A method and device for solving lithium battery working condition sequence and a readable storage medium

Abstract

The application discloses a method and device for solving a working condition sequence of a lithium battery and a readable storage medium, and the method comprises the following steps: establishing a training electrochemical model, and calculating an iteration to generate an electrochemical parameter of the lithium battery; performing normalization processing on the electrochemical parameter of the lithium battery to obtain equivalent electrochemical parameters concentrated in an order of magnitude; and substituting the equivalent electrochemical parameters into a prediction electrochemical model, wherein the prediction electrochemical model is arranged on an edge computing device, and the prediction electrochemical model is used to predict the working condition sequence of the lithium battery. According to the application, the cache occupation of a related algorithm on a hardware computing device can be reduced, and the calculation speed can be improved.

Description

Technical Field

[0001] This invention relates to the field of battery parameters, and more specifically to a method, apparatus, and readable storage medium for solving lithium battery operating condition sequences. Background Technology

[0002] In existing technologies, establishing a physicochemical model for lithium batteries and obtaining simulated values ​​of the physicochemical state quantities in the space and time of the battery's interior can provide a clearer understanding of the real-time operating status of the lithium battery, thereby better ensuring the economy, reliability and safety of the lithium battery.

[0003] Electrochemical modeling of lithium-ion batteries is fundamental for understanding their operational status, system safety and stability, fault analysis, and economic dispatch. However, large-scale lithium-ion battery systems (energy storage power stations, electric vehicles, etc.) involve a vast number of batteries, placing high demands on computational throughput. Therefore, we aim to achieve simulations on GPUs or small edge computing devices. However, because individual computing cores on these devices have limited memory resources, it is necessary to reduce the cache footprint of the algorithm. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method, apparatus, and readable storage medium for solving lithium battery operating condition sequences.

[0005] Specifically, the technical solution of the present invention is as follows:

[0006] On the one hand, a method for solving the lithium battery operating condition sequence includes:

[0007] Establish a training electrochemical model and calculate the electrochemical parameters of the iteratively generated lithium battery;

[0008] The electrochemical parameters of the lithium battery are normalized to obtain equivalent electrochemical parameters with a large order of magnitude.

[0009] The equivalent electrochemical parameters are substituted into the predictive electrochemical model, which is deployed on an edge computing device, and the predictive electrochemical model is used to predict the operating condition sequence of the lithium battery.

[0010] In some embodiments, the equivalent electrochemical parameters include: equivalent parameters of the x-dimensional coordinates of the spatial location of the active material microsphere, equivalent parameters of the r-dimensional coordinates of the internal spatial location of the active material microsphere, equivalent parameters of the simulation time, equivalent parameters of the overpotential, equivalent parameters of the solid phase current, equivalent parameters of the liquid phase lithium ion concentration, and equivalent parameters of the solid phase lithium ion concentration.

[0011] In some embodiments, the normalization formula for the equivalent electrochemical parameters is:

[0012]

[0013] Where x is the x-coordinate of the spatial location of the active material microsphere. Let x be an isoparameter, L be the actual size of the spatial domain, and L be the actual size of the spatial domain. - L represents the actual size of the negative electrode space domain. spe L represents the actual size of the diaphragm space. + This represents the actual size of the positive electrode space domain;

[0014]

[0015] Where r is the r-dimensional coordinate of the internal spatial position of the active material microsphere. Rx is the equivalent parameter of r, and Rx is the radius of the active material microspheres;

[0016]

[0017] in, i is the equivalent parameter of the simulated time. app The reference current density is given, where F is Farady's constant. This represents the maximum lithium loading in the negative electrode solid phase.

[0018]

[0019] Where η is the overpotential. Let η be the equivalent parameter, R be the universal gas constant, and T be the Kelvin temperature;

[0020]

[0021] Among them, i s It is solid-phase current. For the i s Equivalent parameters;

[0022]

[0023] Among them, c e This refers to the concentration of lithium ions in the liquid phase. For reference liquid phase concentration, For the c e Equivalent parameters;

[0024]

[0025] in, For the c e The equivalent parameter, c s Solid-phase lithium-ion concentration, c smax This represents the maximum lithium loading in the solid phase.

[0026] In some embodiments, after obtaining equivalent electrochemical parameters with a range of orders of magnitude, the method further includes:

[0027] Select the first mapped integer data range based on the order of magnitude of the equivalent parameters;

[0028] Based on the first mapped integer data range, a second mapped integer data range is set, taking into account tolerance errors. The second mapped integer data range is larger than the first mapped integer data range.

