Thermal cycle performance optimization method and platform for electric tool battery pack

Abstract

The invention provides a thermal cycle performance optimization method and platform for an electric tool battery pack, and relates to the technical field of batteries, and the method comprises the steps: interacting basic configuration information of an electric tool, and obtaining structural characteristics; arranging a temperature sensing array based on the structural characteristics of the battery pack, and determining a real-time temperature field; the method comprises the following steps: constructing a thermal management module by dividing a main channel and a micro channel and introducing a flow resistance element according to thermal cycle structure characteristics; a verification gate is arranged at a sensing data interface, pre-verification of the thermal runaway risk is carried out on a real-time temperature field, a result is transmitted back to a thermal management module to carry out temperature control decision, and a thermal cycle strategy is determined; and executing a thermal cycle strategy according to a thermal management system of the electric tool, and regulating and controlling the temperature of the battery pack. The technical problem that in the prior art, due to the fact that fine analysis on the structure and thermal cycle characteristics of the battery pack is lacked, thermal management precision is insufficient is solved, the accuracy and efficiency of thermal cycle control of the battery pack are improved, then the safety of the battery pack is enhanced, and the service life is prolonged.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method and platform for optimizing thermal cycle performance of a power tool battery pack. Background Art

[0002] Electric tool battery packs tend to generate a lot of heat when working for a long time and under high load. If the heat cannot be dissipated in a timely and effective manner, it may cause overheating, performance degradation, and even cause safety problems such as thermal runaway.

[0003] At present, the thermal management technology of power tool battery packs mainly relies on physical cooling methods such as air cooling, liquid cooling, heat pipe cooling and phase change material (PCM) thermal management, or a combination of these methods. These methods are usually based on fixed cooling parameters or preset design schemes, and the heat dissipation capacity has certain limitations. There are different heat channels and structural areas inside the battery pack, and the heat distribution and flow are complex and changeable. Especially when working under high load, the temperature change in the battery pack may be very rapid. The existing cooling method cannot be flexibly adjusted according to the real-time thermal state and structural characteristics of the battery pack, resulting in low overall cooling efficiency. In practical applications, the temperature of some areas may not be effectively controlled, resulting in local overheating, which in turn causes performance degradation or safety hazards of the battery cells. In addition, these methods lack monitoring and real-time response mechanisms for temperature changes inside the battery pack, cannot identify and respond to abnormal temperature rise in time, are prone to temperature control lag, increase the risk of thermal runaway under high load conditions, and are difficult to meet the refined thermal management requirements of power tool battery packs in complex working environments. Summary of the invention

[0004] The present application provides a method and platform for optimizing the thermal cycle performance of a power tool battery pack, which solves the technical problems in the prior art of insufficient thermal management accuracy and a high risk of thermal runaway due to the lack of refined analysis of the battery pack structural characteristics and thermal cycle characteristics. It achieves the technical effect of improving the accuracy and efficiency of thermal cycle control of the power tool battery pack, thereby enhancing the safety of the battery pack and extending the service life of the battery pack.

[0005] In view of the above problems, on the one hand, the present application provides a method for optimizing the thermal cycle performance of a battery pack for an electric tool, the method comprising: interacting with basic configuration information of the electric tool to obtain structural characteristics, wherein the structural characteristics include battery pack structural characteristics and thermal cycle structural characteristics; arranging a temperature sensor array based on the battery pack structural characteristics, and determining the real-time temperature field through sensor sampling; according to the thermal cycle structural characteristics, constructing a thermal management module by dividing the main channel and microchannel and introducing a flow resistance element, wherein the microchannel is a channel arranged in the gap between battery cells in the battery pack, and the flow resistance element characterizes the magnitude of the fluid flow resistance, and the thermal management module uses the temperature field as an input variable and the thermal cycle parameter as a response variable; arranging a checkpoint on the sensor data interface, pre-checking the thermal runaway risk of the real-time temperature field, and transmitting it back to the thermal management module for temperature control decision-making and determining the thermal cycle strategy; executing the thermal cycle strategy according to the thermal management system of the electric tool to control the battery pack temperature.

[0006] On the other hand, the present application also provides a thermal cycle performance optimization platform for an electric tool battery pack, the platform comprising: a structural characteristic acquisition unit, used to interact with basic configuration information of the electric tool and acquire structural characteristics, wherein the structural characteristics include battery pack structural characteristics and thermal cycle structural characteristics; a temperature sensing unit, used to arrange a temperature sensor array based on the battery pack structural characteristics, and determine the real-time temperature field through sensor sampling; a thermal management module construction unit, used to construct a thermal management module according to the thermal cycle structural characteristics by dividing the main channel and the microchannel and introducing a flow resistance element, wherein the microchannel is a channel arranged in the gap between battery cells in the battery pack, and the flow resistance element characterizes the magnitude of the fluid flow resistance, and the thermal management module uses the temperature field as an input variable and the thermal cycle parameter as a response quantity; a thermal cycle strategy formulation unit, used to arrange a verification checkpoint on the sensor data interface, perform a pre-verification of the thermal runaway risk on the real-time temperature field, and transmit it back to the thermal management module for temperature control decision and determination of the thermal cycle strategy; a strategy execution unit, used to execute the thermal cycle strategy according to the thermal management system of the electric tool to perform battery pack temperature control.

