CN119199572B - Method, device, vehicle and readable storage medium for determining battery core temperature - Google Patents
Method, device, vehicle and readable storage medium for determining battery core temperature Download PDFInfo
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Abstract
本申请涉及一种电池核心温度的确定方法、装置、车辆和可读存储介质。所述方法包括：获取电池在预设热量传递方向上满足的传热方程和量化热量的伯纳第产热方程；确定与电池核心温度关联的关联参数，根据关联参数对伯纳第产热方程中的开路电压进行修正，得到修正伯纳第产热方程；根据传热方程和修正伯纳第产热方程，构建电池的电池半闭环温度估计模型；获取电池在不同预设工况下的工作参数，根据工作参数对电池半闭环温度估计模型进行参数辨识，确定电池半闭环温度估计模型的模型参数，得到用于确定电池核心温度的温度估计模型。采用本方法能够准确估计电池核心温度以及确保电池安全性。
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 associated parameters associated with the core temperature of the battery, 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; 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 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, determining the model parameters of the battery semi-closed loop temperature estimation model, and obtaining a temperature estimation model for determining the core temperature of the battery. The present method can accurately estimate the core temperature of the battery and ensure battery safety.
Description
Technical Field
The present application relates to the field of battery temperature detection technology, and in particular, to a method and apparatus for determining a battery core temperature, a vehicle, and a readable storage medium.
Background
With the improvement of battery material system and the progress of manufacturing process, more heating value is brought while the charge and discharge performance of the battery is continuously improved, the heating value can cause the change of the battery temperature, the temperature is an important index affecting the electrochemical performance, the service life and the safety of the battery, and the obtaining of accurate internal temperature of the battery is always an important technology of a battery management system.
In the conventional technology, more internal state information of the battery is obtained by implanting foreign matters in the battery, such as implanted electrode sensing, temperature sensing and the like, and the risk of thermal runaway is greatly increased by the foreign matters in the battery.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a battery core temperature determination method, apparatus, vehicle, computer-readable storage medium, and computer program product that are capable of ensuring battery safety and core temperature accuracy for battery core temperatures.
In a first aspect, the present application provides a method for determining a core temperature of a battery, including:
Acquiring a heat transfer equation which is satisfied by a battery in a preset heat transfer direction and a primary heat generation equation for quantifying heat;
determining a correlation parameter correlated with the core temperature of the battery, and correcting the open-circuit voltage in the primary heat generation equation according to the correlation parameter to obtain a corrected primary heat generation equation;
constructing a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the modified primary heat generation equation;
And acquiring working parameters of the battery under different preset working conditions, carrying out parameter identification on the battery semi-closed loop temperature estimation model according to the working parameters, and determining model parameters of the battery semi-closed loop temperature estimation model to obtain a temperature estimation model for determining the core temperature of the battery.
In one embodiment, the obtaining the heat transfer equation satisfied by the battery in the preset heat transfer direction includes:
Acquiring a battery heat convection lumped parameter model of a battery;
And converting the battery thermal convection lumped parameter model to obtain a heat transfer equation which is satisfied by the battery in a preset heat transfer direction.
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 converting the battery thermal convection lumped parameter model to obtain a heat transfer equation satisfied by the battery in the preset heat transfer direction includes:
Converting the battery thermal convection lumped parameter model to obtain a first heat transfer equation which is met by the battery in a first heat transfer direction and a second heat transfer equation which is met by the battery in a second preset heat transfer direction;
the first heat transfer equation is used for representing the relation among the battery surface temperature of the battery at the current sampling period time and the next sampling period time, the calorific value input at the current period time and the battery environment temperature, and the second heat transfer equation is used for representing the relation among the acquired battery core internal temperature at the current sampling period time and the next sampling period time, the calorific value input at the current period time and the battery surface temperature.
In one embodiment, the determining the correlation parameter correlated to the battery core temperature, correcting the open-circuit voltage in the primary heat generation equation according to the correlation parameter, to obtain a corrected primary heat generation equation, includes:
Determining all candidate parameters associated with the battery core temperature, and determining the candidate parameter corresponding to the maximum association degree as an association parameter according to the association degree of each candidate parameter and the battery core temperature;
determining a correction relation satisfied by the open-circuit voltage according to the correlation parameter;
and correcting the open-circuit voltage in the primary heat generation equation according to the correction relation to obtain a corrected primary heat generation equation.
In one embodiment, the related parameter is a battery surface temperature, and the determining, according to the related parameter, a correction relationship that the open circuit voltage satisfies includes:
And according to the relation satisfied by the open-circuit voltage of the battery at the current sampling period time and the next sampling period time, the battery surface temperature of the battery at the current sampling period time and the real-time estimated surface temperature of the battery, the relation is used as a correction relation satisfied by the open-circuit voltage.
In one embodiment, the obtaining the working parameters of the battery under different preset working conditions, performing parameter identification on the battery semi-closed loop temperature estimation model according to the working parameters, and determining model parameters of the battery semi-closed loop temperature estimation model includes:
Collecting working parameters of the battery at different preset temperatures by using a pulse working condition, wherein the working parameters comprise core temperature, battery surface temperature, environment temperature, voltage and current under a charging and discharging working condition;
taking the working parameters as the input of the battery semi-closed loop temperature estimation model, and determining the thermophysical parameters of the battery semi-closed loop temperature estimation model by using a least square method;
And inputting preset simulation working parameters into a battery semi-closed loop temperature estimation model comprising the thermophysical parameters, and determining correction parameters of the battery semi-closed loop temperature estimation model.
