Battery temperature distribution estimation method and system, storage medium and electronic equipment

Abstract

The invention belongs to the technical field of temperature distribution estimation, and provides a battery temperature distribution estimation method and system, a storage medium and electronic equipment, and the method comprises the steps: obtaining the distributed temperature data of the surface of a battery, carrying out the preprocessing of the temperature data, and carrying out the spatial mapping of the preprocessed temperature data; constructing an electrochemical model, determining a heat source, establishing a geometric model according to the morphological parameters of the battery, and coupling to form a battery thermal model in combination with the determined heat source; and according to the temperature gradient difference, performing domain division on the battery thermal model to form a plurality of sub-domains, based on a space mapping result, calibrating convective heat transfer coefficients of different sub-domains, and based on the calibrated convective heat transfer coefficients, obtaining a temperature distribution estimation result of the battery surface. According to the method, the spatial resolution and the real-time performance of the surface temperature of the battery are remarkably improved, thermal runaway of the battery can be effectively prevented, and the problem of poor precision in the prior art is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of temperature distribution estimation, and in particular relates to a battery temperature distribution estimation method, system, storage medium and electronic equipment. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

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

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

[0005] In actual applications, whether it is energy storage or electric vehicles, battery cells (such as lithium batteries) and various areas of the battery influence each other. Electrochemical reactions occur inside the battery when it is working, and the electrical heat is unevenly distributed. If the temperature of each part of the battery cannot be dynamically and accurately detected, and the temperature of each part cannot be accurately controlled, it will cause local overheating of the battery, and the thermal management strategy will not match, triggering further reaction 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 temperature distribution of existing methods is poor, especially during the operation of the battery, the spatial resolution and real-time performance of the surface temperature cannot meet the needs of efficient management and safety monitoring. Summary of the invention

[0007] In order to solve the above problems, the present invention proposes a battery temperature distribution estimation method, system, storage medium and electronic device. The present invention obtains temperature through distributed optical fiber sensors, maps the optical fiber data, associates it with various positions 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 calibrates the convective heat transfer coefficient of each area on the battery surface in combination with the mapping results, thereby achieving accurate estimation / prediction of temperature distribution, significantly improving the spatial resolution and real-time performance of the battery surface temperature, and can effectively prevent battery thermal runaway.

[0008] According to some embodiments, the present invention adopts the following technical solutions: A battery temperature distribution estimation method comprises the following steps: 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; Considering the temperature gradient differences at different positions, the battery thermal model is divided into multiple subdomains. Based on the spatial mapping results, the convective heat transfer coefficients of different subdomains are calibrated. Based on the calibrated convective heat transfer coefficients, the temperature distribution estimation result of the battery surface is obtained.

[0009] As an optional implementation, in obtaining distributed temperature data on the battery surface, optical fiber data is obtained using a distributed optical fiber sensor, and the distributed optical fiber sensor is 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; Distributed optical fiber sensors are provided within the set range of the positive and negative poles of the battery, and / or within the set range of the center position of the battery.

[0010] As an optional 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 the temperature; Or further, the converted temperature data is compared with the temperature data of the corresponding position detected by the temperature sensor to obtain an accurate relationship between the wavelength and the temperature, and the converted temperature data is calibrated.

[0011] As an optional implementation, the process of spatially mapping the preprocessed optical fiber data includes: dividing the optical fiber layout path into a plurality of parts, each part being a straight line segment or a circular arc, and using the endpoints 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.

[0012] As an optional implementation, 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 calculation to determine the three-dimensional coordinates of the data point on the spiral.

[0013] As an optional implementation, the process of constructing an electrochemical model includes: according to the actual usage scenario and performance requirements of the battery, selecting to construct a one-dimensional, two-dimensional or three-dimensional electrochemical model, wherein the electrochemical model calculates the heat generated by the battery during the charging and discharging process according to the battery type, the electrochemical reaction process, ion transfer characteristics and charge conduction laws inside the battery of this type, and the heat generated includes ohmic heat, polarization heat, reaction heat and tab heat.

[0014] As an optional implementation, the process of establishing a geometric model based on the morphological parameters of the battery includes: constructing a geometric model of the battery using three-dimensional modeling based on the actual size and shape of the battery, wherein the geometric model reflects the battery's external dimensions, internal structure, and morphological characteristics of the battery surface.