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

[0008] By interacting with the basic configuration information of the power tool, the structural characteristics of the battery pack and the thermal cycle structural characteristics are obtained, providing accurate input data for the subsequent thermal management strategy, so that personalized and precise thermal cycle management can be performed for different types of battery packs. By arranging a temperature sensor array in the battery pack, the temperature distribution inside the battery pack can be monitored in real time, and the complex temperature changes inside the battery pack can be dynamically responded to. By finely designing the thermal management module and optimizing the heat conduction path, the problem of local overheating or overcooling caused by unreasonable flow channel design in traditional methods is solved. The design of the microchannel can effectively utilize the gap between battery cells and improve the heat dissipation efficiency. By arranging verification checkpoints and pre-verifying the risk of thermal runaway on the real-time temperature field, abnormal temperature conditions can be identified in advance, avoiding the risk of thermal runaway caused by response delays, and significantly improving the safety and reliability of the battery pack. By executing the optimized thermal cycle strategy, it is ensured that the battery pack always remains in the optimal temperature range when working under high load, thereby improving battery performance and service life.

[0009] In summary, the present application takes into account the synergistic effect of the battery pack structural characteristics and the thermal cycle structural characteristics, significantly improves the accuracy and efficiency of thermal cycle control, effectively reduces the risk of thermal runaway, enhances the safety of the battery pack, and extends the service life of the battery pack through precise temperature control.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic flow chart of a method for optimizing thermal cycle performance of a power tool battery pack provided in an embodiment of the present application.

[0012] Figure 2 A schematic diagram of a process for constructing a thermal management module in a method for optimizing thermal cycle performance of a power tool battery pack provided in an embodiment of the present application.

[0013] Figure 3 A schematic diagram of a process for pre-checking the thermal runaway risk of the real-time temperature field in the thermal cycle performance optimization method for a power tool battery pack provided in an embodiment of the present application.

[0014] Figure 4 A schematic diagram of the structure of a thermal cycle performance optimization platform for a power tool battery pack provided in an embodiment of the present application.

[0015] Explanation of the reference numerals: structural characteristic acquisition unit 10 , temperature sensing unit 20 , thermal management module construction unit 30 , thermal cycle strategy formulation unit 40 , strategy execution unit 50 . DETAILED DESCRIPTION

[0016] The embodiments of the present application provide a method and platform for optimizing the thermal cycle performance of a power tool battery pack, thereby solving the technical problems in the prior art of insufficient thermal management accuracy and a high risk of thermal runaway due to a lack of refined analysis of the structural characteristics and thermal cycle characteristics of the battery pack. This achieves the technical effect of improving the accuracy and efficiency of thermal cycle control of the power tool battery pack, thereby enhancing the safety of the battery pack and extending the service life of the battery pack.

[0017] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a method for optimizing the thermal cycle performance of a power tool battery pack, the method comprising:

[0018] Step S1: basic configuration information of the interactive electric tool is obtained to obtain structural characteristics, wherein the structural characteristics include battery pack structural characteristics and thermal cycle structural characteristics.

[0019] Specifically, the basic configuration information of the power tool is obtained by interacting with the control system of the power tool or the relevant configuration file. These basic configuration information are some basic parameters and settings related to the power tool, such as the type of power tool, the rated voltage of the battery pack, the rated capacity, the number and arrangement of battery cells, etc. For example, if it is an electric drill, the rated voltage of the electric drill battery pack is 18V, and the battery cell is composed of 10 cylindrical batteries in series, etc., which are read from its product manual or the internally stored configuration data. Then, based on these basic configuration information, the battery pack structural characteristics and thermal cycle structural characteristics are determined, such as performing an image recognition algorithm on the battery cell layout diagram to determine the arrangement structure of the battery cell. Among them, the battery pack structural characteristics include the external dimensions of the battery pack, the packaging form of the battery cell (such as cylindrical, square, etc.), the layout structure of the battery cell (series, parallel or mixed), the shell material of the battery pack and its heat dissipation performance, etc. The thermal cycle structural characteristics include the path of heat transfer inside the battery pack, the heat dissipation method (such as the structural layout of natural heat dissipation, air cooling, liquid cooling, etc.), and the flow channel structure of the heat transfer medium (such as air, coolant, etc.) in the battery pack.

[0020] By accurately obtaining the structural characteristics, we can prepare for more precise thermal management measures for battery packs with specific structures, ensure that subsequent thermal management solutions are more in line with the structural characteristics of the battery pack, and improve the pertinence and effectiveness of thermal management.

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

[0022] Specifically, according to the structural characteristics of the battery pack obtained in step S1, multiple temperature sensors (such as thermocouples, RTD sensors) are arranged at key positions of the battery pack to form a temperature sensing array. For example, if the battery pack is in the shape of a cuboid and the battery cells are arranged in a matrix, temperature sensors can be arranged between the battery cells or at key positions in a row and column manner. These temperature sensors can collect temperature data at each point in real time and transmit the collected temperature data to the data processing unit. The data processing unit integrates the discrete temperature data collected by each sensor into a continuous real-time temperature field through data processing algorithms, such as interpolation algorithms. To reflect the overall temperature distribution of the battery pack at a certain moment.

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

[0024] Step S3: According to the characteristics of the thermal cycle structure, a thermal management module is constructed by dividing the main channel and the microchannel and introducing a flow resistance element, wherein the microchannel is a gap arrangement channel between battery cells in a battery pack, and the flow resistance element characterizes the magnitude of fluid flow resistance. The thermal management module uses the temperature field as an input variable and the thermal cycle parameter as a response variable.