[0029] Based on the first mapped integer data range and the second mapped integer data range, a third mapped integer data range considering redundancy is set, and the equivalent parameter is mapped to the third mapped integer data range to obtain integer data. The third mapped integer data range is larger than the second mapped integer data range.

[0030] The integer data is fed into the edge computing device for calculation to predict the operating condition sequence of the lithium battery.

[0031] In some embodiments, mapping the electrochemical model parameters to a third mapping integer data range to obtain integer data specifically includes:

[0032] Based on the third mapped integer data range, the number of binary digits in the third mapped integer data range is obtained;

[0033] Based on the number of binary digits, the integer data is represented using the number of binary digits to obtain the integer data in binary representation.

[0034] The integer data represented by the binary number is stored in the edge computing device for calculation to predict the operating condition sequence of the lithium battery.

[0035] On the other hand, based on the same technical concept, the present invention also proposes an apparatus for solving the lithium battery operating condition sequence, comprising:

[0036] Establish a module for building and training electrochemical models;

[0037] The calculation module is used to calculate the electrochemical parameters of the iteratively generated battery;

[0038] The normalization module is used to normalize the electrochemical parameters of the lithium battery to obtain equivalent electrochemical parameters with a large order of magnitude.

[0039] A prediction module is used to substitute the equivalent electrochemical parameters into a prediction electrochemical model, which is deployed on an edge computing device, and to predict the operating condition sequence of the lithium battery using the prediction electrochemical model.

[0040] In some implementations, the normalization formula applied to the normalization module is:

[0041]

[0042] Where x is the x-coordinate of the spatial location of the active material microsphere. Let x be an isoparameter, L be the actual size of the spatial domain, and L be the actual size of the spatial domain. - L represents the actual size of the negative electrode space domain. spe L represents the actual size of the diaphragm space. + This represents the actual size of the positive electrode space domain;

[0043]

[0044] Where r is the r-dimensional coordinate of the internal spatial position of the active material microsphere. R is the equivalent parameter of r. s The radius of the active material microspheres;

[0045]

[0046] in, i is the equivalent parameter of the simulated time. app The reference current density is given, where F is Farady's constant. This represents the maximum lithium loading in the negative electrode solid phase.

[0047]

[0048] Where η is the overpotential. Let η be the equivalent parameter, R be the universal gas constant, and T be the Kelvin temperature;

[0049]

[0050] Among them, i s It is solid-phase current. For the i s Equivalent parameters;

[0051]

[0052] Among them, c e This refers to the concentration of lithium ions in the liquid phase. For reference liquid phase concentration, For the c e Equivalent parameters;

[0053]

[0054] in, For the c e The equivalent parameter, c s Solid-phase lithium-ion concentration, c smax This represents the maximum lithium loading in the solid phase.

[0055] In some implementations, the normalization module includes:

[0056] The selection unit is used to select a first mapped integer data range based on the order of magnitude of the equivalent parameter.

[0057] The setting unit is configured to set a second mapped integer data range, taking into account tolerance errors, based on the first mapped integer data range, wherein the second mapped integer data range is larger than the first mapped integer data range.

[0058] The setting unit is further configured to set a third mapped integer data range that takes redundancy into account based on the first mapped integer data range and the second mapped integer data range, and map the equivalent parameter to the third mapped integer data range to obtain integer data, wherein the third mapped integer data range is greater than the second mapped integer data range;

[0059] The prediction module is also used to input the integer data into the edge computing device for calculation to predict the operating condition sequence of the lithium battery.

[0060] In some implementations, the normalization module further includes a conversion unit.

[0061] The calculation module is used to obtain the number of binary digits of the third mapped integer data range based on the third mapped integer data range;

[0062] The conversion unit is used to represent the integer data using the number of binary digits, based on the number of binary digits, to obtain the integer data represented by binary digits.

[0063] The prediction module is used to store the integer data represented by the binary number into the edge computing device for calculation to predict the operating condition sequence of the lithium battery.

