A lithium-ion battery electrothermal coupling modeling method and system based on response characteristics

Abstract

The present invention relates to the technical field of lithium-ion batteries, and discloses a lithium-ion battery electrothermal coupling modeling method based on response characteristics, including: constructing an initial electrical model of the lithium-ion battery, constructing an initial thermal model of the lithium-ion battery, coupling the initial electrical model with the initial thermal model; converting temperature-related factors into temperature-related vectors, calculating the temperature probability value of the lithium-ion battery, and identifying the internal temperature value of the lithium-ion battery; using the internal temperature value to calculate the temperature-related value in the initial electrical model, identifying the parameters to be substituted into the parameters to be adjusted in the electrical model, and measuring the battery terminal voltage of the lithium-ion battery; performing parameter adjustment on the parameters to be adjusted, using the adjustment parameters to substitute the parameters into the electrical model to convert them into a parameter-adjusted electrical model; and substituting the parameters into the coupling model through the parameter-adjusted electrical model to convert them into the electrothermal coupling modeling result of the lithium-ion battery. The present invention can simplify the process of calculating and measuring unknown parameters and temperature data during electrothermal coupling.

Description

Technical Field

[0001] The invention relates to a lithium ion battery electric-thermal coupling modeling method and system based on response characteristics, belonging to the technical field of lithium ion batteries. Background Art

[0002] Lithium battery is a complex nonlinear electrochemical system whose performance is greatly affected by temperature. Under different temperature conditions, the battery's electrical parameters such as capacity, internal resistance, and open circuit voltage will change. For example, under low temperature conditions, the battery's internal resistance will increase, resulting in a decrease in its discharge capacity and a slower charging speed. Under high temperature conditions, the reaction rate of the battery's active substances will accelerate, but it may also trigger side reactions, affecting the battery's life and safety. During the battery's complex electrochemical reaction process, there is a complex coupling relationship between its key states such as state of charge (SOC), thermal state (SOT), and state of health (SOH). Changes in temperature will affect the battery's SOC and SOH, and the battery's charging and discharging process will generate heat, causing temperature changes. This interaction makes it difficult to accurately assess the battery's status.

[0003] At present, the electrothermal coupling modeling of lithium-ion batteries requires the process of modeling a thermal model, modeling an electrical model, and coupling the thermal model with the electrical model. The electrical model refers to a model that generates heat under the action of an electrical signal, and the thermal model refers to a model of temperature change under the action of heat. The prior art constructs the electrical model and the thermal model by means of a mathematical model. Before constructing the electrical model and the thermal model, a large number of parameters need to be tested. If some parameters are too complex to measure, the parameters in the constructed electrical model or thermal model will be unknown, thereby affecting the process of numerical simulation using the constructed model. Secondly, the electrical signal of the battery causes the battery to generate temperature, and the temperature generated by the battery in turn affects the electrical signal parameters. When measuring the temperature parameters that affect the electrical model, the prior art only uses tools such as temperature sensors to measure the temperature of each different subdivided area of ​​the battery one by one. This measurement method is relatively cumbersome. Therefore, the calculation and measurement of unknown parameters and temperature data during electrothermal coupling are relatively cumbersome. Summary of the invention

[0004] The present invention provides a lithium-ion battery electrothermal coupling modeling method and system based on response characteristics, the main purpose of which is to simplify the calculation and measurement process of unknown parameters and temperature data during electrothermal coupling.

[0005] To achieve the above object, the present invention provides a lithium-ion battery electrothermal coupling modeling method based on response characteristics, comprising:

[0006] Constructing an initial electrical model of a lithium-ion battery, and constructing an initial thermal model of the lithium-ion battery, and coupling the initial electrical model with the initial thermal model to obtain an initial electrical-thermal coupling model;

[0007] Collecting temperature-related factors of the lithium-ion battery, converting the temperature-related factors into temperature-related vectors, calculating temperature probability values ​​of the lithium-ion battery according to the temperature-related vectors, and using the temperature probability values ​​to identify internal temperature values ​​of the lithium-ion battery;

[0008] Calculating temperature-related values ​​in the initial electrical model using the internal temperature values, substituting the temperature-related values ​​into the initial electrical model, obtaining parameters to be substituted into the electrical model, identifying parameters to be adjusted in the parameters substituted into the electrical model, and measuring the battery terminal voltage of the lithium-ion battery;

[0009] According to the battery terminal voltage, the parameter to be adjusted is adjusted to obtain an adjustment parameter, and the adjustment parameter is used to substitute the parameter into an electrical model to convert it into a parameter-adjusted electrical model;

[0010] Substituting the internal temperature value into the initial electrothermal coupling model to obtain a parameter-substituting coupling model, and converting the parameter-substituting coupling model into an electrothermal coupling modeling result of the lithium-ion battery through the parameter-adjusting electrical model.

