Battery pack temperature control method

Abstract

The invention relates to the technical field of battery pack temperature control, in particular to a battery pack temperature control method. Comprising the following steps of performing detection parameter configuration on a temperature sensor according to battery pack data, collecting real-time battery pack temperature data, performing temperature verification smooth processing and spatial-temporal characteristic synchronous processing on the real-time battery pack temperature data, and generating synchronous verification battery pack temperature data; performing battery pack aging analysis and battery pack temperature dynamic threshold calculation according to the battery pack data, and performing battery pack abnormal temperature detection on the synchronous verification battery pack temperature data according to the battery pack temperature dynamic threshold data; and battery pack temperature regulation and control parameters are set based on the battery pack condition data and the battery pack abnormal temperature data to generate the battery pack temperature regulation and control parameters, and the battery pack temperature regulation and control parameters are transmitted to the battery pack temperature-regulation and control equipment to execute battery pack temperature regulation and control. The method has the effect of performing real-time temperature regulation and control according to different aging degrees and use conditions of the battery pack.

Description

Technical Field

[0001] The present invention relates to the technical field of battery pack temperature control, and in particular to a battery pack temperature control method. Background Technique

[0002] An energy storage container is a modular energy storage system integrating functions such as a battery pack, a battery management system, and a heat dissipation system, and can be used in fields such as photovoltaic power generation, wind power generation, and new energy vehicles. With the increasing demand for energy storage of battery packs, the capacitance and volume of battery packs are getting larger and larger, and the temperature control of battery packs is particularly important. The temperature of the battery pack needs to be maintained within a certain range during use and storage to ensure the performance of the battery pack.

[0003] In the existing related technologies, the temperature regulation threshold of the battery pack adopts a fixed threshold range.

[0004] In view of the above related technologies, batteries used for a long time are difficult to dynamically adjust the temperature control threshold according to their usage conditions, so there is a certain delay when the temperature of the battery pack is abnormal and regulated. Summary of the Invention

[0005] Based on this, the present invention provides a battery pack temperature control method to alleviate the technical problem of delay in the abnormal regulation of the battery pack temperature.

[0006] To achieve the above object, an adaptive cooling regulation method during the production process of a conductive bar includes the following steps: Step S1: Obtain battery pack data; perform detection parameter configuration processing on the temperature sensor according to the battery pack data, and collect the real-time temperature of the battery pack through the temperature sensor after the detection parameter configuration to generate real-time battery pack temperature data; Step S2: Perform temperature verification smoothing processing on the real-time battery pack temperature data to generate verified battery pack temperature data; perform spatio-temporal feature synchronization processing on the verified battery pack temperature data to generate synchronized verified battery pack temperature data; Step S3: Perform battery pack aging analysis according to the battery pack data to generate battery pack aging data; perform battery pack temperature dynamic threshold calculation based on the battery pack aging data to generate battery pack temperature dynamic threshold data; Step S4: Obtain battery pack temperature control device data; perform joint processing of the battery pack temperature control device based on the battery pack temperature control device data and the battery pack data to generate a battery pack - temperature regulation device; Step S5: Detect the abnormal temperature of the battery pack for the synchronized verified battery pack temperature data according to the dynamic threshold data of the battery pack temperature, and generate the abnormal temperature data of the battery pack; obtain the battery pack status data, set the battery pack temperature control parameters based on the abnormal temperature data of the battery pack and the battery pack status data, and generate the battery pack temperature control parameters; transmit the battery pack temperature control parameters to the battery pack temperature - control device to execute the battery pack temperature control.

[0007] By adopting the above technical solutions, the present invention flexibly configures the detection parameters of the temperature sensors by combining the battery pack data, optimizes the data acquisition accuracy and efficiency of the temperature sensors, so as to collect highly reliable real - time battery pack temperature data. Perform temperature calibration and smoothing processing on the real - time battery pack temperature data to eliminate detection errors and interference, and perform synchronous processing in combination with spatio - temporal characteristics to ensure the consistency and comparability of the battery pack temperature data at different positions and times. By evaluating the aging state of the battery pack, dynamically reflect the degree of battery performance decline, and calculate the dynamic threshold of the battery pack temperature according to the battery pack aging data, so that the battery pack temperature control can be adaptively adjusted according to the battery aging state, and accurately control the temperature of the battery. Establish a matching relationship between the battery pack and the temperature control device to ensure that the temperature control instruction can act on the target battery pack area quickly and accurately, thereby enhancing the coordination and energy efficiency ratio of the overall battery pack temperature control. By comparing the synchronized verified battery pack temperature data with the dynamically calculated temperature control threshold in real time, achieve accurate abnormal temperature detection, combine the real - time working condition data of the battery pack, generate the optimal temperature control parameters, and quickly execute accurate battery pack temperature control through the pre - set "battery pack - temperature control device" to achieve rapid adaptive control of the abnormal temperature of the battery pack, effectively prevent the risk of thermal runaway, and optimize the energy consumption efficiency while ensuring the safety of the battery.

[0008] Preferably, step S1 includes the following steps: Step S11: Obtain the battery pack data; Step S12: Analyze the battery pack nodes according to the battery pack data, and generate the battery pack node data; Step S13: Analyze the current transmission link according to the battery pack node data, and generate the current transmission link data; Step S14: Integrate the temperature sensor configuration parameters based on the battery pack node data and the current transmission link data, and generate the sensor configuration detection parameters; Step S15: Configure the detection parameters of the temperature sensor according to the sensor configuration detection parameters, and collect the real - time temperature of the battery pack through the temperature sensor after the detection parameter configuration, and generate the real - time battery pack temperature data.

[0009] By adopting the above technical solutions, the present invention identifies the key thermal management areas of the battery pack and the thermal effect distribution of the current path through battery pack node analysis and current transmission link analysis, optimizes the layout and detection parameters of temperature sensors, and realizes the precise configuration of the temperature monitoring network, so as to collect reliable real-time battery pack temperature data.

[0010] Preferably, step S2 includes the following steps: Step S21: Perform area division processing according to the real-time battery pack temperature data to generate battery pack area temperature data; Step S22: Perform temperature verification and smoothing processing according to the battery pack area temperature data to generate verified battery pack temperature data; Step S23: Perform spatio-temporal feature synchronization processing on the verified battery pack temperature data to generate synchronized verified battery pack temperature data.

[0011] By adopting the above technical solutions, the present invention establishes refined temperature management through battery pack temperature area division, combines verification and smoothing processing to eliminate measurement noise and abnormal fluctuations, and then ensures the data time series continuity and spatial correlation through spatio-temporal feature synchronization, realizing highly accurate and highly consistent synchronized verification of battery pack temperature data, laying a data foundation for subsequent precise temperature control of the battery pack, so as to be able to detect battery pack thermal anomalies in time and make a quick response.

[0012] Preferably, step S21 includes the following steps: Step S211: Calculate the battery pack temperature gradient vector according to the real-time battery pack temperature data to generate battery pack temperature gradient vector data; Step S212: Analyze the battery pack temperature distribution characteristics according to the battery pack temperature gradient vector data to generate temperature distribution characteristic data; Step S213: Perform area division processing based on the temperature distribution characteristic data to generate battery pack area temperature data.

[0013] By adopting the above technical solutions, the present invention accurately quantifies the temperature change rate between different regions by calculating the battery pack temperature gradient vector, and analyzes the temperature distribution characteristics based on the gradient data to identify the core area of the thermal field and the conduction path, realizing reasonable dynamic area division and improving the refinement level and response speed of battery pack temperature management.

[0014] Preferably, step S3 includes the following steps: S31: Divide the battery pack characteristic data according to the battery pack data to generate battery pack characteristic data; S32: Perform battery pack aging analysis according to the battery pack characteristic data to generate battery pack aging data; S33: Calculate the dynamic temperature threshold of the battery pack based on the battery pack aging data to generate the dynamic temperature threshold data of the battery pack.

[0015] By adopting the above technical solution, the present invention analyzes the multi-dimensional characteristic data of the battery pack and couples it with the aging state analysis, constructs a dynamic temperature control threshold generation mechanism driven by the battery health degree, and then establishes a dynamic correction model of the temperature threshold strongly related to the aging degree of the battery pack. The safety boundary of the battery pack temperature is dynamically adjusted with the battery attenuation process, avoiding overprotection or underprotection of the aging battery by a fixed threshold, and realizing early warning of abnormal temperature rise through elastic adjustment over the threshold.

