A method, system, storage medium and electronic device for estimating battery temperature distribution

Abstract

The present invention belongs to the technical field of temperature distribution estimation. The present invention provides a method, a system, a storage medium and an electronic device for estimating the battery temperature distribution. Distributed temperature data on the battery surface is acquired, the temperature data is preprocessed, and the preprocessed temperature data is subjected to spatial mapping; an electrochemical model is constructed to determine the heat source, a geometric model is established according to the morphological parameters of the battery, and in combination with the determined heat source, a battery thermal model is formed by coupling; according to the temperature gradient difference, the battery thermal model is divided into domains to form multiple sub-domains, based on the spatial mapping result, the convective heat transfer coefficients of different sub-domains are calibrated, and based on the calibrated convective heat transfer coefficients, the estimated result of the temperature distribution on the battery surface is obtained. The present invention significantly improves the spatial resolution and real-time performance of the battery surface temperature, can effectively prevent battery thermal runaway, and solves the problem of poor accuracy in the prior art.

Description

Technical Field

[0001] The present invention belongs to the technical field of temperature distribution estimation, and particularly relates to a method, a system, a storage medium and an electronic device for estimating the temperature distribution of a battery. Background Art

[0002] The statements in this part only provide background technical information related to the present invention, and do not necessarily constitute prior art.

[0003] In the estimation of the temperature distribution of a battery, traditional model-based estimation methods usually assume that the battery surface has a uniform convective heat transfer coefficient. This simplified assumption ignores the possible non-uniformity of heat flow on the battery surface, resulting in a significant decrease in the estimation accuracy. In addition, the verification means of existing models mainly rely on the temperature data of a small number of thermocouple measurement points, and cannot effectively reflect the real temperature changes on the surface of a larger-sized battery or a three-dimensional battery.

[0004] Some documents provide the acquisition of temperature data of battery measurement points using distributed multimode optical fibers. However, in general, these documents divide the battery module into different intervals, monitor the temperature of each interval, and compare it with a set threshold. The analysis process is relatively simple, and it still belongs to the single-point temperature distribution estimation assuming that the battery surface has a uniform convective heat transfer coefficient.

[0005] In practical applications, whether in energy storage or electric vehicle applications, etc., battery cells (such as lithium batteries) and various regions of the battery affect each other. When the battery works, electrochemical reactions occur inside, and the electrothermal distribution is non-uniform. If the temperature of each part of the battery cannot be dynamically and accurately detected, and the temperature of each part cannot be precisely controlled, it will cause local overheating of the battery, and the thermal management strategy does not match, triggering further reactions of the battery. Even if the thermal management strategy is strengthened, it cannot be controlled, resulting in thermal runaway.

[0006] In summary, in practical applications, the accuracy of the temperature distribution of existing methods is poor. Especially during the operation of the battery, the spatial resolution and real-time performance of its surface temperature cannot meet the requirements of efficient management and safety monitoring. Summary of the Invention

[0007] In order to solve the above problems, the present invention proposes a method, a system, a storage medium and an electronic device for estimating the temperature distribution of a battery. The present invention obtains the temperature through a distributed optical fiber sensor, maps the optical fiber data so that it is associated with each position on the battery surface, couples the electrochemical model and the thermal model, considers the temperature gradient difference at different positions on the battery surface, and combines the mapping result to calibrate the convective heat transfer coefficient of each region on the battery surface, thereby realizing the accurate estimation / prediction of the temperature distribution, significantly improving the spatial resolution and real-time performance of the battery surface temperature, and being able to effectively prevent battery thermal runaway.

[0008] According to some embodiments, the present invention adopts the following technical solutions:

[0009] A method for estimating the battery temperature distribution, comprising the following steps:

[0010] Obtain the distributed temperature data on the battery surface, preprocess the temperature data, and perform spatial mapping on the preprocessed temperature data;

[0011] Construct an electrochemical model, determine the heat source, establish a geometric model according to the morphological parameters of the battery, and couple with the determined heat source to form a battery thermal model;

[0012] Considering the temperature gradient differences at different positions, divide the battery thermal model into domains to form multiple sub-domains, calibrate the convective heat transfer coefficients of different sub-domains based on the spatial mapping results, and obtain the estimated result of the temperature distribution on the battery surface based on the calibrated convective heat transfer coefficients.

[0013] As an alternative implementation, in obtaining the distributed temperature data on the battery surface, use a distributed fiber optic sensor to obtain fiber optic data. The distributed fiber optic sensor is arranged on the battery surface in a reciprocating manner, or in a spiral interval, or in a grid interval on the battery surface;

[0014] And within the set range of the positive and negative electrodes of the battery, and / or within the set range of the central position of the battery, distributed fiber optic sensors are provided.

[0015] As an alternative implementation, the process of preprocessing includes: converting the wavelength data of the optical fiber into temperature data according to the relationship between the frequency shift caused by Rayleigh scattering and temperature;

[0016] Or further, compare the converted temperature data with the temperature data detected by the temperature sensor at the corresponding position to obtain an accurate relationship between wavelength and temperature, and calibrate the converted temperature data.

[0017] As an alternative implementation, the process of performing spatial mapping on the preprocessed fiber optic data includes: dividing the laying path of the optical fiber into multiple parts, each part being a straight line segment or an arc, and using the endpoints and / or center points of each part as feature points to calibrate the key positions of the fiber optic path;

[0018] For the straight line segment part, use an interpolation algorithm to uniformly map all the fiber optic data corresponding within the straight line segment range onto the straight line segment;

[0019] For the arc part, determine the central angle and radius of the arc, calculate the distribution of the optical fibers within the arc segment through the arc length formula, and use the interpolation algorithm to evenly distribute all the optical fiber data within the arc segment according to the ratio of the arc length, and map it to the corresponding arc path on the battery surface.

[0020] As an alternative implementation, the process of spatial mapping of the preprocessed optical fiber data includes: if the distributed optical fiber sensor is arranged in a helical pattern, determine the basic parameters of the helix, including the radius of the projection circle of the helix on the horizontal plane 、 the pitch and the total length of the helix ， Calculate the parametric equation of the helix, calculate the arc length of the helix using integration, and then determine the arc length corresponding to each data point, and perform reverse calculation to determine the three-dimensional coordinates of the data point on the helix.