In one embodiment, the method further comprises:
acquiring the surface temperature, voltage and current of a battery to be tested and the ambient temperature of the battery;
and inputting the surface temperature, the voltage, the current and the environmental temperature of the battery into the temperature estimation model to obtain the core temperature of the battery to be tested.
In a second aspect, the present application also provides a device for determining a core temperature of a battery, including:
the data acquisition module is used for acquiring a heat transfer equation and a primary heat generation equation for quantifying heat, which are met by the battery in a preset heat transfer direction;
the correction module is used for determining the associated parameters associated with the core temperature of the battery, correcting the open-circuit voltage in the primary sodium first heat generation equation according to the associated parameters, and obtaining a corrected primary sodium first heat generation equation;
The model construction module is used for constructing a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the modified Berner first heat generation equation;
The parameter identification module is used for acquiring working parameters of the battery under different preset working conditions, carrying out parameter identification on the battery semi-closed loop temperature estimation model according to the working parameters, determining model parameters of the battery semi-closed loop temperature estimation model, and obtaining a temperature estimation model for determining the core temperature of the battery.
In a third aspect, the present application also provides a vehicle comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
Acquiring a heat transfer equation which is satisfied by a battery in a preset heat transfer direction and a primary heat generation equation for quantifying heat;
determining a correlation parameter correlated with the core temperature of the battery, and correcting the open-circuit voltage in the primary heat generation equation according to the correlation parameter to obtain a corrected primary heat generation equation;
constructing a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the modified primary heat generation equation;
And acquiring working parameters of the battery under different preset working conditions, carrying out parameter identification on the battery semi-closed loop temperature estimation model according to the working parameters, and determining model parameters of the battery semi-closed loop temperature estimation model to obtain a temperature estimation model for determining the core temperature of the battery.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
Acquiring a heat transfer equation which is satisfied by a battery in a preset heat transfer direction and a primary heat generation equation for quantifying heat;
determining a correlation parameter correlated with the core temperature of the battery, and correcting the open-circuit voltage in the primary heat generation equation according to the correlation parameter to obtain a corrected primary heat generation equation;
constructing a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the modified primary heat generation equation;
And acquiring working parameters of the battery under different preset working conditions, carrying out parameter identification on the battery semi-closed loop temperature estimation model according to the working parameters, and determining model parameters of the battery semi-closed loop temperature estimation model to obtain a temperature estimation model for determining the core temperature of the battery.
In a fifth aspect, the application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of:
Acquiring a heat transfer equation which is satisfied by a battery in a preset heat transfer direction and a primary heat generation equation for quantifying heat;
determining a correlation parameter correlated with the core temperature of the battery, and correcting the open-circuit voltage in the primary heat generation equation according to the correlation parameter to obtain a corrected primary heat generation equation;
constructing a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the modified primary heat generation equation;
And acquiring working parameters of the battery under different preset working conditions, carrying out parameter identification on the battery semi-closed loop temperature estimation model according to the working parameters, and determining model parameters of the battery semi-closed loop temperature estimation model to obtain a temperature estimation model for determining the core temperature of the battery.
According to the method, the device, the vehicle, the computer readable storage medium and the computer program product for determining the battery core temperature, the heat transfer equation and the primary second heat generation equation for quantifying heat which are met by the battery in the preset heat transfer direction are obtained, the open-circuit voltage in the primary second heat generation equation is corrected by utilizing the associated parameters associated with the battery core temperature, the battery semi-closed loop temperature estimation model of the battery is constructed according to the heat transfer equation and the corrected primary second heat generation equation, the situation that errors can be accumulated continuously along with the change of working conditions or battery aging, and convergence of model estimation accuracy cannot be achieved is avoided by the model, further, the temperature estimation model for determining the battery core temperature is determined according to working parameters under different preset working conditions, namely, the heat is corrected by utilizing the associated parameters, the battery core temperature is calculated again after updating, the input battery charge state is not needed, the battery heat transfer process is simulated by using the equivalent circuit model to determine the battery core temperature, and the accuracy of battery core temperature estimation is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are needed in the description of the embodiments of the present application or the related technologies will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other related drawings may be obtained according to these drawings without inventive effort to those of ordinary skill in the art.
