value input at the last sampling cycle can characterize the relationship between the calorific value input at the last sampling cycle and the open circuit voltage and current at the last sampling cycle, and the Bernardi heat production equation corresponding to the calorific value input at the current sampling cycle can characterize the relationship between the calorific value input at the current sampling cycle and the open circuit voltage and current at the current sampling cycle. The correction relationship satisfied by the open circuit voltage at the current sampling cycle can characterize the relationship between the open circuit voltage at the current sampling cycle, the open circuit voltage at the last sampling cycle, and the collected value and estimated value of the battery surface temperature at the current sampling cycle. The first heat transfer equation satisfied by the battery surface temperature at the current sampling cycle can characterize the relationship between the estimated value of the battery surface temperature at the last sampling cycle, the calorific value input at the last sampling cycle, and the ambient temperature at the last sampling cycle. The second heat transfer equation satisfied by the battery core temperature at the next sampling cycle can characterize the relationship between the battery core temperature at the current sampling cycle, the calorific value input at the current sampling cycle, and the collected value of the battery surface temperature at the current sampling cycle.

[0037] Step 208, obtaining the operating parameters of the battery under different preset operating conditions, performing parameter identification on the battery semi-closed loop temperature estimation model according to the operating parameters, determining the model parameters of the battery semi-closed loop temperature estimation model, and obtaining a temperature estimation model for determining the battery core temperature.

[0038] Among them, the working parameters do not include signals related to SOC. The working parameters can be the battery core temperature, battery surface temperature, ambient temperature, voltage and current under different preset working conditions for charging and discharging. The different preset working conditions can be preset ambient temperatures, such as -10℃~30℃ ambient temperature, symmetrical pulse working conditions with a frequency of 1hz between 1C-3C, and charging and discharging can be performed in each pulse working condition, such as charging for 1 second and discharging for 1 second, and the charging and discharging currents are equal.

[0039] Parameter identification can be understood as determining the model parameters of the battery semi-closed-loop temperature estimation model. By determining the model parameters of the battery semi-closed-loop temperature estimation model, the temperature estimation model is obtained. On this basis, the input and output of the battery semi-closed-loop temperature estimation model in the actual vehicle application process can be clarified, that is, the input can be the battery surface temperature, voltage and current, and the ambient temperature obtained by the thermal management system does not involve SOC-related signals, and the ambient temperature can be the inlet and outlet temperature of the liquid cooling plate.

[0040] The battery semi-closed-loop temperature estimation model is constructed and determined based on the heat transfer equation and the modified Bernardi heat generation equation. The parameters of the battery semi-closed-loop temperature estimation model include a first parameter and a second parameter. The first parameter may be a thermophysical parameter related to the battery physical characteristic parameters. The battery physical characteristic parameters may include battery mass, specific heat capacity, heat transfer coefficient, surface area and other parameters. The second parameter may be a correction parameter. The second parameter is determined by simulating the actual vehicle condition based on the determination of the first parameter.

[0041] Exemplarily, the battery core temperature, battery surface temperature, ambient temperature, voltage and current of the battery under different preset operating conditions are obtained, the data collected under different preset operating conditions are used as the input of the model, and the thermophysical parameters related to the physical characteristic parameters of the battery are determined. On this basis, a simulation of the actual vehicle operating conditions is carried out to determine the correction parameters. On this basis, it is clearly determined that the input of the battery semi-closed-loop temperature estimation model in the actual vehicle application process is the battery surface temperature, voltage and current, and the ambient temperature obtained by the thermal management system, and the output is the battery core temperature. There is no need to involve SOC-related signals, and it is decoupled from the SOC estimation in actual application to avoid battery temperature estimation errors caused by SOC estimation errors.

[0042] In the above-mentioned method for determining the core temperature of the battery, the heat transfer equation satisfied by the battery in the preset heat transfer direction and the Bernardi heat generation equation for quantifying the heat are obtained, and the open circuit voltage in the Bernardi heat generation equation is corrected by using the associated parameters associated with the core temperature of the battery. According to the heat transfer equation and the modified Bernardi heat generation equation, a battery semi-closed-loop temperature estimation model of the battery is constructed. This model avoids the situation that the battery core temperature and the associated parameters may change with the working conditions or battery aging, the errors will continue to accumulate, and the model estimation accuracy cannot be converged. Furthermore, the constructed battery semi-closed-loop temperature estimation model is parameterized according to the working parameters under different preset working conditions to determine the temperature estimation model for determining the core temperature of the battery, that is, the heat is corrected by using the associated parameters, and the battery core temperature is calculated again by the updated heat generation. There is no need to use the input battery state of charge, and the battery core temperature is determined by using an equivalent circuit model to simulate the battery heat transfer process, thereby improving the accuracy of the battery core temperature estimation.

