Methods and platforms for optimizing the thermal cycling performance of power tool battery packs

Abstract

This application provides a method and platform for optimizing the thermal cycling performance of power tool battery packs, relating to the field of battery technology. The method includes: obtaining structural characteristics from the basic configuration information of the power tool; deploying a temperature sensor array based on the battery pack's structural characteristics to determine the real-time temperature field; constructing a thermal management module by dividing the thermal cycling structure into main channels and microchannels and introducing flow resistance elements; deploying verification checkpoints at the sensor data interface to pre-verify the risk of thermal runaway in the real-time temperature field, transmitting the data back to the thermal management module for temperature control decisions and determining the thermal cycling strategy; and executing the thermal cycling strategy according to the power tool's thermal management system to regulate the battery pack temperature. This application solves the technical problem of insufficient thermal management accuracy in existing technologies due to a lack of refined analysis of the battery pack structure and thermal cycling characteristics, improving the accuracy and efficiency of battery pack thermal cycling control, thereby enhancing battery pack safety and extending its service life.

Description

Technical Field

[0001] This application relates to the field of battery technology, specifically to a method and platform for optimizing the thermal cycling performance of power tool battery packs. Background Technology

[0002] Power tool battery packs are prone to generating a lot of heat when working under high load for a long time. If the heat cannot be dissipated in a timely and effective manner, it may lead to overheating, performance degradation, or even thermal runaway and other safety problems.

[0003] Currently, thermal management technologies for power tool battery packs primarily rely on physical cooling methods such as air cooling, liquid cooling, heat pipe cooling, and phase change material (PCM) thermal management, or combinations thereof. These methods are typically based on fixed cooling parameters or pre-designed schemes, resulting in limitations in heat dissipation capabilities. The battery pack contains various thermal channels and structural regions, leading to complex and variable heat distribution and flow. Especially under high loads, temperature changes within the battery pack can be very rapid. Existing cooling methods cannot flexibly adjust to the real-time thermal state and structural characteristics of the battery pack, resulting in low overall cooling efficiency. In practical applications, the temperature in some areas may not be effectively controlled, leading to localized overheating and consequently, performance degradation or safety hazards in individual battery cells. Furthermore, these methods lack monitoring and real-time response mechanisms for internal temperature changes within the battery pack, failing to promptly identify and address abnormal temperature rises. This can lead to temperature control lag, increasing the risk of thermal runaway under high load conditions and failing to meet the refined thermal management needs of power tool battery packs in complex working environments. Summary of the Invention

[0004] This application provides a method and platform for optimizing the thermal cycling performance of power tool battery packs. It solves the technical problem that the lack of detailed analysis of the structural characteristics and thermal cycling characteristics of the battery pack in the prior art leads to insufficient thermal management accuracy and a high risk of thermal runaway. It achieves the technical effect of improving the accuracy and efficiency of thermal cycling control of power tool battery packs, thereby enhancing the safety of the battery pack and extending its service life.

[0005] In view of the above problems, this application provides a method for optimizing the thermal cycling performance of a power tool battery pack. The method includes: interacting with the basic configuration information of the power tool to obtain structural characteristics, wherein the structural characteristics include battery pack structural characteristics and thermal cycling structural characteristics; deploying a temperature sensing array based on the battery pack structural characteristics, and determining the real-time temperature field through sensing sampling; constructing a thermal management module according to the thermal cycling structural characteristics by dividing the main channel and microchannels and introducing flow resistance elements, wherein the microchannels are channels for the gaps between battery cells in the battery pack, the flow resistance elements characterize the magnitude of fluid flow resistance, and the thermal management module uses the temperature field as an input variable and thermal cycling parameters as a response quantity; deploying a verification checkpoint at the sensing data interface to perform a pre-verification of the thermal runaway risk of the real-time temperature field, and transmitting the data back to the thermal management module for temperature control decision-making to determine the thermal cycling strategy; and executing the thermal cycling strategy according to the thermal management system of the power tool to regulate the battery pack temperature.

[0006] On the other hand, this application also provides a thermal cycling performance optimization platform for power tool battery packs. The platform includes: a structural characteristic acquisition unit for interacting with basic configuration information of the power tool to acquire structural characteristics, wherein the structural characteristics include battery pack structural characteristics and thermal cycling structural characteristics; a temperature sensing unit for deploying a temperature sensing array based on the battery pack structural characteristics and determining the real-time temperature field through sensing sampling; a thermal management module construction unit for constructing a thermal management module based on the thermal cycling structural characteristics by dividing the main channel and microchannels and introducing flow resistance elements, wherein the microchannels are channels arranged between battery cells in the battery pack, the flow resistance elements characterize the magnitude of fluid flow resistance, and the thermal management module uses the temperature field as an input variable and thermal cycling parameters as a response quantity; a thermal cycling strategy formulation unit for deploying verification checkpoints at the sensing data interface to pre-verify the risk of thermal runaway in the real-time temperature field, and transmitting the data back to the thermal management module for temperature control decision-making to determine the thermal cycling strategy; and a strategy execution unit for executing the thermal cycling strategy according to the power tool's thermal management system to regulate the battery pack temperature.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] By leveraging the basic configuration information of interactive power tools, the system acquires the structural and thermal cycling characteristics of the battery pack, providing precise input data for subsequent thermal management strategies. This enables personalized and precise thermal cycling management for different types of battery packs. By deploying a temperature sensor array within the battery pack, the system can monitor the internal temperature distribution in real time and dynamically respond to complex temperature changes. Through refined design of the thermal management module and optimization of heat conduction paths, the system solves the problem of localized overheating or overcooling caused by improper flow channel design in traditional methods. The microchannel design effectively utilizes the gaps between battery cells, improving heat dissipation efficiency. By deploying verification checkpoints, the system performs pre-emptive verification of the real-time temperature field to identify thermal runaway risks, proactively identifying abnormal temperature conditions and avoiding thermal runaway risks due to response delays, significantly improving the safety and reliability of the battery pack. By implementing optimized thermal cycling strategies, the system ensures that the battery pack remains within its optimal temperature range under high load operation, thereby improving battery performance and lifespan.

