CN116430245A - Battery thermal runaway prediction method based on gradient optimization multi-physical information neural network - Google Patents

Battery thermal runaway prediction method based on gradient optimization multi-physical information neural network Download PDF

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CN116430245A
CN116430245A CN202310699901.3A CN202310699901A CN116430245A CN 116430245 A CN116430245 A CN 116430245A CN 202310699901 A CN202310699901 A CN 202310699901A CN 116430245 A CN116430245 A CN 116430245A
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neural network
physical information
thermal runaway
lithium ion
ion battery
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张艳辉
李卫华
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Weihai Puyue Optoelectronic Technology Co ltd
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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G06F17/12—Simultaneous equations, e.g. systems of linear equations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
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    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
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    • G06N3/0499—Feedforward networks
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    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
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    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
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Abstract

The application belongs to the technical field of batteries, and provides a battery thermal runaway prediction method based on a gradient optimization multi-physical information neural network, which comprises the following steps: establishing a multi-parameter coupling model for thermal runaway of the lithium ion battery; constructing a multi-physical information neural network, wherein the input of the multi-physical information neural network is time, ambient temperature and at least two spatial distribution variables of the lithium ion battery, the output is the temperature of the lithium ion battery and the dimensionless concentration of lithium ions contained in the lithium ion battery, and a loss function is determined based on the multi-parameter coupling model; training the multi-physical information neural network by using training data and optimizing the counter-propagation gradient in the training process; a trained multi-physical information neural network is used to predict thermal runaway processes of lithium ion batteries. According to the method, the training process of the neural network prediction model is supervised and optimized based on a multi-parameter physical mechanism of thermal runaway of the lithium ion battery, and the prediction precision and the robustness degree can be effectively improved.

Description

Battery thermal runaway prediction method based on gradient optimization multi-physical information neural network
Technical Field
The application belongs to the technical field of batteries, relates to a battery safety management technology, and particularly provides a battery thermal runaway prediction method based on a gradient optimization multi-physical information neural network.
Background
The Lithium Ion Battery (LIB) has certain advantages in Battery schemes used by new energy vehicles such as electric automobiles, electric ships and the like due to the characteristics of high energy density, long duration, quick charge rate and the like, and the safety problem existing in the use process of the Lithium Ion Battery is not negligible. The main safety issue of lithium ion batteries is thermal runaway, which refers to an irreversible state in lithium ion batteries, where the local battery temperature rises and causes combustion due to initial internal short circuits until the internal reactive species of the LIB component are burned or even cause explosion of the lithium ion battery. Real-time prediction of thermal runaway of lithium ion batteries and estimation of thermal runaway phenomena under various operating conditions is an active area of research, as it not only predicts thermal runaway, but also provides information about the optimal design of lithium ion battery structures (e.g., optimal design of surface to volume ratio).
There are two main embodiments of predicting thermal runaway processes of lithium ion batteries currently available: numerical simulation is carried out on a thermal runaway process by using a Finite Element Method (FEM), a Finite Difference Method (FDM) and the like, and the physical mechanism of the method is clear and visual, but a large amount of calculation work is needed, and the method cannot respond in time to random and rapid changing environments and various actual operating conditions encountered by a lithium ion battery, so that the method is difficult to be applied to rapid prediction or estimation of the thermal runaway of the lithium ion battery; the method is implemented by using a deep learning neural network and predicting the thermal runaway process of the lithium ion battery, but the method adopts pure data driving as a main part, and repeatedly trains and learns from a given neural network structure and obtained training data to obtain a specific prediction model, namely, a mapping relation between input data and output data is established.
In recent years, physical information neural networks (Physics Informed Neural Networks, PINN) have been widely studied as prospective proxy models for many time-containing systems in engineering, and a major advantage of physical information neural networks is that they can quickly model time-dependent systems of interest without losing generality and accuracy, fundamentally, PINN differ from common data-driven deep learning models in that they are trained in conjunction with the laws of dominance of physics to ensure that they do not violate these laws and produce robust results against outliers.
However, there is no mature physical information neural network applied to lithium ion battery thermal runaway prediction at present, and the main reason is that: firstly, although the neural network learns and optimizes based on a physical model in the optimization process in the training process, the neural network does not necessarily ensure a perfect conformation of non-visible data points and a physical control equation; secondly, studies have found that the convergence of the PINN during training is largely dependent on the physical type involved, since the control equations are provided as soft constraints in the loss function, and are susceptible to out-of-distribution data; moreover, solving these problems using PINN is more challenging and difficult from a mathematical perspective due to the high degree of non-convexity of the loss function and the imbalance caused when optimizing multiple loss functions simultaneously.