[0043] Based on the heat transfer characteristics between fluid and solid, a battery thermal convection lumped parameter model is established. However, the battery thermal convection lumped parameter model cannot be calculated online. In order to estimate the battery core temperature, optionally, in an exemplary embodiment, the determination of the heat transfer equation includes: Obtain a battery thermal convection lumped parameter model of the battery; transform the battery thermal convection lumped parameter model to obtain a heat transfer equation satisfied by the battery in a preset heat transfer direction. The transformation here can be to transform the battery thermal convection lumped heat transfer model into a state space equation, that is, a heat transfer equation satisfied by the battery in a preset heat transfer direction, and the heat transfer equation is a discrete equation.

[0044] Among them, the preset heat transfer direction includes a first heat transfer direction from the battery core to the battery surface and a second heat transfer direction from the battery surface to the battery liquid cooling plate. Furthermore, the battery thermal convection lumped parameter model is converted to obtain a heat transfer equation satisfied by the battery in the preset heat transfer direction, including: converting the battery thermal convection lumped parameter model to obtain a first heat transfer equation satisfied by the battery in the first heat transfer direction, and a second heat transfer equation satisfied in the second preset heat transfer direction; the first heat transfer equation is used to characterize the relationship between the battery surface temperature at the current sampling cycle and the next sampling cycle, the heat input at the current cycle, and the battery ambient temperature; the second heat transfer equation is used to characterize the relationship between the internal temperature of the battery cell collected at the current sampling cycle and the next sampling cycle, the heat input at the current cycle, and the battery surface temperature.

[0045] It should be noted that the actual battery heat transfer is a progressive relationship from the inside to the outside, but it is impossible to detect the overall battery temperature in real time, nor is it necessary to know the temperature of each specific part. Therefore, the battery temperature is divided into two parts: the core and the surface. Only the temperature of the two parts is calculated to simplify the calculation amount, such as Figure 3 1 is a diagram showing the temperature distribution of a battery under charge and discharge conditions in an exemplary embodiment, including a real battery cell temperature distribution and a simplified battery cell temperature distribution.

[0046] Among them, the battery thermal convection lumped parameter model can be expressed as: ; Among them, m bat is the battery mass, C p is the specific heat capacity, is the temperature change rate at the current moment, Q is the heat input, which is generally calculated from the resistance and entropy thermal coefficient of the battery, h bat and A batRepresent the convection heat transfer coefficient and surface area respectively, T1 and T2 represent the temperature distribution of the battery from the inside to the outside along a certain direction. Since the mass, specific heat capacity, heat transfer coefficient, and surface area of ​​the battery can be regarded as constants, these fixed parameters can be represented by the lumped form of the first parameter k1 and the second parameter k2, and the simplified battery thermal convection lumped parameter model can be expressed as: ; The battery thermal convection lumped parameter model is converted to obtain a first heat transfer equation satisfied by the battery in the first heat transfer direction and a second heat transfer equation satisfied in the second preset heat transfer direction. The first heat transfer equation can be expressed as: ; The second heat transfer equation can be expressed as: ; Among them, t and t+1 represent the current sampling cycle time and the next sampling cycle time respectively, T c 、T s and T abt They represent the battery core temperature, battery surface temperature and the average temperature of the battery liquid cooling plate inlet and outlet respectively. The first parameter k1, the second parameter k2 and the fourth parameter k4 and the fifth parameter k5 in the first heat transfer equation correspond to the two lumped parameters in the simplified battery thermal convection lumped parameter model. The third parameter k3 and the sixth parameter k6 are correction parameters introduced to better simulate the heat dissipation performance of the battery in a large temperature difference environment. Heat input Q and ambient temperature T abt The real-time surface temperature T of the battery can be recursively calculated s and core temperature T c .