[0016] Preferably, step S32 includes the following steps: Step S321: Extract the battery pack capacity and the battery pack charge and discharge cycle data of the battery characteristic data respectively to obtain the battery pack capacity data and the battery pack charge and discharge cycle data respectively; Step S322: Calculate the capacitance loss rate of the battery pack according to the battery pack charge and discharge cycle data to generate the capacitance loss rate data of the battery pack; Step S323: Analyze the charge and discharge cycle of the battery pack based on the battery pack capacitance data and the battery pack charge and discharge cycle data to generate the charge and discharge cycle data of the battery pack; Step S324: Analyze the aging of the battery pack based on the capacitance loss rate data of the battery pack and the charge and discharge cycle data of the battery pack to generate the aging data of the battery pack.

[0017] By adopting the above technical solution, the present invention combines the capacity attenuation quantization index with the battery pack charge and discharge cycle data, and through the double verification mechanism of loss rate calculation and cycle analysis, ensures the accuracy of the battery pack aging state evaluation, and provides an accurate basis for the subsequent dynamic temperature threshold adjustment of the battery pack.

[0018] Preferably, step S322 includes the following steps: Extract the charge and discharge characteristic spectrum according to the battery pack charge and discharge cycle data to generate the charge and discharge characteristic spectrum data; Calculate the charge and discharge spectrum time series attenuation matrix coefficient of the charge and discharge characteristic spectrum data to generate the time series attenuation coefficient matrix data; Calculate the capacitance loss rate of the battery pack according to the time series attenuation coefficient matrix data to generate the capacitance loss rate data of the battery pack.

[0019] By adopting the above technical solution, the present invention realizes the accurate quantitative evaluation of the battery capacity loss rate by calculating the charge and discharge spectrum time series attenuation matrix coefficient of the charge and discharge characteristic spectrum data, reflects the spatio-temporal correlation of the energy attenuation rate of the battery pack in different frequency bands through the time series attenuation coefficient matrix, and makes the subsequent dynamic temperature threshold adjustment more accurate and effective by establishing the mapping relationship between the attenuation coefficient matrix and the capacity loss.

[0020] Preferably, step S33 includes the following steps: Step S331: Analyze the temperature power consumption of the battery pack based on the battery pack aging data to generate battery pack temperature group power consumption data; Step S332: Draw a battery pack temperature-power curve based on the battery pack temperature power consumption data to generate battery pack temperature-power curve data; Step S333: Divide the battery pack temperature range according to the battery pack temperature-power curve data to generate high-loss inflection point temperature data and low-loss inflection point temperature data; Step S334: Integrate the battery pack temperature dynamic threshold based on the high-loss inflection point temperature data and the low-loss inflection point temperature data to generate battery pack temperature dynamic threshold data.

[0021] By adopting the above technical solutions, the present invention dynamically reflects the correlation between the battery power consumption at different temperatures through the temperature-power curve, thereby achieving accurate division of high / low temperature thresholds, forming a dual-threshold dynamic correction mechanism for battery pack temperature control, enabling the battery pack temperature range to be dynamically adjusted according to the battery aging degree, and realizing adaptive optimization adjustment of the battery pack temperature control threshold.

[0022] Preferably, step S4 includes the following steps: Step S41: Obtain battery pack temperature control device data; Step S42: Analyze the spatial topology structure of the temperature control device based on the battery pack temperature control device data to generate temperature control device topology structure data; Step S43: Analyze the region-associated battery pack based on the battery pack data to generate region-associated battery pack data; Step S44: Perform joint processing of the battery pack temperature control based on the temperature control device topology structure data and the region-associated battery pack data to generate a battery pack - temperature control device.

[0023] By adopting the above technical solutions, the present invention obtains the temperature control device parameters and analyzes its spatial topology structure, realizes precise collaborative optimization of the battery pack temperature regulation by establishing a dynamic mapping relationship between the temperature control device topology structure and the spatial distribution of the battery pack, and ensures that each temperature control unit can accurately cover the corresponding battery pack area through topology structure analysis.

[0024] Preferably, step S5 includes the following steps: Step S51: Use the battery pack temperature dynamic threshold data to monitor the battery pack temperature for anomalies in the synchronized verification battery pack temperature data to generate battery pack abnormal temperature data; Step S52: Analyze the battery pack load based on the abnormal battery pack temperature data to generate battery pack load data; Step S53: Set the battery pack temperature regulation parameters based on the abnormal battery pack temperature data and the battery pack load data to generate battery pack temperature regulation parameters; Step S54: Transmit the battery pack temperature regulation parameters to the battery pack - temperature regulation device to perform battery pack temperature regulation.

[0025] By adopting the above technical solution, through the synergistic effect of dynamic threshold monitoring and load status analysis, the closed-loop control of battery pack temperature management is realized. The temperature anomaly detection is combined with the real-time working condition analysis of the battery pack. Based on the dynamic threshold, potential thermal risks are accurately identified, and the optimal regulation parameters are intelligently generated according to the load status, and a rapid response is made through the preset temperature control device network.

[0026] The beneficial effects of this application are as follows. First, by dynamically configuring the parameters of the temperature sensor, the data acquisition accuracy is optimized. The real-time battery pack temperature data is processed through calibration smoothing and spatio-temporal synchronization to eliminate measurement noise and timing deviation, ensuring data consistency. The temperature threshold is dynamically adjusted based on the battery pack aging analysis to avoid overprotection or underprotection of aging batteries by fixed thresholds, improving the timeliness of battery pack temperature control. Through the joint processing of the temperature control device and the battery area, the accurate matching of the temperature control device and the heat source of the battery pack is realized, improving the regulation efficiency and accuracy of the battery pack. The abnormal temperature detection of the battery pack is combined with the load analysis to generate the optimal regulation parameters, and a rapid response is made through the preset device network, effectively preventing the thermal runaway of the battery pack, so that the battery pack temperature regulation dynamically adapts to the aging state and usage of the battery pack. Brief Description of the Drawings

[0027] Figure 1 is a schematic flow chart of steps S1 - S5 in a battery pack temperature control method according to an embodiment of the present invention; Figure 2 is Figure 1 the specific flow chart of step S1; Figure 3 is Figure 1 the specific flow chart of step S2; Figure 4 is Figure 1 the specific flow chart of step S3; Figure 5 is Figure 1 the specific flow chart of step S4; Figure 6 is Figure 1 the specific flow chart of step S5; Figure 7 is Figure 1 the specific flow chart of step S21; Figure 8 Yes Figure 1 It is a schematic diagram of the specific process of step S32 in Figure 9 Yes Figure 1 It is a schematic diagram of the specific process of step S33 in Specific implementation mode

[0028] Next, the technical solutions in the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Figure 1-9

[0029] The present invention will be further described in detail below with reference to the accompanying drawings.

[0030] Example 1: Step S1: Obtain battery pack data; perform detection parameter configuration processing on the temperature sensor according to the battery pack data, and collect the real-time temperature of the battery pack through the temperature sensor after the detection parameter configuration to generate real-time battery pack temperature data; Specifically, the battery pack data is collected through the CAN bus interface of the battery management system BMS, including the single-cell voltage, current, charge and discharge times, working environment temperature, and battery pack topology structure (series and parallel quantities) of the battery pack. According to the battery pack topology structure, the battery pack is decomposed into several unit grids by using the grid division method. The center point of each grid corresponds to the physical coordinates of the battery cell, and a temperature sensor is correspondingly configured. An infrared thermal imager is used to perform a full-area scan on the surface of the battery pack to establish a mapping relationship table between the battery cell position and the thermal distribution. Based on the analysis of the battery pack current transmission link data, sensor detection parameters are generated. The calibrated temperature sensor communicates with the BMS through the SPI protocol to collect the real-time temperature of the battery pack.

[0031] The accurate multi-dimensional temperature acquisition of the battery pack is realized by dynamically configuring the temperature sensor parameters, thus effectively solving the monitoring of uneven temperature distribution in large-capacity battery packs.

