Battery cell temperature evaluation method, device and equipment and computer readable storage medium

Abstract

The invention discloses a battery cell temperature evaluation method, device and equipment and a computer readable storage medium, the method comprises the following steps: obtaining measurement parameters and transfer heat of a battery pack, the battery pack comprising at least one battery cell element, the transfer heat characterizing heat transfer of other elements except the battery cell element in the battery pack to the battery cell element; inputting the measurement parameters of the battery pack into the trained battery cell temperature evaluation model to obtain the initial temperature of each battery cell element in the battery pack output by the trained battery cell temperature evaluation model; and correcting the initial temperature of each battery cell element according to the transmitted heat to obtain the target temperature of each battery cell element. According to the scheme, the initial temperature of the battery cell element is evaluated through the battery cell temperature evaluation model, the influence of other elements in the battery pack on the battery cell temperature is also considered, and the accuracy of battery cell temperature evaluation is improved.

Description

Technical Field

[0001] The present invention relates to the field of battery processing technology, and in particular to a battery cell temperature evaluation method, a battery cell temperature evaluation device, an electronic device, and a computer-readable storage medium. Background Art

[0002] As an important component of energy storage systems, electronic products, and various electromechanical equipment, batteries generate a lot of heat during operation, and it is necessary to detect the battery temperature in real time to avoid problems such as overheating or underheating. The temperature outside the battery is relatively easy to measure, but due to the complex internal structure and small size of the battery, its internal temperature is not easy to measure.

[0003] Currently, sensor technology is mainly used to measure the external temperature of the battery. The temperature of each component inside the battery is restricted by factors such as the cost, space and wiring of the battery pack. It is not possible to configure a corresponding sensor for each single cell, resulting in the inability to accurately identify the temperature of each component inside the battery pack. Summary of the invention

[0004] The main technical problem solved by the present application is to provide a battery cell temperature evaluation method, a battery cell temperature evaluation device, an electronic device and a computer-readable storage medium, which can improve the accuracy of battery cell temperature evaluation.

[0005] In order to solve the above technical problems, a technical solution adopted in the present application is: a battery cell temperature evaluation method is provided, and the battery cell temperature evaluation method includes: obtaining measurement parameters and transfer heat of a battery pack, wherein the battery pack includes at least one battery cell element, and the transfer heat characterizes the heat transfer of other elements in the battery pack except the battery cell element to the battery cell element; inputting the measurement parameters of the battery pack into a trained battery cell temperature evaluation model to obtain the initial temperature of each battery cell element in the battery pack output by the trained battery cell temperature evaluation model; and correcting the initial temperature of each battery cell element according to the transfer heat to obtain the target temperature of each battery cell element.

[0006] In some embodiments, the step of obtaining the transfer heat of the battery pack includes: simplifying each battery cell component in the battery pack and components associated with each battery cell component into resistors to construct a simplified circuit structure; and determining the transfer heat of each battery cell component based on each resistor in the simplified circuit structure.

[0007] In some embodiments, the step of determining the heat transfer of each battery cell component based on each resistor in the simplified circuit structure includes: initializing each resistor in the simplified circuit structure to obtain an initial resistance value of each resistor; inputting the initial resistance value of each resistor into a trained neural network model for identification processing to obtain a resistance value output by the neural network model; and determining the heat transfer of each battery cell component based on the resistance value output by the trained neural network model.

[0008] In some embodiments, the method also includes a training step for the trained neural network model: inputting the initial resistance value of each resistor in the simplified circuit structure into the neural network model to obtain the resistance value output by the neural network model; inputting the resistance value output by the neural network model into the simplified circuit structure to obtain the total voltage and total temperature of the battery pack; calculating a first loss value of the loss function between the total voltage of the battery pack and the actual voltage of the battery pack, and calculating a second loss value of the loss function between the total temperature of the battery pack and the actual temperature of the battery pack; training the neural network model with the purpose of reducing the sum of the first loss value and the second loss value to obtain the trained neural network model.

[0009] In some embodiments, the method includes the steps of training the trained cell temperature assessment model: obtaining experimental parameters of the battery pack; inputting the experimental parameters into the cell temperature assessment model to obtain the cell temperature and cell voltage output by the cell temperature assessment model; calculating a third loss value of the loss function between the cell temperature and the actual cell temperature, and a fourth loss value of the loss function between the cell voltage and the actual cell voltage; training the cell temperature assessment model with the purpose of reducing the sum of the third loss value and the fourth loss value to obtain the trained cell temperature assessment model.

[0010] In some embodiments, the method further includes: establishing a target electrochemical model based on the electrochemical properties of the battery cell components; establishing a thermal model based on the thermal properties of the battery cell components; and combining the electrochemical model and the thermal model to obtain the battery cell temperature assessment model.

[0011] In some embodiments, the step of establishing a target electrochemical model based on the electrochemical properties of the battery cell components includes: establishing an initial electrochemical model according to the electrochemical properties of the battery cell components; introducing physical assumptions to simplify the initial electrochemical model to obtain the target electrochemical model.

[0012] To solve the above technical problems, another technical solution adopted in the present application is: a battery cell temperature evaluation device is provided, the device comprising: an acquisition module, used to acquire measurement parameters and transfer heat of a battery pack, the battery pack comprising at least one battery cell element, the transfer heat characterizing the heat transfer of other elements in the battery pack except the battery cell to the battery cell element; an evaluation module, used to input the measurement parameters of the battery pack into a trained battery cell temperature evaluation model, and obtain the initial temperature of each battery cell in the battery pack output by the trained battery cell temperature evaluation model; a correction module, used to correct the initial temperature of each battery cell according to the transfer heat, and obtain the target temperature of each battery cell.

[0013] In order to solve the above technical problems, another technical solution adopted in the present application is: to provide an electronic device, including a memory and a processor, wherein the memory stores program instructions, and the processor retrieves the program instructions from the memory to execute the above battery cell temperature evaluation method.