[0015] As an optional implementation, considering the temperature gradient differences at different locations, the battery thermal model is domain-divided to form multiple sub-domains. The process includes: dividing the battery surface into multiple sub-domains based on the lateral and longitudinal temperature gradients on the battery surface and the model accuracy requirements, each sub-domain corresponds to a different convection heat transfer coefficient to reflect the dynamic characteristics of heat exchange between different areas of the battery, and the heat exchange between each sub-domain and the environment can be set separately.

[0016] As an optional implementation, 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.

[0017] As an optional implementation, 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.

[0018] A battery temperature distribution estimation system, comprising: 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 consider the temperature gradient differences at different locations, divide the battery thermal model into multiple subdomains, calibrate the convective heat transfer coefficients of different subdomains based on the spatial mapping results, and obtain the temperature distribution estimation result of the battery surface based on the calibrated convective heat transfer coefficients.

[0019] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the steps in the above method are completed.

[0020] A 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 according to the above method.

[0021] An electronic device comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps in the above method are completed.

[0022] An electric vehicle includes a vehicle body, 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 is used to perform preprocessing and spatial mapping of the preprocessed optical fiber 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; Considering the temperature gradient differences at different locations, the battery thermal model is divided into multiple subdomains. Based on the spatial mapping results, the convective heat transfer coefficients of different subdomains are calibrated. Based on the calibrated convective heat transfer coefficients, the temperature distribution estimation result of the battery surface is obtained. or, The battery management system includes the above-mentioned battery temperature distribution estimation system; or, The battery management system is equipped with the above-mentioned computer-readable storage medium; or, The battery management system comprises the above electronic device.

[0023] Compared with the prior art, the present invention has the following beneficial effects: The present invention utilizes distributed optical fiber sensors to dynamically acquire the temperature of the battery surface. The optical fiber can evenly cover the key areas of the battery surface to obtain comprehensive and continuous temperature information. The layout scheme can flexibly determine the strategy based on the shape, size and thermal physical properties of the key areas of temperature monitoring of the battery.

[0024] The present invention utilizes a feature point positioning method and a spatial mapping algorithm to accurately map the one-dimensional temperature data collected by the optical fiber to the two-dimensional plane / three-dimensional space of the battery, thereby realizing precise spatial mapping of the battery temperature. When performing battery thermal management, the temperature of different areas can be displayed more accurately and intuitively.

[0025] The present invention can flexibly choose to construct a one-dimensional, two-dimensional or three-dimensional electrochemical model according to the actual usage scenarios and performance requirements of the battery, and fully consider the electrochemical reaction process, ion transmission characteristics and charge conduction laws inside the battery. It can accurately calculate the heat generated by the battery during the charging and discharging process, provide heat source data, and ensure the accuracy of subsequent thermal management.

[0026] The present invention constructs a battery thermal model with multi-domain convective thermal boundaries, fully considering the processes of heat generation, heat conduction inside the battery, and heat exchange with the external environment. It can truly reflect the heat transfer law of the battery during operation, and combined with multi-domain division, improves the prediction accuracy of the model.

[0027] The present invention takes into account the possible heat flow non-uniformity on the battery surface by adjusting and optimizing the convection heat transfer coefficient of each sub-domain, so that the predicted temperature distribution meets the accuracy requirement.

[0028] The present invention has a wide range of applications and can be applied to both power batteries and energy storage batteries. In actual application, it can dynamically and timely monitor the temperature conditions of various areas on the battery surface, especially key areas, 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.