FIG. 1 is an application environment diagram of a method of determining a battery core temperature in one embodiment;
FIG. 2 is a flow chart of a method of determining a battery core temperature in one embodiment;
FIG. 3 is a schematic diagram of a battery temperature distribution under charge and discharge conditions according to an embodiment;
FIG. 4 is a flow chart of step 208 in one embodiment;
FIG. 5 is a schematic diagram of the cell surface and core temperatures during charge and discharge conditions in one embodiment;
FIG. 6 is a flow chart of a method of determining model parameters in one embodiment;
FIG. 7 is a flow chart of a method of determining a battery core temperature in another embodiment;
FIG. 8 is a flow chart of a method of determining a battery core temperature in another embodiment;
FIG. 9 is a block diagram showing the structure of a device for determining the core temperature of a battery in one embodiment;
fig. 10 is an internal structural view of a vehicle in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
SOC ( SOC (State of Charge), for example, can be understood as the percentage of Charge in a cell phone;
OCV (Open Circuit Voltage), the open-circuit voltage of the battery is strongly related to the SOC of the battery, is an important parameter for calculating the heating value, and cannot be directly obtained in the charging and discharging processes of the battery;
entropy heat is the endothermic and exothermic characteristics exhibited during the charge and discharge of the battery, and the intensity of the entropy heat is reflected by different coefficients in different SOC sections.
Temperature is an important indicator affecting electrochemical performance, life, and safety of a battery, and obtaining accurate internal temperature of a battery has been an important technique for battery management systems. The battery is taken as a lithium ion battery for explanation, for example, an iron lithium system positive electrode battery, the lithium ion battery can be a lithium ion battery module, when the internal temperature of the lithium ion battery is measured, as the lithium ion battery is taken as a black box system, the internal information of the battery can be obtained only by externally collecting signals such as voltage, current, temperature and the like, more internal state information of the battery can be obtained by technologies such as implanted electrode sensing, temperature sensing and the like, but the risk of thermal runaway is greatly increased by foreign matters in the battery, and therefore, a related battery thermal model needs to be established to simulate the change trend of the internal temperature under various working conditions in real time.
The existing battery heat generation model mainly comprises two calculation methods of external characteristics and mechanisms, and a heat transfer model can be divided into a one-dimensional model and a three-dimensional model, wherein the mechanism heat generation model and the three-dimensional heat transfer model are mainly applied to finite element analysis in product design and optimization, and the process comprises a large number of parameter and partial differential equation calculation and is not suitable for online application of a real vehicle. When the external characteristic heat generation model and the one-dimensional heat transfer model are used, the heat generation inside the battery is required to be regarded as one particle, the heat transfer rate in the same direction is uniform, and the core temperature of the battery can be estimated on line.
That is, the heat generation model studied in the prior art needs to ensure absolute accuracy of SOC input, high-precision calculation of heat productivity cannot be realized in the lithium iron positive electrode system battery, and simulation precision of the equivalent circuit lumped parameter heat transfer model under complex working conditions such as pulse is low.
Aiming at the situation that the estimation of the battery core temperature is inaccurate in the related technology, a method for determining the battery core temperature is provided, and the open-circuit voltage in the Berner first heat generation equation is corrected through the related parameters related to the battery core temperature, so that the constructed battery semi-closed loop temperature estimation model is closed, and the estimation of the battery core temperature is further realized.
The method for determining the core temperature of the battery disclosed by the embodiment of the application can be applied to electric devices such as vehicles, ships or aircrafts, but is not limited to the method. The embodiment of the application provides an electric device using a lithium ion battery as a power supply, and the electric device can be, but is not limited to, a vehicle, an electric tool, a notebook computer and the like.
For convenience of description, the following embodiment will take an electric device according to an embodiment of the present application as an example of the vehicle 102. The vehicle 102 may be a different type of vehicle, and is not limited herein. The battery 104 is provided in the vehicle 102, and the position of the battery 104 may be set according to actual requirements, for example, may be provided at the bottom or the head of the vehicle 102. The battery 104 is used to power the vehicle 102. The vehicle 102 further comprises a controller 106, wherein the controller acquires a heat transfer equation and a primary first heat generation equation for quantifying heat, which are met by the battery in a preset heat transfer direction, determines associated parameters associated with the core temperature of the battery, corrects the open-circuit voltage in the primary first heat generation equation according to the associated parameters to obtain a corrected primary first heat generation equation, constructs a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the corrected primary first heat generation equation, acquires 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, and determines model parameters of the battery semi-closed loop temperature estimation model to obtain the temperature estimation model for determining the core temperature of the battery.
In an exemplary embodiment, as shown in fig. 2, a method for determining a battery core temperature is provided, and an example in which the method is applied to the vehicle in fig. 1 is described, including the following steps 202 to 208. Wherein:
Step 202, a heat transfer equation satisfied by the battery in a preset heat transfer direction and a primary heat generation equation for quantifying heat are obtained.
The number of the preset heat transfer directions may be determined according to actual requirements, and 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 performing discrete processing on a battery heat convection lumped parameter model of the battery, which can also be called a battery heat convection lumped parameter heat transfer model, and the battery heat convection lumped parameter model is determined based on heat transfer characteristics between fluid and solid and can be used for representing 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 to the outside along a certain direction. Physical characteristic parameters include, but are not limited to, mass, specific heat capacity, convective heat transfer coefficient, and surface area of the cell. The primary heat generation equation can be used for representing the heat generation amount of the battery at the current moment, the terminal voltage and current acquired by the battery in real time in the running process, the open circuit voltage inside the battery and the heat generation relation satisfied between the current temperature of the battery and the entropy heat coefficient of the battery.
