Lithium ion battery pack internal heat source positioning method, system, equipment and medium

Abstract

The invention belongs to the technical field of battery thermal management, and particularly relates to a lithium ion battery pack internal heat source positioning method, system, device and medium, the method comprises the following steps: obtaining measured data through a distributed temperature sensor, after median filtering and normalization preprocessing, constructing an improved three-dimensional thermal diffusion model in combination with battery parameters, and calculating the internal heat source of a lithium ion battery pack; according to the model, a Fourier heat conduction equation is innovatively combined with a convective heat transfer boundary condition, and the internal heat conduction characteristic of the battery is accurately represented. The thermal diffusion model is solved through spatial discretization processing of the battery pack to obtain a simulated temperature field, a mean square error of actually measured and simulated data is used as a target function, and the position of a heat source is iteratively determined by adopting an optimization algorithm. According to the invention, the problems of insufficient precision and poor anti-interference capability of a traditional heat source positioning method are solved, high-precision and high-efficiency heat source positioning is realized, and reliable technical support is provided for battery thermal safety management.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery thermal management, and in particular relates to a method, system, device and medium for locating a heat source inside a lithium-ion battery pack. Background Art

[0002] In new energy vehicles, thermal management of power batteries is crucial for ensuring performance, lifespan, and safety. Batteries generate heat during charging and discharging. If this heat cannot be dissipated promptly, it can lead to localized overheating and, in turn, thermal runaway. Accurately and quickly locating heat sources within the battery pack is crucial for implementing effective heat dissipation measures and diagnosing faults.

[0003] Traditional heat source localization methods are primarily based on Fourier thermal diffusion models or direct measurements using temperature sensor arrays. The Fourier model describes heat propagation through the heat conduction equation, but it assumes continuous and uniform heat transfer, making it difficult to accurately reflect the localized non-uniform heat transfer characteristics within the battery caused by factors such as module gaps and contact thermal resistance. Additionally, some studies have attempted to incorporate the threshold concept of the HK (Hegselmann-Krause) model, determining the significance of heat exchange by setting a temperature difference threshold. However, when combined with the Fourier model, problems such as insufficient integration and low computational efficiency have limited practical application. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, system, device and medium for locating heat sources inside a lithium-ion battery pack to solve the problems of inaccurate positioning and poor anti-interference ability of traditional thermal diffusion models under complex working conditions.

[0005] The present invention achieves the above-mentioned purpose through the following technical solutions: In a first aspect, the present invention provides a method for locating a heat source inside a lithium-ion battery pack, the method comprising: Acquire measured data from distributed temperature sensors in the battery pack, perform median filtering and normalization on the measured data, and form first data to be processed; Combined with the battery pack's self-set parameters, a three-dimensional heat diffusion model is constructed based on Fourier's heat conduction law. The three-dimensional heat diffusion model includes the heat diffusion equation and convection heat transfer boundary conditions. Discretizing the three-dimensional space of the battery pack, converting the thermal diffusion model into a system of linear equations and solving them to obtain second processed data simulating the temperature distribution within the battery pack; An objective function is established based on the mean square error of the first data to be processed and the second data to be processed, an optimization algorithm is used to iterate the heat source position, and a heat source positioning result is output when the objective function converges.

[0006] Furthermore, obtaining measured data of distributed temperature sensors in the battery pack includes: Obtaining and parsing the original message data of the battery pack to obtain the original data of the battery pack; wherein the original message data includes the temperature value of each sampling point in the battery pack; After cleaning and sorting the original data, the discharge segments are obtained; When the data volume of a single discharge segment meets the set data volume threshold, the measured data T is selected from the target segment. measured .