Therefore, if the multi-physical information neural network is adopted to predict the thermal runaway of the lithium ion battery, the key point is how to build a multi-parameter coupling physical model for accurately describing the thermal runaway process and reasonably build a coupling training mechanism of the physical model to the neural network.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provide a battery thermal runaway prediction method based on a gradient optimization multi-physical information neural network, which can monitor and optimize the training process of a neural network prediction model by utilizing a multi-parameter physical mechanism of thermal runaway of a lithium ion battery so as to ensure that the training of the prediction model is performed under the control of a physical law with practical significance and generate a steady prediction result conforming to an actual physical image.
The embodiment of the application can be realized through the following technical scheme:
a battery thermal runaway prediction method based on a gradient optimization multi-physical information neural network is used for predicting a thermal runaway process of a lithium ion battery and comprises the following steps:
establishing a multi-parameter coupling model for thermal runaway of the lithium ion battery;
constructing a multi-physical information neural network, wherein the input of the multi-physical information neural network is time
Figure SMS_1
Ambient temperature->
Figure SMS_2
And at least two spatially distributed variables of the lithium ion battery +.>
Figure SMS_3
、/>
Figure SMS_4
The output is the temperature of the lithium ion battery>
Figure SMS_5
And lithium ions contained thereinDimensionless concentration ∈10->
Figure SMS_6
Loss function->
Figure SMS_7
Determining based on the multiparameter coupling model;
training the multi-physical information neural network by using training data, wherein the reverse propagation gradient of the multi-physical information neural network is optimized based on the multi-parameter coupling model in the training process;
and predicting the thermal runaway process of the lithium ion battery by using the trained multi-physical information neural network.
Preferably, the multiparameter coupling model comprises a first set of equations describing the thermodynamic reaction during thermal runaway of the lithium ion battery, and a second set of equations describing the chemical degradation reaction of various mediums during thermal runaway of the lithium ion battery; the first equation set and the second equation set are based on the rate of heat generation by the volume
Figure SMS_8
Are coupled to each other.
Further, the first equation set is specifically:
Figure SMS_9
，
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_13
、/>
Figure SMS_16
、/>
Figure SMS_19
for the density, heat capacity and heat conductivity of lithium ion battery respectively, < >>
Figure SMS_11
The temperature of lithium ions in the electrolyte of a lithium ion battery, which is the distribution space of lithium ions +.>
Figure SMS_21
Time->
Figure SMS_25
Function of->
Figure SMS_27
Is->Is (are) calculated domain->
Figure SMS_26
To calculate the upper time limit +.>
Figure SMS_10
For convection heat transfer coefficient>
Figure SMS_24
、/>
Figure SMS_15
、/>
Figure SMS_20
、/>
Figure SMS_17
、/>
Figure SMS_18
、/>
Figure SMS_14
The reaction enthalpy, the specific active substance content per unit volume, the reaction factor, the activation energy, the molar gas constant and the reaction progression are respectively +.>
Figure SMS_22
、/>
Figure SMS_12
Initial values of temperature and dimensionless concentration respectively;
the second equation set is specifically:
Figure SMS_28
，
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_29
、/>
Figure SMS_33
heat generation rate of positive electrode and electrolyte decomposition, respectively, +.>
Figure SMS_37
、/>
Figure SMS_32
、/>
Figure SMS_34
Frequency factor, reaction coefficient and thermal activation energy of positive electrode decomposition reaction respectively, +.>
Figure SMS_38
、/>
Figure SMS_40
Respectively->
Figure SMS_30
、/>
Figure SMS_35
Is used for the reaction series of (a),
Figure SMS_39
、/>
Figure SMS_41
、/>
Figure SMS_31
、/>
Figure SMS_36
the frequency factor, the thermal activation energy, the lithium ion concentration and the reaction progression of the electrolyte decomposition reaction are respectively shown.
Preferably, the multi-physical information neural network comprises a first neural network and a second neural network which respectively comprise two independence; the first and second neural networks share the same input
Figure SMS_42
、/>
Figure SMS_43
、/>
Figure SMS_44
、/>
Figure SMS_45
The method comprises the steps of carrying out a first treatment on the surface of the The output of the first neural network is temperature +.>
Figure SMS_46
The output of the second neural network is the dimensionless concentration +.>
Figure SMS_47
Is a predicted value of (a).