It can be appreciated that online temperature estimation of the battery can be completed according to the heat transfer equation and the berna first heat generation equation, but considering that the internal open circuit voltage of the battery is in a strong coupling relationship with the SOC, the SOC estimation error directly affects the accuracy of the calculation of the heat generation amount, and therefore, it is necessary to recursively correct the open circuit voltage using the correlation parameter associated with the core temperature of the battery.
And 204, determining a correlation parameter correlated with the core temperature of the battery, and correcting the open-circuit voltage in the primary heat generation equation according to the correlation parameter to obtain a corrected primary heat generation equation.
Wherein the associated parameter associated with the battery core temperature may be the battery surface temperature.
The method comprises the steps of determining a correlation parameter correlated with a battery core temperature, determining a correction relation satisfied by open-circuit voltage according to the correlation parameter, and correcting the open-circuit voltage in the primary heat generation equation according to the correction relation to obtain a corrected primary heat generation equation.
And 206, constructing a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the modified primary heat generation equation.
It should be noted that, considering that the relationship between the battery surface temperature and the core temperature may change along with the working condition or the aging of the battery, if the open loop model is used to estimate the core temperature, errors are accumulated continuously, so that convergence of the model estimation accuracy cannot be achieved, and further estimation of the core temperature of the battery cannot be achieved.
Illustratively, the battery semi-closed loop temperature estimation model of the battery is constructed according to a heat transfer equation and a modified primary heat generation equation, wherein the modified primary heat generation equation corresponding to the respective heating value input of the previous sampling period time and the current sampling period time, the open circuit voltage satisfying the modified relationship of the current sampling period time, the first heat transfer equation satisfying the battery surface temperature at the current sampling period time, and the second heat transfer equation satisfying the battery core temperature at the next sampling period time are included.
The first heat generation equation corresponding to the heat generation amount input at the previous sampling period time can represent the relationship between the heat generation amount input at the previous sampling period time and the satisfaction of the open-circuit voltage and the current at the previous sampling period time, and the first heat generation equation corresponding to the heat generation amount input at the current sampling period time can represent the relationship between the heat generation amount input at the current sampling period time and the satisfaction of the open-circuit voltage and the current at the current sampling period time. The correction relation satisfied by the open-circuit voltage at the current sampling period time can represent the relation satisfied by the open-circuit voltage at the current sampling period time, the open-circuit voltage at the last sampling period time, the acquired value of the battery surface temperature at the current sampling period time and the estimated value. The first heat transfer equation satisfied by the battery surface temperature at the current sampling period time may represent a relationship of an estimated value of the battery surface temperature at the last sampling period time, a heating value input at the last sampling period time, and an ambient temperature satisfaction at the last sampling period time. The second heat transfer equation satisfied by the battery core temperature at the next sampling period time may represent a relationship satisfied by the battery core temperature at the current sampling period time, the calorific value input at the current sampling period time, and the acquired value of the battery surface temperature at the current sampling period time.
And step 208, acquiring working parameters of the battery under different preset working conditions, carrying out parameter identification on the battery semi-closed loop temperature estimation model according to the working parameters, determining model parameters of the battery semi-closed loop temperature estimation model, and obtaining a temperature estimation model for determining the core temperature of the battery.
The working parameters do not include signals related to the SOC, and the working parameters can be battery core temperature, battery surface temperature, ambient temperature, voltage and current under the condition of charge and discharge under different preset working conditions. The different preset working conditions can be preset environment temperatures, such as symmetrical pulse working conditions with the frequency of 1hz between 1C and 3C at the environment temperature of-10 ℃ to 30 ℃, and each pulse working condition can be charged and discharged, for example, 1 second is charged, 1 second is discharged, and the charge and discharge currents are equal in size.
The parameter identification can be understood as determining the model parameters of the battery semi-closed loop temperature estimation model, the temperature estimation model is obtained by determining the model parameters of the battery semi-closed loop temperature estimation model, on the basis, the input and the output of the battery semi-closed loop temperature estimation model in the actual vehicle application process can be definitely determined, namely, the input can be the battery surface temperature, the voltage and the current, the environmental temperature acquired by the thermal management system does not relate to SOC related signals, and the environmental temperature can be the water inlet and outlet temperature of the liquid cooling plate.
The battery semi-closed loop temperature estimation model is established according to a heat transfer equation and a modified Berner first heat generation equation, parameters of the battery semi-closed loop temperature estimation model comprise a first parameter and a second parameter, the first parameter can be a thermophysical parameter related to battery physical characteristic parameters, the battery physical characteristic parameters can comprise parameters such as mass, specific heat capacity, heat exchange coefficient, surface area and the like of a battery, the second parameter can be a modified parameter, and the second parameter is determined by simulating a real vehicle condition on the basis of determining the first parameter.
The method comprises the steps of obtaining the core temperature, the surface temperature, the ambient temperature, the voltage and the current of the battery under different preset working conditions, taking data collected under different preset working conditions as input of a model, determining thermophysical parameters related to physical characteristic parameters of the battery, carrying out primary simulation on the actual working conditions, determining correction parameters, determining the surface temperature, the voltage and the current of the battery during the application process of the battery semi-closed loop temperature estimation model and the ambient temperature obtained by a thermal management system clearly, outputting the core temperature as the battery core temperature, and avoiding the temperature estimation error caused by the SOC estimation error of the battery because of decoupling with the SOC estimation during the actual application process.