[0011] Optionally, constructing an initial electrical model of a lithium-ion battery includes:

[0012] Obtaining a positive electrode voltage parameter, a negative electrode voltage parameter, an input current parameter, an ohmic resistance parameter, a positive electrode liquid phase potential parameter, and a negative electrode liquid phase potential parameter of the lithium ion battery;

[0013] According to the positive electrode voltage parameter, the negative electrode voltage parameter, the input current parameter, the ohmic resistance parameter, the positive electrode liquid phase potential parameter and the negative electrode liquid phase potential parameter, the initial electrical model of the lithium ion battery is constructed using the following formula:

[0014]

[0015] in, represents the initial electrical model, represents the battery terminal voltage variable, Indicates The changing positive voltage, Indicates Changing negative voltage, Indicates the input current, represents the ohmic resistance, Indicates the amount of positive electrode lithium ions, Indicates the amount of lithium ions in the negative electrode, represents the positive electrode liquid phase potential, represents the negative electrode liquid potential, Represents the positive electrode voltage changing with time t.

[0016] Optionally, constructing an initial thermal model of the lithium-ion battery includes:

[0017] Obtaining temperature parameters and heat generation rate of the lithium-ion battery;

[0018] According to the temperature parameters and the heat generation rate, the initial thermal model of the lithium-ion battery is constructed using the following formula:

[0019]

[0020] in, represents the initial thermal model, represents the temperature variable, express The first derivative of represents the heat capacity of the internal structure of a lithium-ion battery, Represents the surface material heat capacity of lithium-ion batteries, represents the heat generation rate of lithium-ion batteries, Represents the temperature of the surface material of the lithium-ion battery in the temperature parameter The first derivative of Indicates the temperature of the surrounding environment of the lithium-ion battery. Indicates the thermal resistance of the surface material of a lithium-ion battery to the surrounding environment.

[0021] Optionally, coupling the initial electrical model with the initial thermal model to obtain an initial electrical-thermal coupling model includes:

[0022] obtaining a voltage difference in the initial electrical model;

[0023] Substituting the voltage difference into the initial thermal model using the following formula, an initial electrothermal coupling model is obtained:

[0024]

[0025] in, represents the initial electrothermal coupling model, represents the temperature variable, express The first derivative of represents the heat capacity of the internal structure of a lithium-ion battery, Represents the surface material heat capacity of lithium-ion batteries, Represents the temperature of the surface material of the lithium-ion battery in the temperature parameter The first derivative of Indicates the temperature of the surrounding environment of the lithium-ion battery. Indicates the thermal resistance of the surface material of the lithium-ion battery to the surrounding environment. Represents the voltage difference, Indicates the input current.

[0026] Optionally, converting the temperature-related factor into a temperature-related vector includes:

[0027] Dividing the lithium-ion battery into squares to obtain divided squares;

[0028] Identifying target related factors belonging to the divided grid from the temperature related factors;

[0029] Arranging the target-related factors into a related factor sequence;

[0030] The correlation factor sequence is input into a preset self-attention mechanism to output a temperature correlation vector corresponding to the correlation factor sequence through the self-attention mechanism.

[0031] Optionally, calculating the temperature probability value of the lithium-ion battery according to the temperature-related vector includes:

[0032] Inputting the temperature-related vector into a preset BP neural network to output a temperature probability value of the lithium-ion battery through the BP neural network;

[0033] Wherein, the BP neural network includes a hidden layer and an output layer; the hidden layer includes:

[0034]

[0035] in, represents the kth output result of the hidden layer, represents the temperature related vector, represents the kth connection weight between the input layer and the hidden layer, represents the bias of the hidden layer;

[0036] The output layer includes:

[0037]

[0038] in, represents the temperature probability value, represents the kth output result of the hidden layer, represents the total number of connection weights between the input layer and the hidden layer, Table activation function.

[0039] Optionally, the calculating temperature-related values ​​in the initial electrical model using the internal temperature values ​​includes:

[0040] Acquiring target parameters affected by temperature in the initial electrical model;

[0041] Querying the parameter source of the target parameter; determining whether there is a temperature factor in the parameter source;

[0042] When there is a temperature factor in the parameter source, calculating a first parameter value of the target parameter using the internal temperature value;

[0043] When there is no temperature factor in the parameter source, constructing a temperature-parameter regression model between the temperature factor and the target parameter;

[0044] Calculating a second parameter value of the target parameter using the temperature-parameter regression model according to the internal temperature value;

[0045] The first parameter value and the second parameter value are used as temperature-related values ​​in an initial electrical model.

[0046] Optionally, the adjusting the parameter to be adjusted according to the battery terminal voltage to obtain the adjustment parameter includes:

[0047] Initializing the position and speed of the parameter to be adjusted respectively to obtain an initialization position and an initialization speed;

[0048] Selecting the global position and the local position of the parameter to be adjusted;

[0049] Performing speed update on the initialization speed according to the global position and the local position to obtain an updated speed;

[0050] Using the update speed to update the initialization position, to obtain an updated position;

[0051] Calculating a fitness value between a battery terminal voltage corresponding to the update position and the battery terminal voltage;

[0052] The fitness value is used to complete parameter adjustment of the parameter to be adjusted to obtain an adjustment parameter.