[0047] In the above embodiment, the standard formula of the battery thermal convection lumped parameter model is discretized and further improved to make it conform to the thermal characteristics of the battery under severe working conditions while having a small amount of calculation. That is, the heat transfer model fully considers the battery structure and mechanism, and can still maintain estimation accuracy under large-rate complex working conditions with large temperature fluctuations. The model has a small amount of calculation and is capable of online operation on a real vehicle.

[0048] On the basis of determining the heat transfer equation, the online temperature estimation of the battery needs to be completed. The Bernardi heat generation equation also needs to be introduced. Considering that the open circuit voltage inside the battery is strongly coupled with the SOC, the SOC estimation error will directly affect the calculation accuracy of the calorific value. Therefore, the open circuit voltage needs to be corrected. In an exemplary embodiment, Figure 4 As shown, step 208 includes steps 402 to 406. Among them: Step 402, determining all candidate parameters associated with the battery core temperature, and according to the correlation between each candidate parameter and the battery core temperature, determining the candidate parameter corresponding to the maximum correlation as the associated parameter.

[0049] Among them, the candidate parameters include battery charging and discharging parameters, ambient temperature parameters, and battery internal resistance. The method for determining the correlation between each candidate parameter and the battery core temperature can be achieved through existing methods and will not be elaborated here. The associated parameter can be understood as the candidate parameter that has the greatest impact on the battery core temperature. The associated parameter can be the battery surface temperature. It should be noted that, under normal circumstances, the internal temperature is higher than the surface temperature. When the thermal management is turned on in winter, there is a situation where the internal temperature is lower than the surface. The surface temperature can be directly observed, but it does not represent the actual operating temperature of the battery. Figure 5 , which is a schematic diagram of the battery surface and core temperature under charge and discharge conditions in an exemplary embodiment.

[0050] Step 404: determine a correction relationship satisfied by the open circuit voltage according to the associated parameters.

[0051] Among them, the correction relationship satisfied by the open circuit voltage determined according to the associated parameters can be: based on the relationship satisfied by the open circuit voltage of the battery at the current sampling cycle and the next sampling cycle, the battery surface temperature of the battery at the current sampling cycle and the real-time estimated surface temperature of the battery, as the correction relationship satisfied by the open circuit voltage.

[0052] The Bernardi heat production equation can be expressed as: ; Where Q t Represents the heat generated by the battery at the current moment, U t and I t represents the terminal voltage and current collected in real time during the operation of the battery, T represents the current operating temperature of the battery, is the battery entropy thermal coefficient, which can be calculated based on the voltage data measured in the variable temperature shelf experiment. The correction relationship of the open circuit voltage can be expressed as:

[0053] Among them, T t Represents the battery surface temperature obtained by the battery external signal acquisition harness in the current sampling period, that is, it represents the acquisition value of the battery surface temperature, k represents the correction coefficient, T s，t It is a real-time estimate of the battery surface temperature.

[0054] Step 406, correcting the open circuit voltage in the Bernardi heat generation equation according to the correction relationship to obtain a corrected Bernardi heat generation equation.

[0055] Exemplarily, the correction relationship is substituted into the open circuit voltage in the Bernardi heat generation equation, and the open circuit voltage in the Bernardi heat generation equation is corrected to obtain the corrected Bernardi heat generation equation.

[0056] In this embodiment, by correcting the open circuit voltage by the battery surface temperature, the strong coupling relationship between the internal open circuit voltage of the battery and the SOC can be avoided, so that the SOC estimation error will directly affect the calculation accuracy of the calorific value, thereby causing the problem of inaccurate core temperature estimation.

[0057] In an exemplary embodiment, a method for determining model parameters is provided, such as Figure 6 As shown, it includes steps 602 to 606, wherein: Step 602, using pulse operating conditions to collect operating parameters of the battery at different preset temperatures; the operating parameters include core temperature, battery surface temperature, ambient temperature, voltage and current under charge and discharge conditions.

[0058] Among them, the pulse operating condition can be understood as a symmetrical pulse operating condition with a preset frequency within a preset battery charge and discharge rate range. The preset battery charge and discharge rate range can be 1C-3C, and the preset frequency can be 1hz. The symmetrical pulse can be understood as charging and discharging with symmetry in a pulse, for example, charging for 1S and discharging for 1S in a pulse, and the charging and discharging currents are equal.