[0009] In summary, this application takes into account the synergistic effect of battery pack structural characteristics and thermal cycling structural characteristics, significantly improving the accuracy and efficiency of thermal cycling control, effectively reducing the risk of thermal runaway, enhancing the safety of the battery pack, and extending the service life of the battery pack through precise temperature control.

[0010] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating a method for optimizing the thermal cycling performance of a power tool battery pack, as provided in an embodiment of this application.

[0012] Figure 2 This is a flowchart illustrating the construction of a thermal management module in the method for optimizing the thermal cycling performance of a power tool battery pack provided in an embodiment of this application.

[0013] Figure 3 This is a flowchart illustrating the pre-verification of thermal runaway risk in the real-time temperature field in the thermal cycling performance optimization method for power tool battery packs provided in this application embodiment.

[0014] Figure 4 This is a schematic diagram of the structure of a thermal cycling performance optimization platform for power tool battery packs provided in an embodiment of this application.

[0015] Explanation of reference numerals in the attached figures: 10 for structural characteristic acquisition, 20 for temperature sensing, 30 for thermal management module construction, 40 for thermal cycling strategy formulation, and 50 for strategy execution. Detailed Implementation

[0016] This application provides a method and platform for optimizing the thermal cycling performance of power tool battery packs. This solves the technical problem in the prior art where the lack of detailed analysis of the structural and thermal cycling characteristics of the battery pack leads to insufficient thermal management accuracy and a high risk of thermal runaway. The method and platform improve the accuracy and efficiency of thermal cycling control of power tool battery packs, thereby enhancing the safety of the battery pack and extending its service life.

[0017] Example 1, as Figure 1 As shown in the embodiments of this application, a method for optimizing the thermal cycling performance of a power tool battery pack is provided, the method comprising:

[0018] Step S1: Interact with the basic configuration information of the power tool and obtain its structural characteristics, wherein the structural characteristics include battery pack structural characteristics and thermal cycle structural characteristics.

[0019] Specifically, basic configuration information of the power tool is obtained by interacting with its control system or relevant configuration files. This basic configuration information includes fundamental parameters and settings related to the power tool, such as the type of power tool, the rated voltage and capacity of the battery pack, and the number and arrangement of individual battery cells. For example, for a power drill, information such as the rated voltage of the battery pack being 18V and the battery cells being composed of 10 cylindrical cells connected in series is retrieved from its product manual or internally stored configuration data. Then, based on this basic configuration information, the structural characteristics and thermal cycling characteristics of the battery pack are determined, such as using image recognition algorithms to determine the arrangement of the battery cells from the battery cell layout diagram. The structural characteristics of the battery pack include its external dimensions, the packaging form of the battery cells (e.g., cylindrical, square), the layout of the battery cells (series, parallel, or mixed), the casing material of the battery pack, and its heat dissipation performance. The thermal cycling characteristics include the heat transfer path within the battery pack, the heat dissipation method (e.g., natural heat dissipation, air cooling, liquid cooling), and the flow channel structure of the heat transfer medium (e.g., air, coolant) within the battery pack.

[0020] By accurately acquiring structural characteristics, we can prepare for more precise thermal management measures for battery packs with specific structures, ensuring that subsequent thermal management solutions are more aligned with the structural characteristics of the battery pack and improving the targeting and effectiveness of thermal management.

[0021] Step S2: Deploy a temperature sensing array based on the structural characteristics of the battery pack, and determine the real-time temperature field through sensing sampling.

[0022] Specifically, based on the battery pack structural characteristics obtained in step S1, multiple temperature sensors (such as thermocouples and RTD sensors) are deployed at key locations within the battery pack to form a temperature sensing array. For example, if the battery pack is cuboid in shape and the individual battery cells are arranged in a matrix, temperature sensors can be deployed between the battery cells or at key locations in a row-column arrangement. These temperature sensors can collect temperature data from each point in real time and transmit the collected temperature data to the data processing unit. The data processing unit uses data processing algorithms, such as interpolation algorithms, to integrate the discrete temperature data collected by each sensor into a continuous real-time temperature field, reflecting the overall temperature distribution of the battery pack at a given moment.

[0023] By determining the real-time temperature field, the temperature distribution inside the battery pack can be accurately obtained, which helps to detect local overheating or uneven temperature in the battery pack in a timely manner, and provides accurate temperature data for subsequent thermal management decisions.

[0024] Step S3: Based on the thermal cycle structure characteristics, a thermal management module is constructed by dividing the main channel and micro channels and introducing flow resistance elements. The micro channels are the gaps between battery cells in the battery pack, and the flow resistance elements characterize the magnitude of fluid flow resistance. The thermal management module uses the temperature field as the input variable and the thermal cycle parameters as the response quantity.

[0025] Specifically, based on the characteristics of the thermal cycle structure, main channels and micro channels are divided. Main channels refer to the large-scale cooling channels within the battery pack, used for large-scale heat transfer; micro channels are the small gaps within the battery pack (such as the spaces between individual battery cells), used for more precise thermal control and localized heat dissipation. For example, in an air-cooled battery pack thermal management system, the main channel can be the main airflow path from the air inlet on one side of the battery pack to the air outlet on the other, while the micro channels are the narrow gaps between the individual battery cells. Then, based on the resistance factors (i.e., flow resistance elements) affecting the flow of fluids (such as air or liquid cooling media) through these channels, including channel geometry, fluid viscosity, and flow velocity, a thermal management module is constructed using machine learning algorithms. This thermal management module manages the battery pack's thermal cycle and can be a combination of microcontroller-based hardware circuitry and software algorithms. This module uses the temperature field as an input variable, calculates and outputs thermal cycle parameters based on the temperature field conditions to control the thermal cycle process.