[0021] As an alternative implementation, the process of constructing an electrochemical model includes: according to the actual usage scenario and performance requirements of the battery, select to construct a one-dimensional, two-dimensional or three-dimensional electrochemical model, and the electrochemical model calculates the heat generation of the battery during charging and discharging according to the battery type, as well as the electrochemical reaction process, ion transport characteristics and charge conduction law inside the battery of this type, and the heat generation includes ohmic heat, polarization heat, reaction heat and tab heat.

[0022] As an alternative implementation, the process of establishing a geometric model according to the morphological parameters of the battery includes: according to the actual size and morphology of the battery, use three-dimensional modeling to construct a geometric model of the battery, and the geometric model reflects the external dimensions, internal structure and morphological characteristics of the battery surface.

[0023] As an alternative implementation, the process of dividing the battery thermal model into multiple subdomains considering the temperature gradient differences at different positions includes: according to the temperature gradients in the horizontal and vertical directions on the battery surface and the model accuracy requirements, divide the battery surface into multiple subdomains, and each subdomain corresponds to a different convective heat transfer coefficient to reflect the dynamic characteristics of heat exchange between different regions of the battery, and the heat exchange between each subdomain and the environment can be set separately.

[0024] As an alternative implementation, the process of calibrating the convective heat transfer coefficients of different subdomains based on the spatial mapping results includes: comparing the temperature data obtained from the optical fiber data of different subdomains with the calculation results of the battery thermal model, and adjusting and optimizing the convective heat transfer coefficients of the corresponding subdomains according to the difference.

[0025] As an alternative implementation, the process of calibrating the convective heat transfer coefficients of different subdomains includes: recording the temperature change of the battery by the distributed optical fiber sensor, simulating the cooling process using the battery thermal model, and determining the initial value of the convective heat transfer coefficient by matching the experimental temperature decay curve;

[0026] Taking the minimum of the squared difference between the temperature distribution predicted by the model and the obtained true value as the objective function, iterative optimization is performed to adjust the convective heat transfer coefficient until the temperature distribution predicted by the model converges within the set threshold.

[0027] A battery temperature distribution estimation system, comprising:

[0028] A temperature acquisition module, configured to acquire distributed temperature data on the battery surface, preprocess the temperature data, and perform spatial mapping on the preprocessed temperature data;

[0029] A coupled model construction module, configured to construct an electrochemical model, determine the heat source, establish a geometric model according to the morphological parameters of the battery, and couple to form a battery thermal model in combination with the determined heat source;

[0030] A temperature distribution estimation module, configured to consider the temperature gradient differences at different positions, divide the domain of the battery thermal model to form multiple subdomains, calibrate the convective heat transfer coefficients of different subdomains based on the spatial mapping results, and obtain the temperature distribution estimation result on the battery surface based on the calibrated convective heat transfer coefficients.

[0031] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps in the above method.

[0032] A computer program product, the computer program product includes instructions, when the program is executed by at least one data processing device, the instructions cause the data processing device to implement the steps according to the above method.

[0033] An electronic device, comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which, when the computer instructions are run by the processor, complete the steps in the above method.

[0034] An electric vehicle, comprising a vehicle body, the vehicle body includes a battery, a distributed optical fiber sensor, and a battery management system, wherein:

[0035] The battery is used to provide energy for the electric vehicle;

[0036] The distributed optical fiber sensor is used to acquire optical fiber data;

[0037] The battery management system is used to perform preprocessing and perform spatial mapping on the preprocessed optical fiber data;

[0038] Construct an electrochemical model, determine the heat source, establish a geometric model according to the morphological parameters of the battery, and couple to form a battery thermal model in combination with the determined heat source;

[0039] Considering the temperature gradient differences at different positions, the battery thermal model is divided into domains to form multiple sub-domains. Based on the spatial mapping results, the convective heat transfer coefficients of different sub-domains are calibrated. Based on the calibrated convective heat transfer coefficients, the estimated result of the temperature distribution on the battery surface is obtained;

[0040] Or,

[0041] The battery management system includes the above-mentioned battery temperature distribution estimation system;

[0042] Or,

[0043] The battery management system is equipped with the above-mentioned computer-readable storage medium;

[0044] Or,

[0045] The battery management system includes the above-mentioned electronic device.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] The present invention dynamically obtains the temperature on the battery surface by using a distributed fiber optic sensor. The optical fiber can evenly cover the key areas on the battery surface to obtain comprehensive and continuous temperature information. The layout scheme can flexibly determine the strategy according to the shape and size of the battery and the thermophysical properties of the key temperature monitoring areas.

[0048] The present invention accurately maps the one-dimensional temperature data collected by the optical fiber to the two-dimensional plane / three-dimensional space of the battery by using the feature point positioning method and the spatial mapping algorithm, realizing the accurate spatial mapping of the battery temperature. When performing battery thermal management, the temperature of different regions is more accurately and intuitively displayed.

[0049] The present invention can flexibly choose to construct one-dimensional, two-dimensional or three-dimensional electrochemical models according to the actual use scenarios and performance requirements of the battery, and fully considers the electrochemical reaction process, ion transport characteristics and charge conduction law inside the battery, and can accurately calculate the heat generation during the charging and discharging process of the battery, providing heat source data to ensure the accuracy of subsequent thermal management.

[0050] The present invention constructs a battery thermal model with a multi-domain convective heat boundary, fully considering the heat generation, heat conduction inside the battery and heat exchange with the external environment, etc., and can truly reflect the heat transfer law of the battery during operation. Combined with multi-domain division, the prediction accuracy of the model is improved.

[0051] The present invention considers the possible heat flux non-uniformity on the battery surface by adjusting and optimizing the convective heat transfer coefficients of each sub-domain, so that the predicted temperature distribution meets the accuracy requirements.

[0052] The scope of application of the present invention is wide. It can be applied to power batteries as well as energy storage batteries. During actual application, it can dynamically and timely monitor the temperature conditions of various regions on the battery surface, especially the key regions, and can also help to timely correct the thermal management strategy, improve the thermal management effect of the battery, and effectively prevent the battery from thermal runaway.