[0059] Step 604 , using the operating parameters as inputs of a battery semi-closed loop temperature estimation model, and using a least squares method to determine the thermophysical parameters of the battery semi-closed loop temperature estimation model.

[0060] Among them, the specific implementation method of determining the thermophysical parameters of the battery semi-closed loop temperature estimation model by the least squares method can be achieved through existing methods and will not be elaborated here.

[0061] According to the heat transfer equation and the modified Bernardi heat generation equation, the battery semi-closed loop temperature estimation model for the battery can be expressed as:

[0062] Among them, t is the current sampling cycle moment, t-1 is the previous sampling cycle moment, and t +1 is the next sampling cycle moment. It should be noted that the input heat under different preset working conditions is known, and when calculating the thermophysical parameters based on the pulse working condition to collect the working parameters of the battery at different preset temperatures, the input heat can be directly substituted into the calculation.

[0063] Step 606 , inputting preset simulation operating parameters into a battery semi-closed loop temperature estimation model including thermophysical parameters, and determining correction parameters of the battery semi-closed loop temperature estimation model.

[0064] For example, the battery core temperature T collected under pulse conditions is used c , surface temperature T t , Liquid cooling plate inlet and outlet temperature T abt , voltage U and current I are used as input, and the optimal k1, k2, k4, k5 are identified by the least squares method. Then, a simulation of the actual vehicle operating condition is carried out to complete the identification of the correction parameters k3, k6 and k and.

[0065] In the above embodiment, some parameters of the pulse operating condition identification model are used to minimize the influence of entropy heat on parameter identification, thereby ensuring the accuracy of battery core temperature estimation.

[0066] In an exemplary embodiment, based on the identification model parameters, a method for determining the battery core temperature is provided, including: The battery surface temperature, voltage, current, and ambient temperature of the battery to be tested are obtained; the battery surface temperature, voltage, current, and ambient temperature are input into a temperature estimation model to obtain the core temperature of the battery to be tested.

[0067] The ambient temperature may be, but is not limited to, the average temperature of the water inlet and outlet of the battery liquid cooling plate.

[0068] Exemplarily, the observed battery surface temperature, voltage, current and average temperature of the water inlet and outlet of the battery liquid cooling plate are input into the determined temperature estimation model, and the real-time estimated value of the battery surface temperature is calculated. The open circuit voltage is updated based on the real-time estimated value and the observed battery surface temperature, and the calorific value is recalculated to obtain the corrected calorific value, and the battery core temperature is estimated based on the corrected calorific value. The specific implementation can be: the calorific value input at the last sampling cycle is determined according to the terminal voltage, current and open circuit voltage at the last sampling cycle, and the surface temperature is estimated according to the calorific value input at the last sampling cycle, the estimated value of the battery surface temperature at the last sampling cycle, the average temperature of the water inlet and outlet of the battery liquid cooling plate at the last sampling cycle, and the first parameter k1, the second parameter k2 and the third parameter k3, to obtain the surface temperature estimate at the current sampling cycle.

[0069] The open circuit voltage at the current sampling cycle is corrected according to the estimated surface temperature at the current sampling cycle, the collected value of the battery surface temperature at the current sampling cycle and the correction coefficient k, the corrected open circuit voltage is obtained to complete the calculation of the heat input, and the battery core temperature at the next sampling cycle is determined based on the heat input at the current sampling cycle, the fourth parameter, the fifth parameter and the sixth parameter, the battery core temperature at the current sampling cycle and the surface temperature collected in real time during the current sampling cycle.

[0070] In the above embodiment, the surface temperature is used to complete the OCV update in the calorific value calculation process, so that the model is closed-loop, and the estimation error does not amplify over time without using SOC as a model input, thereby avoiding the temperature estimation error caused by the SOC estimation error.

[0071] In an exemplary embodiment, Figure 7 As shown, a method for determining the core temperature of a battery is provided, and the method is applied to Figure 1 The vehicle in FIG. 1 is taken as an example to illustrate, and the method includes the following steps 702 to 712. Among them: Step 702, obtaining a battery thermal convection lumped parameter model of the battery, converting the battery thermal convection lumped parameter model, and obtaining a first heat transfer equation satisfied by the battery in a first heat transfer direction, and a second heat transfer equation satisfied by the battery in a second preset heat transfer direction.

