Intelligent temperature control method and system for commercial battery pack based on battery thermal management

Abstract

This invention discloses an intelligent temperature control method and system for commercial battery packs based on battery thermal management. The invention relates to the technical field of intelligent temperature control methods. Multiple controllable temperature combinations are determined based on multiple temperature anomaly regions and the operating status of the commercial battery pack. A corresponding battery thermal management mode is determined based on each controllable temperature combination, the usage scenario of the commercial battery pack, and the service life of the commercial battery pack, thus improving the accuracy of the battery thermal management mode. Therefore, corresponding temperature control characteristics are determined based on various battery thermal management items, multiple temperature anomaly regions, and temperature anomaly gradient maps. Based on multiple temperature control characteristics, the overall shape of the commercial battery pack, and the usage scenario of the commercial battery pack, a corresponding intelligent temperature control method is determined. The temperature safety level of the commercial battery pack is dynamically optimized based on multiple intelligent temperature control methods, the thermal distribution map of the commercial battery pack, and corresponding temperature change events to ensure the normal operating status of the commercial battery pack.

Description

Technical Field

[0001] This invention relates to the technical field of intelligent temperature control methods, and more particularly to an intelligent temperature control method and system for commercial battery packs based on battery thermal management. Background Technology

[0002] With the development of technology, commercial battery packs are gradually being applied to people's lives and installed in electric buses. Electric buses operate under the power supply of commercial battery packs. The usage of commercial battery packs is monitored in real time. In the existing technology, multiple working data of commercial battery packs are collected. Based on the multiple working data and temperature parameters, abnormal temperature parts of the commercial battery pack are determined. Power is cut off based on the abnormal temperature parts. However, the thermal distribution map of the commercial battery pack cannot be controlled. Various controllable temperature combinations and the usage scenarios of commercial battery packs are ignored, which affects the accuracy of battery thermal management mode and cannot guarantee the normal use status of commercial battery packs. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides an intelligent temperature control method and system for commercial battery packs based on battery thermal management.

[0004] This invention provides an intelligent temperature control method for commercial battery packs based on battery thermal management, comprising:

[0005] Collect multiple operational data points from the commercial battery pack, and determine the operational status of the commercial battery pack based on these data points and the usage scenarios of the commercial battery pack.

[0006] Multiple temperature parameters are determined based on the detection of commercial battery packs. The thermal distribution map of the commercial battery pack is determined based on the multiple dimensional parameters, the corresponding temperature detection locations, and the working status of the commercial battery pack.

[0007] Multiple temperature anomaly areas are identified based on the detection of the thermal distribution map of the commercial battery pack. Multiple temperature controllable combinations are determined based on the multiple temperature anomaly areas and the working status of the commercial battery pack. The corresponding battery thermal management mode is determined based on each temperature controllable combination, the usage scenario of the commercial battery pack, and the service life of the commercial battery pack.

[0008] Based on the detection of battery thermal management mode, multiple battery thermal management items are determined, and the corresponding temperature control characteristics are determined according to each battery thermal management item, multiple temperature anomaly areas, and temperature anomaly gradient map.

[0009] Based on multiple temperature control characteristics, the overall shape of the commercial battery pack, and the usage scenarios of the commercial battery pack, the corresponding intelligent temperature control method is determined. The temperature safety level of the commercial battery pack is dynamically optimized based on multiple intelligent temperature control methods, the thermal distribution map of the commercial battery pack, and the corresponding temperature change events.

[0010] This invention provides an intelligent temperature control system for a commercial battery pack based on battery thermal management. The intelligent temperature control system for a commercial battery pack based on battery thermal management is applied to the aforementioned intelligent temperature control method for a commercial battery pack based on battery thermal management. The intelligent temperature control system for a commercial battery pack based on battery thermal management includes:

[0011] The working status module is used to collect multiple working data of the commercial battery pack and determine the working status of the commercial battery pack based on the multiple working data and the usage scenario of the commercial battery pack.

[0012] The thermal distribution module is used to determine multiple temperature parameters based on the detection of commercial battery packs. It determines the thermal distribution map of commercial battery packs based on multiple dimensional parameters, corresponding temperature detection locations, and the operating status of commercial battery packs.

[0013] The battery thermal management module is used to identify multiple abnormal temperature areas based on the detection of the thermal distribution map of the commercial battery pack, determine multiple controllable temperature combinations based on the multiple abnormal temperature areas and the working status of the commercial battery pack, and determine the corresponding battery thermal management mode based on each controllable temperature combination, the usage scenario of the commercial battery pack, and the service life of the commercial battery pack.

[0014] The temperature control feature module is used to determine multiple battery thermal management items based on the detection of battery thermal management modes, and to determine the corresponding temperature control features based on each battery thermal management item, multiple temperature anomaly regions, and temperature anomaly gradient map.

[0015] The temperature safety level module is used to determine the corresponding intelligent temperature control method based on multiple temperature control characteristics, the overall shape of the commercial battery pack, and the usage scenario of the commercial battery pack. It dynamically optimizes the temperature safety level of the commercial battery pack based on multiple intelligent temperature control methods, the thermal distribution map of the commercial battery pack, and the corresponding temperature change events.

[0016] Compared with the prior art, the beneficial effects of the present invention are:

[0017] In this embodiment of the invention, multiple temperature anomaly regions are determined based on the detection of the thermal distribution map of a commercial battery pack using the method described in this embodiment. Multiple temperature controllable combinations are then determined based on these multiple temperature anomaly regions and the operating status of the commercial battery pack. A corresponding battery thermal management mode is determined based on each temperature controllable combination, the usage scenario of the commercial battery pack, and the service life of the commercial battery pack. The introduction of the thermal distribution map of the commercial battery pack, combined with a holistic consideration of various temperature controllable combinations, the usage scenario of the commercial battery pack, and the service life of the commercial battery pack, improves the accuracy of the battery thermal management mode.

[0018] Therefore, multiple battery thermal management items are identified based on the detection of battery thermal management modes. Corresponding temperature control characteristics are determined based on each battery thermal management item, multiple temperature anomaly regions, and temperature anomaly gradient maps. Based on these multiple temperature control characteristics, the overall shape of the commercial battery pack, and its usage scenarios, corresponding intelligent temperature control methods are determined. The temperature safety level of the commercial battery pack is dynamically optimized based on these intelligent temperature control methods, the thermal distribution map of the commercial battery pack, and corresponding temperature change events. By introducing temperature control characteristics, the intelligent temperature control methods are further controlled, achieving dynamic optimization of the temperature safety level of the commercial battery pack to ensure its normal operation. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the intelligent temperature control method for a commercial battery pack based on battery thermal management in an embodiment of the present invention.

[0020] Figure 2 This is a flowchart illustrating step S11 in the intelligent temperature control method for a commercial battery pack based on battery thermal management, as described in this embodiment of the invention.

[0021] Figure 3 This is a flowchart illustrating step S12 in the intelligent temperature control method for a commercial battery pack based on battery thermal management, as described in this embodiment of the invention.

[0022] Figure 4 This is a flowchart illustrating step S13 in the intelligent temperature control method for a commercial battery pack based on battery thermal management, as described in this embodiment of the invention.

[0023] Figure 5 This is a flowchart illustrating step S14 in the intelligent temperature control method for a commercial battery pack based on battery thermal management, as described in this embodiment of the invention.

[0024] Figure 6 This is a flowchart illustrating step S15 in the intelligent temperature control method for a commercial battery pack based on battery thermal management in an embodiment of the present invention.

[0025] Figure 7 This is a schematic diagram of the structural composition of an intelligent temperature control system for a commercial battery pack based on battery thermal management, as described in an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0027] Please see Figures 1 to 7 A smart temperature control method for commercial battery packs based on battery thermal management is proposed and applied to smart temperature control scenarios. The smart temperature control method for commercial battery packs based on battery thermal management includes:

[0028] Step S11: Collect multiple working data of the commercial battery pack, and determine the working status of the commercial battery pack based on the multiple working data of the commercial battery pack and the usage scenario of the commercial battery pack.

[0029] Step S12: Determine multiple temperature parameters based on the detection of the commercial battery pack, and determine the thermal distribution map of the commercial battery pack according to the multiple dimensional parameters, the corresponding temperature detection locations and the working status of the commercial battery pack;

[0030] Step S13: Based on the detection of the thermal distribution map of the commercial battery pack, identify multiple temperature anomaly areas, determine multiple temperature controllable combinations based on the multiple temperature anomaly areas and the working status of the commercial battery pack, and determine the corresponding battery thermal management mode based on each temperature controllable combination, the usage scenario of the commercial battery pack, and the service life of the commercial battery pack.

[0031] Step S14: Based on the detection of battery thermal management mode, determine multiple battery thermal management items, and determine the corresponding temperature control characteristics according to each battery thermal management item, multiple temperature anomaly areas and temperature anomaly gradient map.

[0032] Step S15: Determine the corresponding intelligent temperature control method based on multiple temperature control characteristics, the overall shape of the commercial battery pack, and the usage scenarios of the commercial battery pack. Dynamically optimize the temperature safety level of the commercial battery pack based on multiple intelligent temperature control methods, the thermal distribution map of the commercial battery pack, and the corresponding temperature change events.

[0033] refer to Figure 2 In step S11, the specific steps are as follows:

[0034] S111: Real-time monitoring of the working process of commercial battery packs, collection of multiple working data of commercial battery packs, determination of corresponding working data combinations based on the cross-combination of multiple working data of commercial battery packs, determination of corresponding sub-working features based on the identification of each working data combination, and collection of multiple sub-working features.

[0035] S112: Collect images of the surrounding environment of the commercial battery pack, determine multiple scene features based on the recognition of the surrounding environment images, determine the usage scenario of the commercial battery pack based on the multiple scene features and the overall shape of the commercial battery pack, and determine the working status of the commercial battery pack based on the usage scenario of the commercial battery pack and multiple sub-working features.