In the method for determining the battery core temperature, the heat transfer equation and the primary first heat generation equation for quantifying heat which are met by the battery in the preset heat transfer direction are obtained, the correlation parameters associated with the battery core temperature are utilized to correct open-circuit voltage in the primary first heat generation equation, the battery semi-closed-loop temperature estimation model of the battery is constructed according to the heat transfer equation and the corrected primary first heat generation equation, the situation that errors can be accumulated continuously and convergence of model estimation accuracy cannot be achieved due to the fact that the battery core temperature and the correlation parameters possibly change along with working conditions or battery aging is avoided, further, parameter identification is conducted on the constructed battery semi-closed-loop temperature estimation model according to working parameters under different preset working conditions, a temperature estimation model for determining the battery core temperature is determined, namely, heat is corrected by utilizing the correlation parameters, the battery core temperature is calculated again after updating, the battery core temperature is determined by simulating a battery heat transfer process by using an equivalent circuit model, and accuracy of battery core temperature estimation is improved.
Based on the heat transfer characteristics between the fluid and the solid, a battery thermal convection lumped parameter model is established, but cannot be calculated on-line, for the purpose of estimating the battery core temperature, optionally, in an exemplary embodiment, the determination of the heat transfer equation includes:
And converting the battery heat convection lumped parameter model to obtain a heat transfer equation which is satisfied by the battery in a preset heat transfer direction. The conversion here may be to convert the battery thermal convection lumped heat transfer model into a state space equation, i.e. a heat transfer equation satisfied by the battery in a preset heat transfer direction, the heat transfer equation being a discrete equation.
The method comprises the steps of converting a battery heat convection lumped parameter model to obtain a heat transfer equation met by the battery in the preset heat transfer direction, converting the battery heat convection lumped parameter model to obtain a first heat transfer equation met by the battery in the first heat transfer direction and a second heat transfer equation met in the second preset heat transfer direction, wherein the first heat transfer equation is used for representing the relation between the battery surface temperature of the battery at the current sampling period time and the next sampling period time, the calorific value input of the current period time and the battery environment temperature, and the second heat transfer equation is used for representing the relation between the collected battery internal temperature of the battery at the current sampling period time and the next sampling period time, the calorific value input of the current period time and the battery surface temperature.
It should be noted that, the actual heat transfer of the battery is in a progressive relationship from inside to outside, but the whole temperature of the battery cannot be detected in real time, and the temperature of each specific part is not required to be known, so that the temperature of the battery is divided into a core part and a surface part, the temperatures of the two parts are only calculated, and the calculation amount is simplified, as shown in fig. 3, the battery temperature distribution condition under the charge and discharge working conditions in an exemplary embodiment, including the actual battery cell temperature distribution and the simplified battery cell temperature distribution, is shown in fig. 3.
Wherein, the battery thermal convection lumped parameter model can be expressed as:
;
Wherein m bat is the battery mass, C p is the specific heat capacity, For the temperature change rate at the current moment, Q is the heat generation amount input, and is generally calculated by the resistance and the entropy coefficient of the battery, h bat and a bat respectively represent the heat convection coefficient and the surface area, and T 1 and T 2 represent the temperature distribution of the battery from the inside to the outside along a certain direction. Since the mass, specific heat capacity, heat exchange coefficient and surface area of the battery can be regarded as constants, the fixed parameters can be represented by a form of lumped first parameter k 1 and second parameter k 2, and a simplified battery heat convection lumped parameter model can be expressed as:
;
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, wherein the first heat transfer equation can be expressed as:
;
the second heat transfer equation may be expressed as:
;
wherein T and t+1 respectively represent the current sampling period time and the next sampling period time, T c、Ts and T abt respectively represent the core temperature of the battery, the surface temperature of the battery, and the average temperature of the water inlet and outlet of the liquid cooling plate of the battery, the first parameter k 1, the second parameter k 2, and the interruption fourth parameter k 4 and the fifth parameter k 5 in the first heat transfer equation correspond to two lumped parameters in the simplified battery heat convection lumped parameter model, and the third parameter k 3 and the sixth parameter k 6 are correction parameters introduced to better simulate the heat dissipation performance of the battery in a large temperature difference environment. The heat productivity input Q and the environment temperature T abt can be used for recursively calculating the real-time surface temperature T s and the core temperature T c of the battery.
In the above embodiment, the standard formula of the battery heat convection lumped parameter model is further improved after discretization, so that the standard formula meets the thermal characteristics of the battery under severe working conditions, and meanwhile, the calculated amount is small, namely, the heat transfer model fully considers the structure and mechanism of the battery, the estimation precision can still be kept under the high-magnification complex working conditions with large temperature fluctuation, and the calculated amount of the model is small, so that the method has the capability of real vehicle on-line operation.
On the basis of determining the heat transfer equation, the online temperature estimation of the battery is completed, and a berna first heat generation equation is also required to be introduced, so that, considering that the open circuit voltage inside the battery and the SOC are in a strong coupling relationship, the SOC estimation error directly affects the calculation accuracy of the heating value, and thus the open circuit voltage needs to be corrected, and in an exemplary embodiment, as shown in fig. 4, step 208 includes steps 402 to 406. Wherein:
Step 402, determining all candidate parameters associated with the battery core temperature, and determining the candidate parameter corresponding to the maximum association degree as the association parameter according to the association degree of each candidate parameter and the battery core temperature.