[0032] Step S2: Perform temperature calibration and smoothing processing on the real-time battery pack temperature data to generate calibrated battery pack temperature data; perform spatio-temporal feature synchronization processing on the calibrated battery pack temperature data to generate synchronized calibrated battery pack temperature data; Specifically, the real-time temperature data is processed by a sliding window filtering algorithm, which uses a cascade of median filtering and mean filtering to eliminate transient noise and abnormal jumps. The time-space feature synchronization processing uses a combination of Kalman filtering and timestamp alignment: first, based on the battery pack grid coordinates, the spatial temperature gradient is extracted, and the temperature change rate of adjacent grids is calculated. Then the timestamp is bound to the temperature data, and the weighted average of multiple sensor data at the same time point is performed to generate synchronized verification battery pack temperature data. The data format is a three-dimensional matrix (time × space coordinate × temperature value).

[0033] Through the real-time battery pack temperature data, temperature verification and smoothing processing as well as time-space feature synchronization processing can be performed to significantly improve the real-time performance and spatial resolution of the temperature data and eliminate the lag error of traditional single-point monitoring.

[0034] Step S3: performing battery pack aging analysis based on the battery pack data to generate battery pack aging data; performing battery pack temperature dynamic threshold calculation based on the battery pack aging data to generate battery pack temperature dynamic threshold data; Specifically, the battery pack aging analysis is based on the charge and discharge cycle database, which extracts the complete charge and discharge records of each battery cell. For example, in the constant current and constant voltage charging stage, the difference between the actual charged capacity and the nominal capacity is accumulated. When the difference exceeds 15% of the nominal value, it is marked as the capacity decay stage. The dynamic threshold calculation adopts a piecewise linear adjustment strategy: the standard threshold (0-45°C) is maintained in the initial aging stage; the mid-term aging stage is adjusted to 5-40°C; the severe aging stage is set to 10-35°C. At the same time, fine-tuning is performed according to the internal resistance growth rate. For every 5% increase in internal resistance, the upper limit temperature is reduced by 0.5°C.

[0035] The battery pack aging analysis is performed through the battery pack data and the dynamic temperature threshold of the battery pack is calculated to achieve adaptive adjustment of the temperature threshold as the battery capacity decays.

[0036] Step S4: obtaining battery pack temperature control device data; performing battery pack temperature control device joint processing based on the battery pack temperature control device data and the battery pack data to generate a battery pack-temperature control device; Specifically, in Example 1 of the present invention, the battery pack temperature control device data includes the air cooling system acquisition parameters: fan speed, current, and cumulative operating hours, etc. The liquid cooling system monitoring parameters: pump flow, inlet and outlet temperature difference, and pressure, etc. The equipment is jointly processed to establish a three-dimensional heat conduction model. For example, the battery pack is divided into 5cm×5cm×5cm control units. The algorithm flow for matching the optimal temperature control device combination for each unit is as follows: the Euclidean distance from the center point of the unit to each temperature control device is calculated, and the thermal conduction efficiency of the equipment is evaluated. The air cooling equipment is calculated according to the air volume-distance attenuation curve, and the liquid cooling equipment is calculated according to the pipeline thermal resistance.

[0037] An intelligent matching of the topology of the temperature control device and the spatial distribution of the battery pack forms a collaborative control network to ensure the precise spatial positioning and execution of thermal management instructions.

[0038] Step S5: Detect the abnormal temperature of the battery pack in the synchronized battery pack temperature data according to the dynamic threshold data of the battery pack temperature, and generate the abnormal temperature data of the battery pack; obtain the battery pack condition data, and set the battery pack temperature control parameters based on the abnormal temperature data of the battery pack and the battery pack condition data, and generate the battery pack temperature control parameters; transmit the battery pack temperature control parameters to the battery pack temperature-control device to perform the battery pack temperature control.

[0039] Specifically, the abnormal temperature detection adopts a dual-criterion mechanism to detect the temperature of the battery pack through the dynamic threshold of the battery pack temperature, and set the battery pack temperature control parameters by combining the abnormal temperature data of the battery pack with the battery pack condition data. The abnormal temperature detection adopts a spatio-temporal joint criterion. For example, when the temperature of any battery cell in the synchronized temperature data exceeds the dynamic threshold for consecutive sampling periods and the temperature difference gradient between adjacent battery cells is too large, an abnormal event is triggered. The record of the abnormal temperature data of the battery pack includes the coordinates of the abnormal battery cell, the over-standard temperature value and the duration. The control parameter setting module queries the device linkage control table according to the abnormal code: for the local over-temperature area and the global temperature rise, the corresponding temperature control device is regulated to perform the battery pack temperature control, and the execution feedback data is compared in real time through the temperature matrix. When the temperature of the abnormal battery cell returns to the threshold range and stabilizes, the preset temperature control parameters are automatically restored.

[0040] Generate dynamic control parameters through the joint analysis of abnormal temperature detection and load status, so that the distribution of heat dissipation resources is precisely matched with the actual needs of the battery pack.

[0041] Embodiment 2: Preferably, step S1 includes the following steps: Step S11: Obtain the battery pack data; Step S12: Perform battery pack node analysis according to the battery pack data, and generate battery pack node data; Step S13: Perform current transmission link analysis according to the battery pack node data, and generate current transmission link data; Step S14: Integrate the temperature sensor configuration parameters based on the battery pack node data and the current transmission link data, and generate the sensor configuration detection parameters; Step S15: Perform detection parameter configuration processing on the temperature sensor according to the sensor configuration detection parameters, and collect the real-time temperature of the battery pack through the temperature sensor after the detection parameter configuration, and generate the real-time battery pack temperature data.

[0042] Specifically, in Embodiment 2 of the present invention, the battery pack data is acquired through the CAN bus interface of the Battery Management System (BMS) at a preset time interval. The data types include single-cell voltage, charge and discharge current, battery pack topology (number of series cells, number of parallel cells), and historical temperature extreme values, etc.

[0043] The battery pack node analysis is carried out based on the cell arrangement grid and the current path topology. The grid division method is used to decompose the battery pack into several equal-area units, and the center point of each unit is defined as a cell node. The node coordinates are encoded in the form of (X, Y). The node current density is calculated through current distribution simulation: a three-dimensional model of the battery pack is established in ANSYS Maxwell, the actual charge and discharge current values are loaded, and the current density values of each node are extracted after solving the Joule heat distribution. The node attribute table includes coordinates, current density, connection status of adjacent nodes, and distance from the bus bar, etc.

[0044] The current transmission link analysis is realized by combining resistance measurement and infrared thermal imaging. A micro-ohmmeter is used to measure the contact resistance of the connecting pieces between the cells in the battery pack. Combining with the current density values in the node data, the weight coefficients of the main current path and the branch path are calculated. The main path is defined as the continuous node sequence with the smallest total resistance from the positive bus bar to the negative bus bar, and the branch path is the set of nodes of the parallel branch. The path resistance value is calculated by the superposition method: the total resistance of the main path and the resistance deviation of the branch path. At the same time, an infrared thermal imager is used to take the thermal distribution map of the battery pack during full-load operation.

[0045] The integration of the temperature sensor configuration parameters is based on the node data and the current link data. For example, resistance temperature sensors are arranged within a radius of 20 mm around the nodes with high current density, and the sensor spacing is set to 10 mm. According to the temperature rise characteristics of the main path, the sensor sampling frequency is divided into three levels: the nodes on the main path are configured with a sampling rate of 500 Hz, the nodes on the branch path are configured with 200 Hz, and the edge nodes are configured with 100 Hz. Thus, the real-time battery pack temperature data is collected.

[0046] Through the joint analysis of the battery pack nodes and the current transmission link, the thermally sensitive areas of the battery pack are accurately identified, so that the arrangement positions and sampling frequencies of the temperature sensors match the actual heat generation characteristics of the battery, avoiding monitoring blind spots. Based on the battery pack node data and the current transmission link data, the temperature sensor configuration parameters are integrated, and the dynamically adjusted detection parameters enable the sensor network to automatically adapt to the thermal field changes under different charge and discharge states, thereby collecting the real-time battery pack temperature data.

[0047] Preferably, step S2 includes the following steps: Step S21: Perform regional division processing on the real-time battery pack temperature data to generate battery pack regional temperature data; Step S22: Perform temperature calibration and smoothing processing based on the battery pack area temperature data to generate calibrated battery pack temperature data; Step S23: Perform spatio-temporal feature synchronization processing on the calibrated battery pack temperature data to generate synchronized calibrated battery pack temperature data.