[0036] In the embodiments of this application, operating parameters are acquired by deploying a multi-type sensor network within the electric bus battery pack. Specifically, temperature sensors are distributed at key locations in the battery module, voltage sensors monitor the voltage of individual battery cells, current sensors record the charging and discharging current, and pressure sensors detect changes in internal pressure within the battery pack. The sampling frequency employs a dynamic adjustment strategy. Optionally, data is collected at a frequency of 1Hz during normal operation of the electric bus, while automatically increasing to 5-10Hz under high-load conditions such as acceleration and hill climbing to ensure the capture of critical state changes. The data acquisition system uses a high-precision ADC chip and anti-interference design to ensure reliable data acquisition even in complex vehicle electromagnetic environments.

[0037] Data correlation analysis is used to construct a parameter combination reflecting the overall state of the battery. Optionally, in the actual operation of electric buses, the "temperature-current" combination can reflect the thermal load state of the battery. When a large current discharge is accompanied by a rapid temperature rise, it indicates that the battery is under high load. The "voltage-temperature change rate" combination can identify the degree of battery aging. Normal batteries have small voltage fluctuations when the temperature changes, while aged batteries show obvious instability. Each combination has a corresponding threshold range. For example, in the "temperature-current" combination, 100A of continuous discharge is allowed below 40℃, while the maximum allowable current drops to 60A when the temperature exceeds 45℃. These thresholds provide a basis for subsequent state judgment.

[0038] Meaningful features are extracted from data combinations using advanced signal processing and pattern recognition algorithms. Optionally, the "thermal time constant" feature is extracted from the "temperature-current" combination to reflect the battery's heat dissipation capability; the "temperature sensitivity coefficient" is extracted from the "voltage-temperature change rate" combination to characterize the battery's response to temperature changes. These features are quantified and standardized and stored in a feature library. For example, the thermal time constant is standardized to a value between 0 and 1, where 0 indicates optimal heat dissipation performance and 1 indicates severely degraded heat dissipation performance. Through these features, the operating state of the battery pack can be accurately identified, such as normal, slightly overheated, or severely overheated, providing precise input for subsequent intelligent temperature control strategies.

[0039] Furthermore, by strategically deploying multiple high-definition cameras around the electric bus body, comprehensive monitoring of the environment surrounding the battery pack can be achieved; the cameras are installed at the bottom, sides, and rear of the vehicle to ensure that road conditions, ambient temperature, and potential obstacles around the battery pack can be captured.

[0040] The image acquisition system employs high dynamic range (HDR) technology, enabling it to operate stably under various lighting conditions, including low light, strong light, and overcast skies. Computer vision algorithms process these images in real time, using deep learning models to identify and extract key environmental features, such as road type (urban roads, highways, mountain roads), traffic conditions (congestion, smooth traffic), weather conditions (sunny, rainy, high temperature, low temperature), and ambient temperature (direct sunlight areas, shady areas). These environmental features provide rich input information for subsequent scene classification.

[0041] The scenario classification model combines environmental features with the physical characteristics of the battery pack to determine the current usage scenario. The scenario classification model is trained based on a large amount of actual operation data and defines a variety of typical usage scenarios, including urban road driving (frequent start-stop, medium load), highway driving (continuous high speed, stable load), mountain climbing (high current discharge, high temperature environment), charging status (external charging, heat accumulation), and parking status (natural heat dissipation, no load).

[0042] During the classification process, special consideration is given to the overall shape of the battery pack, such as the impact of the bottom mounting position on heat dissipation, the limitations of the protection level on environmental adaptability, and the efficiency changes of the heat dissipation structure at different vehicle speeds. For example, when the system detects that the vehicle is driving on mountain roads and the ambient temperature is high, combined with the characteristic that the bottom mounting of the battery pack leads to limited heat dissipation, the usage scenario will be classified as "mountain climbing high temperature scenario". In this scenario, the battery pack is more likely to have heat accumulation problems.

[0043] By using a multi-source information fusion algorithm, the system comprehensively analyzes the usage scenarios and internal operating characteristics to determine the current operating state of the battery pack. The system establishes a detailed scenario-feature mapping relationship and defines typical operating characteristic modes under different scenarios. For example, in the "highway driving" scenario, the normal temperature-current characteristics should be a stable temperature rise curve and a relatively constant current value. In the "mountain climbing" scenario, higher current fluctuations and faster temperature rises are expected. The multi-source information fusion algorithm adopts a weighted decision mechanism, assigning different weights to information from different sources. For example, in high-temperature environments, the weight of temperature-related features will be increased accordingly.

[0044] The operating status is divided into three levels: normal status (all parameters are within a reasonable range), warning status (some parameters are close to the critical value), and abnormal status (parameters exceed the safe range). The system also has dynamic update capability, which can adjust the operating status judgment in real time according to the latest environmental changes and internal parameters, ensuring that accurate status assessment is always provided during the operation of electric buses, and providing a reliable decision-making basis for intelligent temperature control strategies.

[0045] refer to Figure 3 In step S12, the specific steps are as follows:

[0046] S121: During operation, the commercial battery pack performs corresponding online temperature detection simultaneously, and determines multiple temperature parameters based on the online temperature detection of the commercial battery pack; and determines the first redistribution map based on the multiple temperature parameters and the operating status of the commercial battery pack.

[0047] S122: Determine the corresponding temperature detection location based on the traceability of each temperature parameter, determine the second re-distribution map based on the temperature detection location of each temperature parameter and the working state of the commercial battery pack, and determine the thermal distribution map of the commercial battery pack based on the first re-distribution map, the second re-distribution map and the working state of the commercial battery pack.

[0048] In the embodiments of this application, the commercial battery pack performs corresponding online temperature detection simultaneously during operation, and determines multiple temperature parameters based on the online temperature detection of the commercial battery pack; a first redistribution map is determined based on the multiple temperature parameters and the operating state of the commercial battery pack, which takes into account the overall consideration of multiple temperature parameters and the operating state of the commercial battery pack, and ensures the accuracy of the first redistribution map.

[0049] At this point, by deploying a distributed temperature sensor network within the electric bus battery pack, full coverage of key monitoring points is achieved. The sensor network employs multiple types of sensors working collaboratively: NTC thermistors monitor the surface temperature of the battery cells, PT100 platinum resistance thermometers are used for high-precision temperature measurement, and infrared temperature sensors enable non-contact temperature detection. The sampling strategy adopts an intelligent adjustment mechanism, sampling at a frequency of 1-5Hz during normal operation of the electric bus, while automatically increasing to 10-20Hz under high-load conditions to ensure the capture of rapid temperature changes. The data acquisition system has strong anti-interference capabilities, employing shielding design and digital filtering technology to ensure stable operation in the complex electromagnetic environment of the electric bus. The temperature parameters calculated by the system include real-time temperature values ​​(current temperature at each monitoring point), temperature change rate (amount of temperature change per unit time), temperature fluctuation amplitude (range of temperature change over a period of time), and extreme temperatures (highest and lowest temperatures), etc. These parameters provide a rich data foundation for subsequent thermal distribution analysis.

[0050] Through data fusion and correlation analysis, temperature parameters are combined with operating conditions to construct a preliminary characterization of temperature distribution. The system establishes a correlation model between temperature parameters and operating conditions, defining the normal temperature range and variation patterns under different operating conditions. For example, under urban road driving conditions for electric buses, the normal temperature range of the battery pack is typically 30-36°C, while this range will be adjusted accordingly under highway driving or mountain climbing conditions. The data fusion algorithm uses Kalman filtering technology to process multi-source temperature data, eliminating measurement noise and outliers, while applying time series analysis methods to identify temperature change trends. The system classifies and labels temperature data based on operating conditions, such as "normal," "higher," and "abnormal," providing an intuitive reference for subsequent analysis. The final generated first-order distribution map mainly reflects the spatiotemporal distribution characteristics of temperature values, displaying the temperature status of each region of the battery pack in a two-dimensional matrix form, providing an intuitive basis for thermal management decisions.

[0051] Specifically, on an electric bus traveling on urban roads, the implementation process of sub-step S121 demonstrates the practical application of temperature monitoring and analysis; in the 12 modules of the battery pack, each module is equipped with 4 temperature sensors to monitor the temperature of the cell surface, between cells, module shell and coolant interface, respectively. At the same time, the battery pack as a whole is equipped with 8 ambient temperature sensors, forming a full-coverage network of 56 temperature monitoring points.

[0052] The system samples the temperature at a frequency of 5 Hz. The collected data shows that the temperature between the cells in module 6 reaches 37.5°C, which is the highest value, while the temperature at the air inlet is 28.3°C, which is the lowest value. The calculated temperature parameters include: the temperature change rate between the cells in module 6 is +0.8°C / min, the temperature fluctuation range in the past 5 minutes is 1.2°C, the overall average temperature of the battery pack is 33.7°C, and the temperature standard deviation is 2.1°C, indicating that the temperature distribution is relatively uniform.

[0053] When constructing the first re-distribution map, the system establishes a temperature parameter correlation model based on the working state of "normal driving on urban roads, medium load, and ambient temperature of 28°C". Under medium load conditions, the normal temperature range is 30-36°C. The current temperature parameters are generally within the normal range, but the temperature of module 6 is too high. After processing the temperature data using the Kalman filter algorithm, time series analysis shows that the temperature of module 6 is showing a slow upward trend.

[0054] The generated two-dimensional temperature distribution matrix uses the battery module number as the horizontal axis and the temperature value as the vertical axis. The temperature contour map clearly shows the distribution characteristics of "high in the middle and low around the edges". The temperature band is formed in the area of ​​module 5-7, and module 6 is the central hot spot. The first distribution map marks the temperature anomaly areas, temperature change trend arrows and key temperature parameters, showing that the temperature distribution basically matches the current working state. However, the local hot spot of module 6 needs special attention, which provides an important reference for subsequent intelligent temperature control strategies.

