Power battery thermal monitoring management method and system based on big data analysis

Abstract

The present invention discloses a power battery thermal monitoring management method and system based on big data analysis, and specifically relates to the technical field of power battery thermal monitoring management. By arranging multiple temperature sensors on the power battery cells, the temperature distribution data of the entire power battery is obtained in real time, and cluster analysis is performed to calculate the temperature distribution difference coefficient, so as to accurately identify the problem of abnormal temperature rise behavior occurring in different areas of the power battery. When the power battery has regional abnormal temperature rise behavior, the fan speed information of the heat dissipation control system, the timing synchronization information between the heat dissipation control system and the battery charging and discharging, and the coolant circulation information of the heat dissipation control system are obtained, and the fan speed abnormality coefficient, the timing synchronization coefficient, and the coolant circulation blockage coefficient are calculated, and the heat dissipation control system self-test index is output to evaluate the potential hidden dangers of the heat dissipation control system at the current stage, and the heat dissipation control system is early warning managed according to the evaluation results to timely detect potential faults of the heat dissipation system.

Description

Technical Field

[0001] The present invention relates to the technical field of power battery thermal monitoring and management, and more specifically, to a power battery thermal monitoring and management method and system based on big data analysis. Background Art

[0002] With the widespread adoption of electric vehicles and energy storage devices, the safety and performance of power batteries have become a key concern. Batteries generate a certain amount of heat during the charging and discharging process. Excessive temperatures not only affect the battery's cycle life but can also cause safety accidents. Therefore, real-time monitoring and management of power battery temperature distribution and thermal behavior, especially detecting abnormal temperature increases in different areas, is key to ensuring the safe operation of battery systems.

[0003] Existing power battery thermal monitoring and management systems primarily focus on overall battery temperature monitoring and uniform heat dissipation control. However, these systems often suffer from the following issues: First, they inadequately monitor the temperature distribution of different battery regions (such as individual cells, modules, and different locations within the battery pack), making it difficult to identify abnormal heating behavior in the power battery. Second, existing heat dissipation control systems often rely on preset temperature thresholds to initiate heat dissipation, but in actual operation lack monitoring of the heat dissipation system's self-test and status feedback, making it difficult to promptly detect performance degradation or failure of the heat dissipation system. These issues can lead to heat dissipation failure, localized overheating, and decreased battery performance, thereby impacting battery safety and service life. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a power battery thermal monitoring and management method and system based on big data analysis to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The power battery thermal monitoring and management method based on big data analysis includes the following steps:

[0007] Step S1, by arranging multiple temperature sensors on the power battery cells, collecting temperature distribution data of the power battery in real time, and analyzing the temperature distribution data of the power battery for abnormal temperature rise behavior in different regions;

[0008] Step S2: When the power battery has abnormal temperature rise in a certain area, obtain the fan speed information of the heat dissipation control system, and calculate the fan speed abnormality coefficient based on the fan speed information of the heat dissipation control system;

[0009] Step S3: When the power battery has abnormal regional temperature rise behavior, obtain the timing synchronization information between the heat dissipation control system and the battery charging and discharging, and calculate the timing synchronization coefficient based on the timing synchronization information between the heat dissipation control system and the battery charging and discharging;

[0010] Step S4: when the power battery has abnormal temperature rise in a certain area, obtain coolant flow information of the heat dissipation control system, and calculate the coolant flow blockage coefficient based on the coolant flow information of the heat dissipation control system;

[0011] Step S5, constructing a heat dissipation control system self-check model based on the fan speed abnormality coefficient, timing synchronization coefficient, and coolant flow blockage coefficient, outputting a heat dissipation control system self-check index, evaluating the potential hidden dangers of the heat dissipation control system at the current stage, and performing early warning management of the heat dissipation control system based on the evaluation results.