[0048] Specifically, in Embodiment 2 of the present invention, the area division processing is based on the cell arrangement coordinates and temperature distribution characteristics in the real-time battery pack temperature data. For example, the battery pack is divided into a physical grid unit of 8 rows × 12 columns (unit size 50mm × 50mm), and each unit corresponds to the X-Y coordinates of a cell (X = 1-8, Y = 1-12). The area division rule adopts the temperature gradient clustering method: taking the temperature difference between adjacent cells ≤ 1.5°C as the merging condition, and merging the continuously satisfied units into areas. The division process is scanned row by row through a temperature distribution scanner (resolution 0.1°C), and the scanning path starts from the positive bus bar (X = 1, Y = 1) and moves along the long axis direction of the battery pack at a speed of 5mm / s. The area boundary determination basis is temperature mutation point detection: when the temperature difference between adjacent units accumulates more than 4°C within 3 seconds, it is marked as an area dividing line. The attributes of each area include the area number (16-bit code), the number of cells included (1-96), the extreme temperature value of the area (the difference between the highest temperature and the lowest temperature), and the average temperature of the area (arithmetic mean). The division result is stored in a two-dimensional matrix structure, where the row number corresponds to the X coordinate, the column number corresponds to the Y coordinate, and the matrix element value is the area number, and finally a battery pack area temperature data set containing 15-25 areas is generated.

[0049] The temperature calibration and smoothing processing is realized based on the noise and outlier correction requirements in the area temperature data. For example, a three-level filtering mechanism is adopted: the first level is internal area filtering, and the median filtering of a 5×5 sliding window is performed on all cell temperature data within each area, and the temperature value of the cell at the center of the window is replaced by the median of the 25 temperature values within the window to eliminate single-point mutation noise. The second level is cross-area filtering. Based on the area average temperature, the deviation between each cell temperature and the area mean value is calculated, and the data points with a deviation exceeding ±3°C are marked as abnormal, and the abnormal values are replaced by the weighted average of the temperatures of the adjacent 3 cells, and the weight coefficients are distributed inversely proportional to the cell spacing. The third level is smoothing in the time dimension, and Gaussian weighted average filtering is performed on 100 consecutive sampling points of the same cell, and the weight coefficients are distributed according to the time decay curve, with the weight ratio of the latest sampling point being 50%, and the weight decreasing by 2% every 10ms. The smoothed data generates a calibrated battery pack temperature data set.

[0050] Spatio-temporal feature synchronization processing is achieved through timestamp calibration and spatial interpolation techniques. For example, a GPS timing chip is used to mark a unified timestamp for real-time battery pack temperature data, eliminating the timing deviation caused by sensor sampling delay. For data packets with a transmission delay exceeding 2 ms, linear interpolation is used to complete the missing values: based on the two adjacent valid sampling points, the temperature value of the middle point is calculated according to the time interval ratio. Spatial synchronization uses bicubic interpolation algorithm. For example, discrete cell temperature data is converted into a continuous temperature field of 512×512 pixels, with a pixel resolution of 0.2 mm / pixel. During the interpolation process, the temperature value of each pixel is calculated by weighted averaging of the temperatures of 16 surrounding cell nodes, and the weight coefficient is determined by the Euclidean distance between the node and the pixel center. The weight decreases by 15% for every 1 mm increase in distance. Synchronized and verified battery pack temperature data is generated, and the data format is a three-dimensional matrix (time×spatial coordinates×temperature value).

[0051] Through intelligent region division based on temperature gradient, the thermal aggregation region inside the battery pack is accurately identified. Temperature verification and smoothing processing are performed based on the battery pack region temperature data to eliminate sensor noise interference. Spatio-temporal feature synchronization processing effectively solves the time asynchrony in distributed sensor acquisition. By establishing the spatio-temporal correlation of the temperature field, high-precision spatio-temporal feature data support is provided for dynamic battery pack temperature control, ensuring the temperature monitoring reliability of large-capacity battery packs under all working conditions.

[0052] Preferably, step S21 includes the following steps: Step S211: Calculate the battery pack temperature gradient vector based on the real-time battery pack temperature data to generate battery pack temperature gradient vector data; Step S212: Analyze the battery pack temperature distribution characteristics based on the battery pack temperature gradient vector data to generate temperature distribution characteristic data; Step S213: Perform region division processing based on the temperature distribution characteristic data to generate battery pack region temperature data.

[0053] Specifically, in Embodiment 2 of the present invention, the battery pack temperature gradient vector calculation is based on the temperature difference between adjacent cells in the real-time battery pack temperature data. For example, a two-dimensional grid scanner is used to scan 96 cells of the battery pack (arranged in 8 rows×12 columns) row by row, and the temperature difference between the coordinates (X,Y) of each cell and its 4 adjacent cells (above, below, left, and right) is used as the calculation input. For example, the gradient vector calculation rule is: take the maximum difference between the current cell temperature and the adjacent cell temperature as the vector modulus, and the difference direction is determined by the coordinate difference of the cell with the higher temperature. For example, if the temperature of a certain cell (X3,Y5) is 2.1 °C higher than the right cell (X3,Y6), a horizontal right gradient vector with a modulus of 2.1 °C is generated.

[0054] The analysis of temperature distribution characteristics is realized based on the magnitude and direction distribution characteristics in the gradient vector data. The gradient vector database table is identified through calculation. For example, first, the vectors with a magnitude ≥ 3.0 °C are extracted and marked as high-gradient regions, and their distribution density is statistically analyzed. Secondly, the central tendency of the direction coding is analyzed. If the gradient directions of 5 consecutive battery cells in a certain area are the same, it is marked as a unidirectional diffusion area. The high-temperature core localization is realized through the extreme value tracking algorithm. For example, starting from the initial battery cell (X1, Y1), continuously search for consecutive vectors with increasing magnitude along the gradient direction until a local temperature peak point is found.

[0055] The area division process is executed based on the high-temperature core position and gradient diffusion range in the temperature distribution characteristic data. For example, with the high-temperature core as the center, expand outward until the gradient magnitude of the adjacent battery cells drops to ≤ 1.0 °C or the direction coding changes abruptly. The boundary determination adopts the two-way scanning method: scan along the gradient direction of the high-temperature core until the magnitude is lower than the threshold, and then scan in the reverse direction to confirm the boundary stability.

[0056] The direction and intensity of the internal heat flow of the battery pack are accurately captured through the calculation of the temperature gradient vector, realizing the real-time dynamic tracking of the temperature field change. The temperature distribution characteristics of the battery pack are analyzed based on the battery pack temperature gradient vector data, and the area division process is carried out based on the temperature distribution characteristic data. The adaptive area division algorithm dynamically adjusts the partition boundary according to the gradient change rate.

[0057] Preferably, step S3 includes the following steps: S31: Divide the battery pack characteristic data according to the battery pack data to generate battery pack characteristic data; S32: Conduct battery pack aging analysis according to the battery pack characteristic data to generate battery pack aging data; S33: Calculate the dynamic threshold of the battery pack temperature based on the battery pack aging data to generate battery pack temperature dynamic threshold data.

[0058] Specifically, in Embodiment 2 of the present invention, first, the real-time data during the operation of the battery pack is collected, including the voltage value, current value, and temperature value of each battery cell, and the historical charge and discharge cycle times and internal resistance change data are stored through the BMS (Battery Management System). Then, the collected data is classified structurally. The voltage, current, and temperature data are classified as real-time operation characteristic data, the charge and discharge cycle times and internal resistance data are classified as aging-related characteristic data, and the SOC data is classified as state evaluation characteristic data. Next, the data segmentation method is adopted. According to the physical arrangement structure of the battery pack, the battery pack is divided into several regions. Each region contains multiple battery cells, and the voltage, current, and temperature data of each region are respectively classified as regional operation characteristic data.