[0055] Furthermore, the corresponding temperature detection location is determined based on the traceability of each temperature parameter. A second re-distribution map is determined based on the temperature detection location of each temperature parameter and the working state of the commercial battery pack. A thermal distribution map of the commercial battery pack is determined based on the first re-distribution map, the second re-distribution map, and the working state of the commercial battery pack. This approach takes into account the overall consideration of the first re-distribution map, the second re-distribution map, and the working state of the commercial battery pack, ensuring the accuracy of the thermal distribution map of the commercial battery pack.

[0056] At this point, a spatial positioning system for temperature parameters was established, which associates each temperature data point with a specific physical location within the battery pack through precise coordinate mapping. The system establishes a temperature sensor location mapping table, assigning precise three-dimensional coordinates to the 36 temperature sensors within the electric bus battery pack, forming a complete location grid. The temperature parameter tracing process includes not only the sensor ID and physical location (x, y, z coordinates), but also key information such as the battery module it belongs to and its distance from the cooling system.

[0057] Specifically, sensor T06 is located at the center of module 6 with coordinates (1100,0,0), while sensor T18 is located at the edge of module 6 with coordinates (1100,150,0). Through this refined spatial marking, a temperature dataset with location information is formed, and the correlation between temperature parameters and the internal structure of the battery pack, including cells, modules, and cooling pipes, is established, laying the foundation for subsequent spatial temperature distribution analysis.

[0058] By combining location information with the working mode, a three-dimensional temperature field model is formed. Based on the temperature detection location, the system uses a radial basis function interpolation algorithm to estimate the temperature of unmonitored points, filling the data gaps between sensors. In the working mode of the electric bus battery pack being arranged horizontally, the system considers heat transfer mechanisms such as heat conduction, convection, and radiation, and optimizes the temperature distribution calculation.

[0059] Specifically, the temperature at the center of module 6 is 37.5°C, which diffuses outwards to form a temperature gradient. A low-temperature zone is formed near the cooling system, with temperatures generally lower than other areas. The second redistribution map clearly shows that the temperature is highest in the middle of the battery pack (modules 5-7). The temperature decreases from the center to the surrounding areas, forming a significant temperature gradient. The hot spot is concentrated in the center of module 6, with a temperature gradient of 2.3°C / cm, providing a spatial dimension for thermal management strategy decisions.

[0060] By integrating the information from the first two distribution maps and combining them with the operating status, a final thermal distribution map is formed. The system establishes a weighted fusion algorithm, with the first distribution map having a weight of 60% and the second distribution map having a weight of 40% under the medium load condition of electric buses driving normally on urban roads. During the fusion process, the system introduces a heat conduction model for verification to ensure that the thermal distribution conforms to physical laws, and adds a function to predict temperature change trends.

[0061] Specifically, based on the current temperature rise trend, the system predicts that if the temperature of module 6 continues to rise, it will reach 38°C in 10 minutes. The final generated thermal distribution map contains multi-dimensional information such as temperature numerical distribution (28.3°C-37.5°C), spatial distribution characteristics (high in the center and low around the edges), and change trend (the temperature of module 6 rises slowly). It shows that the hot spot area is located in the center of module 6, with a temperature gradient of 2.3°C / cm. The cooling system forms a clear low-temperature channel, and the temperature is maintained at around 32.0°C. The thermal distribution map basically matches the current working state, but the identified local hot spots provide a clear direction for optimization of the intelligent temperature control system, ensuring that the electric bus battery pack operates within the optimal temperature range.

[0062] refer to Figure 4 In step S13, the specific steps are as follows:

[0063] S131: Collect the thermal distribution map of the commercial battery pack, determine multiple temperature anomaly features based on the detection of the thermal distribution map of the commercial battery pack, and determine multiple temperature anomaly regions based on the feature location, corresponding feature shape and thermal distribution map of the commercial battery pack.

[0064] S132: Based on the tracing of each temperature anomaly area, determine the working component corresponding to the commercial battery pack, and determine multiple temperature controllable combinations according to multiple temperature anomaly areas, corresponding working components and the working status of the commercial battery pack;

[0065] S133: Determine the first battery thermal management coefficient based on the usage scenarios of each temperature controllable combination and commercial battery pack, determine the second battery thermal management coefficient based on the service life of each temperature controllable combination and commercial battery pack, and determine the corresponding battery thermal management mode according to the mapping relationship between the first battery thermal management coefficient, the second battery thermal management coefficient and the battery thermal management mode.

[0066] In the embodiments of this application, the thermal distribution map of the battery pack at the current moment is obtained from the thermal distribution map database; the thermal distribution map is presented in the form of a three-dimensional temperature matrix or temperature cloud map, containing complete information such as temperature value, location coordinates and timestamp of each point; in electric bus applications, the system will collect the thermal distribution map periodically (e.g., every 30 seconds) to ensure the real-time and continuity of the data.

[0067] For example, the thermal distribution map acquired at 2:30 pm shows that the overall temperature of the battery pack is in the range of 30-38°C, with an average temperature of 34.5°C. The data format is a 20×15×10 three-dimensional temperature matrix, representing the temperature distribution of 3,000 monitoring points inside the battery pack. Before using the data, the system will perform integrity checks and preprocessing, including data smoothing and noise filtering, to ensure the accuracy and reliability of the basic data for analysis.

[0068] The thermal distribution map is analyzed using image processing algorithms to identify abnormal temperature characteristics. Criteria for judging abnormal temperatures are established, including an absolute temperature threshold (above 37°C is considered a high-temperature anomaly), a relative temperature difference threshold (a difference of more than 3°C from the surrounding average temperature is considered an anomaly), and a temperature gradient threshold (a temperature gradient exceeding 2°C / cm is considered an anomaly). Multiple image processing algorithms are applied for analysis: threshold segmentation identifies regions exceeding safe temperature thresholds; edge detection identifies boundaries with drastic temperature changes; region growing identifies connected abnormal temperature regions starting from a seed point; and gradient calculation identifies regions with abnormal temperature change rates.

[0069] Meanwhile, for electric bus battery packs, these algorithms identified 5 high-temperature points exceeding 37°C, 3 boundary regions with drastic temperature changes, and 2 abnormal temperature gradient regions. Each abnormal feature was extracted in detail. For example, high-temperature point 1 is located at coordinates (15,8,5) with a temperature of 37.8°C and a point-like shape. Boundary region 1 covers the coordinate range (14-18,7-10,4-6) with a temperature change from 35°C to 37.8°C and a band-like shape. All identified abnormal features were stored in a temperature abnormality feature database, providing a data foundation for subsequent region division.

[0070] Related temperature anomaly features are merged into temperature anomaly regions, realizing the transformation from discrete features to continuous regions. Based on feature location, the DBSCAN clustering algorithm is used to group adjacent or similar temperature anomaly features. In the electric bus example, the algorithm divides 10 anomaly features into 3 groups: Group 1 includes high temperature points 1-5, boundary region 1, and gradient anomaly region 1, located at (15-17, 8-9, 4-6); Group 2 includes boundary region 2 and gradient anomaly region 2, located at (6-7, 13-14, 7-8); Group 3 only includes boundary region 3, located at (10-13, 3-6, 2-4); morphological analysis is performed on each feature group. Analysis was conducted to determine whether a region could be merged based on criteria such as the distance between features, similarity of morphological features, and continuity of temperature changes. The analysis results showed that the features of Group 1 and Group 2 were close in location and had continuous temperature changes, so they could be merged into one temperature anomaly region. Group 3 had a single feature and was directly used as a temperature anomaly region. Next, the convex hull algorithm was used to determine the boundary of each temperature anomaly region. For example, the boundary coordinates of temperature anomaly region 1 were (14-18, 7-10, 4-6), and its shape was approximately cuboid. The comprehensive parameters of each temperature anomaly region were calculated, including the region area and volume, temperature statistical parameters, temperature gradient, shape features, and positional relationship with key components of the battery pack.

[0071] For example, temperature anomaly area 1 has an area of ​​12 cm², a volume of 36 cm³, a maximum temperature of 37.8°C, an average temperature of 36.8°C, and a maximum temperature gradient of 2.5°C / cm. It is located in the central area of ​​battery module 6, 3 cm away from the cooling pipe, and its risk level is medium. The system establishes a temperature anomaly area distribution map, which intuitively displays the location and shape of the three anomaly areas in the battery pack. Based on the area parameters, a risk assessment is conducted to determine that temperature anomaly area 1 needs to be given special attention, while areas 2 and 3 need to be continuously monitored, providing precise target areas and optimization directions for the intelligent temperature control strategy.

[0072] Furthermore, based on the tracing of each temperature anomaly region, the corresponding working component of the commercial battery pack is determined. Multiple temperature-controllable combinations are determined according to multiple temperature anomaly regions, corresponding working components, and the working status of the commercial battery pack. This approach takes into account the overall consideration of multiple temperature anomaly regions, corresponding working components, and the working status of the commercial battery pack, ensuring the accuracy of multiple temperature-controllable combinations.

[0073] At this point, the system constructs a battery pack component mapping table, establishes a three-dimensional spatial location model of all key components inside the electric bus battery pack (such as 8 cell modules, BMS main control board, liquid cooling pipeline, busbar, etc.), assigns a unique ID to each component and records its geometric position, shape and functional attributes; for example, cell module 6 is assigned IDM-006, with position coordinates (110-130, 75-95, 15-25) and functional attribute "main energy storage unit".

[0074] Spatial coordinate matching is performed, comparing the center coordinates and boundary coordinates of the temperature anomaly area with the component mapping table, and using spatial geometry algorithms to determine which component or component combination the anomaly area falls on; taking anomaly area 1 as an example, its center coordinates (120,85,20) are determined to fall within the range of cell group C6-12 to C6-18 in module 6 by using the point within polygon judgment algorithm.