[0012] In a preferred embodiment, the temperature distribution data of the power battery includes temperature data of different sub-regions;

[0013] Identify regional abnormal temperature rise behavior based on the temperature distribution data of the power battery, as follows:

[0014] Step A1: Create a temperature clustering dataset {wd i}={wd1,wd2,...,wd I}, where wd i represents the temperature of the i-th sub-region, i∈{1,2,...,I};

[0015] Step A2: Use the elbow rule to determine the initial number of clusters K, randomly select the temperatures of K sub-regions from the temperature clustering dataset as the initial clusters, and use the temperatures of the sub-regions as the cluster centers of the initial clusters;

[0016] Step A3: using the Euclidean distance calculation method to calculate the distance between the temperature of each sub-region in the temperature clustering dataset and the cluster center of each cluster, and assigning it to the cluster with the smallest distance;

[0017] Step A4, calculating the average temperature of the sub-regions in each cluster and using it as the cluster center of the cluster;

[0018] Step A4: repeat steps A3 and A4 until the cluster center no longer changes, then the clustering process ends and the final temperature distribution cluster is obtained;

[0019] Calculate the temperature distribution cluster JL separately n and temperature distribution cluster JL m mean and standard deviation of interior temperature;

[0020] Calculate the nth temperature distribution cluster JL n Cluster JL with the mth temperature distribution m The difference value CY nm , the expression is as follows where σC n Represents the nth temperature distribution cluster JL n Standard deviation of internal temperature, σC m Represents the mth temperature distribution cluster JL m Standard deviation of internal temperature, TC n Represents the nth temperature distribution cluster JL n The mean internal temperature, TC m Represents the mth temperature distribution cluster JL m The mean value of the internal temperature, ∈ represents the minimum constant factor to avoid the denominator being zero;

[0021] Calculate the temperature distribution difference coefficient Wdc, the expression is as follows Where n∈{1,2,...,N}, N is the total number of temperature distribution clusters.

[0022] In a preferred embodiment, the temperature distribution difference coefficient is compared with a preset temperature distribution difference coefficient threshold to identify abnormal temperature rise behavior in the region, as follows:

[0023] If the temperature distribution difference coefficient is greater than the temperature distribution difference coefficient threshold, a regional abnormal temperature rise behavior signal is generated; if the temperature distribution difference coefficient is less than or equal to the temperature distribution difference coefficient threshold, no regional abnormal temperature rise behavior signal needs to be generated.

[0024] In a preferred embodiment, the fan speed information of the heat dissipation control system is obtained, the abnormality of the fan speed of the heat dissipation control system is analyzed, and the fan speed abnormality coefficient is obtained to measure the abnormality of the fan speed of the heat dissipation control system;

[0025] The logic for obtaining the fan speed abnormality coefficient is as follows:

[0026] The actual speed Ract of the fan is obtained through the real-time monitoring system, and the normal speed range of the fan [Rmin, Rmax] is obtained, where Rmin is the lowest speed of the fan under normal conditions, and Rmax is the highest speed of the fan under normal conditions; the expected average speed Ravg of the fan is calculated as follows Calculate the standard deviation of the actual fan speed σfan, the expression is as follows Among them Ract j represents the actual speed of the fan at the jth sampling point, Indicates the average actual fan speed. The calculation expression is as follows Where j∈{1,2,...,J}, J is the total number of sampling points; calculate the fan speed abnormality coefficient Fsyc, the expression is as follows Where ΔR represents the fan speed change rate, and θ is the sensitivity factor that controls the degree of influence of the fan speed change rate on the fan speed abnormality coefficient.

[0027] In a preferred embodiment, the timing synchronization between the heat dissipation control system and the battery charging and discharging is analyzed by acquiring the timing synchronization information between the heat dissipation control system and the battery charging and discharging, and the timing synchronization coefficient is obtained to measure the timing synchronization between the heat dissipation control system and the battery charging and discharging;

[0028] The logic for obtaining the timing synchronization coefficient is as follows:

[0029] Obtain the response time Tcooling of the cooling system to start controlling the battery temperature and the time Theat when the regional abnormal temperature rise behavior signal is generated. Calculate the cooling response delay ΔTresponse using the following expression: ΔTresponse = Tcooling - Theat.