[0059] Battery pack aging analysis is carried out based on the charge and discharge cycle times, internal resistance changes, and capacity attenuation data in the battery pack characteristic data. For example, first, extract the historical charge and discharge cycle times of the battery pack, count the cumulative cycle times of each battery cell, and record the depth of discharge (DOD) for each cycle. Then, measure the internal resistance value of the current battery pack, compare it with the initial internal resistance value, and calculate the internal resistance growth rate. Next, obtain the actual capacity of the current battery pack through constant current charge and discharge tests, compare it with the nominal capacity, and calculate the capacity attenuation rate. Further, analyze the relationship between the charge and discharge cycle times and the capacity attenuation rate, establish a cycle times - capacity attenuation curve, and judge the aging trend of the battery pack. At the same time, in combination with the internal resistance growth rate, if the internal resistance increase exceeds 20% of the initial value and the capacity attenuation exceeds 15% of the nominal capacity, it is determined that the battery pack enters the accelerated aging stage. Finally, integrate the cycle times, internal resistance growth rate, and capacity attenuation rate data to generate battery pack aging data.

[0060] The dynamic temperature threshold calculation of the battery pack is adjusted based on the aging stage, internal resistance growth rate, and capacity attenuation rate in the battery pack aging data. For example, first, divide the temperature control strategy according to the aging stage. If the battery pack is in the initial aging stage, set the temperature threshold to the standard range (such as 0°C - 45°C); if it enters the accelerated aging stage, adjust the threshold range (such as 5°C - 40°C) to reduce the damage of high temperature to the aging battery. Then, in combination with the internal resistance growth rate, if the internal resistance grows rapidly, lower the maximum allowable temperature. For every 5% increase in internal resistance, the maximum temperature threshold is lowered by 1°C. Next, adjust the minimum temperature threshold according to the capacity attenuation rate. If the capacity attenuation exceeds 10%, the minimum temperature threshold is increased by 2°C to prevent further decline in battery performance at low temperatures. Finally, integrate the aging stage, internal resistance, and capacity data to generate dynamic temperature threshold data, including the maximum temperature threshold, minimum temperature threshold, and temperature control response speed parameters in different aging states, for subsequent abnormal temperature detection.

[0061] Establish a multi - dimensional aging evaluation system through the intelligent division of battery pack characteristic data to accurately quantify the degree of battery performance attenuation. Calculate the dynamic temperature threshold of the battery pack based on the battery pack aging data. The dynamic temperature threshold calculation of the battery pack automatically adjusts the temperature control boundary conditions according to the aging data, realizing the intelligent adjustment of the temperature control threshold with the health status of the battery pack.

[0062] Preferably, step S32 includes the following steps: Step S321: Extract the battery pack capacity and battery pack charge and discharge cycle data of the battery characteristic data respectively to obtain the battery pack capacity data and the battery pack charge and discharge cycle data respectively; Step S322: Calculate the battery pack capacitance loss rate based on the battery pack charge and discharge cycle data to generate battery pack capacitance loss rate data; Step S323: Analyze the charge and discharge cycles of the battery pack based on the battery pack capacitance data and the battery pack charge and discharge cycle data, and generate the battery pack charge and discharge cycle data; Step S324: Analyze the aging of the battery pack based on the battery pack capacitance loss rate data and the battery pack charge and discharge cycle data, and generate the battery pack aging data.

[0063] Specifically, in Embodiment 2 of the present invention, the battery characteristic data includes the initial capacity of the battery pack, the current actual capacity, the number of charge and discharge cycles, the charge and discharge depth (DOD) of each cycle, the current rate of charge and discharge, and the cycle timestamp data, etc. First, extract the initial capacity data of the battery pack from the historical database of the battery management system (BMS), which is determined by the standard capacity test at the time of battery factory. Then, calculate the current actual capacity of the battery pack through the real-time charge and discharge data recorded by the BMS. For example, after the battery pack is fully discharged, it is charged to the full charge state with a constant current, and the total charge is recorded as the current actual capacity. Next, extract the cumulative number of charge and discharge cycles of the battery pack from the cycle counter of the BMS, and associate the charge and discharge depth data of each cycle. For example, the discharge depth of a certain cycle is 80% and the charge depth is 100%. Finally, classify the initial capacity and the current actual capacity as the battery pack capacity data, and classify the number of charge and discharge cycles, the depth and current rate of each cycle as the battery pack charge and discharge cycle data.

[0064] The capacitance loss rate is calculated by the measured data comparison method. The calculation process first calls the nominal capacity value at the time of battery pack factory, and compares it with the current measured capacity data. The capacitance loss rate calculation formula is (nominal capacity - current capacity) / nominal capacity × 100%. To ensure the calculation accuracy, at the same time analyze the capacity attenuation trend of the most recent cycle, and use the linear regression method to fit the capacity attenuation curve. For each additional cycle, automatically record the change gradient of the capacity attenuation rate once. When the difference between the attenuation rates of two adjacent capacity tests exceeds 0.5%, trigger the data review mechanism and re-perform the capacity verification test under standard conditions. The finally generated battery pack capacitance loss rate data includes the current absolute loss rate, the relative loss rate of the most recent cycle, and the historical loss rate change trend graph.

[0065] The charge-discharge cycle analysis is achieved by analyzing historical charge-discharge records. The distribution of the number of cycles at different discharge depths (DOD) is statistically analyzed. For example, the charge-discharge cycles are divided into three categories: shallow cycles (DOD ≤ 30%), medium cycles (30% < DOD ≤ 70%), and deep cycles (DOD > 70%). For each type of cycle, its average cycle efficiency, temperature rise amplitude, and capacity attenuation contribution degree are calculated respectively. A correspondence table between the number of cycles and capacity attenuation is established, recording the capacity attenuation amount corresponding to each cycle. At the same time, the equivalent full cycle number experienced by the battery pack is calculated. This value is obtained by converting the number of cycles at different DODs into equivalent cycle numbers at 100% DOD according to the attenuation influence coefficient.

[0066] The aging analysis process synthesizes the capacitor loss rate and charge-discharge cycle characteristics. For example, a relationship matrix between capacity attenuation and the number of cycles is established, and the matrix dimensions include the number of cycles, cycle depth, and temperature conditions, etc. By querying this matrix, the progress percentage of the actual aging state of the current battery pack relative to the design life is determined. The aging state determination adopts a three-level classification standard. For example, when the capacity attenuation first exceeds 10% of the nominal value, it is marked as initial aging; when the capacity attenuation reaches 15% and the internal resistance increases by 20%, it is marked as medium aging; when the capacity attenuation exceeds 25%, it is marked as severe aging. The finally generated battery pack aging data includes the current aging stage identifier, aging progress percentage, predicted remaining cycle life, and the recommended temperature control strategy level.

[0067] A multi-dimensional aging evaluation model is constructed through the collaborative analysis of the battery pack capacity and charge-discharge cycle data to accurately quantify the degree of battery performance attenuation. Based on the calculation of the capacitor loss rate based on the charge-discharge cycle characteristics, a battery attenuation trajectory function is established to achieve real-time dynamic tracking of capacity attenuation. Based on the battery pack capacitor loss rate data and the battery pack charge-discharge cycle data, battery pack aging analysis is carried out. With a dual verification mechanism, an accurate quantitative evaluation of the battery health state is realized, providing reliable aging data support for dynamic temperature regulation.

[0068] Preferably, step S322 includes the following steps: Extract the charge-discharge characteristic spectrum according to the battery pack charge-discharge cycle data to generate charge-discharge characteristic spectrum data; Calculate the charge-discharge spectrum time-series attenuation matrix coefficient for the charge-discharge characteristic spectrum data to generate time-series attenuation coefficient matrix data; Calculate the battery pack capacitor loss rate according to the time-series attenuation coefficient matrix data to generate battery pack capacitor loss rate data.

[0069] Specifically, in Embodiment 2 of the present invention, the charge-discharge cycle data of the battery pack includes the current waveforms, voltage change curves, temperature change curves, and cycle time data for each cycle. For example, a high-speed data acquisition card is used to record the instantaneous current and voltage values of each charge-discharge cycle at a sampling frequency of not less than 1 kHz to form the original time-domain signal. Then, the acquired time-domain signal is preprocessed, and a digital filter is used to eliminate the 50 Hz power frequency interference and high-frequency noise, retaining the effective frequency band of 0.1 - 500 Hz. Next, a fast Fourier transform (FFT) is performed on the filtered charge-discharge current signal to calculate the frequency spectrum distribution in the range of 0 - 500 Hz, and the characteristic frequency points including the fundamental frequency, second harmonic, and third harmonic components are extracted. At the same time, the same processing is performed on the voltage signal to obtain the voltage spectrum characteristics. Finally, the current spectrum amplitude, voltage spectrum amplitude, and phase difference data of the characteristic frequency points are integrated into charge-discharge characteristic spectrum data, including parameters such as the fundamental frequency amplitude, harmonic component amplitude ratio, and voltage-current phase difference.