[0026] Flow resistance determines the cooling efficiency of a thermal management system. By introducing flow resistance, it is possible to simulate and predict the heat flow in different channels, thereby optimizing the cooling path. The constructed thermal management module can more accurately adjust thermal cycling parameters based on real-time temperature field conditions, improving the efficiency and accuracy of thermal management and helping to maintain stable internal temperature of the battery pack.

[0027] Step S4: Deploy a verification checkpoint at the sensor data interface to perform a preliminary verification of the risk of thermal runaway in the real-time temperature field, and transmit the data back to the thermal management module for temperature control decision-making to determine the thermal cycling strategy.

[0028] Specifically, when the internal temperature of a battery pack is excessively high or the temperature distribution is extremely uneven, uncontrollable chemical reactions can occur inside the battery, potentially leading to serious safety accidents such as battery fires and explosions. This is the risk of thermal runaway. To ensure the safety of the battery pack during use, a verification checkpoint is set up at the sensor data interface. This checkpoint can be a software algorithm or a hardware circuit. When the real-time temperature field data collected by the temperature sensor array is transmitted to this interface, the verification checkpoint analyzes the data to check for any abnormal temperatures that could lead to thermal runaway. For example, an upper temperature limit and a temperature difference threshold are set. If the temperature in a certain area exceeds the upper limit or the temperature difference between different areas exceeds the threshold, it is determined that there is a risk of thermal runaway. Then, the temperature field data indicating a risk, or the pre-processed data, is sent back to the thermal management module for temperature control decisions. The thermal management module calculates and judges these decisions to formulate corresponding temperature control measures, such as adjusting cooling strategies and increasing heat dissipation.

[0029] This step, through pre-verification, can identify potential thermal runaway risks in advance and formulate effective temperature control measures through the thermal management module, significantly improving the safety of the battery pack and preventing thermal runaway from occurring.

[0030] Step S5: According to the thermal management system of the power tool, execute the thermal cycling strategy to regulate the battery pack temperature.

[0031] Specifically, a thermal management system is a system that includes components such as temperature sensors, a thermal management module, and actuators (such as fans and liquid circulation systems) to comprehensively manage the temperature of the power tool's battery pack. Based on the power tool's thermal management system, the thermal management module sends the determined thermal circulation strategy to the corresponding actuators to execute the strategy and regulate the battery pack temperature, such as adjusting the operating status of the air-cooled or liquid-cooled system and changing the coolant flow rate.

[0032] This step enables effective regulation of the battery pack temperature based on a defined thermal cycling strategy, ensuring that the battery pack operates within a suitable temperature range, extending its lifespan, and also improving the safety and reliability of power tools.

[0033] Further, such as Figure 2 As shown, step S3 includes:

[0034] Step S31: Based on the characteristics of the thermal cycle structure, the main pipeline and micro pipeline are divided according to the pipeline geometry parameters and pipeline location, and pipeline type labels are generated.

[0035] Step S32: Introduce flow resistance elements, perform flow resistance analysis based on the thermal cycle structure characteristics, and determine the flow resistance coefficient distribution.

[0036] Step S33: Based on the pipe type label and the flow resistance coefficient distribution, mark the thermal cycle structure to determine the marked thermal cycle structure.

[0037] Step S34: Based on the marked thermal cycle structure, construct the thermal management module through data-driven training.

[0038] Specifically, the thermal cycling structure characteristics are analyzed to determine the geometric parameters and location information of the pipes. Pipe geometric parameters include parameters related to the pipe's geometry, such as diameter, length, and shape (e.g., circular, square), which affect the flow characteristics of the fluid within the pipe. Pipe location refers to the pipe's position within the battery pack's thermal cycling structure. Different pipe locations result in different roles in the thermal cycle. For example, in a liquid-cooled battery pack's thermal cycling structure, pipes with larger diameters that penetrate the main area of ​​the battery pack are identified as main pipes based on their geometric parameters (e.g., diameter 10mm, length 500mm) and location (near the center of the battery pack and connecting multiple battery cell groups); while pipes with a diameter of 2mm located between battery cells are identified as micro-pipes. Then, programming or labeling tools are used to generate pipe type labels for these pipes, such as labeling main pipes as "MainPipe" and micro-pipes as "MicroPipe". Accurately classifying main and micro-pipes and generating pipe type labels provides a clear structural framework for subsequent thermal management operations. This facilitates the design of different thermal management strategies for different types of pipelines, improving the targeting and effectiveness of thermal management.

[0039] Based on the structural characteristics of the thermal cycle, flow resistance factors are introduced for flow resistance analysis. For example, for a given pipe, factors such as pipe roughness (if it is a metal pipe, its inner wall roughness may be a certain value) and fluid viscosity (e.g., the viscosity of coolant varies at different temperatures) are considered. Using fluid dynamics principles and related calculation software, such as CFD (Computational Fluid Dynamics) software, the flow resistance coefficient at different pipe locations is calculated. By setting different boundary conditions and fluid parameters in the thermal cycle structural model, the distribution of the flow resistance coefficient throughout the entire thermal cycle structure is obtained.

[0040] Based on pipe type labels and flow resistance coefficient distribution, the thermal cycle structure is labeled using data labeling tools or programming algorithms. For example, in a three-dimensional thermal cycle structure model, for each pipe segment, in addition to labeling it as "MainPipe" or "MicroPipe," the corresponding flow resistance coefficient is also labeled on that pipe segment. In this way, the entire thermal cycle structure is labeled as a labeled thermal cycle structure containing pipe type and flow resistance coefficient information. Labeling the thermal cycle structure can integrate important information such as pipe type and flow resistance coefficient into the thermal cycle structure, allowing the thermal management module to more comprehensively consider the characteristics of the thermal cycle structure when making temperature control decisions, thereby improving the accuracy of thermal management.

