Method, device, vehicle and readable storage medium for determining battery core temperature

Abstract

The present application relates to a method, device, vehicle, and readable storage medium for determining the core temperature of a battery. The method includes: obtaining a heat transfer equation satisfied by the battery in a preset heat transfer direction and a Bernardi heat generation equation for quantifying heat; determining an associated parameter associated with the core temperature of the battery, 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; constructing a battery semi-closed-loop temperature estimation model for the battery according to the heat transfer equation and the corrected Bernardi heat generation equation; obtaining operating parameters of the battery under different preset operating conditions, and performing parameter identification 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 core temperature of the battery. Using this 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 particularly to a method, device, vehicle, and readable storage medium for determining the core temperature of a battery. Background Art

[0002] With the optimization of battery material systems and the progress of manufacturing processes, while the charge and discharge performance of batteries has been continuously improved, it has also brought more heat generation. The heat generation will cause changes in battery temperature, and temperature is an important indicator affecting battery electrochemical performance, lifespan, and safety. Obtaining accurate internal battery temperature has always been an important technology for battery management systems.

[0003] In traditional technologies, by implanting foreign objects inside the battery, such as electrode sensing, temperature sensing, etc. technologies, to obtain more internal battery state information, the foreign objects inside the battery greatly increase the risk of thermal runaway. Summary of the Invention

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

[0005] In a first aspect, the present application provides a method for determining the core temperature of a battery, including:

[0006] Obtain the heat transfer equation satisfied by the battery in a preset heat transfer direction and the Bernardi heat generation equation for quantifying heat;

[0007] Determine the associated parameters related to the core temperature of the battery, and correct the open circuit voltage in the Bernardi heat generation equation according to the associated parameters to obtain a corrected Bernardi heat generation equation;

[0008] Construct a semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the corrected Bernardi heat generation equation;

[0009] Obtain the operating parameters of the battery under different preset working conditions, perform parameter identification on the semi-closed loop temperature estimation model of the battery according to the operating parameters, determine the model parameters of the semi-closed loop temperature estimation model of the battery, and obtain a temperature estimation model for determining the core temperature of the battery.

[0010] In one embodiment, the obtaining of the heat transfer equation satisfied by the battery in a preset heat transfer direction includes:

[0011] Obtain the lumped parameter model of battery thermal convection of the battery;

[0012] Convert the lumped parameter model of battery thermal convection to obtain the heat transfer equation satisfied by the battery in a preset heat transfer direction.

[0013] 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. Converting the lumped parameter model of battery thermal convection to obtain the heat transfer equation satisfied by the battery in the preset heat transfer direction includes:

[0014] Converting the lumped parameter model of battery thermal convection to obtain a first heat transfer equation satisfied by the battery in the first heat transfer direction and a second heat transfer equation satisfied by the battery in the second preset heat transfer direction;

[0015] Wherein, the first heat transfer equation is used to characterize the relationship satisfied by the battery surface temperature at the current sampling period and the next sampling period, the heat generation input at the current period, and the battery ambient temperature; the second heat transfer equation is used to characterize the relationship satisfied by the internal temperature of the battery cell collected at the current sampling period and the next sampling period, the heat generation input at the current period, and the battery surface temperature.

[0016] In one embodiment, determining the 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 includes:

[0017] Determine all candidate parameters associated with the battery core temperature, and determine the candidate parameter corresponding to the maximum correlation degree as the correlation parameter according to the correlation degree between each candidate parameter and the battery core temperature;

[0018] Determine the correction relationship satisfied by the open circuit voltage according to the correlation parameter;

[0019] Correct the open circuit voltage in the Bernardi heat generation equation according to the correction relationship to obtain a corrected Bernardi heat generation equation.

[0020] In one embodiment, the correlation parameter is the battery surface temperature. Determining the correction relationship satisfied by the open circuit voltage according to the correlation parameter includes:

[0021] Taking the relationship satisfied by the open circuit voltage of the battery at the current sampling period and the next sampling period, the battery surface temperature at the current sampling period, and the real-time estimated surface temperature of the battery as the correction relationship satisfied by the open circuit voltage.

[0022] In one embodiment, obtaining the working parameters of the battery under different preset working conditions, and performing parameter identification on the semi-closed-loop temperature estimation model of the battery according to the working parameters to determine the model parameters of the semi-closed-loop temperature estimation model of the battery includes:

[0023] Collect the operating parameters of the battery at different preset temperatures under pulse conditions; the operating parameters include the core temperature, battery surface temperature, ambient temperature, voltage, and current under charge and discharge conditions;

[0024] Use the operating parameters as the input of the battery semi-closed-loop temperature estimation model, and determine the thermophysical parameters of the battery semi-closed-loop temperature estimation model by using the least squares method;

[0025] Input the preset simulated operating parameters into the battery semi-closed-loop temperature estimation model including the thermophysical parameters, and determine the correction parameters of the battery semi-closed-loop temperature estimation model.

[0026] In one embodiment, the method further includes:

[0027] Obtain the battery surface temperature, voltage, and current of the battery to be tested, as well as the ambient temperature;

[0028] Input the battery surface temperature, the voltage, the current, and the ambient temperature into the temperature estimation model to obtain the core temperature of the battery to be tested.