[0075] For abnormal areas spanning multiple components, the system also performs heat conduction path analysis, considering factors such as material thermal conductivity and structural layout to assess the physical causes of temperature anomalies. For example, abnormal area 1 is far from the cooling pipes (about 8cm) and is located in the center of the module, resulting in poor heat dissipation. Therefore, the battery cell module 6 is identified as the main heat source, and the BMS submodule 6-2 is identified as the monitoring component. Finally, the system establishes a traceability table to record information such as the component ID, component type, functional description, and heat source probability corresponding to each temperature anomaly area, forming a structured "abnormal area-component" mapping relationship, providing accurate target location for subsequent temperature management.

[0076] Based on the correspondence between abnormal regions and components, and combined with the current operating status, a "temperature-controllable combination" that can be coordinated and adjusted is formed, providing a combination unit for thermal management strategies. The system constructs a set of operating status parameters, collects the current operating status parameters of the battery pack, such as charging and discharging current, SOC, ambient temperature, cooling system status, etc., and classifies these parameters. In the normal driving scenario of an electric bus on urban roads, the system collects a discharge current of approximately 150A, an SOC of 65%, an ambient temperature of 30℃, and a cooling system flow rate of 30L / min, classifying this state as a "medium-power discharge state".

[0077] Analyze the thermal response characteristics of components, and analyze their temperature response patterns under different operating conditions based on component type and thermal conductivity characteristics. For example, the temperature of a battery cell module rises rapidly during discharge, while the temperature of a cooling pipe drops slowly after startup. Establish temperature-controllable combination rules and formulate combination standards. For example, multiple battery cell modules covered by the same cooling pipe can be grouped into one combination, adjacent components with strong thermal coupling should be grouped into the same combination, and abnormal areas with similar temperature change trends can be merged and controlled.

[0078] Based on these rules, the system generates a list of temperature-controllable combinations. Each combination includes a combination ID, the IDs of the included abnormal areas, a list of involved component IDs, the current temperature status, and thermal management recommendations. For example, combination TC-01 includes abnormal areas 1 and 3, involving modules 6, 4, and cooling branch 2. The current temperature status is continuously rising and the risk is high. The thermal management recommendation is "enhance cooling and limit discharge power." Combination TC-02 includes abnormal area 2, involving bus 3-1 and module 3. The current temperature status is stable and the risk is low. The thermal management recommendation is "monitor, do not intervene for now." These temperature-controllable combinations provide the intelligent temperature control system with clear control units and strategy directions to ensure that the electric bus battery pack operates within the optimal temperature range.

[0079] Therefore, a first battery thermal management coefficient is determined based on the usage scenarios of various temperature-controllable combinations and commercial battery packs, and a second battery thermal management coefficient is determined based on the service life of various temperature-controllable combinations and commercial battery packs. The corresponding battery thermal management mode is determined according to the mapping relationship between the first battery thermal management coefficient, the second battery thermal management coefficient, and the battery thermal management mode. This approach takes into account the overall consideration of the mapping relationship between the first battery thermal management coefficient, the second battery thermal management coefficient, and the battery thermal management mode, ensuring the accuracy of the corresponding battery thermal management mode. At the same time, the thermal distribution map of commercial battery packs is introduced, taking into account the overall consideration of various temperature-controllable combinations, usage scenarios of commercial battery packs, and service life of commercial battery packs, thereby improving the accuracy of the battery thermal management mode.

[0080] At this point, based on the current usage scenario of the temperature-controllable combination, the first battery thermal management coefficient (denoted as K1) is calculated to characterize the degree of influence of the scenario on the thermal management strategy. The system classifies usage scenarios and sets weights, dividing typical usage scenarios of electric buses into several categories such as urban congestion, high-speed cruising, hill climbing, charging status, and static standby, and assigning different thermal management weight coefficients to each scenario.

[0081] For example, the heat load in urban congestion scenarios is moderate, with a weight of 0.7; the heat load in high-speed cruising scenarios is high, with a weight of 1.0; the heat load in hill climbing scenarios is extremely high, with a weight of 1.2; the heat load in charging mode is moderate to high, with a weight of 0.9; and the heat load in standby mode is low, with a weight of 0.3.

[0082] Temperature-controllable combinations are correlated with scenarios, and the thermal risks of each temperature-controllable combination under the current usage scenario are analyzed; for example, during high-speed cruising, if the combination contains multiple high-discharge modules, its thermal risk is significantly increased.

[0083] The first battery thermal management coefficient is calculated using the K1 calculation formula (K1=∑(wi×si)), where wi is the weight of the i-th temperature controllable combination in the current scenario, and si is the thermal risk score of the combination (such as normalized values ​​of temperature value, temperature rise rate, etc.).

[0084] For the high-speed cruising scenario of electric buses, the TC-01 combination includes module 6 and module 4, with a thermal risk score of 0.9 (high temperature continues to rise), and the TC-02 combination includes bus 3-1 and module 3, with a thermal risk score of 0.4 (temperature is stable). Therefore, K1=(1.0×0.9)+(1.0×0.4)=1.3, indicating that the thermal management requirements are high in the current scenario.

[0085] Based on the battery pack's service life, the aging degree of its thermal management system is assessed, and the second battery thermal management coefficient (denoted as K2) is calculated to characterize the impact of aging on thermal management capabilities. The system classifies the service life and sets the degradation coefficient, dividing the battery pack's service life into several stages: 0-2 years is new battery with a degradation coefficient of 1.0; 2-4 years is mild aging with a degradation coefficient of 0.9; 4-6 years is moderate aging with a degradation coefficient of 0.8; and more than 6 years is severe aging with a degradation coefficient of 0.6. Temperature-controllable combinations are correlated with aging, and the aging of components in each temperature-controllable combination is analyzed, such as decreased cooling efficiency and sensor drift, and the conservatism of the thermal management strategy is adjusted according to the degree of aging.

[0086] The second battery thermal management coefficient is calculated using the K2 formula (K2=∑(ai×di)), where ai is the aging degradation coefficient of the i-th combination and di is the importance score of the combination in thermal management (such as the weight of key components). Taking a 5-year-old electric bus battery pack as an example, the degradation coefficient is 0.8, the importance score of the TC-01 combination (including the key discharge module) is 0.9, and the importance score of the TC-02 combination (including auxiliary components) is 0.5. Then K2=(0.8×0.9)+(0.8×0.5)=1.12, indicating that the thermal management capability has decreased due to aging.

[0087] Combining K1 and K2, the most suitable battery thermal management mode is determined through a preset mapping table. The system constructs a thermal management mode mapping table and predefines multiple thermal management modes: Mode A is conventional cooling, suitable for low thermal risk and new batteries; Mode B is enhanced cooling, suitable for medium to high thermal risk and mild aging; Mode C is forced cooling + power limiting, suitable for high thermal risk and moderate to severe aging; Mode D is emergency protection, suitable for extreme thermal risk and severe aging.

[0088] The K1 and K2 scores are combined and weighted to obtain the comprehensive thermal management demand index (TMI = α·K1 + β·K2), where α and β are weighting coefficients, typically α = 0.6 and β = 0.4. According to the mode selection logic, different modes are set for the TMI threshold range: TMI < 0.5 corresponds to mode A, 0.5 ≤ TMI < 0.8 corresponds to mode B, 0.8 ≤ TMI < 1.2 corresponds to mode C, and TMI ≥ 1.2 corresponds to mode D.

[0089] Taking a battery pack used for 5 years in a high-speed cruise scenario of an electric bus as an example, K1=1.3, K2=1.12, then TMI=0.6×1.3+0.4×1.12=1.236. Since TMI≥1.2, the system selects mode D: emergency protection. The specific measures of this mode include starting forced liquid cooling and increasing the cooling flow to the maximum; limiting the battery discharge power to 50% of the rated value; triggering a high temperature alarm and notifying the driver to stop and check immediately; starting BMS active balancing, focusing on cooling module 6. This thermal management mode selection method based on multi-parameter comprehensive analysis ensures that the electric bus battery pack can obtain the most suitable temperature control strategy under various operating conditions.

[0090] refer to Figure 5 In step S14, the specific steps are as follows:

[0091] S141: Collect the battery thermal management mode, determine a sub-management mode based on the detection of the battery thermal management mode, and determine the corresponding battery thermal management items based on the matching of each sub-management mode with the working status of the commercial battery pack.

[0092] S142: Collect multiple temperature anomaly areas, and determine the corresponding thermal management content based on the multiple temperature anomaly areas and the corresponding battery thermal management items.

[0093] S143: Based on the identification of multiple temperature anomaly regions, multiple temperature anomaly data are determined, and a temperature anomaly gradient map is constructed according to the changes of each temperature anomaly data; the corresponding temperature control features are determined based on the thermal management content, the temperature anomaly gradient map, and the thermal distribution map of the commercial battery pack.

[0094] In the embodiments of this application, multiple executable sub-management modes are identified from the overall thermal management mode in order to perform fine-grained control; the system performs thermal management mode identification and obtains the current battery thermal management mode from the previous stage (such as S133), for example, mode D: emergency protection.

[0095] The mode is structured and broken down into multiple sub-management modes, each representing a type of thermal management behavior. Common sub-management modes include cooling control sub-mode, power limiting sub-mode, equalization management sub-mode, and alarm indication sub-mode.

[0096] Sub-management modes are defined, and specific behavioral rules and triggering conditions are set for each sub-management mode. For example, the cooling control sub-mode is defined as activating the liquid cooling system when the battery pack temperature is >45°C; the power limiting sub-mode is defined as limiting the discharge current of a module when its temperature continues to rise; the equalization management sub-mode is defined as activating active equalization when the cell voltage difference is >50mV; and the alarm indication sub-mode is defined as triggering an audible and visual alarm when the temperature exceeds a safety threshold. Taking the commercial battery pack of an electric bus as an example, when the system obtains the current mode as Mode D: Emergency Protection, it is decomposed into four sub-management modes: Sub-mode 1 is the cooling control sub-mode, Sub-mode 2 is the power limiting sub-mode, Sub-mode 3 is the equalization management sub-mode, and Sub-mode 4 is the alarm indication sub-mode.