[0029] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0031] Figure 1 is a schematic flow chart of a battery temperature distribution estimation method according to an embodiment; Figure 2 is a schematic diagram of a distributed optical fiber sensor arrangement scheme for a soft-pack battery according to an embodiment; Figure 3 is a schematic diagram of a distributed optical fiber sensor arrangement scheme for a cylindrical battery according to an embodiment; Figure 4 The schematic diagram of the distributed optical fiber sensor arrangement scheme of a square battery and a battery pack in one embodiment, wherein (a) is the arrangement scheme of a square battery cell, and (b) is the arrangement scheme of a battery pack; Figure 5 is a schematic diagram of spatial mapping feature points of an S-shaped optical fiber arrangement solution according to an embodiment; Figure 6 This is a physical diagram of an S-shaped arrangement of a distributed optical fiber sensor for a soft-pack battery according to an embodiment; Figure 7 The diagram is a schematic diagram of a 0.5C charge and discharge process of an embodiment, wherein (a) is the temperature data obtained by the thermocouple and the 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; Figure 8 The diagram is a schematic diagram of a 1C charge and discharge process of an embodiment, wherein (a) is the temperature data obtained by the thermocouple and the optical fiber during the 1C charge and discharge process, and (b) is the voltage and current data during the 1C charge and discharge process; Fig. 9 The diagram is a schematic diagram of a 1.25C charge and discharge process of an embodiment, wherein (a) is the temperature data obtained by the thermocouple and the 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; Fig.10 1.5C charging and discharging process schematic diagram of an embodiment, wherein (a) is the temperature data obtained by the thermocouple and the optical fiber during the 1.5C charging and discharging process, and (b) is the voltage and current data during the 1.5C charging and discharging process; Fig.11 The present invention is a schematic diagram of the temperature distribution of a battery at different SOCs during a 1C discharge process of an embodiment, wherein (a) is the temperature distribution of the battery at SOC=1.0, (b) is the temperature distribution of the battery at SOC=0.9, (c) is the temperature distribution of the battery at SOC=0.8, (d) is the temperature distribution of the battery at SOC=0.7, (e) is the temperature distribution of the battery at SOC=0.6, (f) is the temperature distribution of the battery at SOC=0.5, (g) is the temperature distribution of the battery at SOC=0.4, (h) is the temperature distribution of the battery at SOC=0.3, (i) is the temperature distribution of the battery at SOC=0.2, and (j) is the temperature distribution of the battery at SOC=0.1; Fig.12 is a schematic diagram of a three-dimensional geometric model of a battery in an embodiment; Fig.13 is a schematic diagram of a multi-domain thermal boundary of an embodiment; Fig.14 is a schematic diagram of the positions of five representative temperature points of an embodiment; Fig.15 A comparison between a model-predicted temperature curve and an optical fiber-monitored temperature curve in an embodiment; Fig.16 is a schematic diagram of temperature distribution predicted by a model of an embodiment, wherein (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; Fig.17 A schematic diagram of an electronic device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0032] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0033] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0034] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit 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, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0035] In the absence of conflict, the embodiments in this application and the features in the embodiments may be combined with each other.

[0036] Embodiment 1 A battery temperature distribution estimation method, such as Figure 1 As shown, the following steps are included: S1 obtains distributed temperature data on the battery surface, preprocesses the temperature data, and performs spatial mapping on the preprocessed temperature data; S2 builds an electrochemical model, determines the heat source, establishes a geometric model based on the battery's morphological parameters, combines the determined heat source, and couples to form a battery thermal model; S3 considers the temperature gradient differences at different locations and divides the battery thermal model into multiple subdomains. Based on the spatial mapping results, the convective heat transfer coefficients of different subdomains are calibrated. Based on the calibrated convective heat transfer coefficients, the temperature distribution estimation result of the battery surface is obtained.

[0037] It should be noted that batteries generally refer to electrochemical batteries, such as lithium-ion batteries.

[0038] The details of each step are described below.

[0039] First, obtain the fiber data.

[0040] In this embodiment, optical fiber data is obtained by distributed optical fiber sensors. In this embodiment, the layout scheme is not limited, and the layout scheme can flexibly design the distributed optical fiber sensor layout strategy according to the shape and size of the battery and the thermal physical characteristics of the key temperature monitoring area.

[0041] For different batteries, as long as the layout plan can meet the needs 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.

[0042] In order to enable those skilled in the art to more clearly understand the solution of this embodiment, the following arrangement solution is given as an example.

[0043] like Figure 2 As shown, in actual applications, for rectangular soft-pack batteries, or batteries with surface dimensions greater than a set value, an S-shaped arrangement or other reciprocatingly curved continuous arrangement schemes, such as a zigzag shape, a W shape, etc., can be adopted, which are not exhaustively listed here.

[0044] For each type of battery, the spacing and bending amplitude of the reciprocating bending 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 areas close to the positive and negative poles of the battery and the central area where the heat dissipation of the battery is more difficult, ensuring the acquisition of comprehensive and representative temperature data.

[0045] For cylindrical batteries or other circular-shaped batteries, a spiral winding arrangement can be used to evenly cover the cylindrical surface of the battery to obtain the circumferential and axial temperature changes of the battery.