[0029] In a second aspect, the present application also provides a device for determining the core temperature of a battery, including:

[0030] A data acquisition module, configured to acquire the heat transfer equation satisfied by the battery in the preset heat transfer direction and the Bernardi heat generation equation for quantifying heat;

[0031] A correction module, configured to determine the correlation parameters associated with the core temperature of the battery, 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;

[0032] A model construction module, configured to construct the battery semi-closed-loop temperature estimation model of the battery according to the heat transfer equation and the corrected Bernardi heat generation equation;

[0033] A parameter identification module, configured to acquire the operating parameters of the battery under different preset 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 core temperature of the battery.

[0034] In a third aspect, the present application also provides a vehicle, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0035] Acquire the heat transfer equation satisfied by the battery in the preset heat transfer direction and the Bernardi heat generation equation for quantifying heat;

[0036] Determine the 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;

[0037] Construct a battery semi-closed-loop temperature estimation model of the battery according to the heat transfer equation and the corrected Bernardi heat generation equation;

[0038] 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.

[0039] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0040] Obtain the heat transfer equation satisfied by the battery in the preset heat transfer direction and the Bernardi heat generation equation for quantifying heat;

[0041] Determine the 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;

[0042] Construct a battery semi-closed-loop temperature estimation model of the battery according to the heat transfer equation and the corrected Bernardi heat generation equation;

[0043] 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.

[0044] In a fifth aspect, the present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0045] Obtain the heat transfer equation satisfied by the battery in the preset heat transfer direction and the Bernardi heat generation equation for quantifying heat;

[0046] Determine the 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;

[0047] Construct a battery semi-closed-loop temperature estimation model of the battery according to the heat transfer equation and the corrected Bernardi heat generation equation;

[0048] 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 core temperature of the battery.

[0049] The above 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 heat, use the correlation parameters associated with the core temperature of the battery to correct the open-circuit voltage in the Bernardi heat generation equation, and construct a battery semi-closed-loop temperature estimation model for the battery according to the heat transfer equation and the corrected Bernardi heat generation equation. This model avoids the situation where the core temperature of the battery and the correlation parameters may change with the working conditions or battery aging, and the error will continuously accumulate, making it impossible to achieve the convergence of the model estimation accuracy. Further, perform parameter identification on the constructed battery semi-closed-loop temperature estimation model according to the operating parameters under different preset working conditions, and determine the temperature estimation model for determining the core temperature of the battery, that is, use the correlation parameters to correct the heat, and then calculate the core temperature of the battery from the updated heat generation amount, without using the input battery state of charge, and use an equivalent circuit model to simulate the battery heat transfer process to determine the core temperature of the battery, improving the accuracy of the core temperature estimation of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0051] Figure 1 It is an application environment diagram of the method for determining the core temperature of the battery in an embodiment;

[0052] Figure 2 It is a schematic flowchart of the method for determining the core temperature of the battery in an embodiment;

[0053] Figure 3 It is a schematic diagram of the battery temperature distribution under charge and discharge conditions in an embodiment;

[0054] Figure 4 It is a schematic flowchart of step 208 in an embodiment;

[0055] Figure 5 It is a schematic diagram of the battery surface and core temperature under charge and discharge conditions in an embodiment;

[0056] Figure 6Flow diagram of the method for determining model parameters in one embodiment;

[0057] Figure 7 Flow diagram of the method for determining the core temperature of the battery in another embodiment;

[0058] Figure 8 Flow diagram of the method for determining the core temperature of the battery in another embodiment;

[0059] Figure 9 Structural block diagram of the device for determining the core temperature of the battery in one embodiment;

[0060] Figure 10 Internal structure diagram of a vehicle in one embodiment. Detailed implementation manners

[0061] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to 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.

[0062] SOC: (SOC (State of Charge, battery charge state), for example, it can be understood as the percentage of the battery power in a mobile phone;

[0063] OCV: (Open Circuit Voltage, battery open circuit voltage) is strongly related to the battery SOC and is an important parameter for calculating the heat generation amount, and it cannot be directly obtained during the battery charge and discharge process;

[0064] Entropy heat: The endothermic and exothermic characteristics exhibited during the battery charge and discharge process are reflected by different coefficients in different SOC segments to reflect its severity.

[0065] Temperature is an important indicator affecting the electrochemical performance, life and safety of the battery. Obtaining the accurate internal temperature of the battery has always been an important technology for the battery management system. Taking the battery as a lithium-ion battery for illustration, for example, a lithium iron phosphate cathode battery, the lithium-ion battery can be a lithium-ion battery module. When measuring the internal temperature of the lithium-ion battery, since the lithium-ion battery is a black-box system, only external signals such as voltage, current, and temperature can be collected to obtain the internal information of the battery. At present, more internal state information of the battery can be obtained through technologies such as implanting electrode sensors and temperature sensors, but foreign objects inside the battery greatly increase the risk of thermal runaway. Therefore, it is necessary to establish a relevant battery thermal model to simulate the change trend of the internal temperature under various working conditions in real time.