[0014] In order to solve the above technical problems, another technical solution adopted by the present application is: providing a computer-readable storage medium including program data stored therein, wherein the program data is used to implement the above-mentioned battery cell temperature evaluation method when executed by a processor.

[0015] Beneficial effects of the present application: The battery cell temperature evaluation method of the embodiment of the present application obtains the measurement parameters and transfer heat of the battery pack, the battery pack includes at least one battery cell element, and the transfer heat characterizes the heat transfer of other elements in the battery pack to the battery cell element except the battery cell element; the measurement parameters of the battery pack are input into the trained battery cell temperature evaluation model to obtain the initial temperature of each battery cell element in the battery pack output by the trained battery cell temperature evaluation model; the initial temperature of each battery cell element is corrected according to the transfer heat to obtain the target temperature of each battery cell element. The above scheme, in addition to evaluating the initial temperature of the battery cell element through the battery cell temperature evaluation model, also takes into account the influence of other elements in the battery pack on the battery cell temperature, thereby improving the accuracy of the battery cell temperature evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0017] Figure 1 is a flow chart of an exemplary embodiment of a method for evaluating a battery cell temperature shown in the present application;

[0018] Figure 2 yes Figure 1 A flow chart of an exemplary embodiment of step S110 in the method for evaluating the temperature of a battery cell is shown;

[0019] Figure 3 is a structural schematic diagram of an exemplary embodiment of a simplified circuit structure shown in the present application;

[0020] Figure 4 is a schematic diagram of the structure of an exemplary embodiment of a neural network model shown in the present application;

[0021] Figure 5 It is a specific flow chart of an exemplary embodiment of a battery cell temperature evaluation method shown in the present application;

[0022] Figure 6 is a structural schematic diagram of an exemplary embodiment of a battery core temperature evaluation device shown in the present application;

[0023] Figure 7 It is a structural schematic diagram of an embodiment of an electronic device provided by the present application;

[0024] Figure 8 It is a structural schematic diagram of an embodiment of a computer-readable storage medium provided by the present application. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It will be appreciated that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only some but not all structures related to the present application are shown in the drawings. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the art without making creative work are within the scope of protection of the present application.

[0026] First of all, it should be noted that most of the current battery cell temperature assessment methods are temperature assessment methods for single battery cells, including direct measurement methods and methods based on electrochemical impedance spectroscopy. Among them, the direct measurement method uses a scanner with infrared thermal imaging function to directly act on the working lithium battery, and measures the thermal radiation of the battery and converts it into a thermal image to display the temperature distribution in different areas, dynamically understand the temperature field distribution inside the battery, and thus obtain the internal temperature of the battery. The electrochemical impedance method studies the relationship between the imaginary part of the battery impedance spectrum and the internal temperature of the battery based on the internal electrochemical and thermal characteristics of the battery, and effectively establishes a lithium battery internal temperature estimation model, but these two methods can only be used in experimental research and are difficult to apply in real life.

[0027] Based on this, the present application proposes a battery cell temperature evaluation method, a battery cell temperature evaluation device, an electronic device and a computer-readable storage medium, which can improve the accuracy of battery cell temperature evaluation. Figure 1 , Figure 1 It is a flow chart of an exemplary embodiment of a battery cell temperature evaluation method shown in the present application.

[0028] The execution subject of the battery cell temperature evaluation method may be a terminal device or a server or other processing device, wherein the terminal device may be a user equipment (UE), a computer, a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The execution subject of the battery cell temperature evaluation method may also be a battery cell temperature evaluation device. In some possible implementations, the battery cell temperature evaluation method may be implemented by a processor calling a computer-readable instruction stored in a memory.

[0029] Specifically, the battery cell temperature evaluation method of this embodiment includes the following steps:

[0030] S110: Acquire measurement parameters and transfer heat of a battery pack, wherein the battery pack includes at least one battery cell component, and the transfer heat represents heat transfer from other components in the battery pack to the battery cell component except the battery cell component.

[0031] A battery pack is generally assembled by connecting at least one battery cell in series and parallel and using a battery management system. Common battery packs include lithium-ion battery packs, nickel-cadmium battery packs, and nickel-metal hydride battery packs. Among them, lithium-ion batteries have been widely used in energy storage systems, electronic products, and various electromechanical equipment due to their advantages such as large specific energy, long cycle life, fast charge and discharge, and low self-discharge rate. Lithium-ion batteries generate a lot of heat during operation, and the arrangement structure of the battery module leads to uneven temperature distribution. At the same time, the operating temperature of the battery has a great influence on the performance of the battery. The suitable operating temperature range of lithium-ion batteries is 10-30°C. Too low temperature will induce lithium precipitation reaction in the battery, and too high temperature will aggravate side reactions such as SEI film growth in the battery, thereby reducing the service life of the battery. The battery pack can contain dozens to hundreds of battery cell components, but the volume of the battery pack is limited, so it is impossible to equip each battery cell component with a temperature sensor, which makes it difficult to measure the battery cell temperature.

[0032] The measurement parameters refer to the parameters of the battery pack that can be measured by physical methods, including some obtainable parameters of the battery cell components in the battery pack. Exemplarily, the measurement parameters can be real-time measurement parameters, including but not limited to: the total voltage, total temperature, current input power, energy, etc. of the battery pack.

[0033] Transfer heat refers to the heat transfer from other components in the battery pack to the battery cell components except the battery cell components to be tested. Specifically, the components in the battery pack are relatively complex, and the internal connecting wires, busbars, and control components will affect the temperature of the battery cell components. Therefore, it is necessary to determine the temperature impact of other components on the battery cell components in order to obtain a more accurate battery cell temperature and realize the evaluation and calculation of the temperature of all batteries in the entire battery pack.