[0053] To make the above objects, features and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The specification drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0055] Figure 1 It is a schematic flowchart of a method for estimating the battery temperature distribution in an embodiment;

[0056] Figure 2 It is a schematic diagram of the layout scheme of a distributed optical fiber sensor for a pouch battery in an embodiment;

[0057] Figure 3 It is a schematic diagram of the layout scheme of a distributed optical fiber sensor for a cylindrical battery in an embodiment;

[0058] Figure 4 It is a schematic diagram of the layout scheme of a distributed optical fiber sensor for a prismatic battery and a battery pack in an embodiment, where (a) is the layout scheme of a single prismatic battery cell and (b) is the layout scheme of the battery pack;

[0059] Figure 5 It is a schematic diagram of the spatial mapping feature points of an S-shaped optical fiber layout scheme in an embodiment;

[0060] Figure 6 It is a physical diagram of the S-shaped layout of a distributed optical fiber sensor for a pouch battery in an embodiment;

[0061] Figure 7 It is a schematic diagram of a 0.5C charge and discharge process in an embodiment, where (a) is the temperature data obtained by a thermocouple and an optical fiber during the 0.5C charge and discharge process, and (b) is the voltage and current data during the 0.5C charge and discharge process;

[0062] Figure 8 It is a schematic diagram of a 1C charge and discharge process in an embodiment, where (a) is the temperature data obtained by a thermocouple and an optical fiber during the 1C charge and discharge process, and (b) is the voltage and current data during the 1C charge and discharge process;

[0063] Figure 9It is a schematic diagram of the 1.25C charge and discharge process of an embodiment. Among them, (a) is the temperature data obtained by the thermocouple and optical fiber during the 1.25C charge and discharge process, and (b) is the voltage and current data during the 1.25C charge and discharge process;

[0064] Figure 10 It is a schematic diagram of the 1.5C charge and discharge process of an embodiment. Among them, (a) is the temperature data obtained by the thermocouple and optical fiber during the 1.5C charge and discharge process, and (b) is the voltage and current data during the 1.5C charge and discharge process;

[0065] Figure 11 It is a schematic diagram of the temperature distribution of the battery at different SOCs during the 1C discharge process of an embodiment. Among them, (a) is the temperature distribution of the battery when SOC = 1.0, (b) is the temperature distribution of the battery when SOC = 0.9, (c) is the temperature distribution of the battery when SOC = 0.8, (d) is the temperature distribution of the battery when SOC = 0.7, (e) is the temperature distribution of the battery when SOC = 0.6, (f) is the temperature distribution of the battery when SOC = 0.5, (g) is the temperature distribution of the battery when SOC = 0.4, (h) is the temperature distribution of the battery when SOC = 0.3, (i) is the temperature distribution of the battery when SOC = 0.2, and (j) is the temperature distribution of the battery when SOC = 0.1;

[0066] Figure 12 It is a schematic diagram of the three-dimensional geometric model of the battery of an embodiment;

[0067] Figure 13 It is a schematic diagram of the multi-domain thermal boundary principle of an embodiment;

[0068] Figure 14 It is a schematic diagram of the positions of 5 representative temperature points of an embodiment;

[0069] Figure 15 It is a comparison between the model-predicted temperature curve and the optical fiber-monitored temperature curve of an embodiment;

[0070] Figure 16 It is a schematic diagram of the model-predicted temperature distribution of an embodiment. Among them, (a) is the temperature distribution of the battery when SOC = 1.0, (b) is the temperature distribution of the battery when SOC = 0.8, (c) is the temperature distribution of the battery when SOC = 0.6, (d) is the temperature distribution of the battery when SOC = 0.4, (e) is the temperature distribution of the battery when SOC = 0.2, and (f) is the temperature distribution of the battery when SOC = 0;

[0071] Figure 17 It is a schematic diagram of an electronic device of an embodiment. Detailed implementation mode

[0072] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0073] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0074] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0075] In the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0076] Embodiment 1

[0077] A method for estimating the battery temperature distribution, as Figure 1 shown, includes the following steps:

[0078] S1 Obtain the distributed temperature data on the battery surface, preprocess the temperature data, and perform spatial mapping on the preprocessed temperature data;

[0079] S2 Construct an electrochemical model, determine the heat source, establish a geometric model according to the morphological parameters of the battery, and couple to form a battery thermal model in combination with the determined heat source;

[0080] S3 Consider the temperature gradient differences at different positions, divide the battery thermal model into domains to form multiple subdomains, calibrate the convective heat transfer coefficients of different subdomains based on the spatial mapping results, and obtain the estimated result of the temperature distribution on the battery surface based on the calibrated convective heat transfer coefficients.

[0081] It should be noted that the battery generally refers to an electrochemical battery, such as a lithium-ion battery, etc.

[0082] The details of each step will be introduced in detail below.

[0083] First, obtain the optical fiber data.

[0084] In this embodiment, the optical fiber data is obtained through a distributed optical fiber sensor. In this embodiment, the layout scheme is not limited, and the layout scheme can flexibly design the layout strategy of the distributed optical fiber sensor according to the shape, size of the battery and the thermophysical properties of the key temperature monitoring areas.

[0085] For different batteries, as long as the layout can meet the requirements of temperature distribution monitoring, that is, ensure that the optical fiber can evenly cover the key areas of the battery surface to obtain comprehensive and continuous temperature information.

[0086] To enable those skilled in the art to more clearly understand the solution of this embodiment, the following layout solutions are exemplified.

[0087] As Figure 2 shown, in practical applications, for prismatic soft-pack batteries or batteries with a surface size larger than a set value, an S-shaped layout or other continuous layout solutions with reciprocating bends, such as zigzag or W-shaped, etc., are not exhaustively listed here.

[0088] For each type of battery, the spacing and bending amplitude of the reciprocating bend shape can be reasonably adjusted in advance through finite element simulation or experimental analysis, so that the optical fiber can effectively cover the key parts of the battery surface, such as the high-heat-generation areas near the positive and negative electrodes of the battery and the central area where heat dissipation of the battery is more difficult, to ensure obtaining comprehensive and representative temperature data.

[0089] For cylindrical batteries or other batteries with a circular shape, a spiral winding layout can be adopted to evenly cover the cylindrical surface of the battery to obtain the temperature changes in the circumferential and axial directions of the battery.

[0090] As Figure 3 shown, by winding the optical fiber in a spiral layout, the temperature differences at different heights and circumferential positions of the battery can be captured, providing accurate data support for the optimization of the battery thermal management system.

[0091] Similarly, the spacing and bending amplitude of the spiral shape can be reasonably determined in advance through finite element simulation or experimental analysis, so that the optical fiber can effectively cover the key parts of the battery surface or the temperature acquisition results are more reasonable.