[0041] Based on labeled thermal cycle structures, thermal cycle-related data are collected under different operating conditions (such as different ambient temperatures and different battery usage intensities), including temperature data, flow resistance data, and pipe type data. Then, machine learning algorithms, such as neural network algorithms and linear regression algorithms, are used for data-driven training. For example, the collected data is divided into training and testing sets. The data related to the labeled thermal cycle structures is input into the neural network. By adjusting the weights and biases of the neural network, the model can accurately output thermal management decisions (such as heat dissipation power and heating power) based on the input labeled thermal cycle structure data, thereby constructing a thermal management module. The thermal management module constructed through data-driven training can make accurate thermal management decisions based on the actual thermal cycle structure and different operating conditions, improving the adaptability of the thermal management module to complex operating conditions and the accuracy of thermal management.

[0042] Furthermore, step S32 includes:

[0043] Step S321: Identify the characteristics of the thermal cycle structure and determine the pipe interaction nodes, wherein the pipe interaction nodes include elbows, multi-way valves, and valves.

[0044] Step S322: Traverse the pipeline interaction nodes and determine the node geometric features.

[0045] Step S323: Determine the node flow resistance coefficient based on the node geometric characteristics.

[0046] Step S324: Determine the first flow resistance coefficient based on the main channel and the second flow resistance coefficient based on the microchannel.

[0047] Step S325: Integrate the node flow resistance coefficient, the first flow resistance coefficient, and the second flow resistance coefficient to determine the flow resistance coefficient distribution.

[0048] Specifically, through detailed analysis of the thermal cycle structure characteristics, structural analysis software is used to identify pipe interaction nodes. These pipe interaction nodes are the parts where pipes connect, turn, or control the direction of fluid flow, including elbows for changing the direction of fluid flow, multi-way valves for connecting multiple pipes, and valves for controlling the on / off state and flow rate of fluid. For example, for a complex liquid-cooled battery pack thermal cycle system, all elbows, multi-way valves, and valves are marked as pipe interaction nodes on the pipe layout diagram by observing the pipe routing.

[0049] After identifying the pipe interaction nodes, each node is traversed, and its geometric characteristics are analyzed. These include features such as the bending angle of elbows, the number and angle of multi-way connectors, and the opening size of valves. Detailed identification and analysis of the geometric characteristics of the pipe interaction nodes allows for the accurate allocation of appropriate flow resistance coefficients to each node, making the flow resistance analysis more precise.

[0050] The flow resistance coefficients at nodes are determined based on their geometric characteristics, combined with relevant fluid mechanics formulas and empirical data. For example, for elbows, the flow resistance coefficient is calculated using empirical formulas based on their bending angle and pipe diameter; for valves, the corresponding flow resistance coefficient is obtained by consulting relevant engineering manuals or databases based on their opening size and valve type (e.g., ball valve, butterfly valve). These node flow resistance coefficients reflect the magnitude of resistance to fluid flow at the nodes. Accurately quantifying the resistance to fluid flow at pipe interaction nodes helps to accurately assess the flow resistance distribution throughout the entire thermal cycle structure, providing an important basis for optimizing the thermal cycle structure and thermal management strategies.

[0051] Based on the structural characteristics of the thermal cycle, straight pipe sections without bends or branches are identified. After identifying these straight pipe sections, the flow resistance coefficient is calculated using fluid dynamics formulas based on the geometric parameters (such as diameter and length) of the main channel and microchannels, as well as the properties of the fluid. For example, for a straight pipe section in the main channel with a known diameter of 10 mm, a length of 500 mm, and a specific coolant, the first flow resistance coefficient is calculated using the Hagen-Poiseuille law and other relevant formulas. For straight pipe sections in microchannels, a similar method is used to calculate the second flow resistance coefficient based on their smaller diameter and shorter length. The first and second flow resistance coefficients reflect the resistance of the straight pipes in the main and microchannels to fluid flow. These coefficients allow for a comprehensive assessment of the flow resistance of straight pipe sections in different types of channels (main and microchannels) within the thermal cycle structure, facilitating a more accurate construction of the flow resistance model for the entire thermal cycle structure and providing more precise data support for subsequent thermal management.

[0052] The previously calculated nodal resistance coefficients, the first resistance coefficient based on the main channel, and the second resistance coefficient based on the microchannel are integrated and the resistance coefficient distribution is determined by creating a data table or marking it in the model of the thermal cycling structure. This resistance coefficient distribution is a comprehensive set of information reflecting the resistance characteristics of the thermal cycling structure, fully demonstrating the resistance characteristics within the structure. For example, in a 3D model of a thermal cycling structure, the resistance coefficients of each pipe interaction node, the straight pipe section of the main channel, and the straight pipe section of the microchannel are marked at their corresponding locations, thus visually presenting the resistance coefficient distribution. Determining the resistance coefficient distribution provides a crucial data foundation for the thermal management module to accurately control the thermal cycling process, helping to optimize thermal management strategies to improve the thermal cycling performance of the battery pack.

[0053] Furthermore, the thermal circulation parameters mentioned in step S34 include at least the thermal circulation path, coolant inlet flow rate, coolant temperature, and number of pipe rotations. Step S34 includes:

[0054] Step S341: Interact with the historical thermal management records of the power tool, using the temperature field as the input variable and the thermal cycle parameters as the response quantity, preprocess the historical thermal management records, determine the sample data and mine the response surface relationship, wherein the response surface relationship is the relative linear relationship between the input variable and the response quantity.