[0025] Specifically, according to the characteristics of the thermal cycle structure, the main channel and microchannel are divided. Among them, the main channel refers to the large-scale cooling channel in the battery pack, which is used for large-scale heat transfer; the microchannel is the small gap inside the battery pack (such as the gap between battery cells), which is used for more precise thermal control and local heat dissipation. For example, in an air-cooled battery pack thermal management system, the main channel can be the main air flow path from the air inlet on one side of the battery pack to the air outlet on the other side, while the microchannel is the narrow gap channel between the battery cells. Then, according to the resistance factors (i.e., flow resistance elements) that affect the passage of fluids (such as cooling media such as air or liquid) through these channels, including the geometry of the channel, the viscosity of the fluid, the flow rate, etc., a thermal management module is constructed using a machine learning algorithm. This thermal management module is used to manage the thermal cycle of the battery pack. It can be a combination of a microcontroller-based hardware circuit plus a software algorithm. The module uses the temperature field as an input variable, calculates and outputs thermal cycle parameters according to the temperature field to control the thermal cycle process.

[0026] The flow resistance factor determines the cooling efficiency of the thermal management system. By introducing the flow resistance factor, the heat flow in different channels can be simulated and predicted, thereby optimizing the cooling path. The constructed thermal management module can adjust the thermal cycle parameters more accurately according to the real-time temperature field, improve the efficiency and accuracy of thermal management, and help maintain the stability of the internal temperature of the battery pack.

[0027] Step S4: a checkpoint is arranged at the sensor data interface to perform a pre-check on the thermal runaway risk of the real-time temperature field, and the check is sent back to the thermal management module to make a temperature control decision and determine the thermal cycle strategy.

[0028] Specifically, when the temperature inside the battery pack is too high or the temperature distribution is extremely uneven, uncontrollable chemical reactions will occur inside the battery, which may cause serious safety accidents such as battery fire and explosion. This is the risk of thermal runaway. In order to ensure the safety of the battery pack during use, a verification checkpoint is set at the sensor data interface. This verification 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 will analyze the data to check whether there are temperature anomalies that may cause the risk of thermal runaway. For example, the temperature upper limit value and the temperature difference threshold are set. If the temperature of a certain area exceeds the upper limit value 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 with risks or the data after preliminary processing are sent back to the thermal management module for temperature control decisions. The corresponding temperature control measures are formulated through the calculation and judgment of the thermal management module, such as adjusting the cooling strategy and increasing heat dissipation.

[0029] This step can detect possible thermal runaway risks in advance through pre-verification, and formulate effective temperature control measures through the thermal management module, significantly improving the safety of the battery pack and avoiding the occurrence of thermal runaway.

[0030] Step S5: Execute the thermal cycle strategy according to the thermal management system of the electric tool to control the temperature of the battery pack.

[0031] Specifically, the thermal management system is a system that includes temperature sensors, thermal management modules, actuators (such as fans, liquid circulation systems, etc.), etc., which is used to comprehensively manage the temperature of the power tool battery pack. According to the thermal management system of the power tool, the thermal management module sends the determined thermal cycle strategy to the corresponding actuator to execute the thermal cycle strategy and control the battery pack temperature, such as adjusting the operating status of the air cooling and liquid cooling system, changing the coolant flow, etc.

[0032] This step can effectively regulate the battery pack temperature according to the determined thermal cycle strategy, ensuring that the battery pack operates within the appropriate temperature range, extending the service life of the battery pack, and also improving the safety and reliability of the power tool.

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

[0034] Step S31: Based on the thermal cycle structural characteristics, the main pipeline and micro pipeline are divided according to pipeline geometric parameters and pipeline positions, and pipeline type labels are generated.

[0035] Step S32: introducing flow resistance elements, performing flow resistance analysis based on the thermal cycle structural characteristics, and determining flow resistance coefficient distribution.

[0036] Step S33: marking the thermal cycle structure based on the pipeline type label and the flow resistance coefficient distribution, and determining the marked thermal cycle structure.

[0037] Step S34: Based on the labeled thermal cycle structure, the thermal management module is constructed by performing data-driven training.

[0038] Specifically, the characteristics of the thermal cycle structure are analyzed to determine the geometric parameters and position information of the pipeline. The geometric parameters of the pipeline include parameters related to the geometric shape of the pipeline, such as the diameter, length, shape (such as round, square, etc.), etc., which will affect the flow characteristics of the fluid in the pipeline. The pipeline position refers to the layout position of the pipeline in the thermal cycle structure of the battery pack. Different pipeline positions have different roles in the thermal cycle. For example, in a liquid-cooled battery pack thermal cycle structure, for pipelines with a larger diameter and running through the main area of ​​the battery pack, according to their geometric parameters (such as a diameter of 10mm, a length of 500mm, etc.) and positions (close to the center of the battery pack and connecting multiple battery cell groups), they are determined to be main pipelines; and for pipelines with a diameter of 2mm and located in the gap between battery cells, they are determined to be micropipes. Then, use programming or marking tools to generate pipeline type labels for these pipelines, such as marking the main pipeline as "MainPipe" and the micropipe as "MicroPipe". By accurately dividing the main pipeline and micropipeline and generating pipeline type labels, a clear structural framework can be provided for subsequent thermal management operations. It is convenient to design different thermal management strategies for different types of pipelines, thereby improving the pertinence and effectiveness of thermal management.