[0092] For batteries with a large surface area, square batteries or battery modules, a grid layout can also be adopted to fully utilize the monitoring efficiency of distributed optical fiber sensors.

[0093] As Figure 4 in (a) and Figure 4 in (b) shown, an optical fiber network is constructed on the surface of a single battery or battery pack, and by utilizing the spatial division characteristics of the grid structure, the temperature changes in different areas of the battery surface can be accurately captured. This layout divides the battery surface into regular areas, which greatly facilitates the subsequent analysis and processing of temperature data in different areas.

[0094] Similarly, the size and spacing of the network can be reasonably determined in advance through finite element simulation or experimental analysis, so that the optical fiber can effectively cover the key parts of the battery surface or the temperature acquisition results are more reasonable.

[0095] Of course, in other embodiments, distributed optical fiber sensors can be arranged in other shapes. The general principle is that they can cover the entire surface of the battery at intervals. Additionally, the simpler and more regular the shape, the more beneficial it is for later mapping.

[0096] Similarly, in some other embodiments, the intervals of the distributed optical fiber sensors can be the same or different; there can also be curved parts, or a straight-line arrangement scheme can be adopted.

[0097] Next, preprocessing is performed.

[0098] In this embodiment, preprocessing is to accurately convert the wavelength data collected by the distributed optical fiber sensor into temperature data through a standardized calibration process. It includes optical fiber data analysis and calibration.

[0099] First of all, the goal of optical fiber data analysis is to accurately convert the physical relationship between the wavelength data collected by the distributed optical fiber sensor and the temperature change. The temperature measurement of the optical fiber sensor usually depends on the light scattering effects in the optical fiber (such as Brillouin scattering, Raman scattering, and Rayleigh scattering). These scattering effects will cause changes in the optical fiber wavelength, and there is a clear relationship between the wavelength change and the temperature change.

[0100] Taking Rayleigh scattering as an example, in an optical fiber sensor, the change in temperature will cause a frequency shift of the Rayleigh scattering signal in the optical fiber. This frequency shift has a linear relationship with the temperature change inside the optical fiber. By measuring this frequency shift, the temperature of different sensing units of the optical fiber can be deduced. Specifically, the relationship between the frequency shift caused by Rayleigh scattering and temperature can be expressed by the following formula:

[0101] ; (1)

[0102] In the formula, represents the Rayleigh scattering frequency shift, is the central wavelength of the optical fiber, and are the sensitivity coefficients of temperature and strain respectively, indicating the influence of temperature and strain changes on the optical fiber scattering wavelength, and are the change amounts of temperature and strain respectively.

[0103] For an optical fiber sensor coated with perfluoroalkoxy resin, it shields the influence of strain on the optical fiber scattering wavelength. Therefore, the relationship between the frequency shift caused by Rayleigh scattering and temperature can be expressed as:

[0104] ; (2)

[0105] Of course, in other embodiments, other existing algorithms can be selected for temperature data conversion according to the type of distributed optical fiber sensor. For example, for an optical frequency domain reflectometry distributed optical fiber sensor, the contrast between the local reference signal and the measurement signal can be utilized, and the spectrum determined by the optical scattering effect can be used to calculate the temperature at the corresponding position. Details are not elaborated herein.

[0106] Then, it is necessary to calibrate the optical fiber measurement results. The measurement results of the optical fiber sensor may be affected by environmental factors, optical fiber layout, and material differences. Therefore, these deviations must be eliminated through the calibration process.

[0107] In this embodiment, the specific steps of calibration are as follows: By comparing the optical fiber sensor with a high-precision temperature sensor (such as a thermocouple), an accurate relationship between wavelength and temperature is obtained, that is, to determine .

[0108] Next, spatial mapping of the optical fiber data is performed. The purpose of this step is to accurately map the one-dimensional temperature data collected by the optical fiber to the two-dimensional plane / three-dimensional space of the battery through an accurate spatial mapping algorithm.

[0109] In this embodiment, a spatial mapping algorithm based on feature points is adopted. As Figure 5 shown, taking the S-shaped optical fiber arrangement as an example, the specific process of the spatial mapping algorithm based on feature points includes:

[0110] (1) The optical fiber path in the S-shaped arrangement consists of multiple straight lines and arcs. The key positions of the optical fiber path are calibrated by feature points (such as A, B, C, D, E, F, G, H). The coordinates of these feature points are used to determine the optical fiber arrangement path and provide a stable reference for subsequent mapping.

[0111] (2) For a straight line segment, taking the AB segment as an example, first, it is necessary to determine the actual optical fiber lengths and corresponding to points A and B. Then, the data within the interval , are extracted and saved to a separate file. Further, the interpolation algorithm is used to uniformly map all the measured point data of the optical fiber within the ranges of and to the AB segment. Specifically, the linear interpolation algorithm is adopted to convert each sensing unit data between and into the corresponding coordinates on the AB line segment according to the proportional relationship of its optical fiber length within the interval , .

[0112] For other straight line segments, the same method applies. Through this method, the temperature data within each straight line segment can be accurately mapped onto the battery surface, thus providing precise data support for subsequent temperature distribution estimation.

[0113] (3) For the arc segment, first determine the central angle and radius of the arc, and calculate the distribution of the optical fiber within this arc segment through the arc length formula. Then, use the interpolation algorithm to evenly distribute all the optical fiber sensing unit data within the arc segment according to the proportion of the arc length, ensuring that the data of each measurement point can be accurately mapped onto the corresponding arc path on the battery surface.

[0114] Through the above steps, the data of each sensing unit of the optical fiber can be accurately mapped in space on the two-dimensional plane of the battery.

[0115] For example Figure 3 For the spiral arrangement shown, the data mapping scheme is as follows:

[0116] (1) Determine the basic parameters of the spiral

[0117] including: the radius of the projection circle of the spiral on the horizontal plane r ; the height that the spiral rises in the vertical direction for each revolution, i.e., the pitch p ; the total length of the spiral L .

[0118] (2) Calculate the parametric equation of the spiral

[0119] The spiral can be represented by the parametric equation:

[0120] ;

[0121] ;

[0122] ;

[0123] where t is the parameter, usually with a value range of [0, 2πn], and n is the number of turns of the spiral.