[0034] The battery cell component can also be called a single battery, which is an energy storage unit. Multiple battery cells are connected in series or in parallel to form a battery pack. The battery cell component is the core component in the battery pack. It is necessary to perform real-time temperature detection on the battery cell component to prevent the battery cell temperature from being too high or too low, affecting the normal use of the system and ensuring personnel safety.

[0035] Other components can be components other than the cell components in the battery pack. Of course, in practical applications, the cell components in the battery pack will also affect each other, so other components can also be components other than the cell components to be tested in the battery pack. Other components include but are not limited to: connecting wires, busbars, and control components.

[0036] Heat transfer can be the heat generated by other components transferred to the battery cell components, affecting the temperature of the battery cell components. In the battery pack, the components are closely connected, and the heat generated during operation will be transferred to each other, resulting in the temperature of the battery cell components not only including the heat generated by the battery cell components themselves, but also the heat generated by other components transferred to the battery cell components.

[0037] S120: Inputting the measured parameters of the battery pack into the trained battery cell temperature assessment model to obtain the initial temperature of each battery cell component in the battery pack output by the trained battery cell temperature assessment model.

[0038] The battery cell temperature assessment model is a model constructed by digital twin technology based on the electrochemical properties and thermal properties of the battery cell components. For example, a simplified P2D model can be used when constructing an electrochemical model based on the electrochemical properties of the battery cell components. When constructing a thermal model based on the thermal properties of the battery cell components, a zero-dimensional temperature distribution-centralized heat generation thermal model can be used. The electrochemical model and the thermal model are coupled to obtain the battery cell temperature assessment model.

[0039] The initial temperature is the cell temperature predicted by the cell temperature evaluation model. Exemplarily, the measurement parameters of the battery pack are input into the cell temperature evaluation model to obtain the initial temperature of the cell element. The measurement parameters include not only the external measurement parameters of the battery pack, but also the internal measurement parameters of the battery pack, which can improve the accuracy of the cell temperature.

[0040] S130: Correcting the initial temperature of each battery cell component according to the transferred heat to obtain a target temperature of each battery cell component.

[0041] The target temperature is used to evaluate whether the battery cell components are within a safe range. Exemplarily, the initial temperature output by the battery cell temperature evaluation model can be corrected by transferring heat to obtain the target temperature of each battery cell component. The correction method includes but is not limited to: when calculating the temperature of a single battery cell component, the sum of the heat transferred by each other component to the battery cell component and the initial temperature of the battery cell component is determined as the target temperature; the temperature of other components is weighted according to the heat transferred by each other component to the battery cell component to obtain a weighted sum, and the sum between the initial temperature of the battery cell component and the weighted sum is determined as the target temperature.

[0042] It can be seen that the battery cell temperature evaluation method of the embodiment of the present application obtains the measurement parameters and transfer heat of the battery pack, the battery pack includes at least one battery cell element, and the transfer heat represents the heat transfer of other elements in the battery pack except the battery cell element to the battery cell element; the measurement parameters of the battery pack are input into the trained battery cell temperature evaluation model to obtain the initial temperature of each battery cell element in the battery pack output by the trained battery cell temperature evaluation model; the initial temperature of each battery cell element is corrected according to the transfer heat to obtain the target temperature of each battery cell element. In addition to evaluating the initial temperature of the battery cell element through the battery cell temperature evaluation model, the above scheme also takes into account the influence of other elements in the battery pack on the battery cell temperature, thereby improving the accuracy of the battery cell temperature evaluation.

[0043] Based on the above embodiments, the embodiments of the present application adopt Figure 2 The flowchart details how to obtain the transferred heat, see Figure 2 , Figure 2 yes Figure 1 The flowchart of an exemplary embodiment of step S110 in the battery cell temperature evaluation method is shown. Specifically, the process of obtaining the transferred heat of the battery pack in step S110 includes the following steps:

[0044] S210: simplifying each battery cell component in the battery pack and components associated with each battery cell component into resistors to construct a simplified circuit structure.

[0045] The simplified circuit structure is a circuit structure obtained by simplifying the components in the battery pack. For example, since the heat generated by other components in the battery pack will affect the temperature of the battery cell components, this effect can be simplified to resistance. As an example, the simplified circuit structure can be referred to Figure 3, each single cell is simplified to the battery internal resistance, the busbar associated with the single cell is simplified to the busbar resistance, the connecting wire associated with the single cell is simplified to the connecting resistance, and the control element associated with the single cell is simplified to the mounting resistance; this example includes three battery internal resistances, each battery internal resistance forms a parallel relationship, and the battery internal resistance R close to the total current i0 With mounting resistor R t0 , connect the resistor R c0 And mounting resistor R t1 The battery internal resistance R forms a series relationship and is far away from the total current i1 With bus resistance R b0 , connect the resistor R c1 and bus resistance R b2 Form a series relationship, and the battery internal resistance R i2 With bus resistance R b1 , connect the resistor R c2 and bus resistance R b3 A series relationship is formed, and the voltage at the current input terminal of the battery internal resistance is determined by the output of the electrochemical-thermal coupling model (battery cell temperature evaluation model). This is just an example. In other embodiments, multiple battery cell components can be connected in series, or multiple battery cell components can be connected in series into a group, and then multiple groups can be connected in parallel. The simplified circuit structure is obtained based on the simplified circuit structure of the battery pack, and the simplified circuit structure can be constructed according to the actual circuit structure.

[0046] S220: Determine the transferred heat of each battery cell component based on each resistor in the simplified circuit structure.

[0047] The resistance value of each resistor in the simplified circuit structure can be obtained through experiments. For example, after constructing the simplified circuit structure, the battery cell temperature evaluation device substitutes the experimental parameters of the battery pack into the simplified circuit structure to obtain the resistance value of each resistor in the simplified circuit structure; and determines the transferred heat based on the resistance value of each resistor. Among them, the experimental parameters refer to the parameters measured by the battery pack experiment, including charge and discharge cycle data, and the charge and discharge cycle data includes the voltage and temperature of all battery cell components in the battery pack.