[0007] Furthermore, the battery pack self-set parameters are combined to construct a three-dimensional heat diffusion model based on Fourier's heat conduction law. The three-dimensional heat diffusion model includes a heat diffusion equation and convection heat transfer boundary conditions, including: Obtain the battery pack's self-set parameters, including battery thermal conductivity k, specific heat capacity C, density ρ, convection heat transfer coefficient h between the battery and the environment, and ambient temperature T env ; Based on the self-set parameters and Fourier's heat conduction law, a three-dimensional heat diffusion equation is established as follows: in, Indicates the rate of change of temperature at a certain point inside the battery per unit time; The convection heat transfer boundary condition is introduced to simulate the heat exchange between the battery surface and the external environment, as shown in the following formula: in, is the derivative of the temperature along the normal direction outside the boundary, and T represents the current temperature of a point on the battery surface.

[0008] Furthermore, the three-dimensional space of the battery pack is discretized, and the heat diffusion model is converted into a linear equation system for solution to obtain second processed data simulating the temperature distribution in the battery pack, including: The three-dimensional space of the battery pack is divided into a finite number of unit cells. A corresponding discrete equation is established for each unit cell according to the heat diffusion equation and boundary conditions to form a linear equation system. Solve the linear equations to obtain the temperature value of each unit cell, which is integrated into the second to-be-processed data T for simulating the temperature distribution in the battery pack. model (x,y), (x,y) represents the position coordinates of a point on the battery surface.

[0009] Furthermore, before establishing the objective function using the mean square error of the first data to be processed and the second data to be processed, the method further includes: Determine the possible distribution area of ​​the heat source based on the working status of the battery pack. When the battery is in a discharging state, set the initial heat source position to the area around the negative electrode ear. When the battery is in a charging state, set the initial heat source position to the area around the positive electrode ear. Or determine the high-incidence area of ​​thermal runaway based on the historical failure data of the battery pack, and use the coordinates of this area as the initial heat source position. Or set the coordinates of the geometric center point of the battery pack as the initial heat source position.

[0010] Furthermore, the objective function is established by using the mean square error of the first data to be processed and the second data to be processed , as follows: Where M represents the number of temperature sensors; Represents the location coordinates of the i-th temperature sensor.

[0011] Furthermore, the optimization algorithm is used to iterate the heat source position, and when the objective function converges, the heat source positioning result is output, including: Taking the initial heat source position as the iterative starting point, the objective function is optimized using the gradient descent method. , update the heat source position coordinates; When the objective function value is less than the first threshold or the iterative change of the heat source position coordinate is less than the second threshold, it is considered that the iteration has converged and the heat source positioning result is obtained.

[0012] In a second aspect, the present invention provides a lithium-ion battery pack internal heat source location system, which is used to perform the steps of the lithium-ion battery pack internal heat source location method as described above. The system includes: a data acquisition and preprocessing module, configured to obtain measured data from distributed temperature sensors in the battery pack, perform median filtering and normalization on the measured data, and form first data to be processed; A modeling and calculation module is used to build a three-dimensional heat diffusion model based on Fourier's heat conduction law in combination with the battery pack's self-set parameters. The three-dimensional heat diffusion model includes the heat diffusion equation and convection heat transfer boundary conditions; a discrete solution module, configured to discretize the three-dimensional space of the battery pack, convert the thermal diffusion model into a system of linear equations for solution, and obtain second processed data simulating the temperature distribution within the battery pack; The optimization positioning module is used to establish an objective function based on the mean square error of the first data to be processed and the second data to be processed, iterate the heat source position using an optimization algorithm, and output the heat source positioning result when the objective function converges.

[0013] In a third aspect, the present invention proposes an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for locating a heat source inside a lithium-ion battery pack as described above are implemented.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for locating a heat source inside a lithium-ion battery pack as described above.

[0015] The beneficial effects of the present invention are: 1. This invention proposes combining Fourier's law of heat conduction with convective heat transfer boundary conditions to establish a three-dimensional heat diffusion model that is more consistent with actual physical properties. This model not only considers the heat conduction process, but also fully accounts for the convective heat transfer effect between the battery surface and the environment. It can more accurately simulate the complex non-uniform heat transfer phenomenon inside the battery pack, fundamentally solving the problem of traditional models' insufficient consideration of local heat exchange characteristics.