[0030] Obtain the battery's charging process duration ΔTcharge and discharging process duration ΔTdischarge, and calculate the battery's total charge and discharge time ΔT using the following expression: ΔT = ΔTcharge + ΔTdischarge.

[0031] Calculate the timing synchronization coefficient Sxtb, the expression is as follows

[0032] In a preferred embodiment, the coolant circulation information of the heat dissipation control system is obtained, the degree of coolant circulation is analyzed, and the coolant circulation blockage coefficient is obtained to measure the degree of coolant circulation;

[0033] The logic for obtaining the coolant flow blockage coefficient is as follows:

[0034] The coolant density ρcool, coolant flow rate Vflow, pipe inner diameter Dpipe, and coolant viscosity μcoolant are obtained through the monitoring system, and the coolant Reynolds coefficient Re is calculated as follows: The coolant flow type is classified according to the coolant Reynolds coefficient as follows Calculate the coolant flow blockage coefficient Lque, the expression is as follows Transitional flow, where Lc is the length of the pipe, Qt is the volume flow rate of the coolant, fturbulent is the friction factor of turbulent flow, and flaminar is the friction factor of laminar flow.

[0035] In a preferred embodiment, a heat dissipation control system self-check model is constructed based on the fan speed abnormality coefficient, the timing synchronization coefficient, and the coolant flow blockage coefficient, and the heat dissipation control system self-check index CI is output. The model is based on the following formula: Where Wdc z It represents the temperature distribution difference coefficient corresponding to the generation of the regional abnormal temperature rise behavior signal. Fsyc, Sxtb, and Lque represent the fan speed abnormality coefficient, timing synchronization coefficient, and coolant circulation blockage coefficient, respectively. a1, a2, and a3 represent the preset proportional coefficients of the fan speed abnormality coefficient, timing synchronization coefficient, and coolant circulation blockage coefficient, respectively. A1, a2, and a3 are all greater than 0.

[0036] In a preferred embodiment, the heat dissipation control system self-check index is compared with a preset heat dissipation control system self-check index threshold value to perform early warning management on the heat dissipation control system;

[0037] If the heat dissipation control system self-test index is greater than the heat dissipation control system self-test index threshold, a high-risk warning is immediately triggered; if the heat dissipation control system self-test index is less than or equal to the heat dissipation control system self-test index threshold, there is no need to trigger a high-risk warning.

[0038] In a preferred embodiment, the power battery thermal monitoring and management system based on big data analysis includes a regional abnormal temperature rise module, a fan abnormal speed module, a timing synchronization module, a coolant circulation module, and an early warning management module;

[0039] The regional abnormal temperature rise module is used to collect the temperature distribution data of the power battery in real time by arranging multiple temperature sensors on the power battery cells, and analyze the regional abnormal temperature rise behavior of the power battery temperature distribution data;

[0040] The fan abnormal speed module is used to obtain the fan speed information of the heat dissipation control system when there is abnormal temperature rise in a certain area of the power battery, and calculate the fan speed abnormality coefficient based on the fan speed information of the heat dissipation control system;

[0041] The timing synchronization module is used to obtain the timing synchronization information between the heat dissipation control system and the battery charging and discharging when there is abnormal temperature rise in the power battery area, and calculate the timing synchronization coefficient based on the timing synchronization information between the heat dissipation control system and the battery charging and discharging;

[0042] A coolant circulation module is used to obtain coolant circulation information from the heat dissipation control system when abnormal temperature rise occurs in a certain area of the power battery, and calculate the coolant circulation blockage coefficient based on the coolant circulation information from the heat dissipation control system;

[0043] The early warning management module is used to build a self-check model of the heat dissipation control system based on the fan speed abnormality coefficient, timing synchronization coefficient, and coolant circulation blockage coefficient, output the heat dissipation control system self-check index, evaluate the potential hidden dangers of the heat dissipation control system at the current stage, and perform early warning management of the heat dissipation control system based on the evaluation results.