[0053] Optionally, the step of adjusting the electrical model by the parameters and substituting the parameters into the coupling model to convert the parameters into the electrical-thermal coupling modeling result of the lithium-ion battery includes:

[0054] Obtain the parameters and substitute them into the initial electrical model in the coupling model;

[0055] Replacing the initial electrical model with the parameter-adjusted electrical model to obtain a replaced electrical model;

[0056] The replaced electrical model is inserted into the parameter substitution coupling model to obtain the electrical-thermal coupling modeling result of the lithium-ion battery.

[0057] In order to solve the above problems, the present invention also provides a lithium-ion battery electrothermal coupling modeling system based on response characteristics, the system comprising:

[0058] A model coupling module, used to construct an initial electrical model of the lithium-ion battery, and to construct an initial thermal model of the lithium-ion battery, and to couple the initial electrical model with the initial thermal model to obtain an initial electrical-thermal coupling model;

[0059] a temperature identification module, used to collect temperature-related factors of the lithium-ion battery, convert the temperature-related factors into temperature-related vectors, calculate the temperature probability value of the lithium-ion battery according to the temperature-related vectors, and identify the internal temperature value of the lithium-ion battery using the temperature probability value;

[0060] a voltage measurement module, used to calculate the temperature-related values ​​in the initial electrical model using the internal temperature values, substitute the temperature-related values ​​into the initial electrical model, obtain parameters to be substituted into the electrical model, identify the parameters to be adjusted in the electrical model, and measure the battery terminal voltage of the lithium-ion battery;

[0061] A parameter adjustment module, used to adjust the parameter to be adjusted according to the battery terminal voltage to obtain an adjustment parameter, and use the adjustment parameter to substitute the parameter into the electrical model to convert it into a parameter-adjusted electrical model;

[0062] The electrothermal coupling module is used to substitute the internal temperature value into the initial electrothermal coupling model to obtain a parameter substitution coupling model, and convert the parameter substitution coupling model into an electrothermal coupling modeling result of the lithium-ion battery through the parameter adjustment electrical model.

[0063] Compared with the problem described in the background technology, the embodiment of the present invention converts the temperature-related factors into temperature-related vectors to obtain the characteristic vector representation of the temperature-related factors of each small area of ​​the lithium-ion battery, so as to facilitate the subsequent prediction of temperature changes based on the characteristic vector. Furthermore, the embodiment of the present invention calculates the temperature probability value of the lithium-ion battery according to the temperature-related vector, so as to use a neural network to analyze the temperature data of the lithium-ion battery based on the electrical signal parameters and material parameters of the lithium-ion battery, thereby simplifying the cumbersome process of measuring the temperature of each different subdivided area of ​​the battery one by one using tools such as temperature sensors. The embodiment of the present invention calculates the temperature-related values ​​in the initial electrical model by using the internal temperature values, so as to use the temperature values ​​obtained by the neural network model analysis to affect the parameters affected by temperature in the initial electrical model, thereby realizing the coupling loop of the electrical signal affecting the heat, the heat affecting the temperature, and the temperature affecting the electrical signal. The embodiment of the present invention adjusts the parameters to be adjusted according to the battery terminal voltage to obtain the adjustment parameters, so as to quickly converge to obtain the specific values ​​of the parameters to be adjusted through the particle swarm optimization algorithm, thereby avoiding the phenomenon that the parameters in the constructed electrical model or thermal model are unknown due to the difficulty of measurement due to excessive complexity. Therefore, the lithium-ion battery electrothermal coupling modeling method based on response characteristics proposed in the present invention can simplify the calculation and measurement process of unknown parameters and temperature data during electrothermal coupling. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A schematic flow chart of a lithium-ion battery electrothermal coupling modeling method based on response characteristics provided by an embodiment of the present invention;

[0065] Figure 2 A schematic diagram of a module for implementing the response characteristic-based lithium-ion battery electrothermal coupling modeling method provided in one embodiment of the present invention.

[0066] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0067] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0068] The embodiment of the present application provides a lithium-ion battery electrothermal coupling modeling method based on response characteristics. The execution subject of the lithium-ion battery electrothermal coupling modeling method based on response characteristics includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the lithium-ion battery electrothermal coupling modeling method based on response characteristics can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0069] Embodiment 1:

[0070] Reference Figure 1 FIG. 1 is a flow chart of a method for modeling a lithium-ion battery electrothermal coupling based on response characteristics according to an embodiment of the present invention. In this embodiment, the method for modeling a lithium-ion battery electrothermal coupling based on response characteristics includes:

[0071] S1. constructing an initial electrical model of a lithium-ion battery, and constructing an initial thermal model of the lithium-ion battery, coupling the initial electrical model with the initial thermal model to obtain an initial electrical-thermal coupling model.