[0066] The existing battery heat generation models mainly have two calculation methods: external characteristics and mechanism. The heat transfer models can be divided into one-dimensional models and three-dimensional models. Among them, the mechanism heat generation model and the three-dimensional heat transfer model are mainly applied to the finite element analysis during product design and optimization. The process involves a large number of parameters and partial differential equation calculations, which are not suitable for on-line applications in 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 as to estimate the core temperature of the battery on-line.

[0067] That is to say, the heat generation models studied by the existing technologies need to ensure the absolute accuracy of the SOC input, and cannot achieve high-precision calculation of the heat generation in the lithium iron phosphate cathode system battery. In addition, the simulation accuracy of the equivalent circuit lumped parameter heat transfer model under complex working conditions such as pulses is relatively low. These two problems lead to the inability to ensure error convergence in the process of applying the existing technologies to real vehicles.

[0068] In view of the inaccurate estimation of the battery core temperature in the related technologies, a method for determining the battery core temperature is proposed. By using the correlation parameter associated with the battery core temperature, the open circuit voltage in the Bernardi heat generation equation is corrected, so that the constructed semi-closed-loop temperature estimation model of the battery is closed, and then the estimation of the battery core temperature is realized.

[0069] The method for determining the battery core temperature disclosed in the embodiments of the present application can be but not limited to being applied to electrical devices such as vehicles, ships or aircraft. The embodiments of the present application provide an electrical device using a lithium-ion battery as a power source. The electrical device can be but not limited to vehicles, power tools and laptop computers, etc.

[0070] For the convenience of description, the following embodiments take a vehicle 102, which is an electrical device in an embodiment of the present application, as an example for description. 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 installation position of the battery 104 can be set according to actual needs. For example, it can be set at the bottom or the head of the vehicle 102. The battery 104 is used to supply power to the vehicle 102. The vehicle 102 also includes a controller 106. The controller 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 correlation parameter associated with the battery core temperature, corrects the open circuit voltage in the Bernardi heat generation equation according to the correlation parameter to obtain a corrected Bernardi heat generation equation; constructs a 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, and performs parameter identification on the semi-closed-loop temperature estimation model of the battery according to the working parameters to determine the model parameters of the semi-closed-loop temperature estimation model of the battery, so as to obtain a temperature estimation model for determining the battery core temperature.

[0071] In an exemplary embodiment, asFigure 2 As shown, a method for determining the core temperature of a battery is provided. Taking the vehicle in Figure 1 as an example, the method includes the following steps 202 to 208. Among them:

[0072] Step 202: Obtain the heat transfer equation satisfied by the battery in the preset heat transfer direction and the Bernardi heat generation equation for quantifying heat.

[0073] Among them, the number of preset heat transfer directions can be determined according to actual needs. The preset heat transfer directions may 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 the fluid and the solid, and can be used to characterize the physical characteristic parameters of the battery, the temperature change rate at the current moment, and the relationship satisfied by the temperature distribution of the battery from the inside along a certain direction to the outside. The physical characteristic parameters include but are not limited to the mass, specific heat capacity, convective heat transfer coefficient, and surface area of the battery. The Bernardi heat generation equation can be used to characterize the heat generation relationship satisfied between the heat generation amount 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 current temperature and entropy heat coefficient of the battery.

[0074] It can be understood that the online temperature estimation of the battery can be completed according to 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 calculation accuracy of the heat generation amount. Therefore, it is necessary to use the associated parameter related to the battery core temperature to recursively correct the open circuit voltage.

[0075] Step 204: Determine the associated parameter related to the battery core temperature, and correct the open circuit voltage in the Bernardi heat generation equation according to the associated parameter to obtain the corrected Bernardi heat generation equation.

[0076] Among them, the associated parameter related to the battery core temperature can be the battery surface temperature.

[0077] Exemplarily, determine the associated parameter related to the battery core temperature, and determine the correction relationship satisfied by the open circuit voltage according to the associated parameter; correct the open circuit voltage in the Bernardi heat generation equation according to the correction relationship to obtain the corrected Bernardi heat generation equation.

[0078] Step 206: Construct a semi-closed-loop temperature estimation model of the battery according to the heat transfer equation and the corrected Bernardi heat generation equation.

[0079] It should be noted that considering that the relationship between the battery surface temperature and the core temperature may change with the working conditions or battery aging, if an open-loop model is used to estimate the core temperature, the errors will accumulate continuously, and the convergence of the model estimation accuracy cannot be achieved, thus the estimation of the battery core temperature cannot be realized.

[0080] Exemplarily, according to the heat transfer equation and the modified Bernardi heat generation equation, the battery semi-closed-loop temperature estimation model of the battery is constructed, including the Bernardi heat generation equation corresponding to the heat generation inputs at the previous sampling period moment and the current sampling period moment respectively, the correction relationship satisfied by the open-circuit voltage at the current sampling period moment, the first heat transfer equation satisfied by the battery surface temperature at the current sampling period moment, and the second heat transfer equation satisfied by the battery core temperature at the next sampling period moment.