[0046] like Figure 3 As shown, by spirally winding the optical fiber, the temperature differences of the battery at different heights and circumferential positions can be captured, providing accurate data support for the optimization of the battery thermal management system.

[0047] Similarly, the spiral spacing and bending amplitude 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.

[0048] For batteries with larger surface areas, square batteries or battery modules, a grid arrangement can also be used to give full play to the monitoring efficiency of distributed fiber optic sensors.

[0049] like Figure 4 (a) and Figure 4 As shown in (b), a crisscross optical fiber network is constructed on the surface of a single cell or battery pack. The spatial division characteristics of the grid structure can accurately capture the temperature changes in different areas of the battery surface. This arrangement divides the battery surface into regular partitions, greatly facilitating the subsequent analysis and processing of temperature data in different areas.

[0050] 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.

[0051] Of course, in other embodiments, other shapes can be used to arrange distributed light sensors, and the general principle is that they can cover the entire surface of the battery at intervals. In addition, the simpler and more regular the shape, the more beneficial it is for later mapping.

[0052] Similarly, in some other embodiments, the intervals between the distributed light sensors may be the same or different; there may be curved portions, or all may be arranged in a straight line.

[0053] Next, preprocessing is performed.

[0054] In this embodiment, the preprocessing is to accurately convert the wavelength data collected by the distributed optical fiber sensor into temperature data through a standardized calibration process, including optical fiber data analysis and calibration.

[0055] First, the goal of fiber optic data analysis is to accurately convert the physical relationship between wavelength data collected by distributed fiber optic sensors and temperature changes. Temperature measurement by fiber optic sensors usually relies on light scattering effects in the optical fiber (such as Brillouin scattering, Raman scattering, and Rayleigh scattering). These scattering effects will cause changes in the wavelength of the optical fiber, and there is a clear relationship between wavelength changes and temperature changes.

[0056] Taking Rayleigh scattering as an example, in a fiber optic sensor, temperature changes will cause the Rayleigh scattering signal in the fiber to shift in frequency. This frequency shift has a linear relationship with the temperature change inside the fiber. By measuring this frequency shift, the temperature of different sensing units of the fiber can be inferred. Specifically, the relationship between the frequency shift caused by Rayleigh scattering and temperature can be expressed by the following formula: ; (1) In the formula, represents the Rayleigh scatter frequency shift, is the fiber center wavelength, and are the sensitivity coefficients of temperature and strain, respectively, which represent the influence of temperature and strain changes on the scattering wavelength of the optical fiber. and are the changes in temperature and strain, respectively.

[0057] For the optical fiber sensor coated with perfluoroalkoxy resin, the effect of strain on the scattering wavelength of the optical fiber is shielded, so the relationship between the frequency shift caused by Rayleigh scattering and temperature can be expressed as: ; (2) Of course, in other embodiments, other existing algorithms can be selected to convert temperature data according to the type of distributed optical fiber sensor, such as optical frequency domain reflectance distributed optical fiber sensor, which can compare the local reference signal with the measurement signal and use the spectrum determined by the light scattering effect to calculate the temperature at the corresponding position. No further details will be given here.

[0058] Then, the fiber optic measurement results need to be calibrated. The measurement results of the fiber optic sensor can be affected by environmental factors, fiber layout and material differences, so these deviations must be eliminated through the calibration process.

[0059] In this embodiment, the specific steps of calibration are: 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, determining the relationship between wavelength and temperature in equation (2) .

[0060] 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 into the two-dimensional plane / three-dimensional space of the battery through an accurate spatial mapping algorithm.

[0061] This embodiment adopts a spatial mapping algorithm based on feature points, such as Figure 5 As shown, taking the S-shaped optical fiber arrangement as an example, the specific process of the spatial mapping algorithm based on feature points includes: (1) The S-shaped fiber path is composed of multiple straight lines and arcs, and the key positions of the fiber path are calibrated by feature points (such as A, B, C, D, E, F, G, and H). The coordinates of these feature points are used to determine the fiber layout path and provide a stable benchmark for subsequent mapping.

[0062] (2) For a straight line segment, taking segment AB as an example, we first need to determine the actual fiber lengths corresponding to points A and B. and . Then, the interval [ , ] and save it to a separate file. and All the measurement point data within the range are evenly mapped to the AB segment. Specifically, a linear interpolation algorithm is used to and Each sensor unit data between [ , ]The relative position ratio in the interval is converted into the corresponding coordinates on the AB segment.