[0039] Based on the characteristics of the thermal cycle structure, the flow resistance factor is introduced for flow resistance analysis. For example, for a certain pipeline, the roughness of the pipeline (if it is a metal pipeline, the roughness of its inner wall may be a certain value), the viscosity of the fluid (such as the viscosity of the coolant has different values ​​at different temperatures) and other factors are considered. The flow resistance coefficient at different pipeline positions is calculated using the principles of fluid mechanics and related calculation software, such as CFD (computational fluid dynamics) software. By setting different boundary conditions and fluid parameters in the thermal cycle structure model, the distribution of the flow resistance coefficient in the entire thermal cycle structure is obtained.

[0040] Based on the pipe type label and the flow resistance coefficient distribution, the thermal cycle structure is labeled using a data labeling tool or a programming algorithm. 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 marked on the 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. The labeled thermal cycle structure can integrate important information such as pipe type and flow resistance coefficient into the thermal cycle structure, so that the thermal management module can 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 the labeled thermal cycle structure, collect thermal cycle related data under different working conditions (such as different ambient temperatures, different battery usage intensities, etc.), including temperature data, flow resistance data, pipeline type data, etc. Then, use machine learning algorithms, such as neural network algorithms, linear regression algorithms, etc., to perform data-driven training. For example, the collected data is divided into training sets and test sets, and the relevant data of the labeled thermal cycle structure 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, heating power, etc.) 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 working conditions, which improves the adaptability of the thermal management module to complex working conditions and the accuracy of thermal management.

[0042] Further, step S32 includes:

[0043] Step S321: Identify the thermal cycle structural characteristics and determine the pipeline interaction nodes, wherein the pipeline interaction nodes include elbows, multi-ports, and valves.

[0044] Step S322: traverse the pipeline interaction nodes to determine the node geometric features.

[0045] Step S323: Determine the node flow resistance coefficient according to the node geometric characteristics.

[0046] Step S324: Determine a first flow resistance coefficient based on the main channel and a 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 a detailed analysis of the thermal cycle structural characteristics, the structural analysis software is used to determine the pipeline interaction nodes. These pipeline interaction nodes are the parts where pipelines are interconnected, diverted, or control the flow direction of the fluid, including elbows for changing the flow direction of the fluid, multi-passes for connecting multiple pipelines, and valves for controlling the on-off and flow rate of the fluid. For example, for a complex liquid-cooled battery pack thermal cycle system, on the pipeline layout diagram, by observing the direction of the pipeline, all elbows, multi-passes, and valves are marked as pipeline interaction nodes.

[0049] After identifying the pipeline interaction nodes, traverse each pipeline interaction node and analyze its geometric features. For example, the bending angle of the elbow, the number and angle of the multi-pass interface, the opening size of the valve, etc. The detailed identification and analysis of the geometric features of the pipeline interaction nodes can accurately assign the appropriate flow resistance coefficient to each node, making the flow resistance analysis more accurate.

[0050] According to the geometric characteristics of the nodes, the node flow resistance coefficient is determined in combination with relevant formulas and empirical data of fluid mechanics. For example, for elbows, the flow resistance coefficient is calculated using empirical formulas according to its bending angle and pipe diameter; for valves, the corresponding flow resistance coefficient is obtained by looking up relevant engineering manuals or databases according to its opening size and valve type (such as ball valves, butterfly valves, etc.). These node flow resistance coefficients reflect the resistance to fluid flow at the node. Accurately quantifying the resistance of pipeline interaction nodes to fluid flow helps to accurately evaluate the flow resistance distribution in the entire thermal cycle structure, providing an important basis for optimizing thermal cycle structures and thermal management strategies.

[0051] Based on the characteristics of the thermal cycle structure, the straight pipe part without bends, branches, etc. in the thermal cycle structure is identified. After the straight pipe part is identified, the flow resistance coefficient is calculated using the fluid mechanics formula according to the geometric parameters (such as diameter and length) of the main channel and microchannel and the properties of the fluid. For example, for the straight pipe part in the main channel, it is known that its diameter is 10mm and its length is 500mm. The fluid is a certain coolant. The first flow resistance coefficient is calculated according to the Hagen-Poiseuille law and other related formulas. For the straight pipe part in the microchannel, the second flow resistance coefficient is calculated according to parameters such as its smaller diameter and shorter length in a similar way. The first flow resistance coefficient and the second flow resistance coefficient reflect the resistance of the straight pipe in the main channel and microchannel to the flow of the fluid. Through these first flow resistance coefficients and second flow resistance coefficients, the flow resistance of the straight pipe part of different types of channels (main channel and microchannel) in the thermal cycle structure can be comprehensively evaluated, which helps to more accurately construct the flow resistance model of the entire thermal cycle structure and provide more accurate data support for subsequent thermal management.

[0052] The node flow resistance coefficient, the first flow resistance coefficient based on the main channel, and the second flow resistance coefficient based on the microchannel calculated previously are integrated, and the flow resistance coefficient distribution is determined by establishing a data table or marking it in the model of the thermal cycle structure. This flow resistance coefficient distribution is an information set that comprehensively reflects the flow resistance characteristics of the thermal cycle structure, and can fully display the flow resistance characteristics in the thermal cycle structure. For example, in a three-dimensional model of a thermal cycle structure, the flow resistance coefficients of each pipeline interaction node, the straight pipe section of the main channel, and the straight pipe section of the microchannel are marked at the corresponding position, so as to intuitively present the flow resistance coefficient distribution. Determining the flow resistance coefficient distribution provides a key data basis for the thermal management module to accurately control the thermal cycle process, which helps to optimize the thermal management strategy to improve the thermal cycle performance of the battery pack.