Further, the loss function
Figure SMS_48
The method comprises the following steps:
Figure SMS_49
，
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_53
、/>
Figure SMS_62
、/>
Figure SMS_68
、/>
Figure SMS_52
is->
Figure SMS_61
Data fitting loss, partial differential equation loss, ordinary differential equation loss, boundary condition loss and initial condition loss, respectively, < >>
Figure SMS_70
、/>
Figure SMS_76
、/>
Figure SMS_67
、/>
Figure SMS_74
、/>
Figure SMS_56
Respectively, weighting coefficients thereof, ">
Figure SMS_58
、/>
Figure SMS_51
、/>
Figure SMS_60
、/>
Figure SMS_57
Respectively->
Figure SMS_65
、/>
Figure SMS_66
、/>
Figure SMS_75
、/>
Figure SMS_81
Predicted value of +.>
Figure SMS_83
，/>
Figure SMS_50
，
Figure SMS_59
，/>
Figure SMS_69
，/>
Figure SMS_77
，/>
Figure SMS_71
，/>
Figure SMS_79
、/>
Figure SMS_55
Variable space distribution variables ∈>
Figure SMS_64
、/>
Figure SMS_72
Corresponding heat conductivity->
Figure SMS_80
、/>
Figure SMS_78
、/>
Figure SMS_82
Respectively->
Figure SMS_63
、/>
Figure SMS_73
、/>
Figure SMS_54
Upper limit of the value of (2).
Preferably, the multi-physical information neural network further includes a voltage transformation layer, and the step 300 of optimizing a back propagation gradient of the multi-physical information neural network based on the multi-parameter coupling model in the training process, specifically, performing the following steps in back propagation:
first, calculate
Figure SMS_84
Wherein->
Figure SMS_85
To find the gradient operation;
second, respectively obtaining
Figure SMS_86
And->
Figure SMS_87
、
Figure SMS_88
；
Third, calculate based on the following
Figure SMS_89
、/>
Figure SMS_90
：
Figure SMS_91
；
Fourth, calculate based on the following
Figure SMS_92
、/>
Figure SMS_93
：
Figure SMS_94
，
Wherein, the liquid crystal display device comprises a liquid crystal display device,rateis a preset learning rate;
fifth step, use
Figure SMS_95
、/>
Figure SMS_96
Updating the saidThe weight of the transformer layer.
Preferably, optimizing the counter-propagating gradient of the multi-physical information neural network is only precededNAnd performed in the second back propagation.
Preferably, the battery thermal runaway prediction method based on the gradient optimization multi-physical information neural network further comprises the steps of inputting
Figure SMS_97
、/>
Figure SMS_98
、/>
Figure SMS_99
、/>
Figure SMS_100
Output->
Figure SMS_101
、/>
Figure SMS_102
And performing non-dimensionalization processing.
According to the battery thermal runaway prediction method based on the gradient optimization multi-physical information neural network, a multi-parameter coupling model describing various reaction mechanisms in the thermal runaway process of the lithium ion battery is established, and a loss function with an interconnection structure is generated by utilizing the multi-parameter coupling model, so that the output of the mutually independent neural network sharing input is constrained in a coupling mode, and the multi-physical information neural network trained under the driving and supervision of the coupling reaction mechanisms can be used for predicting the thermal runaway process of the lithium ion battery more accurately, and the prediction robustness is remarkably improved.
Drawings
FIG. 1 is a flow chart of a battery thermal runaway prediction method based on a gradient optimized multi-physical information neural network provided according to an embodiment of the present application;
fig. 2 is a schematic architecture diagram of a multi-physical information neural network according to an embodiment of the present application;
FIG. 3 is a schematic diagram of steps for implementing a method for predicting thermal runaway of a battery based on a gradient-optimized multi-physical information neural network according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a prediction result of a multi-physical information neural network on a temperature of a lithium ion battery according to an embodiment of the present application;
fig. 5 is a partially enlarged schematic view of the prediction result in fig. 4.
Detailed Description
The present application will be further described below based on preferred embodiments with reference to the accompanying drawings.
By way of example, the present application provides a battery thermal runaway prediction method based on a gradient-optimized multi-physical information neural network, and fig. 1 shows a flowchart of implementation of the prediction method in some preferred embodiments, as shown in fig. 1, and the method includes the following steps:
step 100, establishing a multi-parameter coupling model of thermal runaway of the lithium ion battery;
step 200, constructing a multi-physical information neural network, wherein the input of the multi-physical information neural network is time
Figure SMS_103
Ambient temperature->
Figure SMS_104
And at least two lithium ion battery parameters, the output is the temperature of the lithium ion battery +.>
Figure SMS_105
Dimensionless concentration
Figure SMS_106
A loss function is determined based on the multiparameter coupling model;
step 300, training the multi-physical information neural network by using training data, wherein the reverse propagation gradient of the multi-physical information neural network is optimized based on the multi-parameter coupling model in the training process;
step 400, predicting a thermal runaway process of the lithium ion battery using the trained multi-physical information neural network.