[0063] The same method is applicable to other straight line segments. By this method, the temperature data in each straight line segment can be accurately mapped on the battery surface, thereby providing accurate data support for subsequent temperature distribution estimation.

[0064] (3) For the arc segment, first determine the center angle and radius of the arc, and calculate the distribution of the optical fiber in the arc segment using the arc length formula. Then, use the interpolation algorithm to evenly distribute the data of all optical fiber sensor units in the arc segment according to the proportion of the arc length, ensuring that the data of each measurement point can be accurately mapped to the arc path corresponding to the battery surface.

[0065] Through the above steps, the data of each sensor unit of the optical fiber can be accurately spatially mapped on the two-dimensional plane of the battery.

[0066] For Figure 3 The data mapping scheme for the spiral arrangement shown is as follows: (1) Determine the basic parameters of the helix Includes: The radius of the spiral projection circle on the horizontal plane r ; The height that the helix rises vertically every time it rotates one circle, i.e. the pitch p ; Total length of the helix L .

[0067] (2) Calculate the parametric equation of the helix The helix can be expressed by a parametric equation: ; ; ; Here, t is a parameter, usually in the range of [0, 2πn], and n is the number of turns of the helix.

[0068] (3) Data Mapping (a) Arc length of the helix s ( t ) can be calculated by integration: ; For the above parametric equation, it can be simplified to: ; (b) Assume that the total length of the optical fiber is L , then the arc length corresponding to each data point is: ; By reverse calculation, find the corresponding parameters : ; Then, Substituting the parametric equation of the helix, we get the three-dimensional coordinates: ; ; ; Ultimately, each data point on the optical fiber is mapped to a three-dimensional coordinate on the helix ( , , ).

[0069] Then, build a battery electrochemical model.

[0070] In this embodiment, according to the actual usage scenario and performance requirements of the battery, a one-dimensional, two-dimensional or three-dimensional electrochemical model can be flexibly selected to be constructed.

[0071] The one-dimensional model mainly considers the diffusion and reaction of lithium ions in the thickness direction of the electrode, 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 equation, the battery's potential, current density, lithium ion concentration and other parameters can be obtained, and the battery's performance and life can be predicted.

[0072] The one-dimensional model is suitable for rapid simulation and analysis, especially for the preliminary design and optimization stages; it can quickly obtain results and facilitate design and debugging.

[0073] The ‌Two-Dimensional Model‌ is suitable for scenarios that require high-precision simulation, such as battery management system design and optimization. It can provide more accurate internal information of the battery and is suitable for situations with high requirements for battery performance.

[0074] The electrochemical model fully considers the electrochemical reaction process, ion transport characteristics and charge conduction laws inside the battery, and can accurately calculate the heat generated by the battery during the charging and discharging process, providing heat source data for the subsequent construction of the battery thermal model.

[0075] The process of constructing the electrochemical model and calculating the heat source can both use existing solutions, which will not be introduced in detail here.

[0076] According to the actual size and shape of the battery, a fine geometric model of the battery is constructed using 3D modeling technology. This model accurately reflects the battery's external dimensions, internal structure, and morphological characteristics of the battery surface, providing a basis for subsequent thermal analysis.

[0077] Based on the law of conservation of energy and the heat source calculated by the electrochemical model constructed above, a battery thermal model is constructed. This thermal model fully considers the heat generation, heat conduction and heat exchange with the external environment inside the battery, and can truly reflect the heat transfer law of the battery during operation.

[0078] Then, considering the temperature gradient differences at different locations on the battery surface, the battery surface is further divided into domains.

[0079] In this embodiment, the battery surface is divided into multiple subdomains according to the transverse and longitudinal temperature gradients of the battery surface and the requirements for model accuracy. 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.

[0080] Based on the sub-domains divided into battery surfaces, the convective heat transfer coefficients of different sub-domains are calibrated using the temperature distribution data collected by distributed optical fiber sensors.

[0081] Specifically, in this embodiment, the convective heat transfer coefficient is determined based on the inverse identification of finite element parameters. The initial value of the convective heat transfer coefficient is estimated based on the natural cooling behavior of the battery, where the temperature change is recorded by a distributed optical fiber sensor. The cooling process is then simulated using a thermal model, and the initial value of the convective heat transfer coefficient is determined by matching the simulated and experimental temperature decay curves.