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

[0054] Step S341: Preprocess the historical thermal management records of the interactive power tool, using the temperature field as an input variable and the thermal cycle parameter as a response, determine sample data and mine the response surface relationship, wherein the response surface relationship is a relative linear relationship between the input variable and the response.

[0055] Step S342: Based on the response surface relationship and the labeled thermal cycle structure, the thermal management module is acquired by training the sample data until convergence.

[0056] Specifically, the storage system of the power tool is interacted with to obtain its historical thermal management records. These historical thermal management records are relevant data records of thermal management of the power tool in the past, including temperature field conditions at different times, corresponding thermal cycle parameters (including thermal cycle path, coolant inlet flow rate, coolant temperature, number of pipeline rotations, etc.), and thermal management operation records. Then, the temperature field is used as the input variable and the thermal cycle parameters are used as the response quantity, and the historical thermal management records are preprocessed, including data cleaning (removing outliers, erroneous data, etc.), data normalization (converting data of different ranges to the same scale), and other operations. For example, if there are some obviously erroneous records of coolant temperature data in the historical thermal management records (such as temperature values ​​that exceed the physical possibility), they will be removed; at the same time, the temperature field data and thermal cycle parameter data of different magnitudes are normalized to generate sample data. Then, the sample data is analyzed using a data mining algorithm, such as a regression analysis algorithm, to mine the response surface relationship between the input variable and the response quantity. By analyzing a large amount of historical data, the linear relationship between the change in the temperature field and the thermal cycle parameters (such as the adjustment of the coolant inlet flow rate, etc.) is determined.

[0057] Based on the response surface relationship and the marked thermal cycle structure obtained in step S341, sample data (preprocessed historical thermal management record data) is used for training. A machine learning algorithm, such as a neural network algorithm or a support vector machine algorithm, is used. Taking the neural network algorithm as an example, during the training process, the weights and biases of the neural network are continuously adjusted through the back propagation algorithm, so that the error between the thermal cycle parameters predicted by the model and the actual thermal cycle parameters in the sample data is gradually reduced. When the error meets the convergence condition (such as the mean square error is less than a certain set value), the training is stopped 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 conditions based on the marked thermal cycle structure and the rules in the historical data, thereby improving the accuracy and adaptability of the thermal management module.

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

[0059] Step S41: mining temperature features based on thermal runaway risk according to the historical thermal management records, wherein the temperature features are risk critical feature vectors, which at least include spatial distribution features, temperature vector features, and temperature fluctuation features.

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

[0061] Step S43: Based on the verification checkpoint, a thermal runaway risk determination is performed on the real-time temperature field based on temperature characteristics to determine a 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 and corresponding thermal management operations and thermal runaway risk status information are obtained from historical thermal management records. Then, data mining techniques, such as cluster analysis, principal component analysis and other algorithms, are used to analyze these data and mine temperature features related to thermal runaway risks, including temperature features in terms of spatial distribution, temperature vector, temperature fluctuation, etc. For example, historical data points with similar temperature characteristics are classified through cluster analysis, and characteristic patterns related to thermal runaway risks are found out, so as to determine the risk critical feature vector. This risk critical feature vector includes at least spatial distribution features, temperature vector features and temperature fluctuation features. Among them, the spatial distribution feature refers to the spatial distribution of the temperature inside the battery pack, such as whether a certain area is prone to high temperature aggregation, the spatial performance of the temperature difference between different battery cells, etc.; the temperature vector feature includes features such as the size and direction of the temperature, reflecting information such as the intensity and direction of heat transfer; the temperature fluctuation feature indicates the fluctuation of temperature over time, such as the frequency and amplitude of temperature changes, etc., and severe temperature fluctuations may indicate thermal runaway risks.

[0064] Verification levels are configured according to the determined temperature characteristics, and each verification level sets the corresponding risk threshold according to different types of temperature characteristics (spatial distribution, temperature vector, and temperature fluctuation). For example, if the spatial distribution characteristics in the temperature characteristics indicate that the temperature in a specific area of ​​the battery pack (such as a corner position) is too high and is prone to thermal runaway, then the detection logic for the temperature in this area is set in the software program of the verification level, or the weight of the corresponding sensor is adjusted in the hardware circuit. If the temperature fluctuation characteristics show that there is a risk of thermal runaway when the temperature fluctuation amplitude exceeds a certain value, then the corresponding fluctuation amplitude threshold is set in the verification level for detection. The verification level configured based on temperature characteristics can more accurately pre-check the risk of thermal runaway on the real-time temperature field. It improves the pertinence and effectiveness of the verification and reduces the possibility of misjudgment and missed judgment.