The steps described above are described in detail below with reference to the drawings and the specific embodiments.
In the examples of the present application, step 100 is used to construct a multiparameter coupling model describing a thermal runaway process of a lithium ion battery, which refers to the occurrence of a continuous chain reaction of component degradation due to an increase in temperature or an increase in current and power dissipation caused by an exothermic reaction, which is a complex multiphysics-chemical reaction process including various thermodynamic reactions and chemical degradation reactions of various components.
Specifically, the thermodynamic reaction in the thermal runaway process of a lithium ion battery can be described by the following formula (1):
Figure SMS_107
(1)，
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_109
、/>
Figure SMS_112
、/>
Figure SMS_114
for the density, heat capacity and heat conductivity of lithium ion battery respectively, < >>
Figure SMS_108
The temperature of lithium ions in the electrolyte of a lithium ion battery, which is the distribution space of lithium ions +.>
Figure SMS_111
Time->
Figure SMS_115
Function of->
Figure SMS_117
Is->
Figure SMS_110
Is (are) calculated domain->
Figure SMS_113
To calculate the upper time limit +.>
Figure SMS_116
Is the rate of heat generation by the volume.
Equation (1) above is an energy balance equation during thermal runaway of a lithium ion battery, describing the energy balance relationship among the rate of change of temperature, heat conduction and rate of heat generation of a volume, wherein
Figure SMS_118
、/>
Figure SMS_119
The specific forms of (2) and (3) are given by the following formulas (2) and (3), respectively:
Figure SMS_120
(2)，
Figure SMS_121
(3)，
wherein equation (2) describes convective heat exchange between the lithium ion battery and the external environment,
Figure SMS_123
for convective heat transfer coefficient, (3) derives from exothermic reaction caused by stimulated chemical reaction, and dimensionless concentration of lithium in electrolyte of lithium ion battery +.>
Figure SMS_125
Is proportional to the rate of change of (in particular,)>
Figure SMS_128
、/>
Figure SMS_124
、/>
Figure SMS_126
、/>
Figure SMS_127
、/>
Figure SMS_129
、/>
Figure SMS_122
The reaction enthalpy, the specific active substance content per unit volume, the reaction factor, the activation energy, the molar gas constant and the reaction progression are respectively shown.
Further, in
Figure SMS_130
Is->
Figure SMS_131
On (I)>
Figure SMS_132
、/>
Figure SMS_133
The initial condition of (2) is given by the following formula (4):
Figure SMS_134
(4)，
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_135
、/>
Figure SMS_136
the initial values of temperature and dimensionless concentration, respectively.
Meanwhile, in the thermal runaway process of a lithium ion battery, exothermic reaction caused by continuous chemical degradation of various components is an important cause of rapid temperature rise, and in general, chemical degradation occurs in the following order: the Solid Electrolyte Interface (SEI), the anode, the cathode and the electrolyte, the heat generated by the exothermic reaction is simply the sum of each contribution, and therefore,
Figure SMS_137
can be expressed in the form of the following formula (5):
Figure SMS_138
(5)，
wherein the method comprises the steps of
Figure SMS_140
、/>
Figure SMS_142
、/>
Figure SMS_144
、/>
Figure SMS_141
The solid electrolyte interface, the negative electrode, the positive electrode and the rate of heat generation by the electrolyte decomposition are respectively +.>
Figure SMS_143
、/>
Figure SMS_145
Is the main factor of the degradation exotherm during thermal runaway, therefore, in some preferred embodiments, only +.>
Figure SMS_146
、/>
Figure SMS_139
：
Figure SMS_147
(5’)。
Figure SMS_148
、/>
Figure SMS_149
May be represented by Arrhenius' law, e.gIn some specific embodiments, the information may be, in some embodiments,
Figure SMS_150
、/>
Figure SMS_151
can be represented by the following formulas (6), (7), respectively:
Figure SMS_152
(6)，
Figure SMS_153
(7)，
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_156
、/>
Figure SMS_159
、/>
Figure SMS_160
frequency factor, reaction coefficient and thermal activation energy of positive electrode decomposition reaction respectively, +.>
Figure SMS_155
、
Figure SMS_157
Respectively->
Figure SMS_162
、/>
Figure SMS_163
Reaction progression of>
Figure SMS_154
、/>
Figure SMS_158
、/>
Figure SMS_161
、/>
Figure SMS_164
The frequency factor, the thermal activation energy, the lithium ion concentration and the reaction progression of the electrolyte decomposition reaction are respectively shown.
The equation describing the model reaction rate differs, and the temperature reaches a level over time, which results in a significant difference in rate, thereby explaining the fundamental difference in synthesis temperature between the positive electrode decomposition reaction and the electrolyte decomposition reaction.