[0097] Each sub-management mode has clear triggering conditions and execution rules. For example, the cooling control sub-mode activates the liquid cooling system when the battery pack temperature is >45°C, and the power limiting sub-mode limits the discharge current when the module temperature continues to rise. This method of decomposing the overall mode into multiple sub-management modes makes thermal management control more refined and targeted.

[0098] The system matches the sub-management modes with the actual operating status of the current battery pack to generate specific executable thermal management projects. The system monitors the operating status of the battery pack in real time, including the operating status (driving, charging, stationary), load status (high power output, low power output), environmental status (ambient temperature, humidity), and system status (BMS status, cooling system status). The system then matches the sub-management modes and selects the most suitable combination of sub-management modes based on the current operating status.

[0099] For example, in high-speed cruising mode, cooling control and power limiting sub-modes are prioritized; in charging mode, equalization management and alarm prompt sub-modes are prioritized; thermal management items are determined, and specific thermal management items are determined for each selected sub-management mode, including control objectives (such as cooling, current limiting), execution actions (such as starting liquid cooling, adjusting fan speed), execution parameters (such as cooling flow rate, current limiting amplitude), and monitoring indicators (such as temperature change rate, voltage difference); taking the commercial battery pack of electric bus in high-speed cruising mode as an example, the system monitors the operating status as driving (high-speed cruising), the load status as high power output (continuous discharge), the environmental status as ambient temperature 35°C and humidity 60%, and the system status as BMS normal and cooling system standby.

[0100] Based on this status information, the system selects the following sub-management modes: cooling control sub-mode (because high power output requires enhanced heat dissipation), power limiting sub-mode (to prevent overheating caused by continuous high power), equalization management sub-mode (as cell differences increase during high-speed cruising), and alarm prompt sub-mode (ready to alarm for abnormalities at any time).

[0101] Specific thermal management items are determined for each sub-management mode: The control objective of the cooling control sub-mode is to reduce temperature, and the execution action is to start the liquid cooling system and adjust the fan speed to 80%. The execution parameters are a cooling flow rate of 15L / min and a fan speed of 3000rpm. The monitoring indicator is a temperature change rate of <1°C / min. The control objective of the power limiting sub-mode is to limit current, and the execution action is to limit the discharge current of module 6. The execution parameters are a maximum discharge current limit of 70% of the rated value. The monitoring indicator is a module 6 temperature of <45°C.

[0102] The control objective of the equalization management sub-mode is to equalize the cell voltage. The execution action is to initiate active equalization, with equalization current of 2A and equalization time of 30 minutes. The monitoring indicator is that the cell voltage difference is <30mV. The control objective of the alarm prompt sub-mode is to trigger an abnormal alarm. The execution action is to monitor the temperature and prepare to trigger an audible and visual alarm. The execution parameter is an alarm threshold of 50°C, and the monitoring indicator is that an alarm will be triggered immediately if the temperature exceeds the threshold. This method of generating specific thermal management items based on matching sub-management modes with operating states ensures that the electric bus battery pack can obtain the most suitable temperature control strategy under various operating conditions, realizing a complete closed-loop control from the overall mode to specific execution.

[0103] Furthermore, the system identifies areas in the battery pack where abnormal temperatures exist, serving as the basis for subsequent thermal management measures. The system identifies abnormal temperature areas and retrieves a list of identified abnormal temperature areas from step S131. Each abnormal temperature area includes information such as area number (e.g., area 1, area 2), spatial location (e.g., module 6, busbar 3-1), temperature value (e.g., 48.5°C, 46.2°C), temperature trend (e.g., rising, stable, falling), and abnormality level (e.g., high, medium, low).

[0104] Temperature anomaly areas are classified into key concern areas (high and continuously rising temperatures), general concern areas (high but stable temperatures), and monitoring areas (slightly high temperatures but declining temperatures) based on the severity and trend of the temperature anomalies.

[0105] Taking the commercial battery pack for electric buses as an example, the system identified three abnormal temperature areas: Area 1 is located in module 6, with a temperature of 48.5°C, showing a continuous upward trend (rising rate of 1.2°C / min), and an abnormality level of high; Area 2 is located in busbar 3-1, with a temperature of 46.2°C, showing a stable trend, and an abnormality level of medium; Area 3 is located in module 3, with a temperature of 44.8°C, showing a slow downward trend (falling rate of 0.3°C / min), and an abnormality level of low. Through this systematic collection and classification of abnormal areas, accurate target areas and priority rankings are provided for the subsequent formulation of thermal management content.

[0106] The system matches abnormal temperature areas with battery thermal management projects to formulate specific thermal management content. The system performs thermal management project matching, obtains the current thermal management project list from step S141, and matches each abnormal temperature area with a thermal management project, taking into account factors such as the location of the abnormal area and the applicable scope of the thermal management project, the temperature characteristics of the abnormal area and the control objectives of the thermal management project, and the changing trend of the abnormal area and the response characteristics of the thermal management project.

[0107] Develop thermal management content. For each successfully matched abnormal area-thermal management project pair, develop specific thermal management content, including control objectives (such as cooling, current limiting, equalization, etc.), execution actions (such as starting cooling, adjusting parameters, triggering alarms, etc.), execution parameters (such as cooling flow rate, current limiting ratio, equalization current, etc.), monitoring indicators (such as temperature change rate, current value, voltage difference, etc.), and execution conditions (such as temperature threshold, time conditions, state conditions, etc.).

[0108] Taking the commercial battery pack of electric bus as an example, based on the above-mentioned abnormal temperature area and the thermal management items in S141, the system formulates the following thermal management content: Area 1 (Module 6) is matched with the cooling control sub-mode, the control objective is rapid cooling, the execution action is to start the liquid cooling system and adjust the fan speed to the maximum, the execution parameters are cooling flow rate of 20L / min and fan speed of 3500rpm, the monitoring index is temperature change rate <0.5C / min, and the execution condition is temperature >45C and continuous rise.

[0109] Region 1 (Module 6) is matched with the power limiting sub-mode. The control objective is to limit heat generation. The action is to limit the discharge current of Module 6. The execution parameter is to limit the maximum discharge current to 60% of the rated value. The monitoring index is that the temperature of Module 6 is <45°C. The implementation condition is that the temperature is >48°C.

[0110] Zone 2 (busbar 3-1) is matched with the cooling control sub-mode. The control objective is to maintain the temperature. The action is to keep the liquid cooling system running with a medium fan speed. The execution parameters are a cooling flow rate of 10L / min and a fan speed of 2000rpm. The monitoring index is that the temperature is stable below 45°C. The execution condition is that the temperature is >44°C.

[0111] Region 3 (Module 3) is matched with the monitoring sub-mode. The control objective is continuous observation, the execution action is to maintain the current cooling state without adding any additional measures, the execution parameter is to maintain the existing cooling parameters, the monitoring indicator is the temperature continuing to decrease, and the execution condition is that the temperature is showing a decreasing trend. This method of generating specific thermal management content based on the characteristics of abnormal regions and matching them with thermal management items ensures that each temperature abnormal region in the electric bus battery pack can obtain the most suitable control strategy, realizing a complete closed-loop control from anomaly identification to precise execution.

[0112] Therefore, multiple temperature anomaly data are determined based on the identification of multiple temperature anomaly regions, and a temperature anomaly gradient map is constructed according to the changes of each temperature anomaly data. The corresponding temperature control features are determined based on thermal management content, temperature anomaly gradient map and thermal distribution map of commercial battery pack. This approach takes into account the overall consideration of thermal management content, temperature anomaly gradient map and thermal distribution map of commercial battery pack, ensuring the accuracy of the corresponding temperature control features.

[0113] At this point, specific temperature anomaly data is extracted from the identified temperature anomaly areas to provide a quantitative basis for subsequent analysis. The system extracts temperature anomaly data, collecting information such as area number (e.g., area 1, area 2), spatial coordinates (X, Y, Z), current temperature value, temperature change rate (C / min), temperature change trend (rising, stable, falling), historical temperature data (past 5 minutes, 15 minutes, 30 minutes), and ambient temperature for each temperature anomaly area. The system standardizes the temperature anomaly data, unifying the data format of different areas to facilitate comparison and analysis, and establishes a temperature anomaly data table containing detailed information for all anomaly areas.

[0114] Taking the commercial battery pack for electric buses as an example, the system extracts data from three abnormal temperature areas: Area 1 (Module 6) has spatial coordinates of X=120mm, Y=200mm, Z=50mm, current temperature 48.5°C, temperature change rate +1.2°C / min, and an upward temperature trend. Historical temperature data shows 46.1°C 5 minutes ago, 44.3°C 15 minutes ago, and 42.8°C 30 minutes ago, with an ambient temperature of 35°C; Area 2 (Busbar 3-1) has spatial coordinates of X=180mm, Y=150mm, Z=45mm, current temperature 46.2°C, temperature change rate +0.1°C / min, and temperature change... The trend is stable. Historical temperature data shows 46.0°C 5 minutes ago, 45.8°C 15 minutes ago, and 45.5°C 30 minutes ago, with an ambient temperature of 35°C. The spatial coordinates of region 3 (module 3) are X=80mm, Y=220mm, Z=50mm. The current temperature is 43.8°C, with a temperature change rate of -0.5°C / min. The temperature change trend is decreasing. Historical temperature data shows 44.5°C 5 minutes ago, 45.2°C 15 minutes ago, and 45.8°C 30 minutes ago, with an ambient temperature of 35°C. This systematic method for extracting temperature anomaly data provides a precise quantitative basis for subsequent temperature gradient analysis and determination of temperature control characteristics.