[0065] The real-time temperature field data is transmitted to the verification checkpoint, and the verification checkpoint performs detection according to the set temperature feature detection logic to determine whether there is a risk of thermal runaway and obtain a judgment result. For example, the verification checkpoint detects 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, whether the temperature fluctuation exceeds the set threshold, etc. The judgment result has two situations: "yes" or "no". When the judgment 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 through the data transmission line; when the judgment 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 verification levels for pre-verification, real-time temperature field data with thermal runaway risks can be promptly transmitted back to the thermal management module, allowing the thermal management module to quickly make 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 method includes:

[0068] The temperature data of the real-time temperature field is spatially interpolated and transmitted back, wherein the spatial interpolation method includes average interpolation and trend interpolation; if the spatial distance of the neighborhood temperature data is less than or equal to the preset spacing, the mean interpolation method is adopted; if the spatial distance of the neighborhood temperature data is greater than the preset spacing, the trend interpolation method is adopted.

[0069] Specifically, in the real-time temperature field, since the temperature sensor can only collect the temperature value of the spatial position where it is arranged, the amount of temperature data obtained is relatively small and discrete. The spatial interpolation processing can improve the data density and the smoothness of the temperature distribution trend, and obtain a more continuous temperature distribution of the entire temperature field, so as to facilitate the subsequent thermal management module to perform decision analysis. First, determine the position of each temperature sensor in the real-time temperature field and the corresponding collected temperature data. Then, for each position that needs to be interpolated, calculate its spatial distance from the neighboring temperature data (data from the surrounding temperature sensors), and compare the calculated spatial distance with the preset spacing. This preset spacing is a pre-set distance value used to determine which interpolation method to use for spatial interpolation processing.

[0070] If the spatial distance is less than or equal to the preset spacing, the average interpolation method is used, that is, the temperature of the unknown position is estimated by calculating the average value of the neighborhood temperature data. 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 the four sensors are added and divided by 4 to obtain the interpolated temperature of the point.

[0071] If the spatial distance is greater than the preset spacing, the trend interpolation method is used to estimate the temperature of the unknown location based on the trend of the known temperature data (such as the temperature gradient, etc.). This requires analyzing the trend of the known temperature data first. The trend of temperature change can be determined by fitting functions (such as linear fitting, polynomial fitting, etc.), and then the temperature of the interpolation point to be calculated based on this trend.

[0072] Through spatial interpolation processing, the accuracy of temperature field data can be effectively improved. Even when the temperature data is incomplete, more 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 subsequent thermal management modules.

[0073] Furthermore, after step S5, the thermal cycle strategy is executed, and response tracking and feedback regulation of temperature management are performed; wherein the response tracking and feedback regulation methods include:

[0074] Step S51: Determine verification positions, wherein the verification positions are any at least two sensor positions in the temperature sensing array.

[0075] Step S52: collecting temperature data based on the verification position, determining the temperature adjustment deviation based on the temperature adjustment vector and the temperature adjustment direction, and determining the response characteristics.

[0076] Step S53: performing feedback control of the battery pack according to the response characteristics.

[0077] Specifically, after executing the thermal cycle strategy, it is necessary to track and provide feedback on the execution effect of the thermal cycle strategy to ensure the effectiveness of temperature management. First, two or more sensor positions are randomly selected from the temperature sensor array as verification positions, and the temperature data of the verification positions are collected and verified to determine the effect of temperature management.

[0078] According to the specific adjustment content of the thermal cycle strategy, the temperature adjustment vector and temperature adjustment direction are determined. Among them, the temperature adjustment vector is a vector that represents the magnitude and direction of temperature adjustment, reflecting the strength and direction of temperature adjustment expected 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 adjustment, such as the direction of heating or cooling.

[0079] At the determined verification position, the temperature data is collected using a temperature sensor. Then, the collected temperature data is compared with the temperature adjustment vector and the temperature adjustment direction to determine whether the current temperature control effect deviates from the predetermined target. If the temperature deviates from the expected direction, feedback control is required at this time. For example, if the temperature adjustment vector requires that the temperature of a certain area be reduced by a certain amount (such as from 30°C to 25°C), the temperature adjustment direction is cooling, and the actual temperature data collected at the verification position shows that it has only dropped to 28°C, then it can be determined that there is a temperature adjustment deviation. The response characteristics are determined by calculating the difference between the actual temperature and the target temperature and analyzing the trend of temperature change. These response characteristics reflect the response of the temperature regulation, such as over-regulation, under-regulation, or wrong regulation direction, etc., which provides a clear basis for subsequent feedback control, so that the thermal management system can promptly detect problems in the temperature regulation process and take corresponding measures.

[0080] According to the determined response characteristics, the temperature management of the battery pack is feedback-regulated so that the temperature regulation is more in line with the expected temperature regulation vector and temperature regulation direction. If the response characteristics indicate insufficient regulation, for example, the temperature drop is not enough during the cooling process, the thermal management system can increase the heat dissipation power, such as increasing the flow rate of the coolant or increasing the speed of the cooling fan; if it is over-regulated, reduce the heat dissipation power or increase the heating power (if it is necessary to maintain a certain temperature range). If the regulation direction is wrong, adjust the temperature regulation vector and temperature regulation direction, and re-regulate the temperature. The feedback regulation process is achieved by controlling the relevant hardware devices (such as coolant pumps, cooling fans, heating elements, etc.) through the control software of the thermal management system. Through feedback regulation, the accuracy and effectiveness of battery pack temperature management can be improved. Ensure that the temperature of the battery pack is always kept within the appropriate range to improve the performance, safety and service life of the battery.