[0055] Step S342: Based on the response surface relation and the labeled thermal cycling structure, train the module until convergence using the sample data to obtain the thermal management module.

[0056] Specifically, the system interacts with the power tool's storage system to obtain its historical thermal management records. These records contain relevant data on the power tool's thermal management over a past period, including temperature field conditions at different times, corresponding thermal cycling parameters (including thermal cycling path, coolant inlet flow rate, coolant temperature, pipe rotation count, etc.), and thermal management operation records. Then, using the temperature field as the input variable and the thermal cycling parameters as the response variables, the historical thermal management records are preprocessed, including data cleaning (removing outliers and erroneous data) and data normalization (converting data from different ranges to the same scale). For example, if there are obviously erroneous records of coolant temperature data in the historical thermal management records (such as exceeding physically possible temperature values), they are removed. Simultaneously, temperature field data and thermal cycling parameter data of different magnitudes are normalized to generate sample data. Next, data mining algorithms, such as regression analysis, are used to analyze the sample data to uncover the response surface relationship between the input variables and the response variables. By analyzing a large amount of historical data, the linear relationship between changes in the temperature field and thermal cycling parameters (such as adjustments to the coolant inlet flow rate) is determined.

[0057] Based on the response surface relationship and labeled thermal cycle structure obtained in step S341, training is performed using sample data (preprocessed historical thermal management records). Machine learning algorithms, such as neural network algorithms or support vector machine algorithms, are employed. Taking the neural network algorithm as an example, during training, the weights and biases of the neural network are continuously adjusted through backpropagation, gradually reducing the error between the model's predicted thermal cycle parameters and the actual thermal cycle parameters in the sample data. When the error meets the convergence condition (e.g., the mean square error is less than a certain set value), training stops, and the thermal management module is obtained. The thermal management module obtained through data training can accurately adjust the thermal cycle parameters according to the temperature field based on the labeled thermal cycle structure and patterns in historical data, improving the accuracy and adaptability of the thermal management module.

[0058] Further, such as Figure 3 As shown, step S4 includes:

[0059] Step S41: Based on the historical thermal management records, mine temperature features based on the risk of thermal runaway, wherein the temperature features are risk critical feature vectors, including at least spatial distribution features, temperature vector features, and temperature fluctuation features.

[0060] Step S42: Configure the verification checkpoint based on the temperature characteristics.

[0061] Step S43: Based on the verification checkpoint, determine the risk of thermal runaway based on temperature characteristics of the real-time temperature field and determine the determination result.

[0062] Step S44: If the determination result is yes, the real-time temperature field is transmitted back to the thermal management module.

[0063] Specifically, a large amount of temperature data, corresponding thermal management operations, and thermal runaway risk information are obtained from historical thermal management records. Then, data mining techniques, such as cluster analysis and principal component analysis, are used to analyze this data and uncover temperature features related to thermal runaway risk, including spatial distribution, temperature vectors, and temperature fluctuations. For example, cluster analysis is used to classify historical data points with similar temperature characteristics, identifying feature patterns related to thermal runaway risk, thereby determining the critical risk feature vector. This critical risk feature vector includes at least spatial distribution features, temperature vector features, and temperature fluctuation features. Spatial distribution features refer to the spatial distribution of temperature within the battery pack, such as whether a certain area is prone to high-temperature accumulation, and the spatial manifestation of temperature differences between different battery cells. Temperature vector features include the magnitude and direction of temperature, reflecting information such as the intensity and direction of heat transfer. Temperature fluctuation features represent the temperature fluctuations over time, such as the frequency and amplitude of temperature changes; drastic temperature fluctuations may indicate a risk of thermal runaway.

[0064] Verification checkpoints are configured based on defined temperature characteristics. Each checkpoint sets a corresponding risk threshold based on different types of temperature characteristics (spatial distribution, temperature vector, and temperature fluctuation). For example, if the spatial distribution characteristic indicates that the temperature in a specific area of ​​the battery pack (such as a corner) is too high and prone to thermal runaway, then the software program of the verification checkpoint will include detection logic for that area's temperature, or the weights of the corresponding sensors will be adjusted in the hardware circuit. If the temperature fluctuation characteristic shows that there is a risk of thermal runaway when the temperature fluctuation exceeds a certain value, then the corresponding fluctuation threshold will be set in the verification checkpoint for detection. Verification checkpoints configured based on temperature characteristics can more accurately perform pre-verification of thermal runaway risk in the real-time temperature field. This improves the targeting and effectiveness of the verification and reduces the possibility of false positives and false negatives.

[0065] Real-time temperature field data is transmitted to a verification checkpoint. The checkpoint performs checks according to pre-defined temperature feature detection logic to determine if there is a risk of thermal runaway and arrives at a result. For example, the checkpoint checks whether the spatial distribution of the real-time temperature field matches the spatial distribution characteristics in the risk critical feature vector, whether the temperature vector is within the danger range, and whether temperature fluctuations exceed a set threshold. The result is either "yes" or "no." If the result is "yes," it indicates that the current battery pack has a risk of thermal runaway, and the real-time temperature field data is transmitted back to the thermal management module via the data transmission line. If the result is "no," it indicates that the current battery pack does not have a risk of thermal runaway, and no data transmission is required.

[0066] By setting up a verification checkpoint for pre-verification, real-time temperature field data with potential thermal runaway risks can be promptly transmitted back to the thermal management module. This enables the thermal management module to make rapid temperature control decisions, thereby effectively reducing the risk of thermal runaway and ensuring the normal operation of the power tool battery pack.

[0067] Furthermore, before transmitting the real-time temperature field back to the thermal management module, the following steps are included:

[0068] The temperature data of the real-time temperature field is spatially interpolated and then transmitted back. The spatial interpolation methods include average interpolation and trend interpolation. If the spatial distance between neighboring temperature data is less than or equal to a preset interval, the average interpolation method is used; if the spatial distance between neighboring temperature data is greater than the preset interval, the trend interpolation method is used.