The formulas (1) to (7) are multi-parameter coupling models for describing the thermal runaway process of the lithium ion battery, and the thermal runaway process of the lithium ion battery is predicted, namely, the equation of the models is solved to obtain the pair serving as a distribution space
Figure SMS_165
Time->
Figure SMS_166
And ambient temperature->
Figure SMS_167
Temperature of the function of>
Figure SMS_168
Dimensionless concentration->
Figure SMS_169
Is a prediction of (2).
After the multi-parameter coupling model is built through step 100, a multi-physical information neural network can be constructed through step 200, and training can be performed on the multi-physical information neural network through step 300.
In an embodiment of the present application, the multi-physical information neural network constructed in step 200 is input as time
Figure SMS_170
Ambient temperature->
Figure SMS_171
And at least two spatially distributed variables of the lithium ion battery +.>
Figure SMS_172
(e.g. voltage, density, etc. are +.>
Figure SMS_173
Variable with a specific distribution on) output is +.>
Figure SMS_174
、/>
Figure SMS_175
The loss function is determined based on the multiparameter coupling model established in step 100.
The conventional form of physical information neural networks (Physics-Informed Neural Network, PINN) and the manner in which they are trained are known to those skilled in the art, and in general, the network model form of the PINN may be the same as that of a conventional deep neural network with time prediction capability, for example, may include an input layer, a plurality of intermediate layers, an output layer, and the like, unlike a conventional deep neural network, the PINN approximates the solution of a Partial Differential Equation (PDE) by adding an equation (also referred to as a control equation) describing a physical model as a penalty term or constraint term to an empirical loss function of the deep neural network, so that the data fitting and control equation after parameter optimization is satisfied, i.e., the model output thereof is equal to that of an equation or a system response of a time-varying system.
Specifically, the loss function of the multi-physical information neural network provided by the application is formed by adding the formulas (1) to (4) as penalty terms in a conventional empirical loss function, wherein the formulas (3) and (4) are boundary conditions and initial conditions, respectively, which will make the unique solution of the controlled partial differential equation of the formula (1), and such a physical-based constraint is also commonly called residual loss; while conventional empirical losses are also commonly referred to as data fitting losses, ideally, when each residual and data fitting loss is equal to zero, the optimization is complete, meaning that the dominant laws of physics, boundary conditions, and initial conditions are satisfied.
Furthermore, complex reactions of lithium ion batteries during thermal runaway are involved at the same timeThermodynamic and chemical degradation processes, each of formulas (1) to (4) and (5) to (7) are described by differential equations, respectively, and are performed by
Figure SMS_176
The coupling relation between the physical correlation factors and given data is established, and the neural network is supervised together, so that the accuracy and the robustness of the PINN are improved, therefore, in the embodiment of the application, the thermal runaway prediction estimation is carried out through the PINN, each dynamic process is required to be modeled and trained respectively and simultaneously by the independent neural network, and the training of each dynamic process is required to be supervised in a coupling way by a unified penalty term, which is also the reason that the neural network provided by the application is called a multi-physical information neural network.
In particular, in some preferred embodiments of the present application, the loss function
Figure SMS_177
Can be expressed as:
Figure SMS_178
(8)，
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_181
、/>
Figure SMS_182
、/>
Figure SMS_186
、/>
Figure SMS_180
is->
Figure SMS_183
Data fitting loss, partial differential equation loss (which represents the part of the loss function driven by the multiparameter coupling model described above), ordinary differential equation loss, boundary condition loss, and initial condition loss, respectively,/">
Figure SMS_187
、/>
Figure SMS_188
、/>
Figure SMS_179
、/>
Figure SMS_184
、/>
Figure SMS_185
Respectively, their weighting coefficients.
The specific expression of each of the above-described loss functions is given by the following formulas (9) to (13):
Figure SMS_189
(9)，
Figure SMS_190
(10)，
Figure SMS_191
(11)，
Figure SMS_192
(12)，
Figure SMS_193
(13)，
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_195
is mean square error>
Figure SMS_197
、/>
Figure SMS_200
、/>
Figure SMS_196
、/>
Figure SMS_198
Respectively->
Figure SMS_201
、/>
Figure SMS_202
、/>
Figure SMS_194
、/>
Figure SMS_199
Is a predicted value of (a).