[0016] 2. This invention achieves rapid and accurate heat source location by combining discretization processing and optimization algorithms. Spatial discretization transforms a continuous problem into a computable discrete form, while the gradient descent optimization algorithm ensures efficient iterative solution of the heat source location. This combination not only ensures computational efficiency and meets real-time requirements, but also significantly improves positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a flow chart of a method for locating a heat source inside a lithium-ion battery pack provided in an embodiment of the present application; Figure 2 A schematic diagram of another process of locating a heat source inside a lithium-ion battery pack according to an embodiment of the present application; Figure 3 A system block diagram of a lithium-ion battery pack internal heat source positioning system provided in an embodiment of the present application; Figure 4 This is a comparative schematic diagram of the heat source positioning in the case part of the specific implementation method of this application. DETAILED DESCRIPTION

[0018] The present application is described in further detail below in conjunction with the accompanying drawings. It is necessary to point out that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technicians in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0019] Currently, traditional heat source location methods are mostly based on the Fourier diffusion model or direct judgment of temperature sensor arrays. The Fourier diffusion model is a classic model that describes the propagation of heat inside an object. Its basic equation is: Where T is the temperature field, t is the time, α is the thermal diffusivity, is the gradient divergence (Laplacian operator). This model assumes that heat propagation is continuous and uniform, relying primarily on heat conduction. However, in actual battery packs, heat propagation can be affected by a variety of complex factors, such as air convection between battery modules and convective heat transfer between the battery and the environment. These factors limit the traditional Fourier diffusion model in simulating heat propagation within battery packs, resulting in insufficient positioning accuracy.

[0020] The HK model is often referred to as the Hegselmann-Krause model. Proposed by R. Hegselmann and U. Krause, the model was originally used to study the dynamics of opinion within social groups. The HK model assumes that each individual has an opinion value and that individuals influence each other. If the difference in opinion between two individuals is within a certain threshold, they influence each other; otherwise, they do not. By iteratively updating each individual's opinion, the HK model simulates the evolution of opinion within a group.

[0021] Drawing on the threshold concept of the HK model, this approach introduces a threshold to determine whether heat exchange between two regions is significant. Only when the temperature difference exceeds the threshold is the heat exchange considered. This approach can capture the local characteristics of heat propagation to a certain extent, but its advantages have not yet been fully realized when combined with the Fourier diffusion model, especially in the field of battery heat source localization, where effective integration and application are still lacking.

[0022] Heat transfer within a battery pack is not completely uniform and continuous, especially between modules or battery packs. Due to physical separation, contact thermal resistance, and other factors, heat exchange is often localized. The threshold concept introduced by the HK model effectively simulates this localized heat exchange. Significant heat transfer occurs only when the temperature difference exceeds a certain threshold, which is more consistent with actual physical phenomena.

[0023] like Figure 1-2 As shown, in order to address the practical defects of the above-mentioned prior art, the present application proposes a method for locating a heat source inside a lithium-ion battery pack in one embodiment, the method comprising the following steps: S1. Obtain measured data from distributed temperature sensors in the battery pack, perform median filtering and normalization on the measured data, and form first data to be processed.

[0024] In this embodiment, obtaining measured data from distributed temperature sensors in a battery pack includes: obtaining and parsing original message data from the battery pack, wherein the message needs to be parsed according to the data format and requirements of the national standard GB / T32960 to obtain original data from the battery pack; the original message data includes the temperature value of each sampling point in the battery pack; cleaning and sorting the original data to obtain discharge segments, wherein data cleaning includes cleaning duplicate data and empty data, and sorting is performed according to data acquisition time; when the data volume of a single discharge segment meets the set data volume threshold, the measured data T is selected from the target segment. measured .