[0081] Among them, the Bernardi heat generation equation corresponding to the heat generation input at the previous sampling period moment can characterize the relationship satisfied by the heat generation input at the previous sampling period moment, the open-circuit voltage and the current at the previous sampling moment. The Bernardi heat generation equation corresponding to the heat generation input at the current sampling period moment can characterize the relationship satisfied by the heat generation input at the current sampling period moment, the open-circuit voltage and the current at the current sampling period moment. The correction relationship satisfied by the open-circuit voltage at the current sampling period moment can characterize the relationship satisfied by the open-circuit voltage at the current sampling period moment, the open-circuit voltage at the previous sampling period moment, the measured value and the estimated value of the battery surface temperature at the current sampling period moment. The first heat transfer equation satisfied by the battery surface temperature at the current sampling period moment can characterize the relationship satisfied by the estimated value of the battery surface temperature at the previous sampling period moment, the heat generation input at the previous sampling period moment, and the ambient temperature at the previous sampling period moment. The second heat transfer equation satisfied by the battery core temperature at the next sampling period moment can characterize the relationship satisfied by the battery core temperature at the current sampling period moment, the heat generation input at the current sampling period moment, and the measured value of the battery surface temperature at the current sampling period moment.

[0082] Step 208, obtain the working parameters of the battery under different preset working conditions, perform parameter identification on the battery semi-closed-loop temperature estimation model according to the working parameters, determine the model parameters of the battery semi-closed-loop temperature estimation model, and obtain the temperature estimation model for determining the battery core temperature.

[0083] Among them, the working parameters do not include the signals related to SOC. The working parameters can be the battery core temperature, battery surface temperature, ambient temperature, voltage and current under charge and discharge conditions under different preset working conditions. The different preset working conditions can be preset ambient temperatures, such as under the ambient temperature of -10°C to 30°C, the symmetric pulse working condition with a frequency of 1hz between 1C and 3C. Charge and discharge can be performed in each pulse working condition. For example, charge for 1 second, discharge for 1 second, and the magnitudes of the charge and discharge currents are equal.

[0084] 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, a temperature estimation model can be obtained. On this basis, the inputs and outputs of the battery semi-closed-loop temperature estimation model during the actual vehicle application process can be clarified, that is, the inputs can be the battery surface temperature, voltage, and current, as well as the ambient temperature obtained by the thermal management system, without involving SOC-related signals. The ambient temperature can be the inlet and outlet temperatures of the liquid cooling plate.

[0085] The battery semi-closed-loop temperature estimation model is constructed and determined according to 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 can be a thermophysical parameter related to the battery physical characteristic parameters. The battery physical characteristic parameters can include parameters such as the mass, specific heat capacity, heat transfer coefficient, and surface area of the battery. The second parameter can be a correction parameter, which is determined by simulating the actual vehicle conditions on the basis of determining the first parameter.

[0086] Exemplarily, the battery core temperature, battery surface temperature, ambient temperature, voltage, and current of the battery under different preset working conditions are obtained. The data collected under different preset working conditions are used as the inputs of the model to determine the thermophysical parameters related to the battery physical characteristic parameters. On this basis, a simulation of the actual vehicle conditions is carried out once to determine the correction parameter. On this basis, it is clarified that the inputs of the battery semi-closed-loop temperature estimation model during the actual vehicle application process are the battery surface temperature, voltage, and current, as well as the ambient temperature obtained by the thermal management system, and the output is the battery core temperature, without the need to involve SOC-related signals, and it is decoupled from the SOC estimation during the actual application process, avoiding the temperature estimation error of the battery caused by the SOC estimation error.

[0087] In the above method for determining the battery core temperature, by obtaining the heat transfer equation satisfied by the battery in the preset heat transfer direction and the Bernardi heat generation equation for quantifying heat, using the correlation parameter associated with the battery core temperature to correct the open-circuit voltage in the Bernardi heat generation equation, and constructing the battery semi-closed-loop temperature estimation model of the battery according to the heat transfer equation and the modified Bernardi heat generation equation, this model avoids the situation where the battery core temperature and the correlation parameter may change with the working conditions or battery aging, and the error will continuously accumulate, making it impossible to achieve the convergence of the model estimation accuracy. Further, parameter identification is carried out on the constructed battery semi-closed-loop temperature estimation model according to the working parameters under different preset working conditions to determine the temperature estimation model for determining the battery core temperature, that is, using the correlation parameter to correct the heat, and then calculating the battery core temperature from the updated heat generation amount, without using the input battery state of charge, and using the equivalent circuit model to simulate the battery heat transfer process to determine the battery core temperature, improving the accuracy of the battery core temperature estimation.

[0088] Based on the heat transfer characteristics between fluids and solids, a lumped parameter model of battery thermal convection is established. However, the lumped parameter model of battery thermal convection cannot perform online calculations. To estimate the core temperature of the battery, optionally, in an exemplary embodiment, the determination of the heat transfer equation includes:

[0089] Obtain the lumped parameter model of battery thermal convection of the battery; convert the lumped parameter model of battery thermal convection to obtain the heat transfer equation satisfied by the battery in the preset heat transfer direction. Here, the conversion can be to convert the lumped heat transfer model of the battery to a state space equation, that is, the heat transfer equation satisfied by the battery in the preset heat transfer direction, and the heat transfer equation is a discrete equation.