[0072] In one embodiment of the present invention, the initial electrical model of the lithium-ion battery is constructed, including: obtaining the positive electrode voltage parameter, the negative electrode voltage parameter, the input current parameter, the ohmic resistance parameter, the positive electrode liquid phase potential parameter and the negative electrode liquid phase potential parameter of the lithium-ion battery; according to the positive electrode voltage parameter, the negative electrode voltage parameter, the input current parameter, the ohmic resistance parameter, the positive electrode liquid phase potential parameter and the negative electrode liquid phase potential parameter, the initial electrical model of the lithium-ion battery is constructed using the following formula:

[0073]

[0074] in, represents the initial electrical model, represents the battery terminal voltage variable, Indicates The changing positive voltage, Indicates Changing negative voltage, Indicates the input current, represents the ohmic resistance, Indicates the amount of positive electrode lithium ions, Indicates the amount of lithium ions in the negative electrode, represents the positive electrode liquid phase potential, represents the negative electrode liquid potential, Represents the positive electrode voltage changing with time t.

[0075] It should be noted that the positive electrode voltage parameters, negative electrode voltage parameters, input current parameters, ohmic resistance parameters, positive electrode liquid phase potential parameters and negative electrode liquid phase potential parameters will change due to the influence of temperature. This is the response characteristic of the electrical signal under the influence of temperature. Therefore, after the temperature data corresponding to lithium-ion batteries of different materials are obtained through neural network model analysis, the initial electrical model parameters affected by temperature need to be adjusted based on the predicted temperature data.

[0076] In one embodiment of the present invention, the constructing the initial thermal model of the lithium-ion battery includes: obtaining the temperature parameters and the heat generation rate of the lithium-ion battery; and constructing the initial thermal model of the lithium-ion battery according to the temperature parameters and the heat generation rate using the following formula:

[0077]

[0078] in, represents the initial thermal model, represents the temperature variable, express The first derivative of represents the heat capacity of the internal structure of a lithium-ion battery, Represents the surface material heat capacity of lithium-ion batteries, represents the heat generation rate of lithium-ion batteries, Represents the temperature of the surface material of the lithium-ion battery in the temperature parameter The first derivative of Indicates the temperature of the surrounding environment of the lithium-ion battery. Indicates the thermal resistance of the surface material of a lithium-ion battery to the surrounding environment.

[0079] It should be noted that , , , All of them can be obtained through actual measurement or calculation.

[0080] In one embodiment of the present invention, coupling the initial electrical model with the initial thermal model to obtain an initial electrothermal coupling model includes: obtaining a voltage difference in the initial electrical model; substituting the voltage difference into the initial thermal model using the following formula to obtain an initial electrothermal coupling model:

[0081]

[0082] in, represents the initial electrothermal coupling model, represents the temperature variable, express The first derivative of represents the heat capacity of the internal structure of a lithium-ion battery, Represents the surface material heat capacity of lithium-ion batteries, Represents the temperature of the surface material of the lithium-ion battery in the temperature parameter The first derivative of Indicates the temperature of the surrounding environment of the lithium-ion battery. Indicates the thermal resistance of the surface material of the lithium-ion battery to the surrounding environment. Represents the voltage difference, Indicates the input current.

[0083] S2. Collect temperature-related factors of the lithium-ion battery, convert the temperature-related factors into temperature-related vectors, calculate the temperature probability value of the lithium-ion battery according to the temperature-related vector, and use the temperature probability value to identify the internal temperature value of the lithium-ion battery.

[0084] In the embodiment of the present invention, the temperature-related factors refer to parameters in the lithium-ion battery that may affect the temperature, such as the electrical signal of the lithium-ion battery, the thermal conductivity of the material used, the thickness of the material used, etc.

[0085] Furthermore, the embodiment of the present invention converts the temperature-related factors into temperature-related vectors to obtain a characteristic vector representation of the temperature-related factors of each small area of ​​the lithium-ion battery, thereby facilitating subsequent prediction of temperature changes based on the characteristic vectors.

[0086] In one embodiment of the present invention, converting the temperature-related factors into temperature-related vectors includes: dividing the lithium-ion battery into squares to obtain divided squares; identifying target-related factors belonging to the divided squares from the temperature-related factors; arranging the target-related factors into a correlation factor sequence; and inputting the correlation factor sequence into a preset self-attention mechanism to output the temperature-related vector corresponding to the correlation factor sequence through the self-attention mechanism.

[0087] The divided squares refer to squares of different regions of lithium-ion batteries of the same size, the target related factors refer to temperature related factors belonging to the divided squares, and the related factor sequence refers to the result of splicing the target related factors.

[0088] Furthermore, the embodiment of the present invention calculates the temperature probability value of the lithium-ion battery according to the temperature-related vector, so as to use a neural network to analyze the temperature data of the lithium-ion battery based on the electrical signal parameters and material parameters of the lithium-ion battery, thereby simplifying the tedious process of measuring the temperature of different subdivided areas of the battery one by one using only tools such as temperature sensors.

[0089] In one embodiment of the present invention, the temperature probability value of the lithium-ion battery is calculated according to the temperature-related vector, comprising: inputting the temperature-related vector into a preset BP neural network to output the temperature probability value of the lithium-ion battery through the BP neural network; wherein the BP neural network comprises a hidden layer and an output layer; the hidden layer comprises:

[0090]

[0091] in, represents the kth output result of the hidden layer, represents the temperature related vector, represents the kth connection weight between the input layer and the hidden layer, represents the bias of the hidden layer;

[0092] The output layer includes:

[0093]

[0094] in, represents the temperature probability value, represents the kth output result of the hidden layer, represents the total number of connection weights between the input layer and the hidden layer, Table activation function.