[0025] It can be understood that the heat source positioning method proposed in this application can be applied to the vehicle battery management system to monitor and manage the heat source inside the battery pack. During specific implementation, the above-mentioned measured data is obtained according to the position of the preset distributed temperature sensor in the battery pack. However, when the data volume of a single discharge segment does not meet the set data volume threshold, the heat source identification and positioning is terminated.

[0026] S2. Combined with the self-set parameters of the battery pack, a three-dimensional heat diffusion model is constructed based on Fourier's heat conduction law. The three-dimensional heat diffusion model includes the heat diffusion equation and convection heat transfer boundary conditions.

[0027] In this embodiment, step S2 specifically includes: obtaining the battery pack self-setting parameters, including battery thermal conductivity k, specific heat capacity C, density ρ, convection heat transfer coefficient h between the battery and the environment, and ambient temperature T env ; Based on the self-set parameters and Fourier's heat conduction law, a three-dimensional heat diffusion equation is established as follows: in, Indicates the rate of change of temperature at a certain point inside the battery per unit time. is the gradient.

[0028] When building a three-dimensional thermal diffusion model, it is first necessary to obtain the key physical properties of the battery pack, including the inherent characteristic parameters of the battery material such as thermal conductivity k, specific heat capacity C, density ρ, as well as the convection heat transfer coefficient h and ambient temperature T that reflect the heat transfer characteristics between the battery and the environment. env , ambient temperature T env is the ambient temperature outside the battery pack, which is measured by corresponding environmental sensors in the actual operating environment. These parameters together constitute the basic data support for model establishment. Based on Fourier's law of heat conduction, the established three-dimensional heat diffusion equation fully describes the heat conduction process inside the battery. The left side of the equation characterizes the rate of change of heat energy per unit volume over time, the first term on the right side reflects the heat conduction process, and the second term takes into account the contribution of the internal heat source. In particular, the partial derivative of temperature with respect to time accurately describes the dynamic characteristics of the temperature change at each point inside the battery over time.

[0029] The convection heat transfer boundary condition is introduced to simulate the heat exchange between the battery surface and the external environment, as shown in the following formula: in, is the derivative of the temperature along the normal direction outside the boundary, and T represents the current temperature of a point on the battery surface.

[0030] This application introduces a convection heat transfer boundary condition. This boundary condition is established through Newton's law of cooling, which relates the heat flux density on the battery surface to the difference between the surface temperature and the ambient temperature. Among them, the derivative of the temperature along the normal direction outside the boundary accurately expresses the spatial distribution characteristics of the surface heat flux, while the surface temperature T dynamically reflects the heat exchange state between the battery and the environment. The setting of this boundary condition fully considers the complex heat exchange process between the battery surface and the surrounding environment in actual working conditions, including various heat exchange forms such as natural convection and forced convection, so that the model can more realistically simulate the thermal behavior of the battery in the actual working environment.

[0031] It should be noted that in the specific implementation process, the value of thermal conductivity k needs to take into account the anisotropic characteristics of the battery, which is usually obtained through experimental measurement; the convection heat transfer coefficient h needs to be dynamically adjusted according to the actual working environment of the battery pack (such as cooling method, wind speed, etc.). Ambient temperature T env Real-time monitoring data can be used to ensure the timeliness of model input parameters. This modeling approach based on physical laws, combined with precise parameter settings, enables the established thermal diffusion model to accurately reflect the heat transfer process within the battery pack.

[0032] S3. Discretize the three-dimensional space of the battery pack, convert the thermal diffusion model into a set of linear equations and solve them to obtain the second data to be processed that simulates the temperature distribution in the battery pack.

[0033] In this embodiment, the three-dimensional space of the battery pack is discretized, and the thermal diffusion model is converted into a linear equation system for solution to obtain the second data to be processed that simulates the temperature distribution in the battery pack. The method includes: dividing the three-dimensional space of the battery pack into a finite number of unit cells, establishing a corresponding discrete equation for each unit cell according to the thermal diffusion equation and boundary conditions to form a linear equation system; solving the linear equation system to obtain the temperature value of each unit cell, and integrating the obtained value into the second data to be processed T that simulates the temperature distribution in the battery pack. model (x,y), (x,y) represents the position coordinates of a point on the battery surface.