[0070] Parameters such as the fundamental frequency amplitude and harmonic component amplitude ratio in the charge-discharge characteristic spectrum data are arranged in the order of the number of cycles to form a time series. For example, a spectrum parameter time series database is established, and the fundamental frequency amplitude, second harmonic amplitude, and third harmonic amplitude data of each cycle are stored in the order of the number of cycles. Then, the change rate of the spectrum parameters within adjacent cycle windows is calculated. Next, the same calculation is performed on the second harmonic and third harmonic components to obtain the change rate of the harmonic components. For example, a 3×3 attenuation coefficient matrix is constructed. The first row of the matrix stores the fundamental frequency amplitude change rate and its first and second differences, the second row stores the second harmonic change rate and its differences, and the third row stores the third harmonic change rate and its differences. Finally, the matrix is normalized so that the values of each element are in the range of 0 - 1, generating the time series attenuation coefficient matrix data. There is a definite correlation between the fundamental frequency amplitude change rate and the harmonic component change rate in the time series attenuation coefficient matrix data and the battery capacity attenuation. For example, a mapping relationship between the attenuation coefficient matrix and the capacity attenuation is established. When the fundamental frequency amplitude change rate is greater than 0.15, it corresponds to the accelerated stage of capacity attenuation. When the second harmonic change rate exceeds 0.2, it indicates that the capacity attenuation will intensify. Then, the attenuation trend is judged according to the first difference value of the fundamental frequency change rate in the matrix. If the difference value is positive, the attenuation speed increases; if it is negative, the attenuation speed slows down. Next, the weighted summation method is used to calculate the comprehensive attenuation index, with a weight of 0.6 for the fundamental frequency change rate, 0.3 for the second harmonic, and 0.1 for the third harmonic. The comprehensive attenuation index is converted into the capacitance loss rate. For example, a comprehensive attenuation index of 0.1 corresponds to a loss rate of 0.05 Ah / cycle, and 0.2 corresponds to 0.1 Ah / cycle. Finally, according to the value of the comprehensive attenuation index at the current number of cycles, the battery pack capacitance loss rate data is obtained by looking up the table.

[0071] Through the charge-discharge characteristic spectrum extraction technology, deeply analyze the characteristic changes during the battery cycle. Calculate the coefficients of the time-series decay matrix to establish a multi-dimensional aging correlation model, effectively distinguish between reversible capacity loss and irreversible aging. Based on the dynamic decay analysis of spectral characteristics, achieve early warning of battery performance degradation, provide a scientific and reliable aging assessment basis for dynamic temperature control, and ensure the precise matching of the temperature control strategy with the actual internal decay mechanism of the battery.

[0072] Preferably, step S33 includes the following steps: Step S331: Analyze the temperature power consumption of the battery pack based on the battery pack aging data to generate battery pack temperature group power consumption data; Step S332: Draw the battery pack temperature-power consumption curve based on the battery pack temperature power consumption data to generate battery pack temperature-power consumption curve data; Step S333: Divide the battery pack temperature range according to the battery pack temperature-power consumption curve data to generate high-loss inflection point temperature data and low-loss inflection point temperature data; Step S334: Integrate the battery pack temperature dynamic thresholds based on the high-loss inflection point temperature data and the low-loss inflection point temperature data to generate battery pack temperature dynamic threshold data.

[0073] Specifically, in Embodiment 2 of the present invention, the battery pack aging data includes an aging stage identifier, a capacity attenuation rate, an internal resistance growth rate, and a predicted remaining cycle number. For example, set the temperature gradient to be from -20°C to 60°C, with an interval of 5°C as a test point. At each temperature point, use the battery test to discharge at a constant current of 1C to the cut-off voltage, and record the voltage curve and the cumulative discharge capacity during the discharge process. At the same time, measure the input and output power of the battery pack at different temperatures through a high-precision power analyzer, and calculate the energy efficiency (discharge energy / charge energy) at each temperature point. Then, adjust the test parameters according to the aging stage data. For the battery pack with a capacity attenuation exceeding 15%, the temperature range is reduced to -10°C to 50°C to prevent accelerated aging. Then, integrate the discharge capacity, energy efficiency, and internal resistance change data at each temperature point to generate battery pack temperature group power consumption data, including parameters such as the available capacity, energy loss rate, and heat power dissipation value at each temperature point.

[0074] The temperature points, energy efficiency, and heat power dissipation values in the battery pack temperature group power consumption data form the basis for curve plotting. A three-dimensional coordinate system is established. For example, the horizontal axis represents the temperature value (-20°C to 60°C), the vertical axis represents the energy efficiency (0 - 100%), and the z-axis represents the heat power dissipation value (0 - 500W). Then, the data of each temperature point obtained from the test is marked in the coordinate system, and the discrete data points are connected using the cubic spline interpolation method to form smooth temperature-energy efficiency curves and temperature-heat power curves. Next, the characteristic points of the curves are marked, including the highest point of energy efficiency (usually appearing around 25°C) and the heat power mutation points (usually in the regions below 0°C and above 45°C). Further, the curve characteristics are adjusted according to the aging data. For battery packs with an internal resistance growth rate exceeding 20%, the whole curve shifts to the right (the optimal efficiency point rises by 2 - 3°C). Finally, the two curves and their characteristic parameters are integrated into the battery pack temperature-power consumption curve data, including information such as the curve function expression, characteristic point coordinates, and aging offset amount.

[0075] The characteristic points and aging offset amount in the battery pack temperature-power consumption curve data determine the temperature interval division standard. First, locate the peak point of the temperature-energy efficiency curve, and the range of ±5°C around this point is defined as the optimal working interval. Then, analyze the inflection point of the temperature-heat power curve, and set the temperature point with a heat power growth rate exceeding 10% / °C as the critical threshold. Next, adjust the interval boundary in combination with the aging data. For battery packs with a capacity attenuation of more than 20%, the lower limit of the optimal interval is increased by 3°C, and the upper limit is decreased by 5°C. Further, determine the safety margin according to the battery pack usage environment. Based on the critical threshold, a 3°C buffer is reserved on the high-temperature side, and a 2°C buffer is reserved on the low-temperature side. Finally, generate high-loss inflection point temperature data and low-loss inflection point temperature data. The high-temperature threshold includes the highest allowable working temperature (such as 45°C) and the forced cooling trigger temperature (such as 42°C); the low-temperature threshold includes the lowest allowable working temperature (such as -10°C) and the preheating trigger temperature (such as -7°C).

[0076] The reference values, adjustment coefficients, and trigger conditions in the high-loss inflection point temperature data and low-loss inflection point temperature data need to be systematically integrated. First, establish a threshold parameter comparison table and list the high and low temperature thresholds corresponding to different aging stages (initial, stable, accelerated). Then, set the threshold transition algorithm. When it is detected that the aging stage of the battery pack changes, the threshold is gradually adjusted to the new stage standard within 10 charge-discharge cycles. Next, configure the exception handling rules. When the temperature exceeds the forced cooling trigger temperature, immediately start the maximum power heat dissipation; when the temperature is lower than the preheating trigger temperature, activate the heating film to work at full power. Further, add a regional compensation coefficient according to the actual temperature distribution of the battery pack. For battery packs with uneven temperature distribution, the high-temperature threshold is lowered by 2°C, and the low-temperature threshold is raised by 1°C. Finally, integrate all parameters and rules.

[0077] Through the correlation analysis between battery pack aging data and temperature and power consumption characteristics, the thermal behavior changes of batteries under different health states are accurately quantified. The temperature-power consumption curve drawing technology reveals the energy loss characteristics of batteries in different temperature zones, accurately identifies the optimal operating temperature window, and dynamically adjusts the temperature control boundary based on the interval division of the curve inflection point, thereby realizing autonomous optimization of the battery pack temperature control interval parameters.