[0048] As an example, each resistor in a simplified circuit structure is initialized to obtain an initial resistance value of each resistor; the initial resistance value of each resistor is input into a trained neural network model for identification processing to obtain a resistance value output by the neural network model; and the transferred heat of each battery cell component is determined based on the resistance value output by the trained neural network model.

[0049] Among them, the battery cell temperature evaluation device first initializes each resistor in the simplified circuit structure. The initialization can be to assume the resistance value of each resistor to obtain the initial resistance value of each resistor; the initial resistance value of each resistor is input into the trained neural network model for identification processing to obtain the resistance value output by the neural network model; the resistance value output by the trained neural network model is determined as the target resistance value; and the transferred heat of each battery cell component is determined according to the target resistance value.

[0050] The neural network model can be a BP (Back propagation) neural network model, and its structure can be found in Figure 4 First, the connection resistance, bus resistance, mounting resistance and battery internal resistance in the simplified circuit structure are determined as the input parameters of the neural network model. The total number of input parameters is N, the number of input nodes of the neural network model is N, the number of nodes in the first hidden layer is 2N+1, the number of nodes in the second hidden layer is 3N, the number of nodes in the third hidden layer is 2N+1, and the number of output nodes is N, that is, the number of parameters after identification is N.

[0051] Among them, the process of training the trained neural network model includes: inputting the initial resistance value of each resistor in the simplified circuit structure into the neural network model to obtain the resistance value output by the neural network model; inputting the resistance value output by the neural network model into the simplified circuit structure to obtain the total voltage and total temperature of the battery pack; calculating the first loss value of the loss function between the total voltage of the battery pack and the actual voltage of the battery pack, and calculating the second loss value of the loss function between the total temperature of the battery pack and the actual temperature of the battery pack; training the neural network model with the purpose of reducing the sum of the first loss value and the second loss value to obtain a trained neural network model. Thus, identifying the resistance value through the neural network model can improve the efficiency and accuracy of resistance value identification. In other embodiments, the initial resistance value of each resistor in the simplified circuit structure can be continuously adjusted by manually setting rules to obtain the target resistance value.

[0052] Specifically, the battery cell temperature evaluation device first inputs the experimental parameters of the battery pack into the simplified circuit structure to obtain the initial resistance value of each resistor, including the connection resistor, busbar resistor, mounting resistor and battery internal resistance. The initial resistance value of each resistor is input into the neural network model for identification processing to obtain the resistance value after identification processing; then the resistance value after identification processing is substituted into the simplified circuit structure to obtain the total voltage and total temperature of the battery pack; the loss function is used to evaluate the difference between the prediction result of the neural network model and the true label, and minimizing the loss function allows the neural network model to learn the optimal parameters, thereby improving the accuracy of the prediction.

[0053] Among them, when measuring the difference between the prediction result of the neural network model and the true label, the residual between the actual voltage of the battery pack obtained by the experiment, the actual temperature of the battery pack and the total voltage and total temperature of the battery pack obtained by the simplified circuit structure can be input into the neural network model, and the network parameter values ​​of the neural network model are updated by the stochastic gradient descent method to continuously reduce the residuals between the actual voltage of the battery pack and the total voltage obtained by the simplified circuit structure, and the actual temperature of the battery pack and the total temperature obtained by the simplified circuit structure; after the residuals converge, the resistance values ​​of the connection resistance, bus resistance, mounting resistance and battery internal resistance output by the neural network model are the optimal resistance values ​​of each resistor in the simplified circuit structure.

[0054] Furthermore, the cell temperature assessment model can also be trained based on the experimental parameters of the battery pack. The experimental parameters of the battery pack are obtained, including the charge and discharge cycle data of the battery pack and some available electrochemical parameters of the cell components in the battery pack, battery size parameters and thermophysical parameters. Among them, the charge and discharge cycle data include the battery pack voltage, battery pack current, actual cell temperature, actual cell voltage, ambient temperature, battery pack temperature, key position probe temperature, battery pack series-parallel structure, etc.; the available electrochemical parameters include electrode height, width, initial concentration of electrolyte, initial concentration of positive and negative electrodes, cut-off voltage, positive and negative electrode diffusion coefficient, maximum concentration of positive and negative electrodes, conductivity, density, specific heat capacity, positive and negative particle radius, etc.; battery size parameters include battery pack length, width and height, number of modules, number of series and parallel connections, etc.; thermophysical parameters include battery cooling surface area, edge heat transfer coefficient, positive and negative current collector surface heat transfer coefficient, thermal conductivity, positive and negative electrode thermal conductivity, diaphragm thermal conductivity, total heat transfer coefficient, etc. The cell temperature assessment model is trained to fit other unknown parameters, including unknown electrochemical parameters and thermophysical parameters. Exemplarily, the parameters that need to be adjusted include the lithium ion solid phase diffusion coefficient, liquid phase volume fraction, electrolyte diffusion coefficient, solid-liquid phase conductivity, electrode maximum lithium ion concentration, battery density, specific heat capacity, and convection heat transfer coefficient. In the battery pack, due to the material and temperature differences between the cell components, the electrochemical parameters and thermophysical parameters of each cell component may be different.

[0055] After obtaining the experimental parameters of the battery pack, the experimental parameters are input into the cell temperature evaluation model to obtain the cell temperature and cell voltage output by the cell temperature evaluation model. In other embodiments, the experimental parameters may also be preprocessed. Preprocessing may be data processing methods such as data cleaning, data conversion, and feature extraction of the experimental parameters of the battery. Data cleaning refers to the removal of invalid data and abnormal data, such as null values. Data conversion refers to the conversion of some non-numeric type fields into data types, and methods such as unique hot encoding may be used for data conversion. Feature extraction refers to the need for statistical extraction of extreme value parameters such as the historical highest and lowest single cell voltages. It should be noted that only the parameters required by the cell temperature evaluation model are selected from the experimental parameters and input.