[0072] Step 704, obtaining a Bernardi heat generation equation, determining a correlation parameter associated with the battery core temperature, and correcting the open circuit voltage in the Bernardi heat generation equation according to the correlation parameter to obtain a corrected Bernardi heat generation equation; Step 706, constructing a battery semi-closed loop temperature estimation model of the battery according to the first heat transfer equation, the second heat transfer equation and the modified Bernardi heat generation equation; Step 708, obtaining the operating parameters of the battery under different preset operating conditions, performing parameter identification on the battery semi-closed loop temperature estimation model according to the operating parameters, determining the model parameters of the battery semi-closed loop temperature estimation model, and obtaining a temperature estimation model for determining the battery core temperature.

[0073] Step 710, obtaining the battery surface temperature, voltage and current of the battery to be tested, as well as the ambient temperature.

[0074] Step 712 , the battery surface temperature, voltage, current and ambient temperature are input into a temperature estimation model to obtain the core temperature of the battery to be tested.

[0075] In an exemplary embodiment, Figure 8 As shown, a flow chart of a method for determining the core temperature of a battery is provided, specifically including: based on the heat transfer characteristics between fluids and solids, a battery thermal convection lumped parameter model is established, the battery thermal convection lumped heat transfer model is converted into a state space equation, that is, the heat transfer equation satisfied by the battery in a preset heat transfer direction is obtained, and a battery semi-closed loop temperature estimation model is constructed based on the space state equation and the Bernardi heat generation equation; the voltage, current and temperature data under specific working conditions are obtained to perform parameter identification on the battery semi-closed loop temperature estimation model to determine the model parameters, wherein the voltage, current and temperature data include the collected terminal voltage, current, battery core temperature, battery surface temperature and the average temperature of the inlet and outlet of the liquid cooling plate.

[0076] The terminal voltage and current data of the battery to be tested are obtained, and the data are input into a battery semi-closed-loop temperature estimation model that determines the model parameters to determine the estimated value of the battery surface temperature of the battery to be tested. The open circuit voltage is corrected according to the collected battery surface temperature, and the heat generation of the battery is recalculated. The core temperature of the battery is estimated based on the updated heat generation to obtain the battery core temperature.

[0077] It should be noted that the specific implementation of this embodiment can be achieved through the above-mentioned limited manner, which will not be elaborated here.

[0078] In the above embodiment, by considering the battery structure and mechanism, the standard formula of thermal convection is discretized and further improved to make it conform to the thermal characteristics of the battery under severe working conditions while having a small amount of calculation, thereby ensuring the vehicle's online computing capability and the ability to maintain estimation accuracy under large-rate complex working conditions with large temperature fluctuations. On this basis, the surface temperature is used to complete the OCV update in the calorific value calculation process, so that the model is closed-loop, and the estimation error does not amplify over time without using SOC as the model input. In addition, the sampling pulse working condition is used to identify some parameters of the model, thereby minimizing the influence of entropy heat on parameter identification, thereby ensuring the accuracy and reliability of the battery core temperature estimation.

[0079] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0080] Based on the same inventive concept, the embodiment of the present application also provides a device for determining the core temperature of a battery for implementing the method for determining the core temperature of a battery involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method above, so the specific limitations in the embodiments of one or more devices for determining the core temperature of a battery provided below can refer to the limitations of the method for determining the core temperature of a battery above, and will not be repeated here.

[0081] In an exemplary embodiment, Fig. 9As shown, a device for determining the core temperature of a battery is provided, comprising: a data acquisition module 902, a correction module 904, a model building module 906 and a parameter identification module 908, wherein: The data acquisition module 902 is used to obtain the heat transfer equation satisfied by the battery in a preset heat transfer direction and the Bernardi heat generation equation for quantifying the heat.

[0082] The correction module 904 is used to determine the correlation parameters associated with the battery core temperature, and correct the open circuit voltage in the Bernardi heat generation equation according to the correlation parameters to obtain a corrected Bernardi heat generation equation.

[0083] The model building module 906 is used to build a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the modified Bernardi heat generation equation.

[0084] The parameter identification module 908 is used to obtain the operating parameters of the battery under different preset working conditions, perform parameter identification on the battery semi-closed loop temperature estimation model according to the operating parameters, determine the model parameters of the battery semi-closed loop temperature estimation model, and obtain a temperature estimation model for determining the battery core temperature.