[0078] Preferably, step S4 comprises the following steps: Step S41: Acquire battery pack temperature control device data; Step S42: performing a spatial topological structure analysis of the temperature control device according to the battery pack temperature control device data to generate temperature control device topological structure data; Step S43: performing regional associated battery group analysis according to the battery group data to generate regional associated battery group data; Step S44: Perform battery pack temperature control joint processing based on the temperature control device topology data and the area-related battery pack data to generate a battery pack-temperature control device.

[0079] Specifically, in Example 2 of the present invention, the temperature control device data regularly polls the device status through the Modbus RTU protocol, and the collected data include the speed parameters of the cooling fan, the working current of the refrigeration plate, the power output of the heating film, the flow meter reading of the liquid cooling pump, and the location information of each temperature sensor. For air cooling, the model specification, rated air volume, maximum static pressure and speed adjustment range of each fan are recorded. For the liquid cooling system, operating parameters such as coolant flow, inlet and outlet temperature difference and pump operating voltage are collected. The equipment management module also reads the physical installation coordinate data of the temperature control device, which is derived from the spatial positioning measurement record when the equipment is installed and is stored in the form of a three-dimensional coordinate system. After data verification, all collected equipment parameters contain a complete temperature control equipment data set including equipment type, performance parameters, spatial location and real-time status.

[0080] The analysis process first establishes a three-dimensional spatial model of the battery pack system and maps the installation coordinate data of each temperature control device to the model. For air-cooled systems, the relative position relationship between the air supply direction of the fan and the battery module is analyzed, and the overlap of the airflow coverage area is calculated. For liquid cooling systems, the direction of the cooling pipeline and the contact area distribution of the battery module are analyzed. The topological analysis module identifies the linkage relationship between each temperature control device and establishes a device control priority list. For example, the device closest to the high-temperature area is marked as first-level priority, and the next closest is marked as second-level priority. At the same time, the effective radius of each temperature control device is calculated. For cooling fans, the effective heat dissipation distance is determined according to the wind pressure-air volume curve; for heating films, the effective heating range is calculated based on the thermal conductivity coefficient.

[0081] Perform regional associated battery pack analysis on battery pack data. For example, use the K-means clustering algorithm to divide the battery pack into several temperature characteristic regions, and each region contains battery cells with similar temperature characteristics. For each temperature region, calculate its geometric center coordinates, average temperature value, and temperature gradient vector. Then establish a mapping relationship table between the temperature region and the battery cell, and record the temperature region number to which each cell belongs. Further analyze the heat generation characteristics of each region, and calculate the regional heat load parameters, including the average temperature rise rate during charge and discharge, the occurrence frequency of the highest temperature, etc. The finally generated regional associated battery pack data includes the temperature region division scheme, the thermal characteristic parameters of each region, and the corresponding relationship table between the region and the cell.

[0082] Thermostatic control joint processing is achieved through the device-region matching algorithm. For example, in the matching process, first establish the spatial position correspondence between the action range of the thermostatic control device and the temperature region, and calculate the coverage coefficient of each thermostatic control device for each temperature region. For the regions completely covered by a single thermostatic control device, directly establish a one-to-one control relationship. For the regions overlapped and covered by multiple devices, calculate the optimal device combination according to the device performance parameters and the regional heat load. The control strategy generation module assigns the main control device and the auxiliary device to each temperature region, and sets the output ratio of each device. For the air-cooling system, adjust the fan speed to optimize the air flow coverage of the target region; for the liquid-cooling system, dynamically adjust the opening of the flow distribution valve to make the coolant flow through the high-temperature region preferentially. After the device control parameters are iteratively optimized, generate the corresponding relationship between the device and the region, and form the final joint control of the battery pack - temperature control device.

[0083] Establish a three-dimensional thermal management network model through the analysis of the spatial topology structure of the thermostatic control device to achieve the precise matching of the heat dissipation resources and the thermal distribution of the battery pack. The regional associated analysis technology can intelligently identify the spatial mapping relationship between the battery pack and the thermostatic control device, optimize the heat conduction path, and dynamically adjust the refrigeration power distribution based on the joint processing algorithm of the topology structure and regional association, so as to realize the adaptive collaborative operation of the thermostatic control device and the battery pack, and ensure that each thermostatic control unit precisely corresponds to the thermal management requirements of a specific battery pack region.

[0084] Preferably, step S5 includes the following steps: Step S51: Use the battery pack temperature dynamic threshold data to monitor the battery pack temperature data for battery pack temperature anomalies, and generate battery pack abnormal temperature data; Step S52: Perform battery pack load analysis based on the battery pack temperature anomaly data, and generate battery pack load data; Step S53: Set the battery pack temperature control parameters based on the battery pack temperature anomaly data and the battery pack load data, and generate battery pack temperature control parameters; Step S54: Transmit the battery pack temperature regulation parameter to the battery pack - temperature regulation device to perform battery pack temperature regulation.

[0085] Specifically, in Embodiment 2 of the present invention, the temperature anomaly monitoring process is carried out through real-time data comparison. Receive and synchronize the verified battery pack temperature data, which has been processed by spatio-temporal feature synchronization and contains the accurate temperature values of each battery cell under a unified time reference. The monitoring module calls the battery pack temperature dynamic threshold data, which contains the high and low threshold ranges of each temperature region under different aging states. The comparison process adopts a point-by-point scanning method to match the real-time temperature value of each battery cell with the dynamic threshold of the corresponding region. When it is detected that the temperature of a certain cell exceeds the upper threshold, it is marked as a high-temperature anomaly; when the temperature is lower than the lower threshold, it is marked as a low-temperature anomaly. The anomaly determination adopts a continuous confirmation mechanism, and only the anomaly that exceeds the threshold for three consecutive sampling periods will be confirmed. For the confirmed anomaly points, parameters such as the anomaly type, the exceeding amplitude, and the duration are recorded, and the physical position code of the abnormal cell is marked at the same time. The monitoring system generates battery pack abnormal temperature data containing information such as the position of the anomaly point, the anomaly degree level, and the duration.

[0086] The load analysis process is completed by the operating state analysis module. First, associate the abnormal temperature data with the real-time operating parameters of the battery pack, including data such as the total output current, the cell voltage difference, and the ambient temperature. The analysis process establishes the corresponding relationship between the abnormal temperature distribution and the electrical load: for the anomaly points that appear concentrated in the high-current output area, it is determined as a load-type temperature anomaly; for the randomly distributed anomaly points, it is determined as an equipment failure-type anomaly. The load analysis calculates three key parameters: the current density, the voltage drop, and the power loss in the abnormal area. By querying the historical load curve of the battery pack, compare the deviation degree of the current load from the rated load, and calculate the load coefficient. At the same time, analyze the temperature rise rate in the abnormal area and establish the quantitative relationship between the temperature rise and the load. The finally generated battery pack load data includes the current load state assessment, the correlation analysis between the anomaly and the load, and the load distribution characteristics of each region.

[0087] Generate specific control parameters according to the preset regulation rule library. For the load-type high-temperature anomaly, the regulation strategy calculates the required heat dissipation intensity and converts it into specific device control parameters: the fan speed percentage corresponding to the air-cooling system, the flow valve opening value corresponding to the liquid-cooling system, and the drive current value corresponding to the semiconductor refrigeration. For the low-temperature anomaly, calculate the heating power requirement and convert it into the working voltage and the energization duration of the heating film. The regulation parameter setting considers the dynamic change of the load state. When it is detected that the load continuously increases, a preventive regulation strategy is adopted to enhance the refrigeration intensity in advance. The parameter calculation adopts a hierarchical adjustment mechanism, which is divided into four levels: normal, warning, minor anomaly, and serious anomaly according to the anomaly degree, and each level corresponds to a different regulation intensity.

[0088] According to the established mapping relationship between the battery pack and the temperature control device, the general control parameters are converted into control instructions for specific devices. The instruction transmission adopts a hierarchical distribution mechanism: first, the main control instructions are sent to the regional main control device, and then the coordination instructions are sent to the auxiliary devices. For the air-cooling system, the control signal changes the fan speed through PWM modulation; for the liquid-cooling system, the flow control valve is adjusted through analog output; for the heating system, the power-on duration is controlled by a relay. The device status monitoring module real-time feedbacks the execution situation of the instructions. When abnormal device response is detected, the standby device is automatically started or the control strategy is adjusted. The complete control log is recorded during the execution process, including data such as the instruction sending time, device response parameters, and actual control effect. When the abnormality is not eliminated, a new round of control parameter calculation and execution process is automatically triggered to form a closed-loop battery pack temperature control.