[0095] Optionally, the process of using the temperature probability value to identify the internal temperature value of the lithium-ion battery refers to using the size of the temperature probability value to determine which temperatures have the highest probability, thereby using this temperature as the internal temperature value. It should be noted that the internal temperature value refers to the temperature of the internal structure of the lithium-ion battery.

[0096] S3. Calculate the temperature-related values ​​in the initial electrical model using the internal temperature values, substitute the temperature-related values ​​into the initial electrical model, obtain parameters to be substituted into the electrical model, identify the parameters to be adjusted in the parameters substituted into the electrical model, and measure the battery terminal voltage of the lithium-ion battery.

[0097] The embodiment of the present invention calculates the temperature-related values ​​in the initial electrical model by utilizing the internal temperature values ​​so as to use the temperature values ​​obtained through the neural network model analysis to influence the temperature-affected parameters in the initial electrical model, thereby realizing a coupling loop in which electrical signals affect heat, heat affects temperature, and temperature affects electrical signals.

[0098] The temperature-related numerical value refers to the specific numerical value of the relevant parameter affected by temperature in the initial electrical model.

[0099] In one embodiment of the present invention, the use of the internal temperature value to calculate the temperature-related values ​​in the initial electrical model includes: obtaining the target parameter affected by temperature in the initial electrical model; querying the parameter source of the target parameter; determining whether there is a temperature factor in the parameter source; when there is a temperature factor in the parameter source, calculating the first parameter value of the target parameter using the internal temperature value; when there is no temperature factor in the parameter source, constructing a temperature-parameter regression model between the temperature factor and the target parameter; according to the internal temperature value, calculating the second parameter value of the target parameter using the temperature-parameter regression model; and using the first parameter value and the second parameter value as the temperature-related values ​​in the initial electrical model.

[0100] Among them, the target parameters are, for example, the positive electrode voltage parameter, the negative electrode voltage parameter, the input current parameter, the ohmic resistance parameter, the positive electrode liquid phase potential parameter and the negative electrode liquid phase potential parameter in the aforementioned electrical model; the parameter source refers to the parameter based on which the target parameter is calculated or obtained. For example, when calculating the positive electrode voltage parameter, it is necessary to calculate based on the positive electrode lithium ion amount, and the positive electrode lithium ion amount is obtained based on the positive electrode lithium ion concentration, then the positive electrode lithium ion concentration is used as the parameter source; the temperature factor refers to the parameter source dependent variable that is affected by temperature and can be calculated by the temperature independent variable. For example, the aforementioned positive electrode lithium ion concentration is affected by temperature, but the lithium ion concentration is not calculated by temperature, but there is a certain correlation between the lithium ion concentration and the temperature, then the lithium ion concentration is calculated by the temperature-parameter regression model.

[0101] Furthermore, in an embodiment of the present invention, the parameters to be adjusted refer to unknown values ​​that are not affected by temperature when substituted into the electrical model and cannot be calculated based on the relationship with temperature. These unknown values ​​need to be calculated using an optimization algorithm, and these values ​​are mainly some unknown parameters in the aforementioned parameter sources.

[0102] It should be noted that the battery terminal voltage refers to the voltage value of the lithium-ion battery actually measured by the tool, which is different from the terminal voltage in the aforementioned initial electrical model. The terminal voltage in the aforementioned initial electrical model is a variable and changes due to the influence of other electrical signals.

[0103] S4. According to the battery terminal voltage, the parameter to be adjusted is adjusted to obtain an adjustment parameter, and the adjustment parameter is used to substitute the parameter into an electrical model to convert the parameter-adjusted electrical model.

[0104] The embodiment of the present invention adjusts the parameter to be adjusted according to the battery terminal voltage to obtain the adjustment parameter, so as to quickly converge the specific value of the parameter to be adjusted through the particle swarm optimization algorithm, thereby avoiding the phenomenon that the parameters in the constructed electrical model or thermal model are unknown due to measurement difficulties due to excessive complexity.

[0105] In one embodiment of the present invention, the parameter to be adjusted is adjusted according to the battery terminal voltage to obtain the adjustment parameter, including: respectively initializing the position and speed of the parameter to be adjusted to obtain the initialization position and the initialization speed; selecting the global position and the local position of the parameter to be adjusted; updating the initialization speed according to the global position and the local position to obtain the update speed; updating the position of the initialization position using the update speed to obtain the update position; calculating the fitness value between the battery terminal voltage corresponding to the update position and the battery terminal voltage; and using the fitness value to complete the parameter adjustment of the parameter to be adjusted to obtain the adjustment parameter.

[0106] The fitness value refers to the voltage difference between the battery terminal voltage corresponding to the update position and the battery terminal voltage.