[0069] Specifically, in a real-time temperature field, since temperature sensors can only collect temperature values ​​at their spatial locations, the amount of temperature data obtained is relatively small and highly discrete. Spatial interpolation can improve data density and the smoothness of temperature distribution trends, resulting in a more continuous temperature distribution across the entire temperature field, which facilitates decision analysis by the subsequent thermal management module. First, the locations of each temperature sensor in the real-time temperature field and the corresponding collected temperature data are determined. Then, for each location requiring interpolation, its spatial distance to neighboring temperature data (data from surrounding temperature sensors) is calculated, and this calculated spatial distance is compared with a preset distance. This preset distance is a pre-defined value used to determine which interpolation method to use for spatial interpolation.

[0070] If the spatial distance is less than or equal to the preset spacing, the average interpolation method is used, that is, the temperature at the unknown location is estimated by calculating the average value of the temperature data of the neighborhood. For example, in a two-dimensional battery pack temperature field, for a certain point to be interpolated, if the distance between it and the four surrounding temperature sensors is less than or equal to the preset spacing, the temperature data of these four sensors are added together and divided by 4 to obtain the interpolated temperature of that point.

[0071] If the spatial distance is greater than the preset interval, trend interpolation is used to estimate the temperature at the unknown location based on the trend of known temperature data (such as the temperature gradient). This requires first analyzing the trend of the known temperature data, which can be determined by using a fitting function (such as linear fitting, polynomial fitting, etc.), and then calculating the temperature of the point to be interpolated based on this trend.

[0072] Spatial interpolation can effectively improve the accuracy of temperature field data. Even when the temperature data is incomplete, relatively accurate temperature distribution information can still be obtained, thereby more accurately and comprehensively reflecting the temperature distribution inside the battery pack and improving the accuracy of temperature control decisions made by the subsequent thermal management module.

[0073] Furthermore, after step S5, the process includes executing the thermal cycling strategy and performing response tracking and feedback control for temperature management; wherein the response tracking and feedback control methods include:

[0074] Step S51: Determine the verification location, wherein the verification location is any two sensor locations in the temperature sensing array.

[0075] Step S52: Collect temperature data based on the verification location, determine the temperature deviation by using the temperature adjustment vector and temperature adjustment direction, and determine the response characteristics.

[0076] Step S53: Perform feedback control of the battery pack based on the response characteristics.

[0077] Specifically, after implementing the thermal cycling strategy, it is necessary to track and provide feedback on its effectiveness to ensure the effectiveness of temperature management. First, two or more sensor locations are randomly selected from the temperature sensor array as calibration locations. By collecting and verifying the temperature data at the calibration locations, the effectiveness of temperature management can be determined.

[0078] Based on the specific adjustments made in the thermal cycling strategy, the temperature adjustment vector and direction are determined. The temperature adjustment vector represents the magnitude and direction of the temperature adjustment, reflecting the desired intensity and direction of temperature regulation by the thermal management module. For example, the temperature adjustment vector indicates that the temperature of a certain area should be reduced, and provides information such as the magnitude of the reduction. The temperature adjustment direction refers to the direction of temperature regulation, such as the direction of heating or cooling.

[0079] Temperature data is collected using temperature sensors at the designated verification locations. The collected temperature data is then compared with the temperature control vector and direction to determine if the current temperature control effect deviates from the predetermined target. If the temperature deviates from the expected direction, feedback control is required. For example, if the temperature control vector requires a certain reduction in temperature in a region (e.g., from 30℃ to 25℃), and the direction is cooling, but the actual temperature data collected at the verification location shows a reduction to only 28℃, then a temperature control deviation can be identified. By calculating the difference between the actual and target temperatures and analyzing the temperature change trend, response characteristics are determined. These response characteristics reflect the temperature regulation response, such as over-regulation, under-regulation, or incorrect regulation direction, providing a clear basis for subsequent feedback control. This allows the thermal management system to promptly identify problems in the temperature regulation process and take appropriate measures.

[0080] Based on the determined response characteristics, feedback regulation is implemented for battery pack temperature management to ensure that temperature adjustment better matches the expected temperature regulation vector and direction. If the response characteristics indicate insufficient regulation, such as insufficient temperature drop during cooling, the thermal management system can increase heat dissipation power, such as increasing the coolant flow rate or increasing the cooling fan speed. If the regulation is excessive, heat dissipation power is reduced or heating power is increased (if maintaining a certain temperature range is required). If the regulation direction is incorrect, the temperature regulation vector and direction are adjusted, and temperature regulation is repeated. The feedback regulation process is achieved by the thermal management system's control software controlling relevant hardware devices (such as coolant pumps, cooling fans, heating elements, etc.). Through feedback regulation, the accuracy and effectiveness of battery pack temperature management can be improved, ensuring that the battery pack temperature is always maintained within a suitable range, thereby improving battery performance, safety, and lifespan.

[0081] In summary, the method for optimizing the thermal cycling performance of power tool battery packs provided in this application has the following technical effects:

[0082] This application embodiment considers the synergistic effect of battery pack structural characteristics and thermal cycling structural characteristics. By combining a temperature sensor array with thermal cycling structural analysis, it accurately monitors the real-time temperature field of the battery pack and optimizes the heat flow path to ensure efficient heat dissipation. Furthermore, a pre-verification mechanism provides early warning of temperature anomalies, ensuring that risks such as thermal runaway can be identified and addressed in a timely manner. Temperature control decisions are made based on real-time data and pre-verification results, achieving dynamic adjustment of the battery pack temperature. Overall, this application embodiment significantly improves the accuracy and efficiency of thermal cycling control, effectively reduces the risk of thermal runaway, enhances battery pack safety, and extends battery pack lifespan through precise temperature control.