[0089] The battery pack temperature data is synchronously verified through the dynamic threshold data of the battery pack temperature to monitor the battery pack temperature anomaly, realizing the accurate identification of the battery pack temperature anomaly. The battery pack load analysis is carried out on the battery pack temperature anomaly data. Based on the battery pack temperature anomaly data and the battery pack load data, the battery pack temperature control parameters are set. The adaptive control algorithm based on multi-parameter fusion makes the temperature control accurately match the actual demand. The battery pack temperature control parameters are transmitted to the battery pack - temperature control device to execute the battery pack temperature control, so that the battery pack temperature is controlled within the optimal temperature range.

[0090] The beneficial effects of this application are as follows: First, by dynamically configuring the temperature sensor parameters, the data acquisition accuracy is optimized. The real-time battery pack temperature data undergoes calibration smoothing and spatio-temporal synchronization processing to eliminate measurement noise and timing deviation, ensuring data consistency. The temperature threshold is dynamically adjusted based on the battery pack aging analysis to avoid overprotection or underprotection of the aging battery pack by the fixed threshold, improving the timeliness of the battery pack temperature control. Through the joint processing of the temperature control device and the battery pack area, the accurate matching between the temperature control device and the battery pack heat source is realized, improving the battery pack control efficiency and accuracy. The battery pack abnormal temperature detection combines with the load analysis to generate the optimal control parameters and quickly responds through the preset device network, effectively preventing the battery pack thermal runaway. Thus, the battery pack temperature control dynamically adapts to the aging state and usage conditions of the battery pack.

[0091] The above are all the preferred embodiments of this application, and the protection scope of this application is not limited by this. Therefore, all equivalent changes made according to the structure, shape, and principle of this application should be covered within the protection scope of this application.

Claims

1. A method for temperature control of a battery pack, characterized in that, It includes the following steps: Step S1: Obtain battery pack data; perform detection parameter configuration processing on the temperature sensor according to the battery pack data, and collect the real-time temperature of the battery pack through the temperature sensor after the detection parameter configuration to generate real-time battery pack temperature data; Step S2: Perform temperature calibration and smoothing processing on the real-time battery pack temperature data to generate calibrated battery pack temperature data; Perform spatio-temporal feature synchronization processing on the calibrated battery pack temperature data to generate synchronized calibrated battery pack temperature data; Step S3: Perform battery pack aging analysis according to the battery pack data to generate battery pack aging data; calculate the dynamic threshold of the battery pack temperature based on the battery pack aging data to generate battery pack temperature dynamic threshold data; Step S4: Obtain battery pack temperature control device data; perform joint processing of the battery pack temperature control device based on the battery pack temperature control device data and the battery pack data to generate a battery pack - temperature regulation device; Step S5: Perform abnormal temperature detection of the battery pack on the synchronized calibrated battery pack temperature data according to the battery pack temperature dynamic threshold data to generate battery pack abnormal temperature data; Obtain battery pack condition data, set battery pack temperature regulation parameters based on the battery pack abnormal temperature data and the battery pack condition data to generate battery pack temperature regulation parameters; transmit the battery pack temperature regulation parameters to the battery pack temperature - regulation device to perform battery pack temperature regulation.

2. The battery pack temperature control method according to claim 1, wherein Step S1 includes the following steps: Step S11: Obtain battery pack data; Step S12: Perform battery pack node analysis according to the battery pack data to generate battery pack node data; Step S13: Perform current transmission link analysis according to the battery pack node data to generate current transmission link data; Step S14: Integrate the temperature sensor configuration parameters based on the battery pack node data and the current transmission link data to generate sensor configuration detection parameters; Step S15: Perform detection parameter configuration processing on the temperature sensor according to the sensor configuration detection parameters, and collect the real-time temperature of the battery pack through the temperature sensor after the detection parameter configuration to generate real-time battery pack temperature data.

3. The battery pack temperature control method according to claim 1, wherein Step S2 includes the following steps: Step S21: Perform region division processing on the real-time battery pack temperature data to generate battery pack regional temperature data; Step S22: Perform temperature calibration and smoothing processing on the battery pack regional temperature data to generate calibrated battery pack temperature data; Step S23: Perform spatio-temporal feature synchronization processing on the calibrated battery pack temperature data to generate synchronized calibrated battery pack temperature data.

4. The battery pack temperature control method according to claim 3, characterized in that, Step S21 includes the following steps: Step S211: Calculate the battery pack temperature gradient vector according to the real-time battery pack temperature data to generate battery pack temperature gradient vector data; Step S212: Analyze the battery pack temperature distribution characteristics according to the battery pack temperature gradient vector data to generate temperature distribution characteristic data; Step S213: Perform region division processing based on the temperature distribution characteristic data to generate battery pack regional temperature data.

5. The battery pack temperature control method according to claim 1, characterized in that, Step S3 includes the following steps: S31: Divide the battery pack characteristic data according to the battery pack data to generate battery pack characteristic data; S32: Perform battery pack aging analysis according to the battery pack characteristic data to generate battery pack aging data; S33: Calculate the dynamic temperature threshold of the battery pack based on the battery pack aging data to generate the dynamic temperature threshold data of the battery pack.

6. The battery pack temperature control method according to claim 5, wherein Step S32 includes the following steps: Step S321: Extract the battery pack capacity and the battery pack charge and discharge cycle data of the battery characteristic data respectively to obtain the battery pack capacity data and the battery pack charge and discharge cycle data respectively; Step S322: Calculate the capacitance loss rate of the battery pack according to the battery pack charge and discharge cycle data to generate the capacitance loss rate data of the battery pack; Step S323: Analyze the charge and discharge cycle of the battery pack based on the battery pack capacitance data and the battery pack charge and discharge cycle data to generate the charge and discharge cycle data of the battery pack; Step S324: Perform battery pack aging analysis based on the battery pack capacitance loss rate data and the battery pack charge and discharge cycle data to generate the battery pack aging data.

7. The battery pack temperature control method according to claim 6, wherein Step S322 includes the following steps: Extract the charge and discharge characteristic spectrum according to the battery pack charge and discharge cycle data to generate the charge and discharge characteristic spectrum data; Calculate the charge and discharge spectrum time series attenuation matrix coefficient for the charge and discharge characteristic spectrum data to generate the time series attenuation coefficient matrix data; Calculate the capacitance loss rate of the battery pack according to the time series attenuation coefficient matrix data to generate the capacitance loss rate data of the battery pack.

8. The battery pack temperature control method according to claim 6, wherein Step S33 includes the following steps: Step S331: Perform battery pack temperature power consumption analysis based on the battery pack aging data to generate the battery pack temperature group power consumption data; Step S332: Draw the battery pack temperature-power curve according to the battery pack temperature power consumption data to generate the battery pack temperature-power curve data; Step S333: Divide the battery pack temperature range according to the battery pack temperature-power curve data to generate the high-loss inflection point temperature data and the low-loss inflection point temperature data; Step S334: Integrate the battery pack temperature dynamic threshold based on the high-loss inflection point temperature data and the low-loss inflection point temperature data to generate the battery pack temperature dynamic threshold data.

9. The battery pack temperature control method according to claim 1, wherein Step S4 includes the following steps: Step S41: Obtain the battery pack temperature control device data; Step S42: Analyze the spatial topology structure of the temperature control device according to the battery pack temperature control device data to generate the topology structure data of the temperature control device; Step S43: Perform regional associated battery pack analysis according to the battery pack data to generate the regional associated battery pack data; Step S44: Perform combined battery pack temperature control based on the topology structure data of the temperature control device and the regional associated battery pack data to generate the battery pack - temperature control device.

10. The battery pack temperature control method according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Use the battery pack temperature dynamic threshold data to monitor the battery pack temperature anomaly for the synchronized verification battery pack temperature data to generate the battery pack abnormal temperature data; Step S52: Perform battery pack load analysis according to the battery pack temperature anomaly data to generate the battery pack load data; Step S53: Set the battery pack temperature control parameters based on the battery pack temperature anomaly data and the battery pack load data to generate the battery pack temperature control parameters; Step S54: Transmit the battery pack temperature control parameters to the battery pack - temperature control device to perform battery pack temperature control.

Images