The candidate parameters comprise parameters such as battery charge and discharge parameters, environment temperature parameters, battery internal resistance and the like. The determination of the association degree between each candidate parameter and the battery core temperature may be implemented in an existing manner, which is not described herein. The associated parameter may be understood as a candidate parameter that has the greatest influence on the battery core temperature, and the associated parameter may be the battery surface temperature. It should be noted that, in general, the internal specific surface temperature is high, and in winter, the internal temperature is lower than the surface temperature when the thermal management is started, and the surface temperature is directly observed, but it does not represent the actual operating temperature of the battery, as shown in fig. 5, which is a schematic diagram of the surface and core temperature of the battery under the charge and discharge conditions in an exemplary embodiment.
Step 404, determining a correction relation satisfied by the open circuit voltage according to the correlation parameter.
The correction relation for determining the open-circuit voltage according to the correlation parameter may be a relation satisfied by the open-circuit voltage of the battery at the current sampling period time and the next sampling period time, the battery surface temperature of the battery at the current sampling period time and the real-time estimated surface temperature of the battery, and the relation is used as the correction relation for satisfying the open-circuit voltage.
The primary heat generation equation can be expressed as:
;
Wherein Q t represents the heat productivity of the battery at the current moment, U t and I t represent the terminal voltage and current acquired by the battery in real time during the operation process, T represents the current working temperature of the battery, The entropy heat coefficient of the battery can be calculated and determined according to voltage data measured by a temperature change shelving experiment. The correction relation of the open circuit voltage can be expressed as:
Wherein T t represents the battery surface temperature obtained from the battery external signal acquisition harness in the current sampling period, that is, represents the acquired 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.
And step 406, correcting the open-circuit voltage in the primary heat generation equation according to the correction relation to obtain a corrected primary heat generation equation.
Illustratively, the correction relation is substituted into the open circuit voltage in the primary heat generation equation, and the open circuit voltage in the primary heat generation equation is corrected to obtain a corrected primary heat generation equation.
In this embodiment, the battery surface temperature pair corrects the open-circuit voltage, so that a strong coupling relationship between the open-circuit voltage and the SOC in the battery can be avoided, so that the SOC estimation error directly affects the calculation accuracy of the calorific value, and further the problem of inaccurate core temperature estimation is caused.
In an exemplary embodiment, a method for determining model parameters is provided, as shown in fig. 6, including steps 602 to 606, where:
step 602, collecting working parameters of the battery at different preset temperatures by using a pulse working condition, wherein the working parameters comprise core temperature, battery surface temperature, environment temperature, voltage and current under a charging and discharging working condition.
The pulse condition may be understood as a symmetric pulse condition with a frequency of a preset frequency within a preset battery charge-discharge rate range, the preset battery charge-discharge rate range may be 1C-3C, the preset frequency may be 1hz, and the symmetric pulse may be understood as a pulse with symmetric charge-discharge, for example, charging 1S, discharging 1S, and charging and discharging currents with equal magnitudes in one pulse.
Step 604, using the working parameter as the input of the battery semi-closed loop temperature estimation model, and determining the thermophysical parameter of the battery semi-closed loop temperature estimation model by using a least square method.
The specific implementation manner of determining the thermophysical parameter of the battery semi-closed loop temperature estimation model by the least square method can be implemented in an existing manner, and details are not repeated here.
The construction of a battery semi-closed loop temperature estimation model of a battery according to a heat transfer equation and a modified berna first heat generation equation can be expressed as:
Where t is the current sampling period time, t-1 is the last sampling period time, and t+1 is the next sampling period time. It should be noted that, the input heat under different preset conditions is known, and when the thermophysical parameters are calculated based on the working parameters of the pulse condition acquisition battery under different preset temperatures, the input heat can be directly substituted into the calculation.
Step 606, inputting the preset simulated operating parameters into the battery semi-closed loop temperature estimation model including the thermophysical parameters, and determining the correction parameters of the battery semi-closed loop temperature estimation model.
By way of example, the optimum k 1、k2、k4、k5 is identified by the least square method by using the battery core temperature T c, the surface temperature T t, the liquid cooling plate water inlet and outlet temperature T abt, the voltage U and the current I acquired under the pulse working condition as inputs, and then the actual vehicle working condition is simulated once, so as to complete the identification of the correction parameters k 3、k6 and k.
In the embodiment, the parameters of the model are identified by using the pulse working condition, so that the influence of entropy heat on parameter identification is reduced to the greatest extent, and the accuracy of battery core temperature estimation is ensured.
In an exemplary embodiment, a method for determining a battery core temperature based on identifying model parameters is provided, comprising:
and inputting the surface temperature, the voltage, the current and the environmental temperature of the battery to a temperature estimation model to obtain the core temperature of the battery to be tested.
The ambient temperature may be, but is not limited to, an average temperature of the water inlet and outlet of the battery liquid cooling plate.