[0124] (3) Data mapping

[0125] (a) The arc length of the spiral s ( t ) can be calculated by integration:

[0126] ;

[0127] For the above parametric equation, it can be simplified to:

[0128] ;

[0129] (b)Assume the total length of the optical fiber is L , then the arc length corresponding to each data point is:

[0130] ;

[0131] Through reverse calculation, find the corresponding parameter :

[0132] ;

[0133] Then, substitute into the parametric equation of the helix to obtain the three-dimensional coordinates:

[0134] ;

[0135] ;

[0136] ;

[0137] Finally, each data on the optical fiber is mapped to a three-dimensional coordinate on the helix ( , , ).

[0138] Then, construct a battery electrochemical model.

[0139] In this embodiment, according to the actual usage scenario and performance requirements of the battery, an electrochemical model of one dimension, two dimensions or three dimensions can be flexibly selected for construction.

[0140] The one-dimensional model mainly considers the diffusion and reaction of lithium ions in the electrode thickness direction, while the two-dimensional and three-dimensional models can consider the distribution of lithium ions in the electrode plane and space. By solving the electrochemical equations, parameters such as the battery potential, current density, and lithium ion concentration can be obtained, and then the performance and life of the battery can be predicted.

[0141] The one-dimensional model is suitable for rapid simulation and analysis, especially suitable for the preliminary design and optimization stage; results can be obtained quickly, facilitating design and debugging.

[0142] The two-dimensional model is suitable for scenarios that require high-precision simulation, such as the design and optimization of battery management systems, and can provide more accurate internal information of the battery, suitable for occasions with high requirements for battery performance.

[0143] The electrochemical model fully considers the electrochemical reaction process, ion transport characteristics, and charge conduction law inside the battery, and can accurately calculate the heat generation during the charge and discharge process of the battery, providing heat source data for the subsequent construction of the battery thermal model.

[0144] The construction process of the electrochemical model and the calculation process of the heat source can both adopt existing solutions, and will not be introduced in detail here.

[0145] According to the actual size and shape of the battery, a fine geometric model of the battery is constructed using three-dimensional modeling technology. This model accurately reflects the external dimensions, internal structure, and morphological characteristics of the battery surface, providing a basis for subsequent thermal analysis.

[0146] Based on the law of conservation of energy and combined with the heat source calculated from the electrochemical model constructed above, a battery thermal model is constructed. This thermal model fully considers processes such as heat generation, heat conduction inside the battery, and heat exchange with the external environment, and can truly reflect the heat transfer law of the battery during operation.

[0147] Then, considering the temperature gradient differences at different positions on the battery surface, the battery surface is further divided into domains.

[0148] In this embodiment, according to the temperature gradients in the horizontal and vertical directions on the battery surface and the requirements for model accuracy, the battery surface is divided into multiple sub-domains. Through domain division, the heat exchange between different regions and the environment can be set separately, thereby improving the prediction accuracy of the thermal model.

[0149] Based on the sub-domains divided on the battery surface, the convective heat transfer coefficients of different sub-domains are calibrated using the temperature distribution data collected by the distributed fiber optic sensor.

[0150] Specifically, in this embodiment, the convective heat transfer coefficient is determined based on the inverse parameter identification of the finite element. The initial value of the convective heat transfer coefficient is estimated according to the natural cooling behavior of the battery, where the temperature change is recorded by the distributed fiber optic sensor. Then, the cooling process is simulated using the thermal model, and the initial value of the convective heat transfer coefficient is determined by matching the simulated and experimental temperature decay curves.

[0151] To further optimize the convective heat transfer coefficient, an iterative parameter identification process is adopted. This optimization process minimizes the difference between the temperature distribution predicted by the model and the fiber optic measurement values, and the objective function is defined as:

[0152] ;

[0153] In the formula represents the simulated temperature at measurement point i, while is the experimental temperature obtained from the distributed fiber optic sensor.

[0154] In this embodiment, the Levenberg-Marquardt algorithm is used to iteratively adjust the convective heat transfer coefficient until the temperature distribution predicted by the model converges within an acceptable error range of the experimental data.

[0155] Embodiment 2

[0156] To make the implementation of the technical solution of the present invention clearer to those skilled in the art, this embodiment is provided as a specific application example for description.

[0157] This embodiment takes a soft-pack battery with dimensions of 318mm×96mm×12mm as an example. This battery is a ternary lithium-ion battery with a nominal capacity of 80.4Ah. As Figure 6 shown, the distributed optical fiber sensor is arranged in an S shape, and thermocouples are arranged at the positive electrode tab, negative electrode tab, and the center position of the battery for subsequent calibration of optical fiber data.

[0158] The battery is charged and discharged at different rates, here 0.5C, 1C, and 1.5C are adopted. The charge-discharge conditions are constant current discharge and constant current constant voltage charging, and the battery is allowed to stand for 3 hours respectively after the discharge and charging are completed. During the charge-discharge process, battery voltage, current, and thermocouple temperature data are collected and stored. At the same time, the wavelength data of the optical fiber are collected and stored.

[0159] Based on the thermocouple data, the coefficients in Equation (2) are calibrated to obtain accurate temperature data based on optical fiber measurement. In this embodiment, the coefficient in Equation (2) = 11.5. As Figures 7 - 10 shown, Figure 7 (a) of Figure 8 (a) of Figure 9 (a) of Figure 10 (a) of Figure 7 (b) of Figure 8 (b) of Figure 9 (b) of Figure 10 (b) of

[0160] Figure 5 shown, the S-shaped optical fiber is divided into five segments, the straight segment AB, the arc segment BCD, the straight segment DE, the arc segment EFG, and the straight segment GH. Among them, there are 8 feature points (A, B, C, D, E, F, G, H) for positioning, and the coordinates of each feature point are shown in Table 1.

[0161] First, the path of the optical fiber in the two-dimensional plane can be determined through the coordinates in Table 1. Then, based on the actual lengths of each section of the optical fiber in Table 2, the optical fiber data of each section is extracted separately and stored in a file. Finally, the interpolation algorithm is used to uniformly map all the data points included in each section to the above five-section paths, thus completing the mapping from one-dimensional data to two-dimensional space.

[0162] It should be noted that the spatial resolution in each section of data can be set according to the accuracy requirements. In this embodiment, the spatial resolution of the optical fiber is 1.28 mm. In other words, in the above straight line segment AB, arc segment BCD, straight line segment DE, arc segment EFG, and straight line segment GH, there is a sensing unit every 1.28 mm.