[0034] It is understood that the entire process of model simulation in this application locates the heat source by optimizing the objective function, which is constructed based on the heat diffusion equation and boundary conditions mentioned above. Therefore, visualizing the optimization process of the objective function is equivalent to calculating the temperature of each point on the plane based on the diffusion function and boundary conditions, and selecting the point that best meets the optimization conditions of the objective function among all the calculated results.

[0035] S4. Establish an objective function based on the mean square error of the first data to be processed and the second data to be processed, use an optimization algorithm to iterate the heat source position, and output the heat source positioning result when the objective function converges.

[0036] In this embodiment, before establishing the objective function with the mean square error of the first data to be processed and the second data to be processed, the method also includes: determining the possible distribution area of ​​the heat source based on the working state of the battery pack, when the battery is in a discharging state, setting the initial heat source position in the area around the negative electrode ear, and when the battery is in a charging state, setting the initial heat source position in the area around the positive electrode ear; or determining a high-incidence area of ​​thermal runaway based on historical failure data of the battery pack, and using the coordinates of this area as the initial heat source position; or setting the coordinates of the geometric center point of the battery pack as the initial heat source position.

[0037] In the heat source positioning optimization stage, it is first necessary to reasonably set the initial heat source position based on the battery working principle and engineering experience. Different initial position strategies are adopted for different battery working states: in the discharge state, since the area around the negative electrode ear is prone to generate large polarization heat and reaction heat due to the lithium ion embedding process, the initial heat source position is preset in this area; in the charging state, the area around the positive electrode ear becomes the main heat generation area due to the possible occurrence of side reactions such as lithium deposition, so this area is selected as the initial position. For battery packs with historical operating data, their thermal runaway event database can be called, and the coordinates of the historical high-heating area can be used as the initial value. This data-driven initialization method can significantly improve positioning efficiency. When there is a lack of prior information, a conservative strategy is adopted to set the geometric center of the battery pack as the initial position to ensure the universality of the algorithm.

[0038] The established objective function comprehensively considers the difference between the measured and simulated values ​​at all temperature sensor locations, and quantifies the degree of match between the two using the mean square error. In the specific implementation, the temperature difference at each sensor location is squared and then averaged. This processing method not only amplifies the contribution of significant differences, but also maintains the smoothness of the objective function, which is conducive to the convergence of the optimization algorithm. The calculation of the objective function requires the synchronous call of the pre-processed measured temperature data T measured And simulated temperature field data T model , where T model It will be recalculated based on the heat source position of the current iteration.

[0039] In this embodiment, the objective function is established by using the mean square error of the first data to be processed and the second data to be processed. , as follows: Where M represents the number of temperature sensors; Represents the location coordinates of the i-th temperature sensor.

[0040] In this embodiment, an optimization algorithm is used to iterate the heat source position, and the heat source positioning result is output when the objective function converges, including: taking the initial heat source position as the iterative starting point, and using the gradient descent method to optimize the objective function , update the heat source position coordinates; when the objective function value is less than the first threshold or the iterative change of the heat source position coordinates is less than the second threshold, it is considered that the iteration has converged and the heat source positioning result is obtained.

[0041] During the implementation of the optimization algorithm, the gradient descent method is used for iterative search. Each iteration includes three key steps: first, the heat diffusion model is solved based on the current heat source position to obtain the temperature field distribution; then, the objective function value and its partial derivatives with respect to each coordinate direction are calculated; finally, the heat source position coordinates are updated according to the negative gradient direction. To improve convergence efficiency, an adaptive step size adjustment mechanism is introduced. When the objective function decreases rapidly, the step size is increased, and when it approaches the optimal solution, the step size is reduced to improve positioning accuracy. The iterative termination condition is set as a composite criterion. When the objective function value is less than the set threshold or the coordinate change approaches zero in multiple consecutive iterations, it is judged to be converged and the final three-dimensional coordinates of the heat source are output. The entire optimization process realizes the organic combination of physical model and data-driven, which not only ensures the physical rationality of the positioning results, but also makes full use of the feedback information of the measured data.