FIG. 2 illustrates a specific architecture of the multiple physical information neural network established by step 200 in some preferred embodiments, as illustrated in FIG. 2, where the multiple physical information neural network includes two independent first (neural network T) and second (neural network C) neural networks, respectively, each of which may employ a neural network architecture with time prediction capability known to those skilled in the art that shares the same inputs, respectively learns and outputs temperatures
Figure SMS_203
And non-dimensional concentration of lithium ions +.>
Figure SMS_204
After the partial derivative term is calculated by automatic differentiation of the above-mentioned outputs, the total loss function is constructed using the loss terms of the formula>
Figure SMS_205
：
Figure SMS_206
，
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_207
，/>
Figure SMS_209
，/>
Figure SMS_221
，/>
Figure SMS_210
，
Figure SMS_216
，/>
Figure SMS_212
，/>
Figure SMS_218
、/>
Figure SMS_214
variable space distribution variables ∈>
Figure SMS_215
、/>
Figure SMS_208
The thermal conductivity coefficient of the material is corresponding to that of the material,
Figure SMS_220
、/>
Figure SMS_213
、/>
Figure SMS_219
respectively->
Figure SMS_217
、/>
Figure SMS_222
、/>
Figure SMS_211
Upper limit of the value of (2).
Among the above-mentioned loss terms, what is important is the PDE loss, i.e., the energy balance equation, which, due to its coupling with the outputs of two differently trained networks, enables it to take into account the multi-physical characteristics of thermal runaway of the lithium ion battery and makes the multi-physical information neural network proposed in this application have a structure of interconnected loss functions.
The training data is input into the neural network, the loss function is calculated based on the output, and the training mode of updating the weights of each layer of the neural network through back propagation is known to those skilled in the art, and in some preferred embodiments, in order to prevent the gradient explosion phenomenon, as shown in fig. 2, a transformation layer (transformer) is arranged between the input and the first and second neural networks, so as to smooth the diffusion term of the nonlinear equilibrium equation.
Meanwhile, since gradient change speeds of different items of the loss function are different, in order to avoid severe oscillation of gradient values during model training caused by unbalanced back propagation gradient calculation of the multi-physical information neural network and further unstable prediction accuracy, as shown in fig. 2, in the training process of the multi-physical information neural network, the back propagation gradient of the multi-physical information neural network is optimized based on the multi-parameter coupling model so as to balance interaction among different items in the loss function during model training.
Specifically, for each iteration the total loss function
Figure SMS_223
Gradient operation (i.e. acquire +.>
Figure SMS_228
) Then find +.>
Figure SMS_231
And->
Figure SMS_226
Maximum value of item gradient, designated->
Figure SMS_229
I.e. +.>
Figure SMS_230
The method comprises the steps of carrying out a first treatment on the surface of the Respectively find +.>
Figure SMS_234
And->
Figure SMS_224
Is recorded as->
Figure SMS_227
、/>
Figure SMS_232
I.e. +.>
Figure SMS_233
、/>
Figure SMS_225
；
Then, the learning rate is setrateAnd calculate the optimization coefficient based on the following formulas (14), (15)
Figure SMS_235
、/>
Figure SMS_236
：
Figure SMS_238
(14)，
Figure SMS_239
(15)，
Then based on
Figure SMS_240
、/>
Figure SMS_241
Counter-propagating is performed to update the weights of the transformer layers while based on propagation loss +.>
Figure SMS_242
The weights of the first and second neural networks are updated by conventional back propagation.
Further, in some preferred embodimentsIn an example, can use
Figure SMS_243
、/>
Figure SMS_244
For->
Figure SMS_245
、/>
Figure SMS_246
The coefficients of (2) are updated to achieve the purpose of gradient optimization.
In some preferred embodiments, the above-mentioned utilization is used to further improve the high prediction accuracy of the physical information neural network while reducing the calculation time
Figure SMS_247
、/>
Figure SMS_248
Optimization of the transformer layer weights may be precededNIn a plurality of iterations (whereinNIs a preset positive integer), inN+In 1 and later iterations, only the weights of the first neural network and the second neural network are updated, so that the calculation time is reduced while the high prediction precision of the physical information neural network is improved.
In addition, the difference between the input and output scales is another factor causing the weight imbalance between the loss terms, in order to eliminate the problem that the physical quantity normalized neural network output does not match the partial differential equation having the physical dimensions, the variables and coefficients of the partial differential equation, that is, the input and output of the multi-physical information neural network should also be normalized and dimensionless processed, specifically, the non-dimensionalization processing is performed on each of the input and output variables using the following formula (16):
Figure SMS_249
(16)，
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_250
、/>
Figure SMS_251
respectively->
Figure SMS_252
、/>
Figure SMS_253
Lower limit of the value of (2).
Accordingly, the formula (1), the formula (3) and the formula (4) become:
Figure SMS_254
(17)，
Figure SMS_255
(18)，
Figure SMS_256
(19)。
after the construction and training of the multi-physical information neural network model are completed through the steps 200 and 300, the trained multi-physical information neural network can be used to predict the thermal runaway process of the lithium ion battery in the step 400, and the prediction of specific parameters or variables using the trained neural network model is known to those skilled in the art and will not be described herein.