[0115] The system visualizes temperature anomaly data, showing the spatial and temporal trends of temperature changes, providing an intuitive basis for thermal management decisions. The system performs temperature gradient calculations, calculating the temperature difference between each anomaly area and the surrounding area, the temporal gradient of temperature change (rate of temperature change), and the spatial gradient of temperature change (rate of temperature change per unit distance).

[0116] Construct a temperature anomaly gradient map, establish a three-dimensional coordinate system, with X, Y, and Z representing spatial locations, use color coding to indicate high and low temperatures (red for high temperatures and blue for low temperatures), use arrows to indicate the direction and rate of temperature change, use isotherms to represent areas of the same temperature, and add a time axis to show the temperature change over time.

[0117] Taking the commercial battery pack for electric buses as an example, the temperature anomaly gradient map constructed based on the above temperature anomaly data shows the following characteristics: In terms of spatial distribution, module 6 (region 1) has the highest temperature (48.5C), with a temperature difference of about 3-5C compared to the surrounding modules; busbar 3-1 (region 2) has the second highest temperature (46.2C), with a temperature difference of about 1-2C compared to the surrounding busbars; module 3 (region 3) has a relatively low temperature (43.8C), close to the temperature of the surrounding modules; In terms of temporal variation, the temperature of module 6 rises rapidly (+1.2C / min), with the gradient arrow pointing upward and being relatively long; the temperature of busbar 3-1 is basically stable (+0.1C / min), with the gradient arrow pointing horizontally and being relatively short; the temperature of module 3 decreases slowly (-0.5C / min), with the gradient arrow pointing downward and being of medium length.

[0118] The temperature anomaly gradient map is visually displayed as a three-dimensional thermal map. Module 6 shows obvious red hotspots, and the red area is expanding; the busbar 3-1 area is orange-yellow with a stable color; the module 3 area is yellow-green, and the color gradually lightens; there is a clear temperature gradient around module 6, with the temperature gradually decreasing from the center outwards; the time axis shows that the temperature of module 6 has been rising continuously over the past 30 minutes, and the rate of increase is accelerating; this visualized temperature anomaly gradient map provides intuitive spatial and temporal data for thermal management decisions.

[0119] By comprehensively analyzing thermal management content, temperature anomaly gradient maps, and thermal distribution maps, key temperature control features are extracted to guide the optimization of thermal management strategies. The system extracts temperature control features by extracting control parameters and execution conditions from thermal management content, temperature change trends and spatial distribution characteristics from temperature anomaly gradient maps, and overall temperature distribution and hotspot locations from thermal distribution maps. By comprehensively analyzing the above information, key temperature control features are determined. Temperature control features can be divided into spatial features (hotspot locations, temperature distribution uniformity, temperature gradient magnitude), temporal features (temperature change rate, temperature change trend, temperature fluctuation period), control features (cooling intensity, power limitation degree, monitoring frequency), and response features (temperature response speed and degree to control measures).

[0120] Taking the commercial battery pack of electric bus as an example, combined with the thermal management content, temperature anomaly gradient map and thermal distribution map in S142, the system determines the following temperature control characteristics: In terms of spatial characteristics, the hot spot location characteristic shows that module 6 is the central hot spot with the highest temperature and the largest gradient; the temperature distribution characteristic shows that the overall temperature distribution is uneven, and the temperature difference between module 6 and the surrounding modules is large; the temperature gradient characteristic shows that the temperature gradient around module 6 is large (about 2C / cm), while the gradient in other areas is small (about 0.5C / cm).

[0121] Regarding time characteristics, the temperature change rate characteristic shows that module 6's temperature rises rapidly (1.2°C / min), requiring rapid intervention; the temperature change trend characteristic shows that module 6's temperature continues to rise, busbar 3-1's temperature is stable, and module 3's temperature decreases; the temperature fluctuation characteristic shows that module 6's temperature fluctuates greatly, while other areas are relatively stable. Regarding control characteristics, the cooling intensity characteristic shows that module 6 requires high-intensity cooling (liquid cooling + air cooling, flow rate 15L / min, fan 3000rpm); the power limitation characteristic shows that module 6 requires strict power limitation (maximum discharge current limited to 60% of the rated value); the monitoring frequency characteristic shows that module 6 requires high-frequency monitoring (data collected every 10 seconds).

[0122] In terms of response characteristics, the cooling response characteristic display module 6 responds well to the liquid cooling system, with a cooling rate of up to 2°C / min; the power limiting response characteristic display module 6 responds quickly to power limiting, with a significantly slower rate of temperature rise; the comprehensive response characteristic display shows that when cooling and power limiting are used simultaneously, the temperature of module 6 can be reduced by 3°C within 5 minutes; this method of extracting temperature control characteristics by integrating multi-dimensional information provides a scientific basis for optimizing thermal management strategies, ensuring that the electric bus battery pack can obtain the most suitable temperature control under various operating conditions.

[0123] refer to Figure 6 In step S15, the specific steps are as follows:

[0124] S151: Collect multiple temperature control features, determine the first level of intelligent temperature control content based on the multiple temperature control features and the overall shape of the commercial battery pack, and determine the second level of intelligent temperature control content based on the multiple temperature control features and the usage scenario of the commercial battery pack.

[0125] S152: Collect data on the working components corresponding to multiple abnormal temperature areas, and determine the corresponding intelligent temperature control method based on the first level of intelligent temperature control content, the second level of intelligent temperature control content, and the working components corresponding to multiple abnormal temperature areas;

[0126] S153: Based on real-time detection of the thermal distribution map, determine multiple temperature change data, and determine the corresponding temperature change events based on the multiple temperature change data and the operating status of the commercial battery pack; determine temperature safety optimization measures based on multiple intelligent temperature control methods, the thermal distribution map of the commercial battery pack, and the corresponding temperature change events, so as to dynamically optimize the temperature safety level of the commercial battery pack and keep the temperature safety level of the commercial battery pack dynamically within the safe range.

[0127] In the embodiments of this application, multiple temperature control features are collected, and the first level of intelligent temperature control content is determined based on the multiple temperature control features and the overall shape of the commercial battery pack. The second level of intelligent temperature control content is determined based on the multiple temperature control features and the usage scenario of the commercial battery pack. This approach takes into account the overall consideration of multiple temperature control features and the usage scenario of the commercial battery pack, ensuring the accuracy of the second level of intelligent temperature control content.

[0128] At this point, the temperature control characteristics obtained from the previous analysis are summarized to provide a basis for the subsequent formulation of dual intelligent temperature control content. The system extracts the identified temperature control characteristics from step S143, including spatial characteristics (such as the distribution location of temperature anomaly areas, such as module 6, busbar 3-1, and module 3), temporal characteristics (such as temperature change trends, such as continuous rise, fluctuation, and fall), control characteristics (such as the type of temperature control measures, such as cooling control and power limiting), and response characteristics (such as the temperature response speed and amplitude after the implementation of temperature control measures). These temperature control characteristics comprehensively reflect the thermal state characteristics and control requirements of the battery pack, providing a scientific basis for the subsequent formulation of intelligent temperature control content.

[0129] Based on the physical structural characteristics of the battery pack, a global temperature control strategy is formulated; the overall form of the battery pack is systematically analyzed, including the battery module layout (for example, electric bus battery packs are usually composed of multiple modules arranged in a matrix, with poor heat dissipation conditions in the middle modules), cooling system structure (for example, using a combination of liquid cooling plates and air ducts, with the liquid cooling plates covering the bottom and the air ducts distributed on the sides), heat conduction path (heat is conducted from the inside of the module to the liquid cooling plate, and then carried away by the coolant circulation), and shell material (for example, aluminum alloy shell, which has good thermal conductivity).

[0130] Based on these analyses, the system formulates the first layer of intelligent temperature control, including an overall cooling strategy (primarily activating the liquid cooling system, supplemented by the air cooling system; dynamically adjusting the coolant flow rate according to the needs of the module with the highest temperature), optimization of temperature monitoring points (adding temperature sensors to intermediate modules with poor heat dissipation; setting temperature monitoring points at the inlet and outlet of the liquid cooling plate), optimization of heat distribution and conduction (adding thermally conductive silicone pads between modules to improve heat conduction efficiency; optimizing the layout of the liquid cooling plate to ensure uniform heat conduction), and a thermal balance strategy (periodically conducting overall thermal balance assessments of the battery pack; adjusting the operating parameters of the cooling system based on the assessment results). These strategies, based on the physical structural characteristics of the battery pack, optimize the thermal management effect from a global perspective, ensuring that the overall temperature of the battery pack is uniform and controllable.

[0131] Based on the actual usage environment of the battery pack, an adaptive temperature control strategy is formulated; the characteristics of the usage scenarios are systematically analyzed, including ambient temperature (high ambient temperature in summer can reach above 40℃; low ambient temperature in winter can drop to -10℃), operating conditions (frequent start-stop in urban conditions, large current fluctuations; continuous high power output in high-speed conditions), charging behavior (fast charging with high current, rapid heat generation; slow charging with low current, slow heat generation), and load characteristics (full load operation during peak periods, high heat generation; partial load during off-peak periods, low heat generation).

[0132] Based on these analyses, the system formulates a second layer of intelligent temperature control, including environmental adaptability strategies (activating the cooling system in advance during high summer temperatures to pre-cool the battery pack and increase coolant circulation; activating the heating system in low winter temperatures to preheat the battery pack, reduce cooling intensity, and prevent overcooling), operating condition adaptability strategies (dynamically adjusting cooling intensity during frequent start-stop cycles in urban conditions, paying attention to temperature changes caused by current fluctuations; maintaining strong cooling during continuous high-power output in high-speed conditions), charging adaptability strategies (activating the cooling system in advance during fast charging, monitoring temperature in real time, and limiting charging power when necessary; using conventional cooling strategies during slow charging), and load adaptability strategies (enhancing cooling during peak periods to ensure temperature does not exceed limits; appropriately reducing cooling intensity during off-peak periods to save energy). These strategies optimize thermal management from an adaptive perspective based on the actual operating environment of the battery pack, ensuring that the battery pack can obtain the most suitable temperature control under various operating conditions. This dual-layer intelligent temperature control formulation method, based on temperature control characteristics, overall form, and usage scenarios, achieves comprehensive thermal management from global to local and from static to dynamic, providing strong protection for the safe and efficient operation of electric bus battery packs.