[0064] The present invention also discloses a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for solving a lithium battery operating condition sequence as described in any of the preceding claims.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] This invention normalizes the electrochemical model parameters of lithium batteries to obtain parameters with relatively concentrated magnitudes. These parameters are then fed into a predictive electrochemical model deployed in an edge computing device to solve for the operating condition sequence of the lithium battery, thereby simulating the electrochemical model of the lithium battery. This invention normalizes the electrochemical parameters of lithium batteries, reducing the cache footprint of related algorithms on hardware computing devices and improving computational speed. Attached Figure Description

[0067] The preferred embodiments will now be described in a clear and easy-to-understand manner, in conjunction with the accompanying drawings, to further explain the above-mentioned characteristics, technical features, advantages, and implementation methods of the present invention.

[0068] Figure 1 This is a flowchart of an embodiment of a method for solving lithium battery operating condition sequences according to the present invention;

[0069] Figure 2 This is a graph showing the normalized magnitude variation of the solid-phase lithium-ion concentration parameter, according to an embodiment of the method for solving the lithium battery operating condition sequence of the present invention.

[0070] Figure 3 This is a graph showing the magnitude variation of the normalized liquid phase lithium ion concentration parameter, according to an embodiment of the method for solving lithium battery operating condition sequences of the present invention.

[0071] Figure 4 This is a graph showing the normalized magnitude variation of solid-phase current parameters in an embodiment of a method for solving lithium battery operating condition sequences according to the present invention.

[0072] Figure 5 This is a graph showing the magnitude variation of the overpotential parameter after normalization, according to an embodiment of the method for solving the lithium battery operating condition sequence of the present invention.

[0073] Figure 6 This is a schematic diagram of an embodiment of the apparatus for solving the operating condition sequence of a lithium battery according to the present invention. Detailed Implementation

[0074] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without any creative effort.

[0075] To keep the drawings concise, each figure only schematically shows the parts relevant to the invention, and these do not represent the actual structure of the product. Furthermore, to facilitate understanding, in some figures, only one of components with the same structure or function is schematically depicted, or only one is labeled. In this document, "one" not only means "only one," but can also mean "more than one."

[0076] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0077] In this document, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0078] Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0079] One embodiment of the present invention is described in the appendix to the specification. Figure 1 A method for solving the operating condition sequence of a lithium battery includes:

[0080] S100: Establish and train an electrochemical model to calculate the electrochemical parameters of the iteratively generated lithium battery.

[0081] Specifically, training operating condition sequences are obtained from actual operating condition data of lithium batteries, an electrochemical model is trained using the training operating condition sequences, and then the electrochemical parameters of lithium batteries are calculated iteratively using the trained electrochemical model.

[0082] S200, the electrochemical parameters of the lithium battery are normalized to obtain equivalent electrochemical parameters with a large order of magnitude.

[0083] Specifically, the electrochemical parameters of the lithium battery generated by the above iteration are normalized to obtain a set of electrochemical parameters of the lithium battery with a large order of magnitude. These equivalent electrochemical parameters include equivalent parameters of the x-dimensional coordinates of the spatial position of the active material spheres, equivalent parameters of the r-dimensional coordinates of the internal spatial position of the active material spheres, equivalent parameters of the simulation time, equivalent parameters of the overpotential, equivalent parameters of the solid phase current, equivalent parameters of the liquid phase lithium ion concentration, and equivalent parameters of the solid phase lithium ion concentration.

[0084] S300, the equivalent electrochemical parameters are substituted into a preset predictive electrochemical model, the predictive electrochemical model is deployed on an edge computing device, and the predictive electrochemical model is used to predict the operating condition sequence of the lithium battery.

[0085] Specifically, the normalized electrochemical parameters of the lithium battery are fed into the predictive electrochemical model in the edge computing device, and the predictive electrochemical model predicts the operating condition sequence of the lithium battery.

[0086] In this embodiment, the electrochemical parameters of the lithium battery generated by the calculation iteration are normalized and then input into the edge computing device, thereby improving the calculation speed of the edge computing device.

[0087] In one embodiment, step S200 involves normalizing the electrochemical parameters of the lithium battery to obtain equivalent electrochemical parameters with a large order of magnitude; specifically including:

[0088] The normalization formula for the equivalent electrochemical parameters is as follows:

[0089]

[0090] Where x is the x-coordinate of the spatial location of the active material microsphere. Let x be an isoparameter, L be the actual size of the spatial domain, and L be the actual size of the spatial domain. - L represents the actual size of the negative electrode space domain. spe L represents the actual size of the diaphragm space. + This represents the actual size of the positive electrode space domain.

[0091]

[0092] Where r is the r-dimensional coordinate of the internal spatial position of the active material microsphere. R is the equivalent parameter of r. s The radius of the active material microsphere is given.