[0044] Technical effects and advantages of the present invention:

[0045] 1. The present invention arranges multiple temperature sensors on the power battery cells to obtain the temperature distribution data of the entire power battery in real time, and calculates the temperature distribution difference coefficient by clustering analysis on the temperature distribution data, accurately identifying the problem of abnormal temperature rise behavior between different areas of the power battery. When the power battery has regional abnormal temperature rise behavior, the fan speed information of the heat dissipation control system is obtained, and the fan speed abnormality coefficient is calculated based on the fan speed information of the heat dissipation control system. The timing synchronization information between the heat dissipation control system and the battery charging and discharging is obtained, and the timing synchronization coefficient is calculated based on the timing synchronization information between the heat dissipation control system and the battery charging and discharging. The coolant circulation information of the heat dissipation control system is obtained, and the coolant circulation information of the heat dissipation control system is calculated based on the coolant circulation information of the heat dissipation control system. The coolant circulation blockage coefficient builds a heat dissipation control system self-test model based on the fan speed abnormality coefficient, timing synchronization coefficient, and coolant circulation blockage coefficient, outputs the heat dissipation control system self-test index, evaluates the potential hidden dangers of the heat dissipation control system at the current stage, and performs early warning management of the heat dissipation control system based on the evaluation results, and promptly detects potential faults of the heat dissipation system. It can also monitor the operating performance of the heat dissipation system in real time, and promptly discover problems such as abnormal fan speed and poor coolant circulation, providing a scientific basis for early warning management of the heat dissipation system, avoiding heat dissipation failure or system performance degradation, and preventing safety hazards caused by heat dissipation system failure or performance degradation, thereby effectively improving the safety, reliability and service life of the power battery, and avoiding battery accidents and performance degradation caused by overheating. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0047] Figure 1 This is a flow chart of the method of Example 1 of the present invention;

[0048] Figure 2 This is a flow chart of the system of Example 2 of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] Example 1: Figure 1 The present invention provides a power battery thermal monitoring and management method based on big data analysis, which includes the following steps:

[0051] Step S1, by arranging multiple temperature sensors on the power battery cells, collecting temperature distribution data of the power battery in real time, and analyzing the temperature distribution data of the power battery for abnormal temperature rise behavior in different regions;

[0052] Step S2: When the power battery has abnormal temperature rise in a certain area, obtain the fan speed information of the heat dissipation control system, and calculate the fan speed abnormality coefficient based on the fan speed information of the heat dissipation control system;

[0053] Step S3: When the power battery has abnormal regional temperature rise behavior, obtain the timing synchronization information between the heat dissipation control system and the battery charging and discharging, and calculate the timing synchronization coefficient based on the timing synchronization information between the heat dissipation control system and the battery charging and discharging;

[0054] Step S4: when the power battery has abnormal temperature rise in a certain area, obtain coolant flow information of the heat dissipation control system, and calculate the coolant flow blockage coefficient based on the coolant flow information of the heat dissipation control system;

[0055] Step S5: constructing a heat dissipation control system self-check model based on the fan speed abnormality coefficient, the timing synchronization coefficient, and the coolant flow obstruction coefficient, outputting a heat dissipation control system self-check index, evaluating potential hidden dangers of the heat dissipation control system at the current stage, and performing early warning management of the heat dissipation control system based on the evaluation results;

[0056] In step S1, multiple temperature sensors are arranged on the power battery cells to collect temperature distribution data of the power battery in real time, and the abnormal temperature rise behavior of the region is identified based on the temperature distribution data of the power battery;

[0057] By arranging multiple temperature sensors on the power battery cell, it is necessary to ensure that the arrangement of the temperature sensors should cover all areas of the entire power battery cell, and divide all areas of the power battery cell into multiple sub-areas according to the working coverage range of each temperature sensor;

[0058] The temperature distribution data of the power battery includes temperature data of different sub-areas;