[0133] Furthermore, the system collects data on the working components corresponding to multiple temperature anomaly areas, and determines the corresponding intelligent temperature control method based on the first level of intelligent temperature control, the second level of intelligent temperature control, and the working components corresponding to multiple temperature anomaly areas. This approach takes into account the overall considerations of the first level of intelligent temperature control, the second level of intelligent temperature control, and the working components corresponding to multiple temperature anomaly areas, ensuring the accuracy of the corresponding intelligent temperature control method.

[0134] At this point, the system obtains the correspondence between temperature anomaly areas and working components from step S132. Each temperature anomaly area typically corresponds to one or more specific working components; for example: Module 6 (temperature anomaly area A) → single cell group 6, BMS management unit 6, liquid cooling plate 6; Busbar 3-1 (temperature anomaly area B) → high voltage connector 3-1, copper busbar 3-1; Module 3 (temperature anomaly area C) → single cell group 3, BMS management unit 3, liquid cooling plate 3; through this clear mapping relationship, the system can accurately locate the physical source of the temperature anomaly, laying the foundation for subsequent development of targeted temperature control strategies.

[0135] By combining the aforementioned two levels of temperature control with specific working components, an intelligent temperature control execution method is formed for each component, ensuring the accurate implementation of the temperature control strategy. The system applies the first (global) and second (scenario-adaptive) levels of intelligent temperature control to each working component corresponding to each temperature anomaly area, forming a combined temperature control strategy. The intelligent temperature control methods mainly include the following categories:

[0136] (1) Cooling regulation method: The cooling regulation method achieves active control of battery pack temperature by adjusting parameters such as liquid cooling flow rate and fan speed; for example: liquid cooling flow rate adjustment: for the liquid cooling plate 6 in module 6, the coolant flow rate is increased from 10L / min to 15L / min to enhance the heat dissipation effect; fan speed control: for the area where the busbar 3-1 is located, the fan speed is increased from 1500rpm to 2500rpm to enhance air convection heat dissipation.

[0137] (2) Power management method: The power management method reduces the heat generation inside the battery pack by limiting the charging and discharging current and dynamically allocating power. For example: Charging and discharging current limit: For the single cell group 6, the maximum discharge current is limited to 60% of the rated value to prevent the temperature from rising further due to continuous high power operation; Dynamic power allocation: When multiple modules work in parallel, the power output ratio of module 6 is reduced and part of the load is transferred to module 3 with a lower temperature.

[0138] (3) Structural optimization method: The structural optimization method improves the overall heat dissipation performance of the battery pack by improving the thermal conductive material and adjusting the cooling structure. For example: thermal conductive material supplementation: thermal conductive silicone pads are added to the contact surface between the copper busbar 3-1 and the outer shell to improve the heat conduction efficiency; cooling structure adjustment: the flow channel layout of the liquid cooling plate 3 is optimized to ensure that the coolant can evenly cover the surface of the single cell group 3.

[0139] (4) Status monitoring method: The status monitoring method improves the real-time monitoring capability of temperature anomalies by adjusting the temperature sampling frequency and optimizing the alarm threshold. For example: Temperature sampling frequency adjustment: The temperature sampling frequency of BMS management unit 6 is increased from once every 30 seconds to once every 10 seconds to achieve more accurate temperature monitoring; Alarm threshold optimization: The temperature alarm threshold of high voltage connector 3-1 is adjusted from 50℃ to 48℃ to provide early warning of potential risks.

[0140] Therefore, multiple temperature change data are determined based on real-time detection of the thermal distribution map. Corresponding temperature change events are then identified based on these data and the operating status of the commercial battery pack. Temperature safety optimization measures are determined based on multiple intelligent temperature control methods, the thermal distribution map of the commercial battery pack, and the corresponding temperature change events. This dynamically optimizes the temperature safety level of the commercial battery pack, ensuring it remains within a safe range. This approach considers multiple intelligent temperature control methods, the thermal distribution map of the commercial battery pack, and the corresponding temperature change events, guaranteeing the accuracy of the temperature safety optimization measures. Furthermore, temperature control features are introduced to further control the intelligent temperature control methods, achieving dynamic optimization of the temperature safety level of the commercial battery pack and ensuring its normal operation.

[0141] At this time, by continuously monitoring the thermal distribution map, the temperature changes in various areas inside the battery pack are obtained, providing a data basis for subsequent analysis of temperature change events; the system uses a temperature sensor array arranged inside the battery pack to continuously collect temperature data in various areas, and the collection frequency can be dynamically adjusted according to the working status (once every 30 seconds in normal state, once every 5 seconds in abnormal state).

[0142] The system calculates the temperature change rate (°C / min) at each temperature monitoring point, records the direction of temperature change (rising / falling / stable), duration of temperature change, and magnitude of temperature change (the amount of change compared to the previous state). For example: Module 6: temperature rise rate 2.5°C / min, lasting 5 minutes, cumulative rise of 12.5°C; Busbar 3-1: temperature fluctuation, rise rate 1.0°C / min, fall rate 0.8°C / min, continuous fluctuation; Module 3: temperature fall rate 0.5°C / min, lasting 10 minutes, cumulative fall of 5°C. Through this real-time monitoring and data extraction, the system can comprehensively grasp the temperature changes in various areas inside the battery pack, providing data support for subsequent event identification.

[0143] By combining raw temperature change data with the battery pack's operating status, the system identifies temperature change events with specific significance, providing a basis for subsequent safety optimization measures. The system collects current operating status data of the battery pack, including operating mode (charging / discharging / idle), load current, SOC status, ambient temperature conditions, and cooling system operating status. Based on preset identification rules, the system combines temperature change data with the operating status to identify different types of temperature change events. For example: Event Type 1: Rapid temperature rise event; Judgment condition: temperature rise rate > 2℃ / min and duration > 3 minutes; Event Type 2: Temperature fluctuation event; Judgment condition: temperature rise and fall alternate more than 3 times within 10 minutes; Event Type 3: Abnormal temperature drop event; Judgment condition: temperature drop rate > 1℃ / min and not caused by active cooling from the cooling system.

[0144] By combining event recognition with operational status, the system can accurately determine the nature and cause of temperature changes. For example: Event A (Module 6): Rapid temperature rise event; Triggering condition: Temperature rise rate 2.5℃ / min, lasting 5 minutes; Operational status: High current discharge (300A), ambient temperature 35℃, cooling system operating normally; Event B (Bus 3-1): Temperature fluctuation event; Triggering condition: Temperature rise and fall alternate 4 times within 10 minutes; Operational status: Intermittent high current charging and discharging, ambient temperature 32℃, cooling system operating normally; Event C (Module 3): Normal temperature drop event; Triggering condition: Temperature drop rate 0.5℃ / min, lasting 10 minutes; Operational status: Low current discharge (50A), ambient temperature 28℃, cooling system operating normally. This event recognition method based on data and status provides accurate event evidence for subsequent development of targeted safety optimization measures.

[0145] By combining existing intelligent temperature control methods, real-time thermal distribution maps, and identified temperature change events, targeted temperature safety optimization measures are formulated to dynamically adjust the temperature safety level of the battery pack. The system formulates corresponding optimization measures based on different event types and dynamically adjusts the temperature safety level.

[0146] For example: Measure type 1: Temperature control enhancement measure: Applicable scenario: rapid temperature rise event; Specific measures: enhance cooling, limit power, start backup cooling system; Measure type 2: Temperature control adjustment measure: Applicable scenario: temperature fluctuation event; Specific measures: optimize cooling control parameters, adjust working mode; Measure type 3: Temperature control maintenance measure: Applicable scenario: normal temperature drop event; Specific measures: maintain the current temperature control strategy, appropriately reduce cooling intensity.

[0147] Meanwhile, the system has established a dynamic temperature safety level adjustment mechanism, including four levels: Safety Level 1 (Normal): The temperature is within the safe range and the temperature control system is working normally; Safety Level 2 (Caution): The temperature is close to the safety boundary and requires enhanced monitoring; Safety Level 3 (Warning): The temperature exceeds the safety boundary and requires immediate action; Safety Level 4 (Danger): The temperature seriously exceeds the safety boundary and requires emergency handling. Through this optimization measure that combines event type and safety level, the system can formulate the most appropriate response strategy for different events.

[0148] Please see Figure 7 , Figure 7 This is a schematic diagram of the structural composition of an intelligent temperature control system for a commercial battery pack based on battery thermal management, according to an embodiment of the present invention; the intelligent temperature control system for the commercial battery pack based on battery thermal management includes:

[0149] The working status module 21 is used to collect multiple working data of the commercial battery pack and determine the working status of the commercial battery pack based on the multiple working data of the commercial battery pack and the usage scenario of the commercial battery pack.

[0150] The thermal distribution module 22 is used to determine multiple temperature parameters based on the detection of the commercial battery pack, and to determine the thermal distribution map of the commercial battery pack based on multiple dimensional parameters, the corresponding temperature detection locations and the working status of the commercial battery pack.

[0151] The battery thermal management module 23 is used to determine multiple abnormal temperature areas based on the detection of the thermal distribution map of the commercial battery pack, determine multiple controllable temperature combinations based on the multiple abnormal temperature areas and the working status of the commercial battery pack, and determine the corresponding battery thermal management mode based on each controllable temperature combination, the usage scenario of the commercial battery pack and the service life of the commercial battery pack.