[0085] The above-mentioned device for determining the core temperature of the battery obtains the heat transfer equation satisfied by the battery in the preset heat transfer direction and the Bernardi heat generation equation for quantifying the heat, and uses the associated parameters associated with the core temperature of the battery to correct the open circuit voltage in the Bernardi heat generation equation. According to the heat transfer equation and the modified Bernardi heat generation equation, a battery semi-closed-loop temperature estimation model of the battery is constructed. This model avoids the situation that the battery core temperature and the associated parameters may change with the working conditions or battery aging, the errors will continue to accumulate, and the model estimation accuracy cannot be converged. Furthermore, according to the working parameters under different preset working conditions, the constructed battery semi-closed-loop temperature estimation model is parameter identified to determine the temperature estimation model for determining the core temperature of the battery, that is, the heat is corrected by using the associated parameters, and the battery core temperature is calculated again by the updated heat generation. There is no need to use the input battery state of charge, and the battery core temperature is determined by using an equivalent circuit model to simulate the battery heat transfer process, thereby improving the accuracy of the battery core temperature estimation.

[0086] In an exemplary embodiment, the data acquisition module 902 is also used to obtain a battery thermal convection lumped parameter model of the battery; the battery thermal convection lumped parameter model is converted to obtain a heat transfer equation satisfied by the battery in a preset heat transfer direction.

[0087] In an exemplary embodiment, the preset heat transfer direction includes a first heat transfer direction from the battery core to the battery surface and a second heat transfer direction from the battery surface to the battery liquid cooling plate. The data acquisition module 902 is further used to convert the battery thermal convection lumped parameter model to obtain a first heat transfer equation satisfied by the battery in the first heat transfer direction and a second heat transfer equation satisfied in the second preset heat transfer direction; Among them, the first heat transfer equation is used to characterize the relationship between the battery surface temperature at the current sampling cycle and the next sampling cycle, the heat input at the current cycle, and the battery ambient temperature; the second heat transfer equation is used to characterize the relationship between the internal temperature of the battery cell collected at the current sampling cycle and the next sampling cycle, the heat input at the current cycle, and the battery surface temperature.

[0088] In an exemplary embodiment, the correction module 904 is further configured to determine all candidate parameters associated with the battery core temperature, and according to the degree of association between each candidate parameter and the battery core temperature, determine the candidate parameter corresponding to the maximum degree of association as the associated parameter; Determine the correction relationship satisfied by the open circuit voltage according to the associated parameters; The open circuit voltage in the Bernardi heat generation equation is corrected according to the correction relationship to obtain the corrected Bernardi heat generation equation.

[0089] In an exemplary embodiment, the correction module 904 is also used to use the relationship satisfied by the open circuit voltage of the battery at the current sampling cycle and the next sampling cycle, the battery surface temperature of the battery at the current sampling cycle, and the real-time estimated surface temperature of the battery as a correction relationship satisfied by the open circuit voltage.

[0090] In an exemplary embodiment, the parameter identification module 908 is further used to collect the working parameters of the battery at different preset temperatures using the pulse working condition; the working parameters include the core temperature, the battery surface temperature, the ambient temperature, the voltage and the current under the charging and discharging working condition; The operating parameters are used as inputs of a battery semi-closed loop temperature estimation model, and the thermophysical parameters of the battery semi-closed loop temperature estimation model are determined using the least square method; The preset simulation working parameters are input into a battery semi-closed loop temperature estimation model including thermophysical parameters, and correction parameters of the battery semi-closed loop temperature estimation model are determined.

[0091] In an exemplary embodiment, the device for determining the core temperature of the battery also includes a temperature estimation module, which is used to obtain the battery surface temperature, voltage and current, and ambient temperature of the battery to be tested; the battery surface temperature, voltage, current and ambient temperature are input into the temperature estimation model to obtain the core temperature of the battery to be tested.