[0107] Optionally, the process of using the adjustment parameters to substitute the parameters into the electrical model to convert it into a parameter-adjusted electrical model refers to using the adjustment parameters to calculate unknown parameters in the electrical model by substituting the parameters into the electrical model to obtain the parameter-adjusted electrical model.

[0108] S5. Substituting the internal temperature value into the initial electrothermal coupling model to obtain a parameter-substituting coupling model, and converting the parameter-substituting coupling model into an electrothermal coupling modeling result of the lithium-ion battery through the parameter-adjusting electrical model.

[0109] It should be noted that the initial electrothermal coupling model is an unknown parameter, so After replacing this internal temperature variable with the internal temperature value, it can be calculated that The numerical value of .

[0110] In one embodiment of the present invention, the parameter-adjusted electrical model is substituted into the coupling model to convert the parameters into the electrothermal coupling modeling result of the lithium-ion battery, including: obtaining the initial electrical model in the parameter-substituted coupling model; replacing the initial electrical model with the parameter-adjusted electrical model to obtain the replaced electrical model; inserting the replaced electrical model into the parameter-substituted coupling model to obtain the electrothermal coupling modeling result of the lithium-ion battery.

[0111] Optionally, the initial electrical model is replaced with the parameter-adjusted electrical model to obtain a replaced electrical model; and the process of inserting the replaced electrical model into the parameter substitution coupling model refers to the process of replacing all parts of the parameter substitution coupling model belonging to the initial electrical model with the parameter-adjusted electrical model.

[0112] Compared with the problem described in the background technology, the embodiment of the present invention converts the temperature-related factors into temperature-related vectors to obtain the characteristic vector representation of the temperature-related factors of each small area of ​​the lithium-ion battery, so as to facilitate the subsequent prediction of temperature changes based on the characteristic vector. Furthermore, the embodiment of the present invention calculates the temperature probability value of the lithium-ion battery according to the temperature-related vector, so as to use a neural network to analyze the temperature data of the lithium-ion battery based on the electrical signal parameters and material parameters of the lithium-ion battery, thereby simplifying the cumbersome process of measuring the temperature of each different subdivided area of ​​the battery one by one using tools such as temperature sensors. The embodiment of the present invention calculates the temperature-related values ​​in the initial electrical model by using the internal temperature values, so as to use the temperature values ​​obtained by the neural network model analysis to affect the parameters affected by temperature in the initial electrical model, thereby realizing the coupling loop of the electrical signal affecting the heat, the heat affecting the temperature, and the temperature affecting the electrical signal. The embodiment of the present invention adjusts the parameters to be adjusted according to the battery terminal voltage to obtain the adjustment parameters, so as to quickly converge to obtain the specific values ​​of the parameters to be adjusted through the particle swarm optimization algorithm, thereby avoiding the phenomenon that the parameters in the constructed electrical model or thermal model are unknown due to the difficulty of measurement due to excessive complexity. Therefore, the lithium-ion battery electrothermal coupling modeling method based on response characteristics proposed in the present invention can simplify the calculation and measurement process of unknown parameters and temperature data during electrothermal coupling.

[0113] Embodiment 2:

[0114] like Figure 2 The figure shows a functional module diagram of a lithium-ion battery electrothermal coupling modeling system based on response characteristics of the present invention.

[0115] The lithium-ion battery electrothermal coupling modeling system 200 based on response characteristics of the present invention can be installed in an electronic device. According to the functions implemented, the lithium-ion battery electrothermal coupling modeling system based on response characteristics can include a model coupling module 201, a temperature identification module 202, a voltage measurement module 203, a parameter adjustment module 204 and an electrothermal coupling module 205. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0116] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0117] The model coupling module 201 is used to construct an initial electrical model of the lithium-ion battery, and to construct an initial thermal model of the lithium-ion battery, and to couple the initial electrical model with the initial thermal model to obtain an initial electrical-thermal coupling model;

[0118] The temperature identification module 202 is used to collect temperature-related factors of the lithium-ion battery, convert the temperature-related factors into temperature-related vectors, calculate the temperature probability value of the lithium-ion battery according to the temperature-related vector, and identify the internal temperature value of the lithium-ion battery using the temperature probability value;

[0119] The voltage measurement module 203 is used to calculate the temperature-related values ​​in the initial electrical model using the internal temperature values, and substitute the temperature-related values ​​into the initial electrical model to obtain parameters to be substituted into the electrical model, identify the parameters to be adjusted in the parameters substituted into the electrical model, and measure the battery terminal voltage of the lithium-ion battery;

[0120] The parameter adjustment module 204 is used to adjust the parameter to be adjusted according to the battery terminal voltage to obtain an adjustment parameter, and use the adjustment parameter to substitute the parameter into the electrical model to convert it into a parameter-adjusted electrical model;

[0121] The electrothermal coupling module 205 is used to substitute the internal temperature value into the initial electrothermal coupling model to obtain a parameter substitution coupling model, and convert the parameter substitution coupling model into an electrothermal coupling modeling result of the lithium-ion battery through the parameter adjustment electrical model.