[0082] To further optimize the convective heat transfer coefficient, an iterative parameter identification process was used. This optimization process minimizes the difference between the temperature distribution predicted by the model and the fiber measurement, and the objective function is defined as: ; In the formula represents the simulated temperature at the measuring point i, and is the experimental temperature obtained from the distributed fiber optic sensor.

[0083] 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 to an acceptable error range of the experimental data.

[0084] Embodiment 2 In order to make the implementation of the technical solution of the present invention more clear to those skilled in the art, this embodiment is provided as a specific application example for description.

[0085] This embodiment takes a 318mm×96mm×12mm soft-pack battery as an example. The battery is a ternary lithium-ion battery with a nominal capacity of 80.4Ah. Figure 6 As shown, the distributed optical fiber sensor is arranged in an S shape, and thermocouples are arranged at the positive pole ear, negative pole ear and center of the battery for subsequent optical fiber data calibration.

[0086] The battery is charged and discharged at different rates, here 0.5C, 1C and 1.5C are used. The charging and discharging conditions are constant current discharge and constant current constant voltage charging. The battery is left to stand for 3 hours after discharge and charging. The battery voltage, current and thermocouple temperature data are collected and stored during the charging and discharging process. At the same time, the wavelength data of the optical fiber is collected and stored.

[0087] Based on the thermocouple data, the coefficients in formula (2) are calibrated to obtain accurate temperature data based on optical fiber measurement. In this embodiment, the coefficients in formula (2) are = 11.5. Figure 7-10 As shown, Figure 7 (a) Figure 8 (a) Fig. 9 (a) and Fig.10 (a) shows the fiber temperature data and thermocouple temperature data converted based on formula (2) at 0.5C, 1C, 1.25C and 1.5C respectively. Figure 7 (b) Figure 8 (b) Fig. 9 (b) and Fig.10 (b) shows the battery voltage and current data at 0.5C, 1C, 1.25C and 1.5C respectively. It can be seen that the calibrated temperature data is highly consistent with the thermocouple data.

[0088] A spatial mapping algorithm based on feature points is used to map the one-dimensional optical fiber data to the two-dimensional battery surface. Figure 5As shown, the S-shaped optical fiber is divided into five segments, straight segment AB, arc segment BCD, straight segment DE, arc segment EFG, and straight segment GH. There are 8 feature points (A, B, C, D, E, F, G, H) used for positioning, and the coordinates of each feature point are shown in Table 1.

[0089] First, the path of the optical fiber on the two-dimensional plane can be determined by the coordinates in Table 1. Then, based on the actual length of each segment of optical fiber in Table 2, the optical fiber data of each segment is extracted and stored in a file. Finally, the interpolation algorithm is used to evenly map all the data points contained in each segment to the above five paths, thereby completing the mapping of one-dimensional data to two-dimensional space.

[0090] It should be noted that the spatial resolution of each segment 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-mentioned 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.

[0091] Table 1 Feature point coordinates

[0092] Table 2 The actual length of optical fiber corresponding to each path segment

[0093] After completing the above steps, you can get Fig.11 The temperature distribution diagrams shown in (a)-(j) show the battery surface temperature distribution corresponding to different SOCs during 1C discharge. Fig.11 (a) is the temperature distribution of the battery when SOC=1.0. Fig.11 (b) is the temperature distribution of the battery when SOC=0.9. Fig.11 (c) is the temperature distribution of the battery when SOC=0.8. Fig.11 (d) is the temperature distribution of the battery when SOC=0.7. Fig.11 (e) is the temperature distribution of the battery when SOC=0.6. Fig.11 (f) is the temperature distribution of the battery when SOC=0.5, Fig.11 (g) is the temperature distribution of the battery when SOC=0.4, Fig.11 (h) is the temperature distribution of the battery when SOC=0.3, Fig.11 (i) is the temperature distribution of the battery when SOC=0.2, Fig.11 (j) is the temperature distribution of the battery when SOC=0.1. So far, accurate analysis and two-dimensional visualization of optical fiber data have been achieved.

[0094] Further, a battery electrochemical model is constructed. This embodiment adopts a 1D P2D model, 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 heat generated by the tabs.