[0090] Among them, the preset heat transfer directions 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. Further, converting the lumped parameter model of battery thermal convection to obtain the heat transfer equation satisfied by the battery in the preset heat transfer direction includes: converting the lumped parameter model of battery thermal convection to obtain a first heat transfer equation satisfied by the battery in the first heat transfer direction and a second heat transfer equation satisfied by the battery in the second preset heat transfer direction; the first heat transfer equation is used to characterize the relationship satisfied by the battery surface temperature at the current sampling period moment and the next sampling period moment, the heat generation input at the current period moment, and the battery ambient temperature; the second heat transfer equation is used to characterize the relationship satisfied by the internal temperature of the battery cell collected at the current sampling period moment and the next sampling period moment, the heat generation input at the current period moment, and the battery surface temperature.

[0091] It should be noted that the actual heat transfer of the battery is a progressive relationship from the inside to the outside, but the overall temperature of the battery cannot be detected in real time, and it is not necessary to know the temperature of each specific part. Therefore, the battery temperature is divided into two parts: the core and the surface, and only the temperatures of the two parts are calculated to simplify the calculation amount. As Figure 3 shown, it is the battery temperature distribution under charge and discharge conditions in an exemplary embodiment, including the actual battery cell temperature distribution and the simplified battery cell temperature distribution.

[0092] Among them, the lumped parameter model of battery thermal convection can be expressed as:

[0093] ;

[0094] 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 generation input, generally calculated from the resistance and entropy heat coefficient of the battery, h bat and A batrespectively represent the convective heat transfer coefficient and the surface area, and 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, the first parameter k1 and the second parameter k2 can be used to represent these fixed parameters in the form of lumped parameters, and a simplified lumped parameter model of the battery thermal convection can be obtained, which can be expressed as:

[0095] ;

[0096] By transforming the lumped parameter model of the battery thermal convection, the first heat transfer equation satisfied by the battery in the first heat transfer direction and the second heat transfer equation satisfied by the battery in the second preset heat transfer direction are obtained. The first heat transfer equation can be expressed as:

[0097] ;

[0098] The second heat transfer equation can be expressed as:

[0099] ;

[0100] where t and t + 1 represent the current sampling period time and the next sampling period time respectively, T c 、T s and T abt respectively represent the core temperature of the battery collected inside the battery cell, the surface temperature of the battery, and the average temperature of the inlet and outlet of the battery liquid cooling plate. The first parameter k1, the second parameter k2 in the first heat transfer equation and the fourth parameter k4, the fifth parameter k5 in the second heat transfer equation correspond to two lumped parameters in the simplified lumped parameter model of the battery thermal convection. 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. The heat input Q and the ambient temperature T abt can be used to recursively calculate the real-time surface temperature T s and the core temperature T c .

[0101] In the above embodiment, the standard formula of the lumped parameter model of the battery 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 calculation amount. That is, the heat transfer model fully considers the battery structure and mechanism, can still maintain the estimation accuracy under large-rate complex working conditions with large temperature fluctuations, and has a small model calculation amount and the ability to perform on-vehicle online operation.

[0102] On the basis of determining the heat transfer equation, to complete the online temperature estimation of the battery, it is also necessary to introduce the Bernardi heat generation equation. Considering that the open-circuit voltage inside the battery and the SOC are strongly coupled, the SOC estimation error will directly affect the calculation accuracy of the heat generation. Therefore, it is necessary to correct the open-circuit voltage. In an exemplary embodiment, such as Figure 4As shown, step 208 includes steps 402 to 406. Among them:

[0103] Step 402: Determine all candidate parameters associated with the battery core temperature. According to the correlation degree between each candidate parameter and the battery core temperature, determine the candidate parameter corresponding to the maximum correlation degree as the correlation parameter.

[0104] Among them, the candidate parameters include battery charge and discharge parameters, ambient temperature parameters, battery internal resistance and other parameters. The determination method of the correlation degree between each candidate parameter and the battery core temperature can be implemented by existing methods and will not be elaborated here. The correlation parameter can be understood as the candidate parameter that has the greatest impact on the battery core temperature. The correlation parameter can be the battery surface temperature. It should be noted that usually, 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 temperature. The surface temperature can be directly observed, but it does not represent the true operating temperature of the battery. As Figure 5 shown, it is a schematic diagram of the battery surface and core temperatures under charge and discharge conditions in an exemplary embodiment.

[0105] Step 404: Determine the correction relationship satisfied by the open circuit voltage according to the correlation parameter.

[0106] Among them, determining the correction relationship satisfied by the open circuit voltage according to the correlation parameter can be: According to the relationship satisfied by the open circuit voltage of the battery at the current sampling period moment and the next sampling period moment, the battery surface temperature at the current sampling period moment and the real-time estimated surface temperature of the battery, as the correction relationship satisfied by the open circuit voltage.