[0056] Then, the third loss value of the loss function between the cell temperature and the actual cell temperature is calculated, and the fourth loss value of the loss function between the cell voltage and the actual cell voltage is calculated, and the cell temperature evaluation model is trained with the purpose of reducing the sum of the third loss value and the fourth loss value, and other unknown parameters in the cell temperature evaluation model are adjusted to obtain a trained cell temperature evaluation model. For example, other position parameters can be adjusted using algorithms such as genetic algorithms or particle swarm optimization algorithms to make the cell temperature and cell voltage output by the cell temperature evaluation model as equal as possible to the actual cell temperature and actual cell voltage of the battery pack.

[0057] In some embodiments, the battery cell temperature assessment model is formed by coupling a target electrochemical model and a thermal model. Specifically, a target electrochemical model is established based on the electrochemical properties of the battery cell components; a thermal model is established based on the thermal properties of the battery cell components; and the battery cell temperature assessment model is obtained by combining the electrochemical model and the thermal model.

[0058] Exemplarily, the target electrochemical model can be a P2D model or a simplified P2D model. Simplifying the P2D model can reduce parameter calculations and reduce model calculation costs. The steps of simplifying the P2D model include: establishing an initial electrochemical model based on the electrochemical properties of the battery cell components; introducing physical assumptions to simplify the initial electrochemical model to obtain a target electrochemical model.

[0059] Among them, firstly, an initial electrochemical model is established according to the electrochemical properties of the battery cell components. The initial electrochemical model can be a P2D model, and each equation of the P2D model is described as follows:

[0060] Lithium ion migration process in electrode spherical materials s (x,r,t) is described by Fick's law in spherical coordinates:

[0061]

[0062] Its boundary conditions are:

[0063] The equation is used to describe the lithium ion concentration c inside the solid particles. s,i The law of change over time and space, r represents the distance from the center of the particle to a certain point, j i is the reaction current density, representing the electrochemical reaction rate at the solid-liquid interface, R is the particle radius of the electrode material, and D s,i is the solid phase diffusion coefficient of lithium ions.

[0064] Changes of lithium ions in liquid phase e (x, t) is described by the following diffusion equation:

[0065]

[0066] The equation is used to describe the rate of change of lithium ion concentration in the electrode pores with time, c e,i is the lithium ion concentration in the electrolyte liquid phase, t + is the lithium ion migration number, ε e,i is the liquid volume fraction, α s,i is the specific surface area of ​​the solid particles, is the effective diffusion coefficient considering the tortuosity of the porous electrode.

[0067] Conforms to the following formula:

[0068]

[0069] in, represents the diffusion coefficient of electrolyte and Brugg is the Bruggman coefficient.

[0070] Solid phase potential Φ s and liquid potential Φ e The distribution of is described by the charge conservation equation based on Ohm's law:

[0071]

[0072] Here, this equation is used to describe the rate of change of the potential gradient inside the electrode, σ eff =σε s represents the solid phase conductivity, Φ s,i represents the electric potential inside the electrode, represents the spatial gradient of the electric potential, α i The ratio of the contact area between the electrode active material and the electrolyte, F represents the Faraday constant, j i Represents the current density on the electrode surface; represents the liquid conductivity, Φ e,i represents the potential inside the electrode, R represents the ideal gas constant, T represents the temperature, and t+ represents the positive ion migration number, c e,i represents the lithium ion concentration inside the electrolyte, α s represents the surface area ratio of the electrodes, and j(x,t) is the current density on the electrode surface.

[0073] The charge transfer reaction of lithium batteries occurs at the solid-liquid interface between the electrode particles and the electrolyte, and the reaction rate j is calculated by the Butler-Volmer electrochemical kinetic equation:

[0074]

[0075] Among them, j i represents the current density on the electrode surface, h represents the convection heat transfer coefficient, F represents the Faraday constant, usually 96487C / mol, T represents the temperature, η i Represents the reaction overpotential.

[0076] i0 is the exchange current density related to the solid and liquid lithium ion concentration gradients, which can be expressed as:

[0077]

[0078] Among them, k i is the reaction rate constant, c e,i represents the lithium ion concentration in the electrolyte liquid phase, c s,i represents the lithium ion concentration, is the maximum lithium ion concentration of the electrode.

[0079] η i represents the reaction overpotential, which is calculated by the following formula:

[0080] η i =Φ s,i -Φ e,i -U ocv,i , formula (9)

[0081] Among them, η i represents the reaction overpotential, Φ s,i The solid phase potential inside the electrode, Φ e,i The electrolyte potential near the electrode surface, U ocv,i is the equilibrium potential of the electrode, is the stoichiometric number function.

[0082] The terminal voltage of the battery is calculated from the solid phase potential difference between the positive and negative electrodes:

[0083] V(t)=φ s (0,t)-φ s (L, t), formula (10)

[0084] Where V(t) represents the terminal voltage of the battery, φ s (0,t) represents the solid phase potential of the positive electrode of the battery, φ s (L, t) represents the solid phase potential of the negative electrode of the battery, and L represents the thickness of the electrode material.

[0085] Further considering the connection internal resistance R caused by the contact internal resistance of the battery tab, current collector and electrode material ct , the terminal voltage can be further corrected to:

[0086] V(t)=φ s (0,t)-φ s (L,t)-R ct I(t), formula (11)

[0087] Where V(t) represents the terminal voltage of the battery, φ s (0,t) represents the solid phase potential of the positive electrode of the battery, φ s (L,t) represents the solid phase potential of the negative electrode of the battery, L represents the thickness of the electrode material, R ct represents the charge transfer resistance of the electrochemical reaction, and I(t) represents the battery current.