[0093]

[0094] in, i is the equivalent parameter of the simulated time. app The reference current density is given, where F is Farady's constant. This represents the maximum lithium loading in the negative electrode solid phase.

[0095]

[0096] Where η is the overpotential. Let η be the equivalent parameter, R be the universal gas constant, and T be the Kelvin temperature;

[0097]

[0098] wherein i s is the solid-phase current, is the equivalent parameter of the i s ;

[0099]

[0100] wherein c e is the liquid-phase lithium ion concentration, is the reference liquid-phase concentration, is the equivalent parameter of the c e ;

[0101]

[0102] wherein, is the equivalent parameter of the c e , c s is the solid-phase lithium ion concentration, c smax is the maximum solid-phase lithium carrying capacity.

[0103] Specifically, simulating the electrochemical model of lithium-ion batteries is the basis for understanding the operating state of lithium-ion batteries, system safety and stability, fault analysis, economic dispatch and other issues. However, in large-scale lithium-ion battery systems (energy storage power stations, electric vehicles, etc.), the number of batteries is extremely large, which puts high requirements on high-throughput computing. In this embodiment, the electrochemical parameters are preprocessed by parameter normalization, which reduces the task load of the edge computing device, thereby improving the computing speed of the edge computing device.

[0104] An embodiment of the present invention provides a method for solving a lithium battery working condition sequence, comprising:

[0105] S100: establishing a training electrochemical model, and calculating and iteratively generating electrochemical parameters of the lithium battery;

[0106] S210: performing normalization processing on the electrochemical parameters of the lithium battery to obtain equivalent electrochemical parameters with concentrated orders of magnitude;

[0107] S220: selecting a first mapped integer data range according to the order of magnitude of the equivalent parameters;

[0108] Specifically, if the magnitude variation of the equivalent parameters is from 10 to the power of a to 10 to the power of b (a<b), then the order of magnitude of the equivalent parameters is mapped to the integer data range from 10 to the power of a to 10 to the power of b.

[0109] S230, based on the first mapped integer data range, a second mapped integer data range considering allowable error is set, wherein the second mapped integer data range is greater than the first mapped integer data range;

[0110] Specifically, if we consider the allowable error of the integer data range of the equivalent parameter mapping and set the allowable error to be 10 to the power of -c, then the integer data range of the equivalent parameter mapping should be b+c.

[0111] S240, based on the first mapped integer data range and the second mapped integer data range, a third mapped integer data range considering redundancy is set, and the equivalent parameter is mapped to the third mapped integer data range to obtain integer data, wherein the third mapped integer data range is greater than the second mapped integer data range;

[0112] Specifically, considering the allowable error of the integer data range of the equivalent parameter mapping, and also taking into account a certain amount of redundancy, generally the redundancy is taken as 10 to the power of 2, then the integer data range of the equivalent parameter mapping should be b+c+2.

[0113] S300, the equivalent electrochemical parameters are substituted into the predictive electrochemical model, which is deployed on an edge computing device, and the predictive electrochemical model is used to predict the operating condition sequence of the lithium battery.

[0114] Specifically, through the normalized parameters, it can be seen that the calculation process is basically concentrated between the -2nd and 1st orders of 10. For example, see the appendix to the instruction manual. Figure 2 Appendix Figure 3 Appendix Figure 4 Appendix Figure 5 The examples demonstrate the electrochemical parameter calculation process of edge computing devices, c s Solid-phase lithium-ion concentration parameters, c e Liquid phase lithium ion concentration parameters, i s The normalized order of magnitude changes of the solid-phase current parameter and the η overpotential parameter are shown in the appendix to the instruction manual. Figure 2 Appendix Figure 3 Appendix Figure 4 Appendix Figure 5 The horizontal axis in the graph shows the edge computing device's performance during the computation process, c s Solid-phase lithium-ion concentration parameters, c e Liquid phase lithium ion concentration parameters, i sThe normalized orders of magnitude of the solid-phase current parameters and η overpotential parameters are basically concentrated between 10⁻² and 1. If the edge computing device considers the allowable error during the calculation process and sets the allowable error to -8 (or -4), then we need to map the normalized electrochemical parameters to an integer range greater than 10⁹. If the edge computing device considers both the allowable error and a 2-bit redundancy during the calculation process, then the mapped integer range is chosen to be 10¹¹. This embodiment maps the normalized electrochemical parameters to an integer data range, making the calculation of the edge computing device simpler.