[0107] The Bernardi heat generation equation can be expressed as:

[0108] ;

[0109] Among them, Q t represents the heat generation amount of the battery at the current moment, U t and I t represent the terminal voltage and current collected in real time during the operation of the battery, T represents the current working temperature of the battery, is the battery entropy heat coefficient, which can be determined by calculating the voltage data measured in the variable temperature shelf experiment. The correction relationship of the open circuit voltage can be expressed as:

[0110]

[0111] Among them, T t represents the battery surface temperature obtained by the battery external signal acquisition harness during the current sampling period, that is, it represents the acquisition value of the battery surface temperature. k represents the correction coefficient, and T s，t is the real-time estimated value of the battery surface temperature.

[0112] Step 406, correct the open-circuit voltage in the Bernardi heat generation equation according to the correction relationship to obtain a corrected Bernardi heat generation equation.

[0113] Exemplarily, substitute the correction relationship into the open-circuit voltage in the Bernardi heat generation equation to correct the open-circuit voltage in the Bernardi heat generation equation, and obtain a corrected Bernardi heat generation equation.

[0114] In this embodiment, by using the battery surface temperature to correct the open-circuit voltage, 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 heat generation, and further lead to the problem of inaccurate core temperature estimation.

[0115] In an exemplary embodiment, a method for determining model parameters is provided, as Figure 6 shown, including steps 602 to 606, where:

[0116] Step 602, collect the working parameters of the battery at different preset temperatures by using a pulse working condition; the working parameters include the core temperature, battery surface temperature, ambient temperature, voltage and current under the charge and discharge working conditions.

[0117] Among them, the pulse working condition can be understood as a symmetric pulse working condition with a preset frequency within the 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 symmetric pulse can be understood as having symmetry in charge and discharge within a pulse. For example, in a pulse, charge for 1S, discharge for 1S, and the magnitudes of the charge and discharge currents are equal.

[0118] Step 604, use the working parameters as the input of the battery semi-closed-loop temperature estimation model, and use the least squares method to determine the thermophysical parameters of the battery semi-closed-loop temperature estimation model.

[0119] Among them, the specific implementation of using the least squares method to determine the thermophysical parameters of the battery semi-closed-loop temperature estimation model can be realized by existing methods, which will not be elaborated here.

[0120] According to the heat transfer equation and the corrected Bernardi heat generation equation, the battery semi-closed-loop temperature estimation model of the battery can be constructed as:

[0121]

[0122] Among them, t is the current sampling period moment, t - 1 is the previous sampling period moment, and t + 1 is the next sampling period moment. It should be noted that the input heat under different preset working conditions is known. When calculating the thermophysical parameters by collecting the working parameters of the battery at different preset temperatures based on the pulse working condition, the input heat can be directly substituted into the calculation.

[0123] Step 606: Input the preset simulation working parameters into the battery semi-closed-loop temperature estimation model including thermophysical parameters to determine the correction parameters of the battery semi-closed-loop temperature estimation model.

[0124] Exemplarily, use the battery core temperature T c , surface temperature T t , inlet and outlet water temperatures of the liquid cooling plate T abt , voltage U, and current I as inputs. Identify the optimal k1, k2, k4, k5 through the least squares method, and then conduct a simulation of the actual vehicle working condition once to complete the identification of the correction parameters k3, k6, and k.

[0125] In the above embodiments, some parameters of the identification model are identified using the pulse working condition, which minimizes the influence of entropy heat on parameter identification and ensures the accuracy of battery core temperature estimation.

[0126] In an exemplary embodiment, based on the identification of model parameters, a method for determining the battery core temperature is provided, including:

[0127] Obtain the battery surface temperature, voltage, and current of the battery to be tested, as well as the ambient temperature; input the battery surface temperature, voltage, current, and ambient temperature into the temperature estimation model to obtain the core temperature of the battery to be tested.

[0128] Among them, the ambient temperature can be but is not limited to the average temperature of the inlet and outlet of the battery liquid cooling plate.

[0129] Exemplarily, input the observed battery surface temperature, voltage, current, and the average temperature of the inlet and outlet of the battery liquid cooling plate into the determined temperature estimation model, calculate the real-time estimated value of the battery surface temperature, update the open-circuit voltage based on the real-time estimated value and the observed battery surface temperature, recalculate the heat generation, obtain the corrected heat generation, and estimate the battery core temperature based on the corrected heat generation. The specific implementation can be: Determine the heat generation input at the previous sampling period according to the terminal voltage, current, and open-circuit voltage at the previous sampling period, and perform surface temperature estimation and the first parameter k1, the second parameter k2, and the third parameter k3 based on the heat generation input at the previous sampling period, the estimated value of the battery surface temperature at the previous sampling period, and the average temperature of the inlet and outlet of the battery liquid cooling plate at the previous sampling period to obtain the surface temperature estimation at the current sampling period.

[0130] The open-circuit voltage at the current sampling period is corrected based on the estimated surface temperature at the current sampling period, the acquired value of the battery surface temperature at the current sampling period, and the correction coefficient k to obtain the corrected open-circuit voltage, thereby completing the calculation of the heat input. Based on the heat input at the current sampling period, the fourth parameter, the fifth parameter, the sixth parameter, the battery core temperature at the current sampling period, and the surface temperature collected in real time during the current sampling period, the battery core temperature at the next sampling period is determined.