[0163] Table 1 Characteristic Point Coordinates

[0164]

[0165] Table 2 Actual Lengths of Optical Fibers Corresponding to Each Section of the Path

[0166]

[0167] After completing the above steps, the temperature distribution diagrams as shown in (a)-(j) can be obtained. The figures show the battery surface temperature distributions corresponding to different SOCs during the 1C discharge process. Figure 11 Among them, (a) shows the temperature distribution of the battery when SOC = 1.0. Figure 11 Among them, (b) shows the temperature distribution of the battery when SOC = 0.9. Figure 11 Among them, (c) shows the temperature distribution of the battery when SOC = 0.8. Figure 11 Among them, (d) shows the temperature distribution of the battery when SOC = 0.7. Figure 11 Among them, (e) shows the temperature distribution of the battery when SOC = 0.6. Figure 11 Among them, (f) shows the temperature distribution of the battery when SOC = 0.5. Figure 11 Among them, (g) shows the temperature distribution of the battery when SOC = 0.4. Figure 11 Among them, (h) shows the temperature distribution of the battery when SOC = 0.3. Figure 11 Among them, (i) shows the temperature distribution of the battery when SOC = 0.2. Figure 11 Among them, (j) shows the temperature distribution of the battery when SOC = 0.1. Thus, the accurate analysis and two-dimensional visualization of the optical fiber data have been realized. Figure 11 Among them, (j) shows the temperature distribution of the battery when SOC = 0.1. So far, the accurate analysis and two-dimensional visualization of the optical fiber data have been realized.

[0168] Furthermore, a battery electrochemical model is constructed. In this embodiment, a 1D P2D model is adopted, and the relevant control equations and boundary conditions are as follows. The electrochemical model can provide heat sources for the thermal model, including ohmic heat, polarization heat, reaction heat, and tab heat generation.

[0169] The governing equation for charge conservation is:

[0170] ; (3)

[0171] where:

[0172] , ; (4)

[0173] ; (5)

[0174] ; (6)

[0175] where, is the effective solid-phase conductivity, is the solid-phase electric potential, is the solid-phase volume fraction, is the particle radius, is the electrolyte concentration, is the local current density, is the specific surface area, is the solid-phase Bruggeman coefficient, is the solid-phase conductivity, is the vector differential operator, is the effective liquid-phase conductivity, R is the gas constant, T is the temperature, F is the Faraday constant, is the activity coefficient, is the cation transference number, is the liquid-phase Bruggeman coefficient, is the liquid-phase volume fraction.

[0176] The governing equation for mass conservation is:

[0177] ; (7)

[0178] ; (8)

[0179] ; (9)

[0180] where, is the solid-phase concentration, r is the particle radial dimension, J is the lithium-ion flux, De is the liquid-phase diffusion coefficient, is the local current density.

[0181] The governing equation for electrode kinetics is:

[0182] ; (10)

[0183] ; (11)

[0184] ; (12)

[0185] wherein, is the overpotential, is the exchange current density, k is the reaction rate constant, , are the anodic / cathodic transfer coefficients respectively, is the surface lithium ion concentration, is the maximum lithium ion concentration, is the equilibrium potential.

[0186] The control equation for heat generation is:

[0187] ; (13)

[0188] ; (14)

[0189] ; (15)

[0190] ; (16)

[0191] wherein, is the ohmic heat, is the polarization heat, is the reaction heat, is the tab heat generation, is the tab resistance, is the equilibrium potential, is the electrode equilibrium potential.

[0192] The boundary conditions are:

[0193] ; (17)

[0194] ; (18)

[0195] ; (19)

[0196] ; (20)

[0197] ; (21)

[0198] wherein, is the negative electrode thickness, is the separator thickness, is the positive electrode thickness.

[0199] Furthermore, a battery thermal model is constructed. As Figure 12As shown, first, a three-dimensional geometric model of the battery is constructed according to the battery size. The three-dimensional geometric model includes the battery cell, the aluminum-plastic film, and the tab. Then, as shown in Equation (22), the heat conduction, heat generation, and heat exchange processes of the battery are described based on the energy conservation equation.

[0200] ;(22)

[0201] The heat exchange between the battery and the environment is described by Newton's cooling law, as shown in Equation (23).

[0202] ;(23)

[0203] Where is the density, is the specific heat capacity, is the thermal conductivity, is the heat loss, h is the convective heat transfer coefficient, is the ambient temperature, Q gen is the total heat generation.

[0204] As Figure 13 shown, in this embodiment, the battery surface is divided into 12 sub-domains with different heat transfer coefficients, and each sub-domain corresponds to a different convective heat transfer coefficient, reflecting the dynamic characteristics of heat exchange between different regions of the battery. This multi-domain thermal boundary condition modeling method can more accurately capture the temperature asymmetry and non-uniformity during the operation of the battery. The convective heat transfer coefficients of different sub-domains on the battery surface are shown in Table 3.

[0205] Table 3 Convective heat transfer coefficients corresponding to different sub-domains on the battery surface

[0206]

[0207] Based on the temperature data monitored by optical fiber, the convective heat transfer coefficients of 12 regions are calibrated. As Figure 14 shown, in this embodiment, 5 representative points monitored by optical fiber are used to calibrate the convective heat transfer coefficient. It should be noted that more points can be selected to calibrate the convective heat transfer coefficient according to the requirements of the model accuracy. In this embodiment, 5 representative points are used to illustrate the effectiveness of the method proposed by the present invention. Figure 15 The optical fiber monitored temperature curves and the model predicted temperature curves at 5 representative points during the 1C discharge process are compared. It can be seen that the model results are in good agreement with the experimental data at the five points. Figure 16 (a)-(f) of Figure 16 show the cloud maps of the battery temperature distribution predicted by the model under different SOCs. Among them, Figure 16 in (a) of Figure 16Figure (c) shows the temperature distribution of the battery when SOC = 0.6. Figure 16 Figure (d) shows the temperature distribution of the battery when SOC = 0.4. Figure 16 Figure (e) shows the temperature distribution of the battery when SOC = 0.2. Figure 16 Figure (f) shows the temperature distribution of the battery when SOC = 0. It can be seen that at different SOC levels, the model can well depict the non-uniform and asymmetric distribution of temperature during the battery discharge process. These results further illustrate that the method provided by the present invention can well predict the temperature distribution of the battery.

[0208] Embodiment 2

[0209] A battery temperature distribution estimation system includes:

[0210] A temperature acquisition module, configured to acquire distributed temperature data on the battery surface, preprocess the temperature data, and perform spatial mapping on the preprocessed temperature data.