[0088] Based on the above P2D model, physical assumptions are introduced to simplify the P2D model and obtain the target electrochemical model, which is called the single particle model SPM. The physical assumptions are:

[0089] Physical Assumption 1: The solid phase lithium ion concentration is uniformly distributed along the electrode thickness x direction, that is, at any time t, c s (x, r, t) and j(x, r, t) are constants in the electrode thickness direction x.

[0090] Physical Assumption 2: The exchange current density i0(x, t) can be replaced by its mean value, that is, the exchange current density i0(x, t) is a constant along the electrode thickness direction x.

[0091] Physical Assumption 3: The reaction rate j(x, t) at the solid-liquid interface is uniformly distributed along the electrode thickness x direction.

[0092] Physical Assumption 4: The liquid concentration and potential inside the battery are constant values ​​that do not change with time.

[0093] From physical assumptions 1 to 3, it can be seen that the above assumptions eliminate the coupling relationship between the P2D model parameters and the electrode position x. The average reaction rate j(x, t) in the SPM model can be obtained by integrating the solid phase potential equation (1) along the electrode thickness:

[0094]

[0095] Where L represents the thickness of the electrode material, α irepresents the transfer coefficient of the electrode, F represents the Faraday constant, and j i represents the current density of the electrode, I represents the battery current, and A represents the surface area of ​​the electrode.

[0096] Substituting physical assumption 3 into the above equation, the reaction rate of SPM can be directly calculated as:

[0097]

[0098] Substituting the reaction rate expressions into equations (1) and (3), the solid and liquid concentration equations in SPM can be solved independently.

[0099] Substituting the reaction rate into the Butler-Volmer equation, the overpotential of the battery can be obtained as:

[0100]

[0101] Among them, η i represents the overpotential of the battery, R represents the ideal gas constant, α i represents the transfer coefficient of the electrode, T represents the temperature, F represents the Faraday constant, h represents the convection heat transfer coefficient, j i represents the current density of the electrode, i 0,i It represents the exchange current density of the electrode.

[0102] Substituting equation (14) into equation (9) and equation (10), and combining with physical assumption 4, the terminal voltage calculation equation in SPM can be finally obtained as:

[0103]

[0104] Among them, the expression of the intermediate variable ξ is:

[0105]

[0106] V(t) represents the terminal voltage of the battery at time t, U ocv,p represents the equilibrium potential of the positive electrode, U ocv,n represents the equilibrium potential of the negative electrode, R represents the ideal gas constant. T represents temperature, F represents the Faraday constant, ξ p (t) represents the normalized value of the positive electrode lithium ion concentration, which changes with time t. n (t) represents the normalized value of the negative electrode lithium ion concentration, which changes with time t.

[0107] Based on the above SPM model, the internal concentration, potential and reaction current distribution of the battery can be calculated. Comparing the SPM model with the P2D model, it can be seen that the control equations of the SPM model are all in ordinary differential form, while the P2D model has four partial differential equations, so the SPM model greatly reduces the complexity of the model.

[0108] Then, a thermal model is established based on the thermal properties of the battery cell components. For example, a thermal model of zero-dimensional temperature distribution-centralized heat generation can be used. The battery heat generation rate Φ is mainly caused by electrochemical reactions, material phase changes and ohmic heat generation, and its expression is:

[0109]

[0110] Where Φ represents the heat generation rate of the battery, U ocv represents the equilibrium potential, represents the entropy thermal coefficient, I represents the battery current, T represents the temperature, V represents the battery terminal voltage, q pc Indicates the heat generated by the phase change of the material.

[0111] In the actual working process of the battery, the heat generated by the phase change of the material can be ignored. Therefore, the battery heat generation rate Φ can be further simplified as:

[0112]

[0113] Where Φ represents the heat generation rate of the battery, U ocv represents the equilibrium potential, I represents the battery current, V represents the battery terminal voltage, and T represents the temperature.

[0114] Lithium batteries can be regarded as non-steady-state heat conduction systems with convection boundaries. According to heat transfer theory, the heat exchange between the system and the environment mainly takes the form of heat conduction, heat convection and heat radiation. Based on the energy conservation theorem, the zero-dimensional temperature distribution-centralized heat generation thermal model used in the embodiment of the present application can be written as:

[0115]

[0116] Where ρ represents the battery density, c p represents specific heat capacity, T represents temperature, Φ represents battery heat generation rate, h represents convection heat transfer coefficient, A represents battery heat dissipation area, T env is the ambient temperature.

[0117] In addition, the parameters of the SPM model are temperature-dependent. As the temperature changes, the lithium ion conductivity, diffusion rate, and reaction rate will change. Therefore, it is considered to couple the electrochemical SPM model with the thermal model, quantify the relationship between the model parameters and temperature, and establish an electrochemical-thermal coupling model.

[0118] First, consider the effect of temperature on electrochemical parameters. The electrochemical parameter ψ can be corrected based on the Arrhenius formula:

[0119]

[0120] Among them, Tref represents the reference temperature, ψ ref represents the electrochemical parameter value at the reference temperature, represents the reaction activation energy, and R represents the ideal gas constant.

[0121] Based on the above, the temperature correction is performed on the solid phase diffusion coefficient and reaction rate constant of the target electrochemical model. The equilibrium potential curve of the battery is corrected using the entropy thermal coefficient. The correction formula is as follows:

[0122]

[0123] Among them, U ocv represents the equilibrium potential, represents the equilibrium potential at the reference temperature, T represents the temperature, and T ref represents the reference temperature, I represents the battery current, and U represents the battery voltage.

[0124] When the target electrochemical model is corrected by temperature, the voltage output by the target electrochemical model will also change, which will further affect the battery heat generation rate output by the thermal model. Therefore, the temperature correlation of the target electrochemical model is considered at the same time, and the target electrochemical model and thermal model are solved at the same time to establish a battery cell temperature evaluation model.