[0122] In detail, each module in the lithium-ion battery electric-thermal coupling modeling system 200 based on response characteristics in the embodiment of the present invention is used in the same manner as described above. Figure 1 The technical means are the same as the lithium-ion battery electrothermal coupling modeling method based on response characteristics described in, and can produce the same technical effects, so they will not be repeated here.

[0123] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A lithium-ion battery electrothermal coupling modeling method based on response characteristics, characterized in that: The method comprises: Constructing an initial electrical model of a lithium-ion battery, and constructing an initial thermal model of the lithium-ion battery, and coupling the initial electrical model with the initial thermal model to obtain an initial electrical-thermal coupling model; Collecting temperature-related factors of the lithium-ion battery, converting the temperature-related factors into temperature-related vectors, calculating temperature probability values ​​of the lithium-ion battery according to the temperature-related vectors, and using the temperature probability values ​​to identify internal temperature values ​​of the lithium-ion battery; Calculating temperature-related values ​​in the initial electrical model using the internal temperature values, substituting the temperature-related values ​​into the initial electrical model, obtaining parameters to be substituted into the electrical model, identifying parameters to be adjusted in the parameters substituted into the electrical model, and measuring the battery terminal voltage of the lithium-ion battery; According to the battery terminal voltage, the parameter to be adjusted is adjusted to obtain an adjustment parameter, and the adjustment parameter is used to substitute the parameter into an electrical model to convert it into a parameter-adjusted electrical model; Substituting the internal temperature value into the initial electrothermal coupling model to obtain a parameter-substituting coupling model, and converting the parameter-substituting coupling model into an electrothermal coupling modeling result of the lithium-ion battery through the parameter-adjusting electrical model.

2. The method for modeling the electric-thermal coupling of a lithium-ion battery based on response characteristics according to claim 1, characterized in that: The initial electrical model of the lithium-ion battery is constructed, comprising: Obtaining a positive electrode voltage parameter, a negative electrode voltage parameter, an input current parameter, an ohmic resistance parameter, a positive electrode liquid phase potential parameter, and a negative electrode liquid phase potential parameter of the lithium ion battery; According to the positive electrode voltage parameter, the negative electrode voltage parameter, the input current parameter, the ohmic resistance parameter, the positive electrode liquid phase potential parameter and the negative electrode liquid phase potential parameter, the initial electrical model of the lithium ion battery is constructed using the following formula: I(t)R1+U p (t)+β p (t)-β n (t)=U p (α p )-U n (α n )-U(t) Among them, I(t)R1+U p (t)+β p (t)-β n (t) = U p (α p )-U n (α n )-U(t) represents the initial electrical model, U(t) represents the battery terminal voltage variable, U p (α p ) indicates that with α p Variation of positive voltage, U n (α n ) indicates that with α n The changing negative voltage, I(t) represents the input current, R1 represents the ohmic resistance, α p Indicates the amount of positive electrode lithium ions, α n Indicates the amount of lithium ions in the negative electrode, β p (t) represents the positive electrode liquid potential, β n (t) represents the negative electrode liquid potential, U p (t) represents the positive electrode voltage changing with time t.

3. The method for modeling the electric-thermal coupling of a lithium-ion battery based on response characteristics according to claim 1, characterized in that: The constructing of the initial thermal model of the lithium-ion battery comprises: Obtaining temperature parameters and heat generation rate of the lithium-ion battery; According to the temperature parameters and the heat generation rate, the initial thermal model of the lithium-ion battery is constructed using the following formula: in, represents the initial thermal model, T(t) represents the temperature variable, T (1) (t) represents the first-order derivative of T(t), C1 represents the heat capacity of the internal structure of the lithium-ion battery, C2 represents the heat capacity of the surface material of the lithium-ion battery, Q(t) represents the heat generation rate of the lithium-ion battery, It represents the first derivative of the temperature T1(t) of the surface material of the lithium-ion battery in the temperature parameter, T2(t) represents the temperature of the surrounding environment of the lithium-ion battery, and R2 represents the thermal resistance of the surface material of the lithium-ion battery to the surrounding environment.

4. The method for modeling the electric-thermal coupling of a lithium-ion battery based on response characteristics according to claim 1, characterized in that: The coupling of the initial electrical model and the initial thermal model to obtain an initial electrical-thermal coupling model includes: obtaining a voltage difference in the initial electrical model; Substituting the voltage difference into the initial thermal model using the following formula, an initial electrothermal coupling model is obtained: in, represents the initial electrothermal coupling model, T(t) represents the temperature variable, T (1) (t) represents the first-order derivative of T(t), C1 represents the heat capacity of the internal structure of the lithium-ion battery, C2 represents the heat capacity of the surface material of the lithium-ion battery, represents the first derivative of the temperature T1(t) of the surface material of the lithium-ion battery in the temperature parameter, T2(t) represents the temperature of the surrounding environment of the lithium-ion battery, R2 represents the thermal resistance of the surface material of the lithium-ion battery to the surrounding environment, and U p (α p )-U n (α n )-U(t) represents the voltage difference, I(t) represents the input current, U(t) represents the battery terminal voltage variable, U p (α p ) indicates that with α p Variation of positive voltage, U n (α n ) indicates that with α n Changing negative voltage.