[0059] Identify regional abnormal temperature rise behavior based on the temperature distribution data of the power battery, as follows:

[0060] Step A1: Create a temperature clustering dataset {wd i}={wd1,wd2,...,wd I}, where wd i represents the temperature of the i-th sub-region, i∈{1,2,...,I};

[0061] Step A2: Use the elbow rule to determine the initial number of clusters K, randomly select the temperatures of K sub-regions from the temperature clustering dataset as the initial clusters, and use the temperatures of the sub-regions as the cluster centers of the initial clusters;

[0062] Step A3: using the Euclidean distance calculation method to calculate the distance between the temperature of each sub-region in the temperature clustering dataset and the cluster center of each cluster, and assigning it to the cluster with the smallest distance;

[0063] Step A4, calculating the average temperature of the sub-regions in each cluster and using it as the cluster center of the cluster;

[0064] Step A4: repeat steps A3 and A4 until the cluster center no longer changes, then the clustering process ends and the final temperature distribution cluster is obtained;

[0065] It should be noted that the above cluster analysis aggregates different sub-regions of the power battery based on the similarity of temperature. Each temperature distribution cluster contains the temperatures of multiple sub-regions, which means that the temperatures of these sub-regions are similar.

[0066] Calculate the temperature distribution cluster JL separately n and temperature distribution cluster JL m mean and standard deviation of interior temperature;

[0067] Calculate the nth temperature distribution cluster JL n Cluster JL with the mth temperature distribution m The difference value CY nm , the expression is as follows where σC n Represents the nth temperature distribution cluster JL n Standard deviation of internal temperature, σC m Represents the mth temperature distribution cluster JL m Standard deviation of internal temperature, TC n Represents the nth temperature distribution cluster JL n The mean internal temperature, TC m Represents the mth temperature distribution cluster JL mThe mean of the internal temperature, ε represents the minimum constant factor to avoid the denominator being zero;

[0068] Calculate the temperature distribution difference coefficient Wdc, the expression is as follows Where n∈{1,2,...,N}, N is the total number of temperature distribution clusters;

[0069] The temperature distribution difference coefficient is compared with the preset temperature distribution difference coefficient threshold to identify the abnormal temperature rise behavior in the area, as follows:

[0070] If the temperature distribution difference coefficient is greater than the temperature distribution difference coefficient threshold, it means that not only are the temperatures of different temperature distribution clusters significantly different, but the temperature fluctuations within each cluster are also significant, indicating that abnormal temperature rise behavior has occurred in different regions of the power battery, and a regional abnormal temperature rise behavior signal is generated. If the temperature distribution difference coefficient is less than or equal to the temperature distribution difference coefficient threshold, it means that the temperature differences between different temperature distribution clusters are small, and the temperature fluctuations within the cluster are also small. The temperature distribution of the power battery is relatively uniform, and there is no need to generate a regional abnormal temperature rise behavior signal.

[0071] Step S2: When the power battery has abnormal temperature rise in a certain area, obtain the fan speed information of the heat dissipation control system, and calculate the fan speed abnormality coefficient based on the fan speed information of the heat dissipation control system;