Fig. 3 shows a flow chart of the implementation of the prediction method in a specific embodiment that first generates a sufficient amount of data using a COMSOL Multiphysics 5.6.6 high-fidelity model and then provides the data for training to a multi-physical information neural network model that includes two different neural networks, where the neural network T is trained to predict the evolution of the temperature profile in space and time, and the neural network C is trained to predict the concentration of reactive species that degrade over time due to temperature rise, each neural network being assigned a different task, thus each network requires a different loss function, however, the networks are optimized simultaneously because they are coupled to each other by the overall control equation.
FIG. 4 shows the prediction results of the multi-physical information neural network on the temperature of the lithium ion battery provided by the embodiment, wherein the prediction results of the case of setting the learning rate optimization and the case of not setting the learning rate optimization are provided respectively and compared with the actually measured reference values; fig. 5 shows the predicted result of fig. 4 at 230 to 270 minutes in an enlarged manner.
From fig. 4 and fig. 5, it can be seen that, by using the battery thermal runaway prediction method based on the gradient optimization multi-physical information neural network provided by the application, the temperature and dimensionless concentration changes along with time in the thermal runaway process of the lithium ion battery can be accurately predicted, especially, the temperature and dimensionless concentration changes along with time have good prediction effects at the inflection point where the state is suddenly changed, and further, the prediction capability at the mutation point is further improved under the condition of setting the learning rate.
While the foregoing is directed to embodiments of the present application, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.

Claims (8)

1. A battery thermal runaway prediction method based on a gradient optimization multi-physical information neural network is used for predicting a thermal runaway process of a lithium ion battery and is characterized by comprising the following steps of:
establishing a multi-parameter coupling model for thermal runaway of the lithium ion battery;
constructing a multi-physical information neural network, wherein the input of the multi-physical information neural network is time
Figure QLYQS_1
Ambient temperature
Figure QLYQS_2
And at least two spatially distributed variables of the lithium ion battery +.>
Figure QLYQS_3
、/>
Figure QLYQS_4
The output is the temperature of the lithium ion battery>
Figure QLYQS_5
And the dimensionless concentration of lithium ions contained therein +.>
Figure QLYQS_6
Loss function->
Figure QLYQS_7
Determining based on the multiparameter coupling model;
training the multi-physical information neural network by using training data, wherein the reverse propagation gradient of the multi-physical information neural network is optimized based on the multi-parameter coupling model in the training process;
and predicting the thermal runaway process of the lithium ion battery by using the trained multi-physical information neural network.
2. The battery thermal runaway prediction method based on the gradient optimization multi-physical information neural network according to claim 1, wherein the method is characterized by comprising the following steps of:
the multi-parameter coupling model comprises a first equation set for describing a thermodynamic reaction in the thermal runaway process of the lithium ion battery and a second equation set for describing chemical degradation reactions of various mediums in the thermal runaway process of the lithium ion battery;
the first equation set and the second equation set are based on the rate of heat generation by the volume
Figure QLYQS_8
Are coupled to each other.
3. The battery thermal runaway prediction method based on the gradient optimization multi-physical information neural network according to claim 2, wherein the first equation set specifically comprises:
Figure QLYQS_9
，
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure QLYQS_25
、/>
Figure QLYQS_12
、/>
Figure QLYQS_22
for the density, heat capacity and heat conductivity of lithium ion battery respectively, < >>
Figure QLYQS_15
The temperature of lithium ions in the electrolyte of a lithium ion battery, which is the distribution space of lithium ions +.>
Figure QLYQS_21
Time->
Figure QLYQS_23
Function of->
Figure QLYQS_27
Is->
Figure QLYQS_17
Is (are) calculated domain->
Figure QLYQS_18
To calculate the upper time limit +.>
Figure QLYQS_11
For convection heat transfer coefficient>
Figure QLYQS_19
、/>
Figure QLYQS_13
、/>
Figure QLYQS_26
、/>
Figure QLYQS_10
、/>
Figure QLYQS_20
、/>
Figure QLYQS_14
The reaction enthalpy, the specific active substance content per unit volume, the reaction factor, the activation energy, the molar gas constant and the reaction progression are respectively +.>
Figure QLYQS_24
、/>
Figure QLYQS_16
Initial values of temperature and dimensionless concentration respectively;
the second equation set is specifically:
Figure QLYQS_28
，
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure QLYQS_31
、/>
Figure QLYQS_36
heat generation rate of positive electrode and electrolyte decomposition, respectively, +.>
Figure QLYQS_39
、/>
Figure QLYQS_29
、/>
Figure QLYQS_33
Respectively is positive electrodeFrequency factor, reaction coefficient and thermal activation energy of the solution reaction,/->
Figure QLYQS_38
、/>
Figure QLYQS_41
Respectively->
Figure QLYQS_30
、/>
Figure QLYQS_35
Reaction progression of>
Figure QLYQS_37
、
Figure QLYQS_40
、/>
Figure QLYQS_32
、/>
Figure QLYQS_34
The frequency factor, the thermal activation energy, the lithium ion concentration and the reaction progression of the electrolyte decomposition reaction are respectively shown.