[0042] In a specific embodiment, the present invention proposes a lithium-ion battery pack internal heat source positioning system, which is used to execute the steps of the lithium-ion battery pack internal heat source positioning method in the above embodiment. The system includes a data acquisition and preprocessing module, a modeling calculation module, a discrete solution module and an optimization positioning module.

[0043] The data acquisition and preprocessing module acquires measured data from distributed temperature sensors within the battery pack, performs median filtering and normalization on the data, and generates the first batch of processed data. This module directly interfaces with the battery management system (BMS) to obtain real-time data from distributed temperature sensors. The module's built-in filtering algorithm utilizes a sliding window mechanism to dynamically process continuously sampled temperature data, effectively eliminating pulse interference and random noise. The normalization process automatically identifies the temperature range and achieves standardized data conversion.

[0044] The modeling and calculation module is used to construct a three-dimensional heat diffusion model based on Fourier's law of heat conduction, combining the battery pack's self-defined parameters. The three-dimensional heat diffusion model includes a heat diffusion equation and convective heat transfer boundary conditions. It also includes a built-in parameter library for typical lithium-ion battery materials and supports user-defined parameter input. This module displays the three-dimensional battery pack geometry through a graphical interface, allowing engineers to intuitively set boundary conditions and initial conditions. In new energy vehicle applications, this module can receive real-time parameters such as ambient temperature and wind speed from the on-board environmental monitoring system and dynamically adjust the convective heat transfer coefficient to ensure that the model matches actual operating conditions.

[0045] The discrete solution module is used to discretize the three-dimensional space of the battery pack, convert the heat diffusion model into a set of linear equations for solution, and obtain the second data to be processed that simulates the temperature distribution in the battery pack; the optimization positioning module is used to establish an objective function based on the mean square error of the first data to be processed and the second data to be processed, and use the optimization algorithm to iterate the heat source position. When the objective function converges, the heat source positioning result is output.

[0046] The specific definition of the lithium-ion battery pack internal heat source location system can be found in the definition of the lithium-ion battery pack internal heat source location method described above, and will not be repeated here. It should be noted that each module in the above-mentioned location system corresponds to the implementation of steps S1 to S4 in the above-mentioned location method. The examples and application scenarios implemented by the multiple modules and corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiment.

[0047] This system can be deployed on a variety of hardware platforms: in vehicle-mounted scenarios, it operates as an enhanced BMS (Battery Management System) module running on an automotive-grade computing unit; in test and verification scenarios, it is integrated into the monitoring system of a battery test bench; and in data center environments, it serves as an analysis engine for cloud-based battery health management services. The system supports integration with digital twin platforms, visualizing positioning results and assisting engineers with thermal safety assessments and system optimization. With flexible configuration options, the system can adapt to the needs of lithium-ion battery applications of varying scales, from consumer electronics to grid-scale energy storage power plants.

[0048] In another embodiment of the present invention, an electronic device is proposed, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for locating the internal heat source of a lithium-ion battery pack as in the above embodiment are implemented.

[0049] In another embodiment of the present invention, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the method for locating a heat source inside a lithium-ion battery pack are implemented as in the above embodiment.

[0050] In order to more clearly illustrate the present invention and its advantages, the method provided by the present invention will be further explained below in conjunction with specific embodiments and related partial figures.