5. The method for modeling the electric-thermal coupling of a lithium-ion battery based on response characteristics according to claim 1, characterized in that: The converting the temperature-related factor into a temperature-related vector comprises: Dividing the lithium-ion battery into squares to obtain divided squares; Identifying target related factors belonging to the divided grid from the temperature related factors; Arranging the target-related factors into a related factor sequence; The correlation factor sequence is input into a preset self-attention mechanism to output a temperature correlation vector corresponding to the correlation factor sequence through the self-attention mechanism.

6. The method for modeling the electric-thermal coupling of a lithium-ion battery based on response characteristics according to claim 1, characterized in that: The step of calculating the temperature probability value of the lithium-ion battery according to the temperature-related vector includes: Inputting the temperature-related vector into a preset BP neural network to output a temperature probability value of the lithium-ion battery through the BP neural network; Wherein, the BP neural network includes a hidden layer and an output layer; the hidden layer includes: in, represents the kth output result of the hidden layer, x1 represents the temperature related vector, w k represents the kth connection weight between the input layer and the hidden layer, and b represents the bias of the hidden layer; The output layer includes: Among them, x3 represents the temperature probability value, represents the kth output result of the hidden layer, K represents the total number of connection weights between the input layer and the hidden layer, and f represents the activation function.

7. The method for modeling the electric-thermal coupling of a lithium-ion battery based on response characteristics according to claim 1, characterized in that: The step of calculating the temperature-related value in the initial electrical model using the internal temperature value includes: Acquiring target parameters affected by temperature in the initial electrical model; Querying the parameter source of the target parameter; determining whether there is a temperature factor in the parameter source; When there is a temperature factor in the parameter source, calculating a first parameter value of the target parameter using the internal temperature value; When there is no temperature factor in the parameter source, constructing a temperature-parameter regression model between the temperature factor and the target parameter; Calculating a second parameter value of the target parameter using the temperature-parameter regression model according to the internal temperature value; The first parameter value and the second parameter value are used as temperature-related values ​​in an initial electrical model.

8. The method for modeling the electric-thermal coupling of a lithium-ion battery based on response characteristics according to claim 1, characterized in that: The step of adjusting the parameter to be adjusted according to the battery terminal voltage to obtain the adjustment parameter comprises: Initializing the position and speed of the parameter to be adjusted respectively to obtain an initialization position and an initialization speed; Selecting the global position and the local position of the parameter to be adjusted; Performing speed update on the initialization speed according to the global position and the local position to obtain an updated speed; Using the update speed to update the initialization position, to obtain an updated position; Calculating a fitness value between a battery terminal voltage corresponding to the update position and the battery terminal voltage; Using the fitness value to complete parameter adjustment of the parameter to be adjusted to obtain an adjustment parameter; The fitness value refers to the voltage difference between the battery terminal voltage corresponding to the update position and the battery terminal voltage.

9. The method for modeling the electric-thermal coupling of a lithium-ion battery based on response characteristics according to claim 1, characterized in that: The step of adjusting the electrical model by the parameters and substituting the parameters into the coupling model to convert the parameters into the electrical-thermal coupling modeling result of the lithium-ion battery includes: Obtain the parameters and substitute them into the initial electrical model in the coupling model; Replacing the initial electrical model with the parameter-adjusted electrical model to obtain a replaced electrical model; The replaced electrical model is inserted into the parameter substitution coupling model to obtain the electrical-thermal coupling modeling result of the lithium-ion battery.

10. A lithium-ion battery electrothermal coupling modeling system based on response characteristics, characterized in that: The system comprises: A model coupling module, used to construct an initial electrical model of the lithium-ion battery, and to construct an initial thermal model of the lithium-ion battery, and to couple the initial electrical model with the initial thermal model to obtain an initial electrical-thermal coupling model; a temperature identification module, used to collect temperature-related factors of the lithium-ion battery, convert the temperature-related factors into temperature-related vectors, calculate the temperature probability value of the lithium-ion battery according to the temperature-related vectors, and identify the internal temperature value of the lithium-ion battery using the temperature probability value; a voltage measurement module, used to calculate the temperature-related values ​​in the initial electrical model using the internal temperature values, substitute the temperature-related values ​​into the initial electrical model, obtain parameters to be substituted into the electrical model, identify the parameters to be adjusted in the electrical model, and measure the battery terminal voltage of the lithium-ion battery; A parameter adjustment module, used to adjust the parameter to be adjusted according to the battery terminal voltage to obtain an adjustment parameter, and use the adjustment parameter to substitute the parameter into the electrical model to convert it into a parameter-adjusted electrical model; The electrothermal coupling module is used to substitute the internal temperature value into the initial electrothermal coupling model to obtain a parameter substitution coupling model, and convert the parameter substitution coupling model into the electrothermal coupling modeling result of the lithium-ion battery through the parameter adjustment electrical model.

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