[0131] In the above embodiment, the OCV update in the heat generation calculation process is completed using the surface temperature, so that the model is closed-loop, and the estimation error does not increase with time without taking the SOC as the model input, avoiding the temperature estimation error caused by the SOC estimation error.

[0132] In an exemplary embodiment, as Figure 7 shown, a method for determining the battery core temperature is provided. Taking the vehicle in Figure 1 as an example, the method includes the following steps 702 to 712. Among them:

[0133] Step 702: Obtain the lumped parameter model of battery thermal convection, and convert the lumped parameter model of battery thermal convection to obtain the first heat transfer equation satisfied by the battery in the first heat transfer direction and the second heat transfer equation satisfied by the battery in the second preset heat transfer direction.

[0134] Step 704: Obtain the Bernardi heat generation equation, determine the associated parameters associated with the battery core temperature, and correct the open-circuit voltage in the Bernardi heat generation equation according to the associated parameters to obtain the corrected Bernardi heat generation equation;

[0135] Step 706: Construct a semi-closed-loop temperature estimation model of the battery according to the first heat transfer equation, the second heat transfer equation, and the corrected Bernardi heat generation equation;

[0136] Step 708: Obtain the working parameters of the battery under different preset working conditions, identify the model parameters of the semi-closed-loop temperature estimation model of the battery according to the working parameters, and obtain the temperature estimation model for determining the battery core temperature.

[0137] Step 710: Obtain the battery surface temperature, voltage, and current of the battery to be tested, as well as the ambient temperature.

[0138] Step 712: Input the battery surface temperature, voltage, current, and ambient temperature into the temperature estimation model to obtain the core temperature of the battery to be tested.

[0139] In an exemplary embodiment, as Figure 8As shown in the figure, a flowchart of a method for determining the core temperature of a battery is provided, which specifically includes: based on the heat transfer characteristics between fluids and solids, establishing a lumped parameter model for battery thermal convection, converting the lumped heat transfer model of battery thermal convection into a state space equation, that is, obtaining the heat transfer equation satisfied by the battery in the preset heat transfer direction, and building a semi-closed loop temperature estimation model for the battery based on the space state equation and the Bernardi heat generation equation; obtaining voltage, current, and temperature data under specific working conditions to perform parameter identification on the semi-closed loop temperature estimation model of the battery to determine the model parameters. Among them, 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.

[0140] Obtain the terminal voltage and current data of the battery to be tested, input this data into the semi-closed loop temperature estimation model of the battery for determining model parameters to determine the estimated value of the battery surface temperature of the battery to be tested, correct the open circuit voltage according to the collected battery surface temperature, recalculate the heat generation of the battery, and estimate the core temperature of the battery based on the updated heat generation to obtain the battery core temperature.

[0141] It should be noted that the specific implementation of this embodiment can be achieved through the above-defined manner, which will not be elaborated here.

[0142] In the above embodiment, by considering the battery structure and mechanism, the thermal convection standard formula is discretized and further improved, making it conform to the thermal characteristics under severe battery working conditions while having a small calculation amount, ensuring the online operation ability of the vehicle and still maintaining the estimation accuracy under large-rate complex working conditions with large temperature fluctuations. On this basis, the OCV update in the heat generation calculation process is completed using the surface temperature, making the model closed-loop, ensuring that the estimation error does not increase with time without taking the SOC as the model input, and identifying some parameters of the sampling pulse working condition identification model to minimize the impact of entropy heat on parameter identification, thereby ensuring the accuracy and reliability of the battery core temperature estimation.

[0143] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps does not have a strict order limit, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least some of the steps or stages in other steps or other steps.

[0144] Based on the same inventive concept, an embodiment of the present application further provides a device for determining the battery core temperature for implementing the method for determining the battery core temperature involved above. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the device for determining the battery core temperature provided below can refer to the limitations on the method for determining the battery core temperature in the above text, and will not be elaborated here.

[0145] In an exemplary embodiment, as Figure 9 shown, a device for determining the battery core temperature is provided, including: a data acquisition module 902, a correction module 904, a model construction module 906, and a parameter identification module 908, where:

[0146] The data acquisition module 902 is configured to acquire the heat transfer equation satisfied by the battery in the preset heat transfer direction and the Bernardi heat generation equation for quantifying heat.

[0147] The correction module 904 is configured to determine the associated parameter associated with the battery core temperature, and correct the open-circuit voltage in the Bernardi heat generation equation according to the associated parameter to obtain a corrected Bernardi heat generation equation.

[0148] The model construction module 906 is configured to construct a battery semi-closed-loop temperature estimation model of the battery according to the heat transfer equation and the corrected Bernardi heat generation equation.

[0149] The parameter identification module 908 is configured to acquire the operating parameters of the battery under different preset operating 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.