[0125] After the battery cell temperature evaluation model is established, the experimental parameters are input into the battery cell temperature evaluation model to obtain the voltage and temperature output by the battery cell temperature evaluation model.

[0126] In order to elaborate on the method for evaluating the temperature of a battery cell in this application, Figure 5 The framework diagram shown further illustrates it, as follows:

[0127] The first is the training process of the battery cell temperature assessment model. Taking lithium batteries as an example, the charge and discharge cycle data of lithium batteries and some available electrochemical parameters, thermophysical parameters, etc. are obtained; based on the P2D model, physical assumptions are introduced to construct a simplified P2D model, and the battery cell temperature assessment model is established by combining thermodynamic equations; the charge and discharge cycle data of lithium batteries and other assumed unknown electrochemical parameters and thermophysical parameters are input into the battery cell temperature assessment model for training, and other unknown electrochemical parameters and thermophysical parameters in the battery cell temperature assessment model are fitted to obtain a trained battery cell temperature assessment model. Based on the trained battery cell temperature assessment model, the real-time voltage and initial temperature of each battery cell component in the battery pack can be directly obtained.

[0128] Then comes the determination of the transferred heat. First, the connection resistor, battery internal resistance, bus resistance and mounting resistance are set according to the series-parallel structure of each component in the battery pack to construct a simplified circuit structure; the initial resistance value of each resistor in the simplified circuit structure is assumed, and the initial resistance value is input into the neural network model for identification processing to obtain the resistance value output by the neural network model; the resistance value output by the neural network model and the charge and discharge cycle data of the battery are substituted into the simplified circuit structure to obtain the total voltage and total temperature of the battery pack; the total voltage and total temperature of the battery pack output by the simplified circuit structure are compared with the actual voltage and actual temperature of the battery pack, and the neural network model is continuously trained to obtain the target resistance value; the transferred heat is determined based on the target resistance value, and the transferred heat represents the heat transfer of other components to the battery cell components. In other embodiments, while determining the target resistance value of each resistor in the simplified circuit structure, it is also necessary to determine the target heat transfer parameters between the battery cell components. Specifically, the initial resistance value and the initial heat transfer parameters of the resistor can be input into the neural network model for identification processing to obtain the target resistance value and target heat transfer parameters output by the neural network model. The heat transferred by each battery cell component is calculated based on the identified connection resistance, target resistance, mounting resistance, target resistance value of the battery internal resistance, and heat transfer parameters between the battery cell components.

[0129] Finally, the initial temperature output by the battery cell temperature assessment model is adjusted according to the transferred heat to obtain the target temperature of each battery cell component.

[0130] As an example, S1: The laboratory takes battery packs A and B with the same cell model and the same wiring harness composition 3P1S structure, and performs charge and discharge cycle tests on the two battery packs respectively, and obtains data such as battery pack voltage, current, single cell voltage, ambient temperature, battery pack temperature, and electrochemical parameters such as initial concentration of positive and negative electrodes, cut-off voltage, positive and negative electrode diffusion coefficients, and maximum concentration of positive and negative electrodes, as well as thermal physical parameters such as battery cooling surface area and total heat transfer coefficient.

[0131] S2: Based on the P2D electrochemical model, physical assumptions are introduced to construct a simplified electrochemical model. At the same time, a battery cell temperature evaluation model is established by combining thermodynamic equations.

[0132] S3: Based on the charge and discharge cycle data of battery pack A obtained in S1 and some known electrochemical parameters and thermophysical parameters, substitute them into the control equation of the battery cell temperature evaluation model established in S2, and assume other unknown electrochemical parameter reference values. Establish residuals between the voltage and temperature output by the model and the voltage and temperature in the charge and discharge cycle data of battery pack A, set the goal to optimize the residuals, and find the optimal electrochemical parameters.

[0133] S3.1: Use genetic algorithms or particle swarm optimization algorithms to identify and adjust other unknown electrochemical parameters so that the voltage and temperature output by the model are as consistent as possible with the actual measurement results of battery pack A.

[0134] S4: Based on the 3P1S series-parallel structure of the battery pack, set the mounting resistance, battery internal resistance, connection resistance, bus resistance and heat transfer parameters between single cells to establish a simplified circuit structure of the battery pack.

[0135] S4.1: Substitute the charge and discharge cycle data of the laboratory lithium battery pack A into the simplified circuit structure, output the total voltage and total temperature of the battery pack, and compare them with the actual voltage and actual temperature of the battery pack of battery pack A to identify the mounting resistance, battery ohmic internal resistance, connection resistance, bus resistance and heat transfer coefficient between battery cell components.

[0136] S5: During the application process, based on the data of battery pack B's current, battery pack voltage, battery cell voltage, ambient temperature, battery pack series-parallel structure, etc., a simplified circuit structure is substituted, and based on the heat transfer parameters between the battery cell components, the heat transferred between the battery cell components is calculated to obtain the temperature of all single cells.

[0137] See also Figure 6 , Figure 6 It is a structural diagram of an exemplary embodiment of the battery cell temperature evaluation device shown in the present application. The information recommendation device 600 includes an acquisition module 610, an evaluation module 620 and a correction module 630. The acquisition module 610 is used to obtain the measurement parameters and transfer heat of the battery pack. The battery pack includes at least one battery cell element. The transfer heat represents the heat transfer of other elements in the battery pack to the battery cell element except the battery cell; the evaluation module 620 is used to input the measurement parameters of the battery pack into the trained battery cell temperature evaluation model to obtain the initial temperature of each battery cell in the battery pack output by the trained battery cell temperature evaluation model; the correction module 630 is used to correct the initial temperature of each battery cell according to the transfer heat to obtain the target temperature of each battery cell.