[0115] An embodiment of the present invention provides a method for solving the operating condition sequence of a lithium battery, comprising the steps of:

[0116] S100, establish and train an electrochemical model to calculate the electrochemical parameters of the iteratively generated lithium battery;

[0117] S210, the electrochemical parameters of the lithium battery are normalized to obtain equivalent electrochemical parameters with a large order of magnitude.

[0118] S220, Select the first mapped integer data range according to the order of magnitude of the equivalent parameter;

[0119] S230, based on the first mapped integer data range, a second mapped integer data range considering allowable error is set, wherein the second mapped integer data range is greater than the first mapped integer data range;

[0120] S240, based on the first mapped integer data range and the second mapped integer data range, a third mapped integer data range considering redundancy is set, and the equivalent parameter is mapped to the third mapped integer data range to obtain integer data, wherein the third mapped integer data range is greater than the second mapped integer data range;

[0121] S240-1, Based on the third mapped integer data range, obtain the number of binary bits in the third mapped integer data range;

[0122] S240-2, Based on the number of binary digits, represent the integer data using the number of binary digits to obtain the integer data represented by binary digits;

[0123] S300, the integer data represented by the binary number is stored in the edge computing device for calculation to predict the operating condition sequence of the lithium battery.

[0124] Specifically, as mentioned earlier, 10 to the power of 11 is approximately equal to 2 to the power of 36. This means that using a 36-bit binary number to represent the equivalent parameter mapped to the third mapping integer data range is beneficial for reducing the storage space occupied by edge computing devices.

[0125] One embodiment of the present invention is described in the appendix to the specification. Figure 6 An apparatus for solving a lithium battery operating condition sequence, comprising:

[0126] Module 10 is established to build a training electrochemical model;

[0127] Calculation module 20 is used to calculate the electrochemical parameters of the iteratively generated battery;

[0128] The normalization module 30 is used to normalize the electrochemical parameters of the lithium battery to obtain equivalent electrochemical parameters with a large order of magnitude.

[0129] The prediction module 40 is used to substitute the equivalent electrochemical parameters into the prediction electrochemical model, which is deployed on an edge computing device, and to predict the operating condition sequence of the lithium battery using the prediction electrochemical model.

[0130] In this embodiment, the electrochemical parameters of the lithium battery generated by the calculation iteration are normalized and then input into the edge computing device, thereby improving the calculation speed of the edge computing device.

[0131] In one embodiment, the normalization formula applied to the normalization module is:

[0132]

[0133] Where x is the x-coordinate of the spatial location of the active material microsphere. Let x be an isoparameter, L be the actual size of the spatial domain, L be the actual size of the spatial domain, and L- be the actual size of the negative electrode spatial domain. spe L represents the actual size of the diaphragm space domain. + This represents the actual size of the positive electrode space domain;

[0134]

[0135] Where r is the r-dimensional coordinate of the internal spatial position of the active material microsphere. R is the equivalent parameter of r. s The radius of the active material microspheres;

[0136]

[0137] in, i is the equivalent parameter of the simulated time.app The reference current density is given, where F is Farady's constant. This represents the maximum lithium loading in the negative electrode solid phase.

[0138]

[0139] Where η is the overpotential. Let η be the equivalent parameter, R be the universal gas constant, and T be the Kelvin temperature;

[0140]

[0141] Among them, i s It is solid-phase current. For the i s Equivalent parameters;

[0142]

[0143] Among them, c e This refers to the concentration of lithium ions in the liquid phase. For reference liquid phase concentration, For the c e Equivalent parameters;

[0144]

[0145] in, For the c e The equivalent parameter, c s Solid-phase lithium-ion concentration, c smax This represents the maximum lithium loading in the solid phase.

[0146] In this embodiment, the preprocessing of parameter normalization of electrochemical parameters reduces the workload of edge computing devices, thereby improving the computing speed of edge computing devices.

[0147] In one embodiment of the present invention, an apparatus for solving a lithium battery operating condition sequence includes a normalization module 30 comprising:

[0148] The selection unit is used to select a first mapped integer data range based on the order of magnitude of the equivalent parameter.

[0149] The setting unit is configured to set a second mapped integer data range, taking into account tolerance errors, based on the first mapped integer data range, wherein the second mapped integer data range is larger than the first mapped integer data range.