[0051] 1. Experimental conditions The experiment employed 20 temperature sensors evenly distributed around a circle with a radius of 5 meters. The initial sensor temperature was the ambient temperature of 25°C, and Gaussian noise with a standard deviation of 0.5°C was superimposed. Heat sources were randomly generated within the circle at 80°C, and the heat transfer between them and the sensors followed Fourier's law of heat conduction (an inverse square law). The physical model parameters included thermal conductivity k = 0.5 W / m·K, mass m = 0.1 kg, specific heat capacity C = 900 J / kg·K, ambient convection coefficient h = 10 W / m²·K, ambient temperature T_env = 25°C, and the HK model threshold ε = 3°C (heat exchange occurs only when the temperature difference is less than this value). The noise settings were designed with a standard deviation of 0.3°C for temperature measurement and 0.2°C for heat flow calculation. The localization algorithm uses the least squares method, employing the inverse square relationship between temperature and distance to construct a system of equations to solve for the heat source coordinates.

[0052] 2. Experimental Process The experiment compared two models: a pure Fourier diffusion model (constant heat exchange between all sensors) and a combined HK model (heat exchange occurs only when the temperature difference between adjacent sensors is less than 3°C). The simulation process involves initializing a random heat source location, updating the sensor temperature every time step dt = 0.01s, recording the positioning results every 10 steps, and terminating after 300 steps to output the final positioning error. In the visualization, red dots represent the actual heat source locations, blue triangles represent the sensor distribution, green and purple "X"s mark the final positioning points for the two models, respectively, and text boxes display the quantitative positioning error (in meters).

[0053] 3. Analysis of experimental results Experimental results show that the Fourier diffusion model has an error of approximately 7.77m, while the HK model has an error of approximately 0.92m, demonstrating that the HK model has higher positioning accuracy, thanks to its suppression of non-local heat exchange. While the pure Fourier model is susceptible to interference from long-range sensor noise, the HK model uses threshold filtering to better focus on localized high-temperature areas. Regarding dynamic characteristics, the positioning trajectory shows that the HK model converges faster, and the actual offset direction of the final positioning point from the heat source reflects the characteristics of the noise distribution.

[0054] IV. Conclusion The HK model effectively improves positioning accuracy by introducing a threshold mechanism. Limiting unnecessary heat exchange in noisy environments enhances system robustness. The least squares method can still achieve sub-meter positioning even with limited sensors. Future improvements include dynamic threshold adjustment and multi-heat source identification.

[0055] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part.

[0056] The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0057] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0058] In addition, the functional modules in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0059] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for locating heat sources inside a lithium-ion battery pack, characterized in that: The method comprises: Acquire measured data from distributed temperature sensors in the battery pack, perform median filtering and normalization on the measured data, and form first data to be processed; Combined with the battery pack's self-set parameters, a three-dimensional heat diffusion model is constructed based on Fourier's heat conduction law. The three-dimensional heat diffusion model includes the heat diffusion equation and convection heat transfer boundary conditions. Discretizing the three-dimensional space of the battery pack, converting the thermal diffusion model into a system of linear equations and solving them to obtain second processed data simulating the temperature distribution within the battery pack; An objective function is established based on the mean square error of the first data to be processed and the second data to be processed, an optimization algorithm is used to iterate the heat source position, and a heat source positioning result is output when the objective function converges.

2. The method for locating heat sources inside a lithium-ion battery pack according to claim 1, wherein: The obtaining of measured data from distributed temperature sensors in the battery pack includes: Obtaining and parsing the original message data of the battery pack to obtain the original data of the battery pack; wherein the original message data includes the temperature value of each sampling point in the battery pack; After cleaning and sorting the original data, the discharge segments are obtained; When the data volume of a single discharge segment meets the set data volume threshold, the measured data T is selected from the target segment. measured .