[0138] In the above scheme, the battery cell temperature evaluation device obtains the measurement parameters and transfer heat of the battery pack, the battery pack includes at least one battery cell element, and the transfer heat represents the heat transfer of other elements in the battery pack to the battery cell element except the battery cell element; the measurement parameters of the battery pack are input into the trained battery cell temperature evaluation model to obtain the initial temperature of each battery cell element in the battery pack output by the trained battery cell temperature evaluation model; the initial temperature of each battery cell element is corrected according to the transfer heat to obtain the target temperature of each battery cell element. In addition to evaluating the initial temperature of the battery cell element through the battery cell temperature evaluation model, the above scheme also considers the influence of other elements in the battery pack on the battery cell temperature, thereby improving the accuracy of the battery cell temperature evaluation.

[0139] The functions of each module can be found in the embodiment of the battery cell temperature evaluation method, which will not be described in detail here.

[0140] In order to implement the battery core temperature evaluation method of the above embodiment, the present application proposes another electronic device, which can be specifically referred to in Figure 7 , Figure 7 It is a structural schematic diagram of an embodiment of an electronic device provided by the present application.

[0141] The electronic device 700 includes a memory 710 and a processor 720 , wherein the memory 710 and the processor 720 are coupled.

[0142] The memory 710 is used to store program data, and the processor 720 is used to execute the program data to implement the battery cell temperature evaluation method of the above embodiment.

[0143] In this embodiment, the processor 720 may also be referred to as a CPU (Central Processing Unit). The processor 720 may be an integrated circuit chip having signal processing capabilities. The processor 720 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. A general-purpose processor may be a microprocessor or the processor 720 may also be any conventional processor, etc.

[0144] The present application also provides a computer-readable storage medium, such as Figure 8 As shown, the computer-readable storage medium 800 is used to store program data 810. When the program data 810 is executed by the processor, it is used to implement the battery cell temperature evaluation method in the method embodiment of the present application.

[0145] The method involved in the embodiment of the battery cell temperature evaluation method of the present application, when implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a device, such as a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or part of the contribution to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0146] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for evaluating battery cell temperature, characterized in that: The battery cell temperature evaluation method comprises: Acquire measurement parameters and transfer heat of a battery pack, wherein the battery pack includes at least one battery cell element, and the transfer heat represents heat transfer from other elements in the battery pack except the battery cell element to the battery cell element; Inputting the measured parameters of the battery pack into a trained battery cell temperature assessment model to obtain the initial temperature of each battery cell component in the battery pack output by the trained battery cell temperature assessment model; The initial temperature of each battery cell component is corrected according to the transferred heat to obtain the target temperature of each battery cell component.

2. The battery core temperature evaluation method according to claim 1, characterized in that: The steps of obtaining the transferred heat of the battery pack include: Simplifying each cell element and components associated with each cell element in the battery pack into resistors to construct a simplified circuit structure; The transferred heat of each battery cell component is determined based on each resistor in the simplified circuit structure.

3. The battery core temperature evaluation method according to claim 2, characterized in that: The step of determining the transferred heat of each battery cell component based on each resistor in the simplified circuit structure includes: Initializing each resistor in the simplified circuit structure to obtain an initial resistance value of each resistor; Inputting the initial resistance value of each resistor into the trained neural network model for identification processing to obtain the resistance value output by the neural network model; The transferred heat of each battery cell component is determined according to the resistance value output by the trained neural network model.

4. The battery core temperature evaluation method according to claim 3, characterized in that: The method also includes a step of training the trained neural network model: Inputting the initial resistance value of each resistor in the simplified circuit structure into the neural network model to obtain the resistance value output by the neural network model; Inputting the resistance value output by the neural network model into the simplified circuit structure to obtain the total voltage and total temperature of the battery pack; Calculating a first loss value of a loss function between a total voltage of the battery pack and an actual voltage of the battery pack, and calculating a second loss value of a loss function between a total temperature of the battery pack and an actual temperature of the battery pack; The neural network model is trained with the purpose of reducing the sum of the first loss value and the second loss value to obtain the trained neural network model.

5. The battery core temperature evaluation method according to claim 1, characterized in that: The method comprises the steps of training the trained battery cell temperature assessment model: Get the experimental parameters of the battery pack; Inputting the experimental parameters into a battery cell temperature evaluation model to obtain a battery cell temperature and a battery cell voltage output by the battery cell temperature evaluation model; Calculating a third loss value of a loss function between a battery cell temperature and an actual battery cell temperature, and a fourth loss value of a loss function between a battery cell voltage and an actual battery cell voltage; The battery cell temperature assessment model is trained with the purpose of reducing the sum of the third loss value and the fourth loss value to obtain the trained battery cell temperature assessment model.

6. The battery core temperature evaluation method according to claim 1, characterized in that: The method further comprises: Establishing a target electrochemical model based on the electrochemical properties of the battery cell components; Establishing a thermal model based on the thermal properties of the battery cell components; The battery cell temperature assessment model is obtained by combining the electrochemical model and the thermal model.

7. The battery core temperature evaluation method according to claim 6, characterized in that: The step of establishing a target electrochemical model based on the electrochemical properties of the battery cell components comprises: Establishing an initial electrochemical model according to the electrochemical properties of the battery cell components; Physical assumptions are introduced to simplify the initial electrochemical model to obtain the target electrochemical model.

8. A battery cell temperature evaluation device, characterized in that: The device comprises: An acquisition module, used for acquiring measurement parameters and transfer heat of a battery pack, wherein the battery pack includes at least one battery cell element, and the transfer heat represents heat transfer from other elements in the battery pack except the battery cell to the battery cell element; An evaluation module, used for inputting the measurement parameters of the battery pack into a trained battery cell temperature evaluation model to obtain the initial temperature of each battery cell in the battery pack output by the trained battery cell temperature evaluation model; The correction module is used to correct the initial temperature of each battery cell according to the transferred heat to obtain the target temperature of each battery cell.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores program instructions, and the processor retrieves the program instructions from the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: include: Program data is stored, and when the program data is executed by a processor, it is used to implement the method according to any one of claims 1 to 7.

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