[0072] The fan speed abnormality coefficient is an indicator used to measure the degree of fan speed abnormality in the cooling control system. It assesses whether the fan is operating properly and whether there are cooling issues caused by low or high fan speeds. It directly reflects the fan's operational status. Abnormal fan speed may indicate a fault or deficiency in the cooling system, which in turn affects the battery's heat dissipation efficiency, leading to localized temperature increases and increasing the risk of battery overheating. By calculating the fan speed abnormality coefficient, fan performance issues, such as low speed or excessive speed fluctuations, can be identified early. These issues may be caused by fan failure, power supply instability, or control system issues. Furthermore, monitoring the fan speed abnormality coefficient provides a basis for timely adjustment of cooling strategies. For example, if the fan speed abnormality coefficient exceeds the standard, the system can trigger an alarm mechanism, alerting operations and maintenance personnel to inspect or adjust the fan or cooling system in advance, preventing battery overheating, performance degradation, or safety hazards caused by insufficient cooling. This proactive monitoring can significantly reduce equipment failures caused by cooling system instability, thereby improving the overall safety and service life of the battery. Furthermore, the fan speed abnormality coefficient provides data support for subsequent cooling optimization decisions. For example, when abnormal fan speed is detected, a comprehensive analysis can be conducted in conjunction with other parameters (such as temperature sensor data) to assess whether the number of fans needs to be increased, the fan speed range needs to be adjusted, or the cooling system design needs to be optimized. By continuously tracking and analyzing the fan speed anomaly coefficient, dynamic adjustments to the cooling system can be made to ensure that the battery remains within a safe temperature control range, avoiding long-term performance degradation or unexpected failures caused by excessive temperatures.

[0073] Therefore, by acquiring the fan speed information of the heat dissipation control system, analyzing the abnormality of the fan speed of the heat dissipation control system, and obtaining the fan speed abnormality coefficient, the abnormality of the fan speed of the heat dissipation control system is measured;

[0074] The logic for obtaining the fan speed abnormality coefficient is as follows:

[0075] The actual speed Ract of the fan is obtained through the real-time monitoring system, and the normal speed range of the fan [Rmin, Rmax] is obtained, where Rmin is the lowest speed of the fan under normal conditions, and Rmax is the highest speed of the fan under normal conditions; the expected average speed Ravg of the fan is calculated as follows Calculate the standard deviation of the actual fan speed σfan, the expression is as follows Among them Ract j represents the actual speed of the fan at the jth sampling point, Indicates the average actual fan speed. The calculation expression is as follows Where j∈{1,2,...,J}, J is the total number of sampling points; calculate the fan speed abnormality coefficient Fsyc, the expression is as follows Where ΔR represents the fan speed change rate, and θ is the sensitivity factor that controls the degree of influence of the fan speed change rate on the fan speed abnormality coefficient;

[0076] It should be noted that the fan speed change rate is the difference change between adjacent sampling points per unit time, which can be calculated by calculating the ratio of the difference between the actual fan speeds of adjacent sampling points and the sampling time interval;

[0077] Step S3: When the power battery has abnormal regional temperature rise behavior, obtain the timing synchronization information between the heat dissipation control system and the battery charging and discharging, and calculate the timing synchronization coefficient based on the timing synchronization information between the heat dissipation control system and the battery charging and discharging;

[0078] The timing synchronization coefficient is an important metric used to measure the timing synchronization between the thermal dissipation control system and the battery's charging and discharging processes. It describes the time difference between battery heat generation and the cooling system's response during charging and discharging. If the cooling control system's response lags behind the battery temperature rise, or reacts too slowly, the battery temperature can overheat, increasing the risk of overheating and even affecting battery safety and lifespan. The goal is to quantify how well the thermal dissipation control system matches the timing of battery heat generation during charging and discharging. By calculating the timing synchronization coefficient, we can determine whether the cooling system can respond promptly to battery thermal changes, ensuring temperature control during charging and discharging. This can effectively identify any lagging cooling system response, thereby predicting and mitigating potential overheating risks. By quantifying the timing synchronization between the thermal dissipation control system and battery charging and discharging, we can ensure that the cooling system responds promptly to the instant significant battery heat generation occurs, avoiding overheating caused by delayed response. A large timing synchronization coefficient indicates that the cooling system is failing to respond promptly to battery temperature changes, posing a potential risk. Adjusting the cooling strategy or optimizing the charging and discharging process can improve the cooling system's response speed, effectively reducing the risk of overheating and ensuring battery safety and stability. At the same time, by evaluating the timing synchronization coefficient, the efficiency of the cooling system can be improved, unnecessary energy waste can be avoided, more precise temperature control can be achieved, battery life can be extended, and overall system reliability can be improved. This evaluation method based on the timing synchronization coefficient not only improves the real-time monitoring capabilities of the battery management system, but also provides early warning of potential risks, ensuring that the system remains safe and stable under high load.