3. The method for locating heat sources inside a lithium-ion battery pack according to claim 2, wherein: The three-dimensional heat diffusion model is constructed based on the Fourier heat conduction law in combination with the self-set parameters of the battery pack. The three-dimensional heat diffusion model includes the heat diffusion equation and the convection heat transfer boundary conditions, including: Obtain the battery pack's self-set parameters, including battery thermal conductivity k, specific heat capacity C, density ρ, convection heat transfer coefficient h between the battery and the environment, and ambient temperature T env ; Based on the self-set parameters and Fourier's heat conduction law, a three-dimensional heat diffusion equation is established as follows: in, Indicates the rate of change of temperature at a certain point inside the battery per unit time; The convection heat transfer boundary condition is introduced to simulate the heat exchange between the battery surface and the external environment, as shown in the following formula: in, is the derivative of the temperature along the normal direction outside the boundary, and T represents the current temperature of a point on the battery surface.

4. The method for locating a heat source inside a lithium-ion battery pack according to claim 3, wherein: The discretization of the three-dimensional space of the battery pack and the conversion of the thermal diffusion model into a linear equation system for solution to obtain second processed data simulating the temperature distribution in the battery pack include: The three-dimensional space of the battery pack is divided into a finite number of unit cells. A corresponding discrete equation is established for each unit cell according to the heat diffusion equation and boundary conditions to form a linear equation system. Solve the linear equations to obtain the temperature value of each unit cell, which is integrated into the second to-be-processed data T for simulating the temperature distribution in the battery pack. model (x,y), (x,y) represents the position coordinates of a point on the battery surface.

5. The method for locating heat sources inside a lithium-ion battery pack according to claim 4, wherein: Before establishing the objective function based on the mean square error of the first data to be processed and the second data to be processed, the method further includes: Determine the possible distribution area of ​​the heat source based on the working status of the battery pack. When the battery is in a discharging state, set the initial heat source position to the area around the negative electrode ear. When the battery is in a charging state, set the initial heat source position to the area around the positive electrode ear. Or determine the high-incidence area of ​​thermal runaway based on the historical failure data of the battery pack, and use the coordinates of this area as the initial heat source position. Or set the coordinates of the geometric center point of the battery pack as the initial heat source position.

6. The method for locating heat sources inside a lithium-ion battery pack according to claim 5, wherein: The objective function is established by using the mean square error of the first data to be processed and the second data to be processed , as follows: Where M represents the number of temperature sensors; Represents the location coordinates of the i-th temperature sensor.

7. The method for locating heat sources inside a lithium-ion battery pack according to claim 6, wherein: The optimization algorithm is used to iterate the heat source position, and when the objective function converges, the heat source positioning result is output, including: Taking the initial heat source position as the iterative starting point, the objective function is optimized using the gradient descent method. , update the heat source position coordinates; When the objective function value is less than the first threshold or the iterative change of the heat source position coordinate is less than the second threshold, it is considered that the iteration has converged and the heat source positioning result is obtained.

8. A lithium-ion battery pack internal heat source positioning system, used to perform the steps of the lithium-ion battery pack internal heat source positioning method according to any one of claims 1 to 7, characterized in that: The system comprises: a data acquisition and preprocessing module, configured to obtain measured data from distributed temperature sensors in the battery pack, perform median filtering and normalization on the measured data, and form first data to be processed; A modeling and calculation module is used to build a three-dimensional heat diffusion model based on Fourier's heat conduction law in combination with the battery pack's self-set parameters. The three-dimensional heat diffusion model includes the heat diffusion equation and convection heat transfer boundary conditions; a discrete solution module, configured to discretize the three-dimensional space of the battery pack, convert the thermal diffusion model into a system of linear equations for solution, and obtain second processed data simulating the temperature distribution within the battery pack; The optimization positioning module is used to establish an objective function based on the mean square error of the first data to be processed and the second data to be processed, iterate the heat source position using an optimization algorithm, and output the heat source positioning result when the objective function converges.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for locating a heat source inside a lithium-ion battery pack according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that A computer program is stored, and when the computer program is executed by a processor, the steps of the method for locating a heat source inside a lithium-ion battery pack according to any one of claims 1 to 7 are implemented.

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