[0150] The above device for determining the battery core temperature acquires the heat transfer equation satisfied by the battery in the preset heat transfer direction and the Bernardi heat generation equation for quantifying heat, uses the associated parameter associated with the battery core temperature to correct the open-circuit voltage in the Bernardi heat generation equation, and 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. This model avoids the situation where the battery core temperature and the associated parameter may change with the operating conditions or battery aging, and the error will continuously accumulate, making it impossible to achieve the convergence of the model estimation accuracy. Further, parameter identification is performed on the constructed battery semi-closed-loop temperature estimation model according to the operating parameters under different preset operating conditions to determine the temperature estimation model for determining the battery core temperature, that is, using the associated parameter to correct the heat, and then calculating the battery core temperature from the updated heat generation amount, without using the input battery state of charge, and using an equivalent circuit model to simulate the battery heat transfer process to determine the battery core temperature, improving the accuracy of the battery core temperature estimation.

[0151] In an exemplary embodiment, the data acquisition module 902 is further configured to acquire a lumped parameter model of battery thermal convection; transform the lumped parameter model of battery thermal convection to obtain a heat transfer equation satisfied by the battery in a preset heat transfer direction.

[0152] 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 configured to transform the lumped parameter model of battery thermal convection to obtain a first heat transfer equation satisfied by the battery in the first heat transfer direction and a second heat transfer equation satisfied by the battery in the second preset heat transfer direction;

[0153] Wherein, the first heat transfer equation is used to characterize the relationship satisfied by the battery surface temperature at the current sampling period moment and the next sampling period moment, the heat generation input at the current period moment, and the battery ambient temperature; the second heat transfer equation is used to characterize the relationship satisfied by the internal temperature of the battery cell collected at the current sampling period moment and the next sampling period moment, the heat generation input at the current period moment, and the battery surface temperature.

[0154] In an exemplary embodiment, the correction module 904 is further configured to determine all candidate parameters associated with the battery core temperature, and determine the candidate parameter corresponding to the maximum correlation degree as the associated parameter according to the correlation degree between each candidate parameter and the battery core temperature;

[0155] Determine the correction relationship satisfied by the open circuit voltage according to the associated parameter;

[0156] Correct the open circuit voltage in the Bernardi heat generation equation according to the correction relationship to obtain a corrected Bernardi heat generation equation.

[0157] In an exemplary embodiment, the correction module 904 is further configured to use the relationship satisfied by the open circuit voltage of the battery at the current sampling period moment and the next sampling period moment, the battery surface temperature at the current sampling period moment, and the real-time estimated surface temperature of the battery as the correction relationship satisfied by the open circuit voltage.

[0158] In an exemplary embodiment, the parameter identification module 908 is further configured to collect the operating parameters of the battery at different preset temperatures by using a pulse condition; the operating parameters include the core temperature, battery surface temperature, ambient temperature, voltage, and current under charge and discharge conditions;

[0159] Take the operating parameters as the input of the battery semi-closed-loop temperature estimation model, and use the least squares method to determine the thermophysical parameters of the battery semi-closed-loop temperature estimation model;

[0160] Input the preset simulation working parameters into the battery semi-closed-loop temperature estimation model including thermophysical parameters to determine the correction parameters of the battery semi-closed-loop temperature estimation model.

[0161] In an exemplary embodiment, the device for determining the battery core temperature further includes a temperature estimation module, configured to obtain the battery surface temperature, voltage, and current of the battery to be tested, as well as the ambient temperature; input the battery surface temperature, voltage, current, and ambient temperature into the temperature estimation model to obtain the core temperature of the battery to be tested.

[0162] Each module in the above device for determining the battery core temperature can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the computer device in hardware form or independent of it, or stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to the above respective modules.

[0163] In an exemplary embodiment, a vehicle is provided, and its internal structure diagram can be as Figure 10 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through the system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, 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 for the processor to exchange information with external devices. The communication interface of the vehicle is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for determining the battery core temperature. 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. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the outer shell of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0164] Those skilled in the art can understand that Figure 10The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component arrangement.

[0165] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0166] In one embodiment, a vehicle is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0167] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0168] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0169] 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 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 need to comply with relevant regulations.

[0170] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a 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), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0171] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered to be within the scope recorded in the present application.

[0172] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended 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 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 correlation parameter; the correlation parameter is the battery surface temperature; The relationship satisfied by the open circuit voltage of the battery at the current sampling period and the next sampling period, the battery surface temperature of the battery at the current sampling period and the real-time estimated surface temperature of the battery is used as a correction relationship satisfied by the open circuit voltage; Correcting the open circuit voltage in the Bernardi heat generation equation according to the correction relationship 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; Acquire the working parameters of the battery under different preset working conditions, and collect the working parameters of the battery under different preset temperatures using pulse working conditions; the working parameters include core temperature, battery surface temperature, ambient temperature, voltage and current under charge and discharge working conditions; The operating parameters are used as inputs of the battery semi-closed-loop temperature estimation model, the thermophysical parameters of the battery semi-closed-loop temperature estimation model are determined using the least squares method, the preset simulation operating parameters are input into the battery semi-closed-loop temperature estimation model including the thermophysical parameters, the correction parameters of the battery semi-closed-loop temperature estimation model are determined, and a temperature estimation model for determining the core temperature of the battery is obtained.

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 any one of claims 1 to 3, 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.

5. 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 4 are implemented.

6. 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 4 are implemented.

7. 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 4 are implemented.

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