[0079] Therefore, by obtaining the timing synchronization information between the heat dissipation control system and the battery charging and discharging, the timing synchronization between the heat dissipation control system and the battery charging and discharging is analyzed, and the timing synchronization coefficient is obtained to measure the timing synchronization between the heat dissipation control system and the battery charging and discharging;

[0080] The logic for obtaining the timing synchronization coefficient is as follows:

[0081] Obtain the response time Tcooling of the cooling system to start controlling the battery temperature and the time Theat when the regional abnormal temperature rise behavior signal is generated. Calculate the cooling response delay ΔTresponse using the following expression: ΔTresponse = Tcooling - Theat.

[0082] Obtain the battery's charging process duration ΔTcharge and discharging process duration ΔTdischarge, and calculate the battery's total charging and discharging time ΔT using the following expression: ΔT = ΔTcharge + ΔTdischarge.

[0083] Calculate the timing synchronization coefficient Sxtb, the expression is as follows

[0084] Step S4: when the power battery has abnormal temperature rise in a certain area, obtain coolant flow information of the heat dissipation control system, and calculate the coolant flow blockage coefficient based on the coolant flow information of the heat dissipation control system;

[0085] The coolant flow obstruction factor measures the smoothness of coolant flow in the thermal control system, reflecting any flow resistance or obstructions within the system. Coolant plays a vital role in the heat dissipation process. If flow is poor or obstructed, heat dissipation effectiveness will be significantly reduced, affecting battery temperature control and increasing the risk of overheating. Therefore, calculating and evaluating the coolant flow obstruction factor is crucial for the health of the thermal system. A smaller coolant flow obstruction factor indicates less resistance to coolant flow, enabling smoother heat dissipation and better heat dissipation efficiency. A larger coolant flow obstruction factor indicates coolant flow obstruction, potentially due to blockages, pipe bends, or insufficient flow rate, leading to poor heat dissipation and an increased risk of temperature rise. By calculating and evaluating the coolant flow obstruction factor, coolant flow obstructions within the thermal control system can be detected promptly. A larger coolant flow obstruction factor may indicate coolant flow obstruction, resulting in inefficient thermal management and potentially leading to battery overheating, damage, or safety incidents. Therefore, the coolant flow blockage coefficient can not only help monitor the health of the cooling system in real time, but also provide an early warning basis for preventing overheating and guide relevant personnel to carry out necessary maintenance or optimization operations.

[0086] Therefore, by obtaining the coolant flow information of the heat dissipation control system, the smoothness of the coolant flow is analyzed, and the coolant flow blockage coefficient is obtained to measure the smoothness of the coolant flow;

[0087] The logic for obtaining the coolant flow blockage coefficient is as follows:

[0088] The coolant density ρcool, coolant flow rate Vflow, pipe inner diameter Dpipe, and coolant viscosity μcoolant are obtained through the monitoring system, and the coolant Reynolds coefficient Re is calculated as follows: The coolant flow type is classified according to the coolant Reynolds coefficient as follows Calculate the coolant flow blockage coefficient Lque, the expression is as follows Transitional flow, where Lc is the length of the pipe, Qt is the volume flow rate of the coolant, fturbulent is the friction factor of turbulent flow, and flaminar is the friction factor of laminar flow;

[0089] It should be noted that the friction factor of turbulent and laminar flow can be calculated by solving the Colebrook-White equation, which is as follows: Where τ is the relative roughness of the pipeline, and f represents the friction factor;

[0090] Step S5: constructing a heat dissipation control system self-check model based on the fan speed abnormality coefficient, the timing synchronization coefficient, and the coolant flow obstruction coefficient, outputting a heat dissipation control system self-check index, evaluating potential hidden dangers of the heat dissipation control system at the current stage, and performing early warning management of the heat dissipation control system based on the evaluation results;