4. The battery thermal runaway prediction method based on the gradient optimization multi-physical information neural network according to claim 1, wherein the method is characterized by comprising the following steps of:
the multi-physical information neural network comprises a first neural network and a second neural network which are independent;
the first and second neural networks share the same input
Figure QLYQS_42
、/>
Figure QLYQS_43
、/>
Figure QLYQS_44
、/>
Figure QLYQS_45
；
The output of the first neural network is temperature
Figure QLYQS_46
The output of the second neural network is a dimensionless concentration
Figure QLYQS_47
Is a predicted value of (a).
5. The gradient-optimized multi-physical information neural network-based battery thermal runaway prediction method of claim 3, wherein the loss functionThe method comprises the following steps:
Figure QLYQS_49
，
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure QLYQS_73
、/>
Figure QLYQS_81
、/>
Figure QLYQS_82
、/>
Figure QLYQS_54
is->
Figure QLYQS_63
Data fitting loss, partial differential equation loss, ordinary differential equation loss, boundary condition loss and initial condition loss, respectively, < >>
Figure QLYQS_69
、/>
Figure QLYQS_76
、/>
Figure QLYQS_67
、/>
Figure QLYQS_75
、/>
Figure QLYQS_51
Respectively, weighting coefficients thereof, ">
Figure QLYQS_61
、/>
Figure QLYQS_72
、/>
Figure QLYQS_78
、/>
Figure QLYQS_80
Respectively->
Figure QLYQS_83
、/>
Figure QLYQS_52
、/>
Figure QLYQS_60
、/>
Figure QLYQS_66
Predicted value of +.>
Figure QLYQS_74
，/>
Figure QLYQS_56
，/>
Figure QLYQS_64
，/>
Figure QLYQS_55
，/>
Figure QLYQS_58
，/>
Figure QLYQS_71
，/>
Figure QLYQS_79
、/>
Figure QLYQS_57
Variable space distribution variables ∈>
Figure QLYQS_62
、/>
Figure QLYQS_50
Corresponding heat conductivity->
Figure QLYQS_65
、/>
Figure QLYQS_70
、/>
Figure QLYQS_77
Respectively->
Figure QLYQS_59
、/>
Figure QLYQS_68
、/>
Figure QLYQS_53
Upper limit of the value of (2).
6. The method for predicting thermal runaway of a battery based on a gradient-optimized multi-physical information neural network of claim 5,
the multi-physical information neural network further includes a voltage transformation layer, and,
in step 300, the optimization of the back propagation gradient of the multi-physical information neural network based on the multi-parameter coupling model in the training process is specifically performed in the back propagation:
first, calculate
Figure QLYQS_84
Wherein->
Figure QLYQS_85
To find the gradient operation;
second, respectively obtaining
Figure QLYQS_86
And->
Figure QLYQS_87
、
Figure QLYQS_88
；
Third, calculate based on the following
Figure QLYQS_89
、/>
Figure QLYQS_90
：
Figure QLYQS_91
；
Fourth, calculate based on the following
Figure QLYQS_92
、/>
Figure QLYQS_93
：
Figure QLYQS_94
，
Wherein, the liquid crystal display device comprises a liquid crystal display device,rateis a preset learning rate;
fifth step, use
Figure QLYQS_95
、/>
Figure QLYQS_96
And updating the weight of the transformation layer.
7. The battery thermal runaway prediction method based on the gradient-optimized multi-physical information neural network according to claim 6, wherein the method is characterized by:
optimizing the counter-propagating gradient of the multi-physical information neural network is only precededNAnd performed in the second back propagation.
8. The battery thermal runaway prediction method based on the gradient optimization multi-physical information neural network according to claim 1, wherein the method is characterized by comprising the following steps of:
and also includes an input to
Figure QLYQS_97
、/>
Figure QLYQS_98
、/>
Figure QLYQS_99
、/>
Figure QLYQS_100
Output->
Figure QLYQS_101
、/>
Figure QLYQS_102
And performing non-dimensionalization processing.
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