[0095] The governing equation for charge conservation is: ; (3) in: , ; (4) ; (5) ; (6) in, is the solid phase effective conductivity, is the solid phase 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 conductivity of the liquid phase, R is the gas constant, T is the temperature, F is the Faraday constant, is the activity coefficient, is the cation migration number, is the liquid phase Bruggeman coefficient, is the liquid volume fraction.

[0096] The governing equation for mass conservation is: ; (7) ; (8) ; (9) in, is the solid phase concentration, r is the radial dimension of the particle, J is the lithium ion flux, De is the liquid phase diffusion coefficient, is the local current density.

[0097] The governing equation for the electrode kinetics is: ; (10) ; (11) ; (12) in, is the overpotential, is the exchange current density, k is the reaction rate constant, , are the anode / cathode transfer coefficients, is the surface lithium ion concentration, is the maximum lithium ion concentration, is the equilibrium potential.

[0098] The governing equation for heat generation is: ; (13) ; (14) ; (15) ; (16) in, For Ohmic heat, is the polarization heat, is the reaction heat, To generate heat for the ear, is the lug resistance, is the equilibrium potential, Balance the potential of the electrodes.

[0099] The boundary conditions are: ; (17) ; (18) ; (19) ; (20) ;(twenty one) in, is the thickness of the negative electrode, is the diaphragm thickness, is the thickness of the positive electrode.

[0100] Furthermore, a battery thermal model is constructed. Fig.12 As shown in Figure 2, firstly, a three-dimensional geometric model of the battery is constructed according to the battery size. The three-dimensional geometric model includes the battery cell, aluminum-plastic film and the tab. Then, as shown in Equation (22), the heat conduction, heat generation and heat exchange process of the battery are described based on the energy conservation equation.

[0101] ;(twenty two) The heat exchange between the battery and the environment is described by Newton's law of cooling, as shown in Equation (23).

[0102] ;(twenty three) in, is the density, is the specific heat capacity, is the thermal conductivity, is the heat loss, h is the convection heat coefficient, is the ambient temperature, Q gen Total heat production.

[0103] like Fig.13 As shown, in this embodiment, the battery surface is divided into 12 subdomains with different heat transfer coefficients, and each subdomain corresponds to a different convective heat transfer coefficient, reflecting the dynamic characteristics of heat exchange between different areas of the battery. This multi-domain thermal boundary condition modeling method can more accurately capture the temperature asymmetry and non-uniformity during battery operation. The convective heat transfer coefficients of different subdomains on the battery surface are shown in Table 3.

[0104] Table 3 Convective heat transfer coefficients corresponding to different sub-domains on the battery surface

[0105] The convective heat transfer coefficients of 12 areas were calibrated based on the temperature data monitored by optical fiber. Fig.14 As shown, in this embodiment, five representative points of optical fiber monitoring 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 for the accuracy of the model. In this embodiment, five representative points are used to illustrate the effectiveness of the method proposed in the present invention. Fig.15 The fiber-optic monitoring temperature curves at five representative points during the 1C discharge process were compared with the model-predicted temperature curves. It can be seen that the model results are in good agreement with the experimental data at all five points. Fig.16 (a)-(f) show the battery temperature distribution cloud diagram predicted by the model under different SOCs, where: Fig.16 (a) is the temperature distribution of the battery when SOC=1.0. Fig.16 (b) is the temperature distribution of the battery when SOC=0.8. Fig.16 (c) is the temperature distribution of the battery when SOC=0.6. Fig.16 (d) is the temperature distribution of the battery when SOC=0.4. Fig.16 (e) is the temperature distribution of the battery when SOC=0.2. Fig.16 (f) is the temperature distribution of the battery when SOC = 0. It can be seen that under different SOCs, the model can well describe the non-uniform and asymmetric distribution of temperature during battery discharge. These results further illustrate that the method provided by the present invention can well predict the temperature distribution of the battery.

[0106] Embodiment 2 A battery temperature distribution estimation system, comprising: 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; The execution process of this module can be specifically referred to the process of step S1 in the first embodiment, which will not be repeated here; 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 execution process of this module can be specifically referred to the process of step S2 in the first embodiment, which will not be repeated here; The temperature distribution estimation module is configured to consider the temperature gradient differences at different locations, divide the battery thermal model into multiple subdomains, calibrate the convective heat transfer coefficients of different subdomains based on the spatial mapping results, and obtain the temperature distribution estimation result of the battery surface based on the calibrated convective heat transfer coefficients.