[0083] Example 2, as Figure 4 As shown, based on the same inventive concept as in Embodiment 1 above, this application provides a thermal cycling performance optimization platform for power tool battery packs, the platform comprising:

[0084] The structural characteristic acquisition unit 10 is used to obtain the basic configuration information of the power tool and acquire structural characteristics, wherein the structural characteristics include battery pack structural characteristics and thermal cycle structural characteristics.

[0085] Temperature sensing unit 20 is used to deploy a temperature sensing array based on the structural characteristics of the battery pack and determine the real-time temperature field through sensing sampling.

[0086] The thermal management module construction unit 30 is used to construct a thermal management module by dividing the main channel and micro channel and introducing flow resistance elements according to the thermal cycle structure characteristics. The micro channel is a channel for arranging gaps between battery cells in the battery pack. The flow resistance elements characterize the magnitude of fluid flow resistance. The thermal management module takes the temperature field as the input variable and the thermal cycle parameters as the response quantity.

[0087] The thermal cycling strategy formulation unit 40 is used to set up verification checkpoints at the sensor data interface, perform pre-verification of the risk of thermal runaway on the real-time temperature field, and send the data back to the thermal management module for temperature control decision-making to determine the thermal cycling strategy.

[0088] The strategy execution unit 50 is used to execute the thermal cycling strategy according to the thermal management system of the power tool to regulate the battery pack temperature.

[0089] Furthermore, in this embodiment of the application, the thermal management module construction unit 30 is also used to perform the following steps:

[0090] Based on the characteristics of the thermal cycle structure, the main pipeline and micro-pipelines are divided according to the pipeline geometry parameters and pipeline location, and pipeline type labels are generated. Flow resistance elements are introduced, and flow resistance analysis is performed based on the characteristics of the thermal cycle structure to determine the flow resistance coefficient distribution. Based on the pipeline type labels and the flow resistance coefficient distribution, the thermal cycle structure is marked to determine the marked thermal cycle structure. Based on the marked thermal cycle structure, the thermal management module is constructed through data-driven training.

[0091] Furthermore, in this embodiment of the application, the thermal management module construction unit 30 is also used to perform the following steps:

[0092] Identify the structural characteristics of the thermal cycle and determine the pipe interaction nodes, wherein the pipe interaction nodes include elbows, multi-way valves, and valves; traverse the pipe interaction nodes and determine the node geometric features; determine the node flow resistance coefficient based on the node geometric features; based on the structural characteristics of the thermal cycle, identify the straight pipe section and determine a first flow resistance coefficient based on the main channel and a second flow resistance coefficient based on the microchannel; integrate the node flow resistance coefficient, the first flow resistance coefficient, and the second flow resistance coefficient to determine the flow resistance coefficient distribution.

[0093] Furthermore, in this embodiment of the application, the thermal management module construction unit 30 is also used to perform the following steps:

[0094] The thermal cycle parameters include at least the thermal cycle path, coolant inlet flow rate, coolant temperature, and pipe rotation number. Historical thermal management records of the interactive power tool are used, with the temperature field as the input variable and the thermal cycle parameters as the response variables. These historical thermal management records are preprocessed to determine sample data and mine response surface relationships, where the response surface relationship is a relative linear relationship between the input variable and the response variable. Based on the response surface relationship and the labeled thermal cycle structure, the module is trained until convergence using the sample data to obtain the thermal management module.

[0095] Furthermore, in this embodiment of the application, the thermal cycling strategy formulation unit 40 is also used to perform the following steps:

[0096] Based on the historical thermal management records, temperature features based on thermal runaway risk are mined, wherein the temperature features are risk critical feature vectors, including at least spatial distribution features, temperature vector features, and temperature fluctuation features; based on the temperature features, the verification checkpoint is configured; based on the verification checkpoint, the real-time temperature field is assessed for thermal runaway risk based on temperature features, and the assessment result is determined; if the assessment result is positive, the real-time temperature field is transmitted back to the thermal management module.

[0097] Furthermore, before transmitting the real-time temperature field back to the thermal management module, the thermal cycling strategy formulation unit 40 in this embodiment of the application is also configured to perform the following steps:

[0098] The temperature data of the real-time temperature field is spatially interpolated and then transmitted back. The spatial interpolation methods include average interpolation and trend interpolation. If the spatial distance between neighboring temperature data is less than or equal to a preset interval, the average interpolation method is used; if the spatial distance between neighboring temperature data is greater than the preset interval, the trend interpolation method is used.

[0099] Furthermore, in this embodiment of the application, the strategy execution unit 50 is also used to perform the following steps:

[0100] The thermal cycling strategy is executed, and response tracking and feedback control of temperature management are performed. The response tracking and feedback control method includes: determining the verification position, wherein the verification position is any two sensor positions in the temperature sensing array; collecting temperature data based on the verification position, determining the temperature adjustment deviation by using the temperature adjustment vector and temperature adjustment direction, and determining the response characteristics; and performing feedback control of the battery pack according to the response characteristics.

[0101] Through the foregoing detailed description of the method for optimizing the thermal cycling performance of power tool battery packs, those skilled in the art can clearly understand that the platform for optimizing the thermal cycling performance of power tool battery packs in this embodiment corresponds to the method disclosed in Embodiment 2, and has corresponding functional units and beneficial effects. For relevant details, please refer to the description in the method section.