[0211] The execution process of this module can specifically refer to the process of step S1 in Embodiment 1, which will not be elaborated here.

[0212] A coupled model construction module, configured to construct an electrochemical model, determine heat sources, establish a geometric model according to the morphological parameters of the battery, and couple to form a battery thermal model in combination with the determined heat sources.

[0213] The execution process of this module can specifically refer to the process of step S2 in Embodiment 1, which will not be elaborated here.

[0214] A temperature distribution estimation module, configured to consider the temperature gradient differences at different positions, divide the domain of the battery thermal model to form multiple subdomains, calibrate the convective heat transfer coefficients of different subdomains based on the spatial mapping results, and obtain the temperature distribution estimation result on the battery surface based on the calibrated convective heat transfer coefficients.

[0215] The execution process of this module can specifically refer to the process of step S3 in Embodiment 1, which will not be elaborated here.

[0216] It can be understood that the above-mentioned various units / modules can be separately or all combined into one or several other units / modules to form, or some of them can be further split into multiple smaller units with functional division to form, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application.

[0217] The above modules of this system are divided based on logical functions. In actual applications, the functions of one module can also be implemented by multiple modules, or the functions of multiple modules can be implemented by one module.

[0218] Similarly, in other embodiments of the present application, the system may also include other units / modules. In practical applications, these functions may also be assisted by other units and may be achieved through the cooperation of multiple units.

[0219] According to another embodiment of the present application, the system described in this embodiment can be constructed and the method of Embodiment 1 can be implemented by running a computer program (including program code) that can execute the respective steps involved in the method described in Embodiment 1 on a general computing device such as a computer including processing elements and storage elements such as a central processing unit (CPU), a random access memory (RAM), and a read-only memory (ROM). The computer program can be recorded on a computer-readable recording medium, for example, and loaded into the above computing device through the computer-readable recording medium and run therein.

[0220] Embodiment 3

[0221] A computer-readable storage medium for storing computer instructions, which when executed by a processor, complete the steps S1 - S3 in the method provided in Embodiment 1.

[0222] Embodiment 4

[0223] As Figure 17 shown, an electronic device includes a processor 1001, a communication interface 1002, and a computer-readable storage medium 1003. Among them, the processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 can be connected through a bus or other means.

[0224] Among them, the communication interface 1002 is used to receive and send data. The computer-readable storage medium 1003 can be stored in the memory of the electronic device. The computer-readable storage medium 1003 is used to store a computer program, and the computer program includes program instructions. The processor 1001 is used to execute the program instructions stored in the computer-readable storage medium 1003.

[0225] The processor 1001 (or CPU (Central Processing Unit, central processor)) is the computing core and control core of the electronic device, and is adapted to implement one or more instructions. Specifically, it is adapted to load and execute one or more instructions to implement the corresponding method flow or corresponding function.

[0226] The processor 1001 is configured to execute the following process:

[0227] Obtain the distributed temperature data on the battery surface, preprocess the temperature data, and perform spatial mapping on the preprocessed temperature data.

[0228] Construct an electrochemical model, determine the heat source, establish a geometric model according to the morphological parameters of the battery, and couple with the determined heat source to form a battery thermal model.

[0229] Considering the temperature gradient differences at different positions, divide the battery thermal model into domains to form multiple sub-domains. Based on the spatial mapping results, calibrate the convective heat transfer coefficients of different sub-domains, and obtain the estimated temperature distribution results on the battery surface based on the calibrated convective heat transfer coefficients.

[0230] Example Five

[0231] A computer program product, the computer program product includes instructions, when the program is executed by at least one data processing device, the instructions cause the data processing device to implement the steps of the method provided in Example One.

[0232] Example Six

[0233] An electric vehicle, including a vehicle body, the vehicle body includes a battery, a distributed optical fiber sensor, and a battery management system, wherein:

[0234] The battery is used to provide energy for the electric vehicle;

[0235] The distributed optical fiber sensor is used to obtain optical fiber data;

[0236] The battery management system is used to perform preprocessing and perform spatial mapping on the preprocessed optical fiber data;

[0237] Construct an electrochemical model, determine the heat source, establish a geometric model according to the morphological parameters of the battery, and couple with the determined heat source to form a battery thermal model;

[0238] Considering the temperature gradient differences at different positions, divide the battery thermal model into domains to form multiple sub-domains. Based on the spatial mapping results, calibrate the convective heat transfer coefficients of different sub-domains, and obtain the estimated temperature distribution results on the battery surface based on the calibrated convective heat transfer coefficients;

[0239] Or,

[0240] The battery management system includes the battery temperature distribution estimation system provided in Example Two;

[0241] Or,

[0242] The battery management system is equipped with the computer-readable storage medium provided in Example Three or the computer program product provided in Example Five;

[0243] Or,

[0244] The battery management system includes the electronic device provided in the fourth embodiment.

[0245] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the present invention. Any modifications, equivalent replacements, improvements, etc. made by those skilled in the art without creative efforts within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A battery temperature distribution estimation method, characterized in that: The following steps are involved: Obtain distributed temperature data on the battery surface, preprocess the temperature data, and perform spatial mapping on the preprocessed temperature data; Construct an electrochemical model, determine the heat source, establish a geometric model based on the battery's morphological parameters, combine the determined heat source, and couple to form a battery thermal model; According to the temperature gradient difference, the battery thermal model is divided into multiple sub-domains. Based on the spatial mapping results, the convection heat transfer coefficients of different sub-domains are calibrated. Based on the calibrated convection heat transfer coefficients, the temperature distribution estimation result of the battery surface is obtained. In obtaining distributed temperature data on the battery surface, optical fiber data is obtained using distributed optical fiber sensors, which are arranged on the battery surface in a reciprocating manner, or arranged on the battery surface in a spiral interval, or arranged on the battery surface in a grid interval; A distributed optical fiber sensor is provided within the setting range of the positive and negative poles of the battery, and / or within the setting range of the center position of the battery; The process of spatial mapping the pre-processed optical fiber data includes: dividing the optical fiber layout path into multiple parts, each part is a straight line segment or a circular arc, and using the end points and / or the center points of each part as feature points to calibrate the key positions of the optical fiber path; For the straight line segment, all the corresponding optical fiber data within the straight line segment are evenly mapped to the straight line segment using the interpolation algorithm; For the arc part, determine the center angle and radius of the arc, calculate the distribution of the optical fiber in the arc segment using the arc length formula, and use the interpolation algorithm to evenly distribute all the optical fiber data in the arc segment according to the proportion of the arc length and map it to the arc path corresponding to the battery surface; The process of building an electrochemical model includes: The process of spatial mapping the pre-processed optical fiber data includes: if the distributed optical fiber sensor is arranged in a spiral line, determining the basic parameters of the spiral line, including the projection circle radius of the spiral line on the horizontal plane 、 Pitch and total length of the helix ， Calculate the parametric equation of the spiral, use integration to calculate the arc length of the spiral, and then determine the arc length corresponding to each data point, perform reverse calculations, and determine the three-dimensional coordinates of the data point on the spiral; Based on the spatial mapping results, the process of calibrating the convective heat transfer coefficients of different subdomains includes: comparing the temperature data obtained from the optical fiber data of different subdomains with the calculation results of the battery thermal model, and adjusting and optimizing the convective heat transfer coefficients of the corresponding subdomains according to the difference.