The method includes the steps of inputting the observed battery surface temperature, the observed voltage and the observed current and the average temperature of a water inlet and a water outlet of a battery liquid cooling plate into a determined temperature estimation model, calculating a real-time estimated value of the battery surface temperature, updating an open-circuit voltage based on the real-time estimated value and the observed battery surface temperature, recalculating the heating value to obtain corrected heating value, and estimating the battery core temperature based on the corrected heating value. The specific implementation can be that the heating value input of the last sampling period moment is determined according to the terminal voltage, the current and the open circuit voltage of the last sampling period moment, and the surface temperature estimation is carried out according to the heating value input of the last sampling period moment, the battery surface temperature estimation value of the last sampling period moment, the average temperature of the water inlet and outlet of the battery liquid cooling plate of the last sampling period moment, and the surface temperature estimation of the current sampling period moment is obtained according to the first parameter k 1, the second parameter k 2 and the third parameter k 3.
And correcting the open-circuit voltage at the current sampling period according to the surface temperature estimated value at the current sampling period, the acquired value of the battery surface temperature at the current sampling period and the correction coefficient k, obtaining the corrected open-circuit voltage so as to finish the calculation of the heating value input, and determining the battery core temperature at the next sampling period based on the heating value input at the current sampling period, the fourth parameter, the fifth parameter and the sixth parameter, the battery core temperature at the current sampling period and the surface temperature acquired in real time at the current sampling period.
In the above embodiment, the surface temperature is used to complete the OCV update in the heating value calculation process, so that the model is closed-loop, the estimation error is not amplified with time on the premise that the SOC is not used as the model input, and the temperature estimation error caused by the SOC estimation error is avoided.
In an exemplary embodiment, as shown in fig. 7, a method for determining a battery core temperature is provided, and an example in which the method is applied to the vehicle in fig. 1 is described, including the following steps 702 to 712. Wherein:
Step 702, obtaining a battery heat convection lumped parameter model of the battery, and converting the battery heat 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.
Step 704, acquiring a primary first heat generation equation, determining a correlation parameter correlated with the core temperature of the battery, and correcting the open-circuit voltage in the primary first heat generation equation according to the correlation parameter to obtain a corrected primary first 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 primary heat generation equation;
Step 708, obtaining working parameters of the battery under different preset working conditions, carrying out parameter identification on the battery semi-closed loop temperature estimation model according to the working parameters, determining model parameters of the battery semi-closed loop temperature estimation model, and obtaining a temperature estimation model for determining the core temperature of the battery.
Step 710, obtaining the battery surface temperature, voltage and current, and the ambient temperature of the battery to be tested.
Step 712, inputting the surface temperature, voltage, current and ambient temperature of the battery into a temperature estimation model to obtain the core temperature of the battery to be tested.
In an exemplary embodiment, as shown in fig. 8, a flowchart of a method for determining a battery core temperature is provided, which specifically includes that a battery heat convection lumped parameter model is built based on heat transfer characteristics between fluid and solid, the battery heat convection lumped parameter model is converted into a state space equation, namely, a heat transfer equation which is met by a battery in a preset heat transfer direction is obtained, a battery semi-closed loop temperature estimation model is built based on the space state equation and a primary heat generation equation, voltage, current and temperature data under specific working conditions are obtained to conduct parameter identification on the battery semi-closed loop temperature estimation model, and model parameters are determined, wherein the voltage, current and temperature data comprise collected terminal voltage, current, battery core temperature, battery surface temperature and average temperature of a water inlet and a water outlet of a liquid cooling plate.
Acquiring terminal voltage and current data of a battery to be tested, inputting the data into a battery semi-closed loop temperature estimation model for determining model parameters, determining a battery surface temperature estimation value of the battery to be tested, correcting an open-circuit voltage according to the acquired battery surface temperature, recalculating the heating value of the battery, and estimating the core temperature of the battery based on the updated heating value to obtain the core temperature of the battery.
It should be noted that, the specific implementation of this embodiment may be implemented in the above-mentioned limiting manner, which is not described herein.
In the embodiment, the battery structure and mechanism are considered, the thermal convection standard formula is further improved after discretization, so that the thermal convection standard formula accords with the thermal characteristics of the battery under severe working conditions, meanwhile, the calculated amount is small, the on-line operation capability of the vehicle is ensured, the estimation precision can still be kept under the high-multiplying-power complex working conditions with large temperature fluctuation, on the basis, the surface temperature is utilized to complete OCV updating in the heating value calculation process, the model is closed loop, estimation errors are not amplified with time on the premise that the SOC is not taken as the input of the model, the parameters of the model are identified under the sampling pulse working condition, the influence of entropy heat on the parameter identification is reduced to the greatest extent, and the precision and the reliability of the estimation of the core temperature of the battery are further ensured.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a device for determining the battery core temperature, which is used for realizing the method for determining the battery core temperature. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiment of the device for determining a battery core temperature or temperatures provided below may refer to the limitation of the method for determining a battery core temperature hereinabove, and will not be repeated herein.
In one exemplary embodiment, as shown in fig. 9, there is provided a device for determining a battery core temperature, including a data acquisition module 902, a correction module 904, a model construction module 906, and a parameter identification module 908, wherein:
The data acquisition module 902 is configured to acquire a heat transfer equation satisfied by the battery in a preset heat transfer direction and a primary heat generation equation for quantifying heat.