[0081] In summary, the thermal cycle performance optimization method for a power tool battery pack provided in the embodiments of the present application has the following technical effects:

[0082] The embodiments of the present application take into account the synergistic effects of the battery pack structural characteristics and the thermal cycle structural characteristics, combine the temperature sensor array with the thermal cycle structure analysis, accurately monitor the real-time temperature field of the battery pack, and optimize the design of the heat flow path to ensure efficient heat dispersion. Further, the pre-verification mechanism is used to provide early warning of temperature anomalies to ensure that risks such as thermal runaway can be identified and adjusted in a timely manner at an early stage; and temperature control decisions are made based on real-time data and pre-verification results to achieve dynamic regulation of the battery pack temperature. Overall, the embodiments of the present application significantly improve the accuracy and efficiency of thermal cycle control, effectively reduce the risk of thermal runaway, enhance the safety of the battery pack, and extend the service life of the battery pack through precise temperature control.

[0083] Embodiment 2, as Figure 4 As shown, based on the same inventive concept as the aforementioned embodiment 1, the embodiment of the present application provides a thermal cycle performance optimization platform for a power tool battery pack, the platform comprising:

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

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

[0086] The thermal management module construction unit 30 is used to construct a thermal management module according to the thermal cycle structural characteristics by dividing the main channel and the microchannel and introducing the flow resistance element, wherein the microchannel is a gap layout channel between battery cells in the battery pack, and the flow resistance element characterizes the magnitude of the fluid flow resistance. The thermal management module uses the temperature field as an input variable and the thermal cycle parameter as a response variable.

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

[0088] The strategy execution unit 50 is used to execute the thermal cycle strategy according to the thermal management system of the electric tool to control the temperature of the battery pack.

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

[0090] In view of the characteristics of the thermal cycle structure, the main pipeline and the micro pipeline are divided according to the pipeline geometric parameters and the pipeline position, and the pipeline type label is generated; the flow resistance element is introduced, and the 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 label 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 by performing data-driven training.

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

[0092] Identify the thermal cycle structural characteristics and determine the pipeline interaction nodes, wherein the pipeline interaction nodes include elbows, multi-passes, and valves; traverse the pipeline interaction nodes to determine node geometric characteristics; determine the node flow resistance coefficient based on the node geometric characteristics; based on the thermal cycle structural characteristics, identify the straight pipe part, determine the first flow resistance coefficient based on the main channel, and the 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, the thermal management module construction unit 30 of the embodiment of the present application is also used to perform the following steps:

[0094] The thermal cycle parameters at least include the thermal cycle path, the coolant inlet flow rate, the coolant temperature, and the number of pipeline rotations; the historical thermal management records of the interactive power tool, with the temperature field as the input variable and the thermal cycle parameters as the response, pre-processing the historical thermal management records, determining the sample data and mining the response surface relationship, wherein the response surface relationship is the relative linear relationship between the input variable and the response; taking the response surface relationship and the marked thermal cycle structure as a benchmark, training based on the sample data until convergence, and obtaining the thermal management module.

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

[0096] According to the historical thermal management records, the temperature characteristics based on the thermal runaway risk are mined, wherein the temperature characteristics are risk critical characteristic vectors, which at least include spatial distribution characteristics, temperature vector characteristics, and temperature fluctuation characteristics; based on the temperature characteristics, the verification checkpoints are configured; based on the verification checkpoints, the real-time temperature field is subjected to a thermal runaway risk determination based on the temperature characteristics, and a determination result is determined; if the determination result is yes, the real-time temperature field is transmitted back to the thermal management module.

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

[0098] The temperature data of the real-time temperature field is spatially interpolated and transmitted back, wherein the spatial interpolation method includes average interpolation and trend interpolation; if the spatial distance of the neighborhood temperature data is less than or equal to the preset spacing, the mean interpolation method is adopted; if the spatial distance of the neighborhood temperature data is greater than the preset spacing, the trend interpolation method is adopted.

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

[0100] Execute the thermal cycle strategy, and perform response tracking and feedback regulation of temperature management; wherein the response tracking and feedback regulation method includes: determining a verification position, wherein the verification position is any at least two sensor positions in the temperature sensor array; collecting temperature data based on the verification position, performing temperature adjustment deviation judgment based on the temperature adjustment vector and the temperature adjustment direction, and determining the response characteristics; and performing feedback regulation of the battery pack according to the response characteristics.

[0101] Through the above-mentioned detailed description of the thermal cycle performance optimization method for power tool battery packs in this specification, those skilled in the art can clearly understand the thermal cycle performance optimization platform for power tool battery packs in this embodiment. For the platform disclosed in Example 2, since it corresponds to the method disclosed in Example 1 and has corresponding functional units and beneficial effects, the relevant parts can be referred to the description of the method part.

[0102] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be 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 the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing thermal cycle performance of a power tool battery pack, characterized in that: The method comprises: Interacting with basic configuration information of the electric tool to obtain structural characteristics, wherein the structural characteristics include battery pack structural characteristics and thermal cycle structural characteristics; Arrange a temperature sensor array based on the structural characteristics of the battery pack, and determine the real-time temperature field through sensor sampling; According to the thermal cycle structural characteristics, a thermal management module is constructed by dividing the main channel and the microchannel and introducing the flow resistance element, wherein the microchannel is a gap arrangement channel between battery cells in the battery pack, and the flow resistance element represents the magnitude of the fluid flow resistance. The thermal management module uses the temperature field as an input variable and the thermal cycle parameter as a response variable; A checkpoint is arranged at the sensor data interface to pre-check the thermal runaway risk of the real-time temperature field, and the checkpoint is sent back to the thermal management module to make a temperature control decision and determine the thermal cycle strategy; According to the thermal management system of the electric tool, the thermal cycle strategy is executed to control the temperature of the battery pack.