[0150] The setting unit is further configured to set a third mapped integer data range that takes redundancy into account based on the first mapped integer data range and the second mapped integer data range, and map the equivalent parameter to the third mapped integer data range to obtain integer data, wherein the third mapped integer data range is greater than the second mapped integer data range;

[0151] The prediction module is also used to input the integer data into the edge computing device for calculation to predict the operating condition sequence of the lithium battery.

[0152] This embodiment maps normalized electrochemical parameters to an integer data range, making calculations easier for edge computing devices.

[0153] In one embodiment of the present invention, an apparatus for solving lithium battery operating condition sequences, the normalization module 30 further includes: a conversion unit.

[0154] The calculation module is used to obtain the number of binary digits of the third mapped integer data range based on the third mapped integer data range;

[0155] The conversion unit is used to represent the integer data using the number of binary digits, based on the number of binary digits, to obtain the integer data represented by binary digits.

[0156] The prediction module is used to store the integer data represented by the binary number into the edge computing device for calculation to predict the operating condition sequence of the lithium battery.

[0157] In this embodiment, binary numbers are used to represent the integer data obtained by mapping the above equivalent parameters to the third mapping integer data range, which helps to reduce the storage space occupied by the edge computing device.

[0158] It should be noted that the embodiments of the apparatus for solving the lithium battery operating condition sequence provided by the present invention and the embodiments of the method for solving the lithium battery operating condition sequence provided above are both based on the same inventive concept and can achieve the same technical effects. Therefore, other specific details of the embodiments of the apparatus for solving the lithium battery operating condition sequence can be referred to the description of the embodiments of the method for solving the lithium battery operating condition sequence described above.

[0159] Finally, this application also discloses a readable storage medium storing a computer program thereon, wherein the computer program, when executed, implements the steps of a method for solving a lithium battery operating condition sequence according to any of the above embodiments of this application.

[0160] It should be noted that the above embodiments can be freely combined as needed. The above are merely preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for solving the operating condition sequence of a lithium battery, characterized in that, include: Establish a training electrochemical model and calculate the electrochemical parameters of the iteratively generated lithium battery; The electrochemical parameters of the lithium battery are normalized to obtain equivalent electrochemical parameters with a large order of magnitude. The equivalent electrochemical parameters are substituted into the predictive electrochemical model, which is deployed on an edge computing device, and the predictive electrochemical model is used to predict the operating condition sequence of the lithium battery. The equivalent electrochemical parameters include: the equivalent parameters of the x-dimensional coordinates of the spatial position of the active material microsphere, the equivalent parameters of the r-dimensional coordinates of the internal spatial position of the active material microsphere, the equivalent parameters of the simulation time, the equivalent parameters of the overpotential, the equivalent parameters of the solid phase current, the equivalent parameters of the liquid phase lithium ion concentration, and the equivalent parameters of the solid phase lithium ion concentration. The equivalent parameters of the x-dimensional coordinates of the spatial location of the active material microspheres The normalization formula is Where x is the actual size of the spatial domain, This represents the actual size of the negative electrode space domain. The actual size of the diaphragm space domain. This represents the actual size of the positive electrode space domain; The equivalent parameters of the r-dimensional coordinates of the internal spatial position of the active material microspheres The normalization formula is: Where r is the r-dimensional coordinate of the internal spatial position of the active material microspheres. Let r be the equivalent parameter. The radius of the active material microspheres; The equivalent parameter of the simulated time The normalization formula is ;in, The equivalent parameter for the simulated time is... The reference current density is given, where F is Farady's constant. This represents the maximum lithium loading in the negative electrode solid phase. The equivalent parameter of the overpotential The normalization formula is ;in, This is an overpotential. For the The equivalent parameters are R, which is the universal gas constant, and T, which is the Kelvin temperature. The equivalent parameters of the solid-phase current The normalization formula is ;in, It is solid-phase current. For the Equivalent parameters; The equivalent parameter of the liquid phase lithium ion concentration The normalization formula is ;in, This refers to the concentration of lithium ions in the liquid phase. For reference liquid phase concentration, For the Equivalent parameters; The equivalent parameter of the solid-phase lithium-ion concentration The normalization formula is ;in, For the The equivalent parameters, This refers to the concentration of lithium ions in the solid phase. This represents the maximum lithium loading in the solid phase.