[0107] The execution process of this module may refer to the process of step S3 of the first embodiment for details, which will not be repeated here.

[0108] It can be understood that the above-mentioned units / modules can be separately or completely combined into one or several other units / modules, or one (some) of the units can be further divided into multiple functionally smaller units, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application.

[0109] The above modules of the system are divided based on logical functions. In practical applications, the function of one module can also be implemented by multiple modules, or the functions of multiple modules can be implemented by one module.

[0110] Similarly, in other embodiments of the present application, the system may also include other units / modules. In actual applications, these functions may also be implemented with the assistance of other units, and may be implemented by collaboration of multiple units.

[0111] 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) capable of executing the steps involved in the corresponding method described in Embodiment 1 on a general-purpose computing device such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, and loaded into the above-mentioned computing device through the computer-readable recording medium and run therein.

[0112] Embodiment 3 A computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, steps S1-S3 in the method provided in embodiment 1 are completed.

[0113] Embodiment 4 like Fig.17 As shown, an electronic device includes a processor 1001, a communication interface 1002, and a computer-readable storage medium 1003. The processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 may be connected via a bus or other means.

[0114] 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, the computer program includes program instructions, and the processor 1001 is used to execute the program instructions stored in the computer-readable storage medium 1003.

[0115] The processor 1001 (or CPU (Central Processing Unit)) is the computing core and control core of the electronic device, which is suitable for implementing one or more instructions, and specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions.

[0116] The processor 1001 is configured to execute the following process: 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; Considering the temperature gradient differences at different positions, the battery thermal model is divided into multiple subdomains. Based on the spatial mapping results, the convective heat transfer coefficients of different subdomains are calibrated. Based on the calibrated convective heat transfer coefficients, the temperature distribution estimation result of the battery surface is obtained.

[0117] Embodiment 5 A 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 according to the first embodiment.

[0118] Embodiment 6 An electric vehicle includes a vehicle body, 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 is used to perform preprocessing and spatial mapping of the preprocessed optical fiber 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; Considering the temperature gradient differences at different locations, the battery thermal model is divided into multiple subdomains. Based on the spatial mapping results, the convective heat transfer coefficients of different subdomains are calibrated. Based on the calibrated convective heat transfer coefficients, the temperature distribution estimation result of the battery surface is obtained. or, The battery management system includes the battery temperature distribution estimation system provided in the second embodiment; or, The battery management system is equipped with the computer-readable storage medium provided in the third embodiment or the computer program product provided in the fifth embodiment; or, The battery management system includes the electronic device provided in the fourth embodiment.

[0119] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principle of the present invention without creative labor shall be included in 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 subdomains. Based on the spatial mapping results, the convective heat transfer coefficients of different subdomains are calibrated. Based on the calibrated convective heat transfer coefficients, the temperature distribution estimation result of the battery surface is obtained.

2. A battery temperature distribution estimation method as claimed in claim 1, characterized in that: 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; Distributed optical fiber sensors are provided within the set range of the positive and negative poles of the battery, and / or within the set range of the center position of the battery.

3. 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; Or further, the converted temperature data is compared with the temperature data of the corresponding position detected by the temperature sensor to obtain an accurate relationship between the wavelength and the temperature, and the converted temperature data is calibrated.

4. A battery temperature distribution estimation method as claimed in claim 1, characterized in that: 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.

5. A battery temperature distribution estimation method as claimed in claim 1, characterized in that: 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 to determine the three-dimensional coordinates of the data point on the spiral.

6. 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.

7. 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.

8. 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.

9. A battery temperature distribution estimation method as claimed in claim 1, characterized in that: 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.

10. 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.

11. 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 convective heat transfer coefficients of different sub-domains based on the spatial mapping results, and obtain the temperature distribution estimation result of the battery surface based on the calibrated convective heat transfer coefficients.

12. 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 10.

13. 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 10 are completed.

14. 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 10.

15. 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 is used to perform preprocessing and spatial mapping of the preprocessed optical fiber 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. or, The battery management system comprises the battery temperature distribution estimation system according to claim 11; or, The battery management system is equipped with a computer-readable storage medium according to claim 12, or stores / equipped with a computer program product according to claim 14; or, The battery management system comprises the electronic device according to claim 13.

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