2. The thermal cycle performance optimization method for a power tool battery pack according to claim 1, characterized in that: By dividing the main channel and microchannel and introducing flow resistance elements, a thermal management module is constructed, including: According to the characteristics of the thermal cycle structure, the main pipeline and micro pipeline are divided according to the pipeline geometric parameters and pipeline positions, and pipeline type labels are generated; Introducing a flow resistance factor, performing a flow resistance analysis based on the thermal cycle structural characteristics, and determining a flow resistance coefficient distribution; Based on the pipeline type label and the flow resistance coefficient distribution, marking the thermal cycle structure to determine the marked thermal cycle structure; Based on the labeled thermal cycle structure, the thermal management module is constructed by performing data-driven training.

3. The thermal cycle performance optimization method for a power tool battery pack according to claim 2, characterized in that: The determining of the flow resistance coefficient distribution comprises: Identify the thermal cycle structural characteristics and determine pipeline interaction nodes, wherein the pipeline interaction nodes include elbows, multi-ports, and valves; Traversing the pipeline interaction nodes to determine the node geometric features; Determining the node flow resistance coefficient according to the node geometric characteristics; Based on the thermal cycle structural characteristics, the straight tube portion is identified, and a first flow resistance coefficient based on the main channel and a second flow resistance coefficient based on the microchannel are determined; The node flow resistance coefficient, the first flow resistance coefficient, and the second flow resistance coefficient are integrated to determine the flow resistance coefficient distribution.

4. The thermal cycle performance optimization method for a power tool battery pack according to claim 3, characterized in that: Based on the labeled thermal cycle structure, the thermal management module is constructed by performing data-driven training, including: The thermal cycle parameters at least include thermal cycle path, coolant inlet flow rate, coolant temperature, and pipeline rotation times; Interactive power tool historical thermal management records, using temperature field as input variable and thermal cycle parameter as response quantity, preprocessing the historical thermal management records, determining sample data and mining response surface relationship, wherein the response surface relationship is a relative linear relationship between input variable and response quantity; The response surface relationship and the labeled thermal cycle structure are used as a reference, and the thermal management module is obtained by training the sample data until convergence.

5. The thermal cycle performance optimization method for a power tool battery pack according to claim 4, characterized in that: Verification checkpoints are arranged at the sensor data interface to perform pre-verification of the thermal runaway risk of the real-time temperature field, including: According to the historical thermal management records, mining temperature features based on thermal runaway risk, 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 characteristics, configuring the verification checkpoint; Based on the verification checkpoint, a thermal runaway risk determination is performed on the real-time temperature field based on temperature characteristics to determine a determination result; If the determination result is yes, the real-time temperature field is transmitted back to the thermal management module.

6. The thermal cycle performance optimization method for a power tool battery pack according to claim 5, characterized in that: Before transmitting the real-time temperature field back to the thermal management module, the method includes: Performing spatial interpolation processing on the temperature data of the real-time temperature field and transmitting it back, wherein the spatial interpolation method includes average interpolation and trend interpolation; If the spatial distance of the neighborhood temperature data is less than or equal to the preset spacing, the mean interpolation method is used; If the spatial distance of the neighborhood temperature data is greater than the preset spacing, the trend interpolation method is used.

7. The thermal cycle performance optimization method for a power tool battery pack according to claim 1, characterized in that: After the battery pack temperature is regulated, it includes: Execute the thermal cycle strategy and perform response tracking and feedback control of temperature management; Among them, response tracking and feedback control methods include: Determine a verification position, wherein the verification position is any at least two sensor positions in the temperature sensing array; Based on the temperature data collected at the verification position, the temperature adjustment deviation is determined by the temperature adjustment vector and the temperature adjustment direction to determine the response characteristics; Feedback control of the battery pack is performed based on the response characteristics.

8. A thermal cycle performance optimization platform for power tool battery packs, characterized in that: The platform is used to execute the thermal cycle performance optimization method for a power tool battery pack according to any one of claims 1 to 7, comprising: A structural characteristic acquisition unit, used to exchange basic configuration information of the electric tool and acquire structural characteristics, wherein the structural characteristics include battery pack structural characteristics and thermal cycle structural characteristics; A temperature sensing unit, used to arrange a temperature sensing array based on the structural characteristics of the battery pack and determine a real-time temperature field through sensing sampling; A thermal management module construction unit, configured to construct a thermal management module according to the thermal cycle structural characteristics by dividing the main channel and the microchannel and introducing a flow resistance element, wherein the microchannel is a gap arrangement channel between battery cells in a battery pack, the flow resistance element represents the magnitude of fluid flow resistance, and the thermal management module uses a temperature field as an input variable and a thermal cycle parameter as a response variable; A thermal cycle strategy formulation unit is used to set up a verification checkpoint on the sensor data interface, perform a pre-verification of the thermal runaway risk on the real-time temperature field, and transmit it back to the thermal management module for temperature control decision-making and determination of the thermal cycle strategy; The strategy execution unit is used to execute the thermal cycle strategy according to the thermal management system of the electric tool to control the temperature of the battery pack.

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