2. A battery temperature distribution estimation method as claimed in claim 1, characterized in that: The pre-processing process includes: converting the wavelength data of the optical fiber into temperature data according to the relationship between the frequency shift caused by Rayleigh scattering and the temperature; The converted temperature data is compared with the temperature data of the corresponding position detected by the temperature sensor to obtain the accurate relationship between the wavelength and the temperature, and the converted temperature data is calibrated.

3. A battery temperature distribution estimation method as claimed in claim 1, characterized in that: The process of constructing an electrochemical model includes: according to the actual usage scenario and performance requirements of the battery, choosing to construct a one-dimensional, two-dimensional or three-dimensional electrochemical model, wherein the electrochemical model is used to calculate the heat generated by the battery during the charging and discharging process, and the heat generated includes ohmic heat, polarization heat, reaction heat and tab heat.

4. A battery temperature distribution estimation method as claimed in claim 1, characterized in that: According to the morphological parameters of the battery, the process of establishing a geometric model includes: according to the actual size and shape of the battery, using three-dimensional modeling to construct a geometric model of the battery, the geometric model is used to reflect the battery's external dimensions, internal structure and morphological characteristics of the battery surface.

5. A battery temperature distribution estimation method as claimed in claim 1, characterized in that: The battery thermal model is divided into domains according to the temperature gradient difference. The process of forming multiple subdomains includes: dividing the battery surface into multiple subdomains according to the lateral and longitudinal temperature gradients on the battery surface and the model accuracy requirements. Each subdomain corresponds to a different convection heat transfer coefficient to reflect the dynamic characteristics of heat exchange between different areas of the battery. The heat exchange between each subdomain and the environment can be set separately.

6. A battery temperature distribution estimation method as claimed in claim 1, characterized in that: The process of calibrating the convective heat transfer coefficients of different subdomains includes: recording the temperature change of the battery according to the distributed optical fiber sensor, simulating the cooling process using the battery thermal model, and determining the initial value of the convective heat transfer coefficient by matching the experimental temperature decay curve; Taking the minimization of the square value of the difference between the temperature distribution predicted by the model and the actual value obtained as the objective function, an iterative optimization is performed to adjust the convective heat transfer coefficient until the temperature distribution predicted by the model converges to the set threshold.

7. A battery temperature distribution estimation system, characterized in that: include: A temperature acquisition module is configured to acquire distributed temperature data on the battery surface, preprocess the temperature data, and perform spatial mapping on the preprocessed temperature data; A coupled model building module is configured to build an electrochemical model, determine the heat source, establish a geometric model based on the morphological parameters of the battery, and combine the determined heat source to form a battery thermal model; The temperature distribution estimation module is configured to divide the battery thermal model into multiple sub-domains according to the temperature gradient difference, calibrate the convection heat transfer coefficients of different sub-domains based on the spatial mapping result, and obtain the temperature distribution estimation result of the battery surface based on the calibrated convection heat transfer coefficient; In obtaining distributed temperature data on the battery surface, optical fiber data is obtained using distributed optical fiber sensors, which are arranged on the battery surface in a reciprocating manner, or arranged on the battery surface in a spiral interval, or arranged on the battery surface in a grid interval; A distributed optical fiber sensor is provided within the setting range of the positive and negative poles of the battery, and / or within the setting range of the center position of the battery; The process of spatial mapping the pre-processed optical fiber data includes: dividing the optical fiber layout path into multiple parts, each part is a straight line segment or a circular arc, and using the end points and / or the center points of each part as feature points to calibrate the key positions of the optical fiber path; For the straight line segment, all the corresponding optical fiber data within the straight line segment are evenly mapped to the straight line segment using the interpolation algorithm; For the arc part, determine the center angle and radius of the arc, calculate the distribution of the optical fiber in the arc segment using the arc length formula, and use the interpolation algorithm to evenly distribute all the optical fiber data in the arc segment according to the proportion of the arc length and map it to the arc path corresponding to the battery surface; The process of building an electrochemical model includes: The process of spatial mapping the pre-processed optical fiber data includes: if the distributed optical fiber sensor is arranged in a spiral line, determining the basic parameters of the spiral line, including the projection circle radius of the spiral line on the horizontal plane 、 Pitch and total length of the helix ， Calculate the parametric equation of the spiral, use integration to calculate the arc length of the spiral, and then determine the arc length corresponding to each data point, perform reverse calculations, and determine the three-dimensional coordinates of the data point on the spiral; Based on the spatial mapping results, the process of calibrating the convective heat transfer coefficients of different subdomains includes: comparing the temperature data obtained from the optical fiber data of different subdomains with the calculation results of the battery thermal model, and adjusting and optimizing the convective heat transfer coefficients of the corresponding subdomains according to the difference.

8. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the steps of the method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the steps in the method according to any one of claims 1 to 6 are completed.

10. A computer program product, characterized in that The computer program product comprises instructions which, when the program is executed by at least one data processing device, cause the data processing device to carry out the steps of the method according to any one of claims 1 to 6.

11. An electric vehicle, characterized in that: The vehicle body includes a battery, a distributed optical fiber sensor and a battery management system, wherein: The battery is used to provide energy for electric vehicles; The distributed optical fiber sensor is used to obtain optical fiber data; The battery management system comprises the battery temperature distribution estimation system according to claim 7.

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