[0092] Each module in the above-mentioned battery core temperature determination device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0093] In an exemplary embodiment, a vehicle is provided, the internal structure of which can be as follows: Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the vehicle is used to provide computing and control capabilities. The memory of the vehicle includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the vehicle is used to exchange information between the processor and an external device. The communication interface of the vehicle is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NearField Communication, NFC) or other technologies. When the computer program is executed by the processor, a method for determining the core temperature of a battery is implemented. The display unit of the vehicle is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0094] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0095] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0096] In one embodiment, a vehicle is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0097] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0098] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0099] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0100] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0101] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0102] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining a battery core temperature, characterized in that: The method comprises: Obtaining a heat transfer equation satisfied by the battery in a preset heat transfer direction and a Bernardi heat generation equation for quantifying the heat; Determine a correlation parameter associated with the battery core temperature, and correct the open circuit voltage in the Bernardi heat generation equation according to the correlation parameter to obtain a corrected Bernardi heat generation equation; Constructing a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the modified Bernardi heat generation equation; The operating parameters of the battery under different preset working conditions are obtained, and parameter identification is performed on the battery semi-closed-loop temperature estimation model according to the operating parameters to determine the model parameters of the battery semi-closed-loop temperature estimation model, so as to obtain a temperature estimation model for determining the battery core temperature.

2. The method according to claim 1, characterized in that: The step of obtaining a heat transfer equation satisfied by the battery in a preset heat transfer direction includes: Obtain a battery thermal convection lumped parameter model of the battery; The battery thermal convection lumped parameter model is converted to obtain a heat transfer equation satisfied by the battery in a preset heat transfer direction.

3. The method according to claim 2, characterized in that The preset heat transfer direction includes a first heat transfer direction from the battery core to the battery surface and a second heat transfer direction from the battery surface to the battery liquid cooling plate. The battery thermal convection lumped parameter model is converted to obtain a heat transfer equation satisfied by the battery in the preset heat transfer direction, including: Converting the battery thermal convection lumped parameter model to obtain a first heat transfer equation satisfied by the battery in a first heat transfer direction and a second heat transfer equation satisfied by the battery in a second preset heat transfer direction; Among them, the first heat transfer equation is used to characterize the relationship between the battery surface temperature at the current sampling cycle and the next sampling cycle, the heat input at the current cycle, and the battery ambient temperature; the second heat transfer equation is used to characterize the relationship between the battery core temperature collected at the current sampling cycle and the next sampling cycle, the heat input at the current cycle, and the battery surface temperature.

4. The method according to claim 1, characterized in that: The determining of the associated parameters associated with the battery core temperature, and correcting the open circuit voltage in the Bernardi heat generation equation according to the associated parameters to obtain a corrected Bernardi heat generation equation, includes: Determine all candidate parameters associated with the battery core temperature, and according to the correlation between each candidate parameter and the battery core temperature, determine the candidate parameter corresponding to the maximum correlation as the associated parameter; Determining a correction relationship satisfied by the open circuit voltage according to the associated parameters; The open circuit voltage in the Bernardi heat generation equation is corrected according to the correction relationship to obtain a corrected Bernardi heat generation equation.

5. The method according to claim 4, characterized in that The associated parameter is the battery surface temperature, and determining the correction relationship satisfied by the open circuit voltage according to the associated parameter includes: The relationship satisfied by the open circuit voltage of the battery at the current sampling cycle and the next sampling cycle, the battery surface temperature of the battery at the current sampling cycle and the real-time estimated surface temperature of the battery is used as the correction relationship satisfied by the open circuit voltage.

6. The method according to claim 1, characterized in that The obtaining the operating parameters of the battery under different preset working conditions, performing parameter identification on the battery semi-closed loop temperature estimation model according to the operating parameters, and determining the model parameters of the battery semi-closed loop temperature estimation model includes: The operating parameters of the battery at different preset temperatures are collected using pulse operating conditions; the operating parameters include core temperature, battery surface temperature, ambient temperature, voltage and current under charge and discharge conditions; Using the operating parameters as inputs of the battery semi-closed-loop temperature estimation model, and determining the thermophysical parameters of the battery semi-closed-loop temperature estimation model using a least squares method; The preset simulation working parameters are input into a battery semi-closed loop temperature estimation model including the thermophysical parameters, and correction parameters of the battery semi-closed loop temperature estimation model are determined.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: Obtain the battery surface temperature, voltage and current of the battery to be tested, as well as the ambient temperature; The battery surface temperature, the voltage, the current and the ambient temperature are input into the temperature estimation model to obtain the core temperature of the battery to be tested.

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

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

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

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