2. The method for solving the lithium battery operating condition sequence according to claim 1, characterized in that, After obtaining the equivalent electrochemical parameters in the order-of-magnitude range, the process further includes: The first mapped integer data range is selected based on the order of magnitude of the equivalent parameters; Based on the first mapped integer data range, a second mapped integer data range considering allowable errors is set, wherein the second mapped integer data range is greater than the first mapped integer data range; Based on the first mapped integer data range and the second mapped integer data range, a third mapped integer data range considering redundancy is set, and the equivalent parameter is mapped to the third mapped integer data range to obtain integer data, wherein the third mapped integer data range is larger than the second mapped integer data range.

3. The method for solving the lithium battery operating condition sequence according to claim 2, characterized in that, The process of mapping the equivalent parameters to a third mapping integer data range to obtain integer data specifically includes: Based on the third mapped integer data range, the number of binary digits in the third mapped integer data range is obtained; Based on the number of binary digits, the integer data is represented using the number of binary digits to obtain the integer data represented by binary numbers; The integer data represented by the binary number is stored in the edge computing device for calculation to predict the operating condition sequence of the lithium battery.

4. An apparatus for solving the operating condition sequence of a lithium battery, characterized in that, include: Establish a module for building and training electrochemical models; The calculation module is used to calculate the electrochemical parameters of the iteratively generated battery; The normalization module is used to normalize the electrochemical parameters of the lithium battery to obtain equivalent electrochemical parameters with a large order of magnitude. A prediction module is used to substitute the equivalent electrochemical parameters into a prediction electrochemical model, which is deployed on an edge computing device, and to predict the operating condition sequence of the lithium battery using the prediction electrochemical model. The equivalent parameter normalization formula applied to the normalization module is as follows: Equivalent parameters of the x-dimensional coordinates of the spatial location of the active material microspheres The normalization formula is Where x is the actual size of the spatial domain, This represents the actual size of the negative electrode space domain. The actual size of the diaphragm space domain. This represents the actual size of the positive electrode space domain; Equivalent parameters of the r-dimensional coordinates of the internal spatial position of the active material microspheres The normalization formula is: Where r is the r-dimensional coordinate of the internal spatial position of the active material microspheres. Let r be the equivalent parameter. The radius of the active material microspheres; Equivalent parameters of the simulation time The normalization formula is ;in, The equivalent parameter for the simulated time is... The reference current density is given, where F is Farady's constant. This represents the maximum lithium loading in the negative electrode solid phase. Equivalent parameters of overpotential The normalization formula is ;in, This is an overpotential. For the The equivalent parameters are R, which is the universal gas constant, and T, which is the Kelvin temperature. Equivalent parameters of solid-state current The normalization formula is ;in, It is solid-phase current. For the Equivalent parameters; Equivalent parameters of liquid phase lithium-ion concentration The normalization formula is ;in, This refers to the concentration of lithium ions in the liquid phase. For reference liquid phase concentration, For the Equivalent parameters; Equivalent parameters of solid-phase lithium-ion concentration The normalization formula is ;in, For the The equivalent parameters, This refers to the concentration of lithium ions in the solid phase. This represents the maximum lithium loading in the solid phase.

5. The apparatus for solving the lithium battery operating condition sequence according to claim 4, characterized in that, The normalization module includes: The selection unit is used to select a first mapped integer data range based on the order of magnitude of the equivalent parameter. The setting unit is configured to set a second mapped integer data range, taking into account tolerance errors, based on the first mapped integer data range, wherein the second mapped integer data range is larger than the first mapped integer data range. The setting unit is further configured to set a third mapped integer data range that takes redundancy into account based on the first mapped integer data range and the second mapped integer data range, and map the equivalent parameter to the third mapped integer data range to obtain integer data, wherein the third mapped integer data range is greater than the second mapped integer data range; The prediction module is also used to input the integer data into the edge computing device for calculation to predict the operating condition sequence of the lithium battery.

6. The apparatus for solving the lithium battery operating condition sequence according to claim 5, characterized in that, The normalization module also includes a conversion unit. The calculation module is used to obtain the number of binary digits of the third mapped integer data range based on the third mapped integer data range; The conversion unit is used to represent the integer data using the number of binary digits, based on the number of binary digits, to obtain the integer data represented by binary digits. The prediction module is used to store the integer data represented by the binary number into the edge computing device for calculation to predict the operating condition sequence of the lithium battery.

7. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a method for solving a lithium battery operating condition sequence as described in any one of claims 1-3.

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