[0102] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the thermal cycling performance of power tool battery packs, characterized in that, The method includes: The basic configuration information of the interactive power tool is obtained, and its structural characteristics are acquired, including battery pack structural characteristics and thermal cycle structural characteristics. A temperature sensing array is deployed based on the structural characteristics of the battery pack, and the real-time temperature field is determined through sensing and sampling. Based on the thermal cycle structure characteristics, a thermal management module is constructed by dividing the main channel and micro channel and introducing flow resistance elements. The micro channel is a channel for arranging gaps between individual battery cells in the battery pack. The flow resistance elements characterize the magnitude of fluid flow resistance. The thermal management module uses the temperature field as the input variable and the thermal cycle parameters as the response quantity. A verification checkpoint is set up at the sensor data interface to perform a preliminary verification of the risk of thermal runaway in the real-time temperature field, and the data is sent back to the thermal management module for temperature control decision-making to determine the thermal cycling strategy. According to the thermal management system of the power tool, the thermal cycling strategy is executed to regulate the battery pack temperature; After regulating the battery pack temperature, the following steps are included: The thermal cycling strategy is executed, and response tracking and feedback control for temperature management are performed. The response tracking and feedback control methods include: Determine the verification location, wherein the verification location is any two sensor locations in the temperature sensing array; Temperature data is collected based on the verification location, and temperature deviation is determined by using the temperature adjustment vector and temperature adjustment direction to identify response characteristics. Based on the aforementioned response characteristics, feedback regulation of the battery pack is performed.

2. The method for optimizing the thermal cycling performance of a power tool battery pack as described in claim 1, characterized in that, A thermal management module is constructed by dividing the main channel and microchannels and introducing flow resistance elements, including: Based on the aforementioned thermal cycle structure characteristics, and according to the pipe geometry parameters and pipe location, the main pipe and micro pipe are divided, and pipe type labels are generated. By introducing flow resistance elements and performing flow resistance analysis based on the aforementioned thermal cycle structural characteristics, the flow resistance coefficient distribution is determined. Based on the pipe type label and the flow resistance coefficient distribution, the thermal cycle structure is marked to determine the marked thermal cycle structure; Based on the labeled thermal cycle structure, the thermal management module is constructed through data-driven training.

3. The method for optimizing the thermal cycling performance of a power tool battery pack as described in claim 2, characterized in that, The determination of the flow resistance coefficient distribution includes: Identify the characteristics of the thermal cycle structure and determine the pipe interaction nodes, wherein the pipe interaction nodes include elbows, multi-way valves, and valves; Traverse the pipeline interaction nodes and determine the node geometric features; The node flow resistance coefficient is determined based on the node's geometric characteristics. Based on the aforementioned thermal cycle structural characteristics, the straight pipe section is identified, and the first flow resistance coefficient based on the main channel and the second flow resistance coefficient based on the microchannel are determined. The flow resistance coefficient distribution is determined by integrating the node flow resistance coefficient, the first flow resistance coefficient, and the second flow resistance coefficient.

4. The method for optimizing the thermal cycling performance of a power tool battery pack as described in claim 3, characterized in that, Based on the labeled thermal cycle structure, the thermal management module is constructed through data-driven training, including: The thermal circulation parameters include at least the thermal circulation path, coolant inlet flow rate, coolant temperature, and number of pipe rotations. Historical thermal management records of interactive power tools are preprocessed using temperature field as input variable and thermal cycle parameters as response quantity to determine sample data and mine response surface relationships, wherein the response surface relationship is a relative linear relationship between input variables and response quantities. Based on the response surface relation and the labeled thermal cycling structure, the thermal management module is trained until convergence using the sample data.

5. The method for optimizing the thermal cycling performance of a power tool battery pack as described in claim 4, characterized in that, A verification checkpoint is deployed at the sensor data interface to perform a preliminary verification of the risk of thermal runaway in the real-time temperature field, including: Based on the historical thermal management records, temperature features based on the risk of thermal runaway are mined, wherein the temperature features are risk critical feature vectors, which at least include spatial distribution features, temperature vector features, and temperature fluctuation features. Configure the verification checkpoint based on the temperature characteristics; Based on the verification checkpoint, the real-time temperature field is assessed for thermal runaway risk based on temperature characteristics, and the assessment result is determined. If the determination result is yes, the real-time temperature field is transmitted back to the thermal management module.

6. The method for optimizing the thermal cycling performance of a power tool battery pack as described in claim 5, characterized in that, Before transmitting the real-time temperature field back to the thermal management module, the process includes: The temperature data of the real-time temperature field is spatially interpolated and then transmitted back. The spatial interpolation methods include average interpolation and trend interpolation. If the spatial distance between neighboring temperature data is less than or equal to the preset interval, mean interpolation is used. If the spatial distance between neighboring temperature data is greater than the preset interval, trend interpolation is used.

7. A platform for optimizing the thermal cycling performance of power tool battery packs, characterized in that, The platform is used to execute the thermal cycling performance optimization method for power tool battery packs according to any one of claims 1-6, including: A structural characteristic acquisition unit is used to obtain the basic configuration information of the interactive power tool and acquire structural characteristics, wherein the structural characteristics include battery pack structural characteristics and thermal cycle structural characteristics. A temperature sensing unit is used to deploy a temperature sensing array based on the structural characteristics of the battery pack and determine the real-time temperature field through sensing sampling. A thermal management module construction unit is used to construct a thermal management module by dividing the main channel and micro channel and introducing flow resistance elements according to the thermal cycle structure characteristics. The micro channel is a channel for the gap between battery cells in the battery pack, and the flow resistance element characterizes the magnitude of fluid flow resistance. The thermal management module takes the temperature field as the input variable and the thermal cycle parameters as the response quantity. The thermal cycling strategy formulation unit is used to deploy verification checkpoints at the sensor data interface, perform pre-verification of the risk of thermal runaway on the real-time temperature field, and transmit the data back to the thermal management module for temperature control decision-making to determine the thermal cycling strategy. The strategy execution unit is used to execute the thermal cycling strategy according to the thermal management system of the power tool to regulate the battery pack temperature.

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