[0152] Temperature control feature module 24 is used to determine multiple battery thermal management items based on the detection of battery thermal management mode, and to determine the corresponding temperature control features according to each battery thermal management item, multiple temperature abnormality areas and temperature abnormality gradient map.

[0153] The temperature safety level module 25 is used to determine the corresponding intelligent temperature control method based on multiple temperature control characteristics, the overall shape of the commercial battery pack and the usage scenario of the commercial battery pack, and to dynamically optimize the temperature safety level of the commercial battery pack according to multiple intelligent temperature control methods, the thermal distribution map of the commercial battery pack and the corresponding temperature change events.

[0154] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A smart temperature control method for commercial battery packs based on battery thermal management, characterized in that, include: Collect multiple operational data points from the commercial battery pack, and determine the operational status of the commercial battery pack based on these data points and the usage scenarios of the commercial battery pack. Multiple temperature parameters are determined based on the detection of commercial battery packs. The thermal distribution map of the commercial battery pack is determined based on the multiple temperature parameters, the corresponding temperature detection locations, and the working status of the commercial battery pack. Multiple temperature anomaly areas are identified based on the detection of the thermal distribution map of the commercial battery pack. Multiple temperature controllable combinations are determined based on the multiple temperature anomaly areas and the working status of the commercial battery pack. The corresponding battery thermal management mode is determined based on each temperature controllable combination, the usage scenario of the commercial battery pack, and the service life of the commercial battery pack. Based on the detection of battery thermal management mode, multiple battery thermal management items are determined. According to each battery thermal management item, multiple temperature anomaly areas and temperature anomaly gradient map, the corresponding temperature control characteristics are determined. The temperature control characteristics include spatial characteristics, temporal characteristics, control characteristics and response characteristics. Based on multiple temperature control characteristics, the overall shape of the commercial battery pack, and its usage scenarios, a corresponding intelligent temperature control method is determined. The temperature safety level of the commercial battery pack is dynamically optimized based on these intelligent temperature control methods, the thermal distribution map of the commercial battery pack, and corresponding temperature change events. This includes: collecting multiple temperature control characteristics; determining the first level of intelligent temperature control content based on these characteristics and the overall shape of the commercial battery pack; determining the second level of intelligent temperature control content based on these characteristics and the usage scenarios of the commercial battery pack; and collecting data on the working components corresponding to multiple abnormal temperature areas, and determining the corresponding intelligent temperature control method based on the first level of intelligent temperature control content, the second level of intelligent temperature control content, and the working components corresponding to the multiple abnormal temperature areas. Intelligent temperature control methods include cooling regulation, power management, structural optimization, and status monitoring; the overall form of commercial battery packs includes battery module layout, cooling system structure, heat conduction path, and shell material.

2. The intelligent temperature control method for commercial battery packs based on battery thermal management according to claim 1, characterized in that, The process of collecting multiple operational data points from the commercial battery pack and determining its operational status based on this data and the usage scenario includes: The system monitors the operation of commercial battery packs in real time and collects multiple operational data from the commercial battery packs. It determines the corresponding operational data combination based on the cross-combination of multiple operational data from the commercial battery packs, and determines the corresponding sub-operational features based on the identification of each operational data combination, so as to collect multiple sub-operational features. The system acquires images of the surrounding environment of the commercial battery pack, identifies multiple scene features based on the recognition of these images, determines the usage scenario of the commercial battery pack based on these scene features and the overall shape of the commercial battery pack, and determines the working status of the commercial battery pack based on the usage scenario and multiple sub-working features.

3. The intelligent temperature control method for commercial battery packs based on battery thermal management according to claim 1, characterized in that, The process involves determining multiple temperature parameters based on the detection of commercial battery packs, and then determining the thermal distribution map of the commercial battery pack based on these multiple temperature parameters, the corresponding temperature detection locations, and the operating status of the commercial battery pack. This includes: During operation, the commercial battery pack performs corresponding online temperature detection simultaneously, and determines multiple temperature parameters based on the online temperature detection of the commercial battery pack; the first redistribution map is determined based on the multiple temperature parameters and the operating status of the commercial battery pack. Based on the traceability of each temperature parameter, the corresponding temperature detection location is determined. The second re-distribution map is determined according to the temperature detection location of each temperature parameter and the working state of the commercial battery pack. The thermal distribution map of the commercial battery pack is determined based on the first re-distribution map, the second re-distribution map and the working state of the commercial battery pack.

4. The intelligent temperature control method for commercial battery packs based on battery thermal management according to claim 1, characterized in that, The process involves identifying multiple temperature anomaly zones based on the detection of thermal distribution maps of commercial battery packs, determining multiple temperature controllable combinations based on these zones and the operating status of the commercial battery packs, and determining corresponding battery thermal management modes based on each temperature controllable combination, the usage scenario of the commercial battery packs, and the service life of the commercial battery packs. This includes: The thermal distribution map of a commercial battery pack is collected. Based on the detection of the thermal distribution map of the commercial battery pack, multiple temperature anomaly features are identified. Based on the feature location, corresponding feature shape and thermal distribution map of the commercial battery pack, multiple temperature anomaly regions are identified.

5. The intelligent temperature control method for commercial battery packs based on battery thermal management according to claim 4, characterized in that, The method of identifying multiple temperature anomaly zones based on the detection of the thermal distribution map of the commercial battery pack, determining multiple temperature controllable combinations based on the multiple temperature anomaly zones and the operating status of the commercial battery pack, and determining the corresponding battery thermal management mode based on each temperature controllable combination, the usage scenario of the commercial battery pack, and the service life of the commercial battery pack, also includes: Based on the tracing of each temperature anomaly region, the corresponding working component of the commercial battery pack is determined, and multiple temperature controllable combinations are determined according to multiple temperature anomaly regions, corresponding working components and the working status of the commercial battery pack. The first battery thermal management coefficient is determined based on the usage scenarios of various temperature-controllable combinations and commercial battery packs. The second battery thermal management coefficient is determined based on the service life of various temperature-controllable combinations and commercial battery packs. The corresponding battery thermal management mode is determined according to the mapping relationship between the first battery thermal management coefficient, the second battery thermal management coefficient and the battery thermal management mode.

6. The intelligent temperature control method for commercial battery packs based on battery thermal management according to claim 1, characterized in that, The detection based on the battery thermal management mode determines multiple battery thermal management items. Based on each battery thermal management item, multiple temperature anomaly regions, and a temperature anomaly gradient map, corresponding temperature control characteristics are determined, including: The battery thermal management mode is collected, and multiple sub-management modes are determined based on the detection of the battery thermal management mode. The corresponding battery thermal management items are determined based on the matching of each sub-management mode with the working status of the commercial battery pack.

7. The intelligent temperature control method for commercial battery packs based on battery thermal management according to claim 6, characterized in that, The method of determining multiple battery thermal management items based on battery thermal management mode detection, and determining corresponding temperature control characteristics based on each battery thermal management item, multiple temperature anomaly regions, and temperature anomaly gradient map, also includes: Collect data on multiple temperature anomaly areas, and determine the corresponding thermal management content based on these areas and the corresponding battery thermal management items. Multiple temperature anomaly data are determined based on the identification of multiple temperature anomaly regions, and a temperature anomaly gradient map is constructed based on the changes in each temperature anomaly data. The corresponding temperature control features are determined based on thermal management content, temperature anomaly gradient map and thermal distribution map of commercial battery pack.

8. The intelligent temperature control method for commercial battery packs based on battery thermal management according to claim 1, characterized in that, The method of determining the corresponding intelligent temperature control method based on multiple temperature control characteristics, the overall shape of the commercial battery pack, and the usage scenarios of the commercial battery pack, and dynamically optimizing the temperature safety level of the commercial battery pack according to multiple intelligent temperature control methods, the thermal distribution map of the commercial battery pack, and corresponding temperature change events, also includes: Based on real-time detection of thermal distribution maps, multiple temperature change data are determined. Corresponding temperature change events are determined based on multiple temperature change data and the operating status of the commercial battery pack. Temperature safety optimization measures are determined based on multiple intelligent temperature control methods, the thermal distribution map of the commercial battery pack, and the corresponding temperature change events, so as to dynamically optimize the temperature safety level of the commercial battery pack and keep the temperature safety level of the commercial battery pack dynamically within the safe range.

9. An intelligent temperature control system for a commercial battery pack based on battery thermal management, characterized in that, The intelligent temperature control system for a commercial battery pack based on battery thermal management is applied to the intelligent temperature control method for a commercial battery pack based on battery thermal management as described in any one of claims 1-8, wherein the intelligent temperature control system for a commercial battery pack based on battery thermal management includes: The working status module is used to collect multiple working data of the commercial battery pack and determine the working status of the commercial battery pack based on the multiple working data and the usage scenario of the commercial battery pack. The thermal distribution map module is used to determine multiple temperature parameters based on the detection of commercial battery packs, and to determine the thermal distribution map of commercial battery packs based on multiple temperature parameters, corresponding temperature detection locations and the operating status of commercial battery packs. The battery thermal management module is used to identify multiple abnormal temperature areas based on the detection of the thermal distribution map of the commercial battery pack, determine multiple controllable temperature combinations based on the multiple abnormal temperature areas and the working status of the commercial battery pack, and determine the corresponding battery thermal management mode based on each controllable temperature combination, the usage scenario of the commercial battery pack, and the service life of the commercial battery pack. The temperature control feature module is used to determine multiple battery thermal management items based on the detection of battery thermal management modes, and to determine the corresponding temperature control features based on each battery thermal management item, multiple temperature anomaly regions, and temperature anomaly gradient map. The temperature safety level module is used to determine the corresponding intelligent temperature control method based on multiple temperature control characteristics, the overall shape of the commercial battery pack, and the usage scenario of the commercial battery pack. It dynamically optimizes the temperature safety level of the commercial battery pack based on multiple intelligent temperature control methods, the thermal distribution map of the commercial battery pack, and the corresponding temperature change events.

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