And the correction module 904 is configured to determine a correlation parameter associated with the core temperature of the battery, and correct the open-circuit voltage in the primary heat generation equation according to the correlation parameter, so as to obtain a corrected primary heat generation equation.
The model building module 906 is configured to build a battery semi-closed loop temperature estimation model of the battery according to the heat transfer equation and the modified berna first heat generation equation.
The parameter identification module 908 is configured to obtain working parameters of the battery under different preset working conditions, identify parameters of the battery semi-closed loop temperature estimation model according to the working parameters, determine 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.
According to the device for determining the battery core temperature, the heat transfer equation and the primary first heat generation equation for quantifying heat which are met by the battery in the preset heat transfer direction are obtained, the correlation parameters associated with the battery core temperature are utilized to correct open-circuit voltage in the primary first heat generation equation, the battery semi-closed-loop temperature estimation model of the battery is constructed according to the heat transfer equation and the corrected primary first heat generation equation, the situation that errors can be accumulated continuously along with change of working conditions or battery aging and convergence of model estimation accuracy cannot be achieved due to the fact that the battery core temperature and the correlation parameters possibly change is avoided, further, parameter identification is conducted on the constructed battery semi-closed-loop temperature estimation model according to working parameters under different preset working conditions, the temperature estimation model for determining the battery core temperature is determined, namely, heat is corrected by utilizing the correlation parameters, the battery core temperature is calculated again by the updated heat productivity, the battery core temperature is determined by simulating a battery heat transfer process by using an equivalent circuit model, and accuracy of battery core temperature estimation is improved.
In an exemplary embodiment, the data obtaining module 902 is further configured to obtain a battery thermal convection lumped parameter model of the battery, and convert the battery thermal convection lumped parameter model to obtain a heat transfer equation satisfied by the battery in a preset heat transfer direction.
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, and the data acquisition module 902 is further configured 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 by the battery in the second preset heat transfer direction;
The first heat transfer equation is used for representing the relation among the battery surface temperature of the battery at the current sampling period time and the next sampling period time, the calorific value input at the current period time and the battery environment temperature, and the second heat transfer equation is used for representing the relation among the collected battery core internal temperature at the current sampling period time and the next sampling period time, the calorific value input at the current period time and the battery surface temperature.
In an exemplary embodiment, the correction module 904 is further configured to determine all candidate parameters associated with the battery core temperature, and determine, as the associated parameter, a candidate parameter corresponding to the greatest association degree according to the association degree of each candidate parameter with the battery core temperature;
determining a correction relation satisfied by the open-circuit voltage according to the association parameter;
and correcting the open-circuit voltage in the primary heat generation equation according to the correction relation to obtain a corrected primary heat generation equation.
In an exemplary embodiment, the correction module 904 is further configured to serve as a correction relationship for the open circuit voltage to be satisfied based on a relationship satisfied by the open circuit voltage of the battery at the current sampling period time and the next sampling period time, the battery surface temperature of the battery at the current sampling period time, and the real-time estimated surface temperature of the battery.
In an exemplary embodiment, the parameter identification module 908 is further configured to collect operating parameters of the battery at different preset temperatures using a pulse condition, where the operating parameters include a core temperature, a battery surface temperature, an ambient temperature, a voltage, and a current under a charge-discharge condition;
the working parameters are used as the input of a battery semi-closed loop temperature estimation model, and the least square method is used for determining the thermophysical parameters of the battery semi-closed loop temperature estimation model;
And inputting the preset simulation working parameters into a battery semi-closed loop temperature estimation model comprising the thermophysical parameters, and determining the correction parameters of the battery semi-closed loop temperature estimation model.
In an exemplary embodiment, the device for determining the core temperature of the battery further comprises a temperature estimation module, which is used for obtaining the surface temperature, voltage and current of the battery to be tested and the ambient temperature, and inputting the surface temperature, voltage, current and ambient temperature of the battery to a temperature estimation model to obtain the core temperature of the battery to be tested.
The respective modules in the above-described determination means of the battery core temperature may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one exemplary embodiment, a vehicle is provided, the internal structure of which may be as shown in fig. 10. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. 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. Wherein the processor of the vehicle is configured 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 computer programs in the non-volatile storage media. The input/output interface of the vehicle is used for exchanging information between the processor and the external device. The Communication interface of the vehicle is used for carrying out wired or wireless Communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, near field Communication (NEAR FIELD Communication) or other technologies. The computer program is executed by a processor to implement a method of determining a battery core temperature. The display unit of the vehicle is used for forming a visual picture, and 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 equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in FIG. 10 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, there is also provided a vehicle including a memory having a computer program stored therein and a processor that when executing the computer program performs the steps of the method embodiments described above.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are both information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to meet the related regulations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile memory and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (RESISTIVE RANDOM ACCESS MEMORY, reRAM), magneto-resistive Memory (MagnetoresistiveRandom Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static Random access memory (Static Random Access Memory, SRAM) or Dynamic Random access memory (Dynamic Random AccessMemory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present 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 computation, an artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) processor, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the present application.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
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