[0091] The heat dissipation control system self-check model is constructed based on the fan speed abnormality coefficient, timing synchronization coefficient, and coolant flow blockage coefficient, and the heat dissipation control system self-check index CI is output. The model is based on the following formula Where Wdc z represents the temperature distribution difference coefficient corresponding to the generation of the regional abnormal temperature rise behavior signal, Fsyc, Sxtb, and Lque represent the fan speed abnormality coefficient, timing synchronization coefficient, and coolant circulation blockage coefficient, respectively, and a1, a2, and a3 represent the preset proportional coefficients of the fan speed abnormality coefficient, timing synchronization coefficient, and coolant circulation blockage coefficient, respectively, and a1, a2, and a3 are all greater than 0;

[0092] It should be noted that before building the heat dissipation control system self-test model, it is necessary to ensure that the fan speed abnormality coefficient, timing synchronization coefficient, coolant flow obstruction coefficient, and the temperature distribution difference coefficient corresponding to the generation of the regional abnormal temperature rise behavior signal are all normalized. a1, a2, and a3 are set according to actual conditions. For example, the expert empowerment method is adopted, that is, experts in related fields are invited to conduct professional opinion surveys and comprehensive evaluations to determine the preset proportional coefficients of various indicators.

[0093] It can be seen from the above calculation expression that the larger the fan speed abnormality coefficient, the larger the timing synchronization coefficient, and the larger the coolant circulation blockage coefficient, the larger the corresponding temperature distribution difference coefficient when generating the regional abnormal temperature rise behavior signal, and the larger the heat dissipation control system self-test index, indicating that the current heat dissipation control system has greater potential hidden dangers and may not be able to effectively deal with the abnormal temperature rise behavior problem occurring between different areas of the power battery. Conversely, the smaller the fan speed abnormality coefficient, the smaller the timing synchronization coefficient, and the smaller the coolant circulation blockage coefficient, the smaller the temperature distribution difference coefficient when generating the regional abnormal temperature rise behavior signal, and the smaller the heat dissipation control system self-test index, indicating that the current heat dissipation control system has less potential hidden dangers and the current stage of the heat dissipation system can more effectively deal with the abnormal temperature rise behavior problem occurring between different areas of the power battery.

[0094] Compare the heat dissipation control system self-check index with the preset heat dissipation control system self-check index threshold to perform early warning management of the heat dissipation control system;

[0095] If the thermal control system self-test index exceeds the thermal control system self-test index threshold, a high-risk warning is immediately triggered. At this time, the warning information will be notified to the relevant operators or technical support team through the monitoring system, alarm system or maintenance platform. If the thermal control system self-test index is less than or equal to the thermal control system self-test index threshold, it means that the thermal control system has no significant potential hazards at the current stage, the system's heat dissipation performance and operating status are normal, and it can effectively deal with abnormal temperature rise behavior in different areas of the power battery, and there is no need to trigger a high-risk warning.

[0096] The present invention arranges multiple temperature sensors on the power battery cells to obtain the temperature distribution data of the entire power battery in real time, and calculates the temperature distribution difference coefficient by clustering analysis on the temperature distribution data, so as to accurately identify the problem of abnormal temperature rise behavior between different areas of the power battery. When the power battery has regional abnormal temperature rise behavior, the fan speed information of the heat dissipation control system is obtained, and the fan speed abnormality coefficient is calculated based on the fan speed information of the heat dissipation control system. The timing synchronization information between the heat dissipation control system and the battery charging and discharging is obtained, and the timing synchronization coefficient is calculated based on the timing synchronization information between the heat dissipation control system and the battery charging and discharging. The coolant circulation information of the heat dissipation control system is obtained, and the coolant circulation information of the heat dissipation control system is calculated based on the coolant circulation information of the heat dissipation control system. Coolant circulation blockage coefficient, builds a heat dissipation control system self-test model according to the fan speed abnormality coefficient, timing synchronization coefficient, and coolant circulation blockage coefficient, outputs the heat dissipation control system self-test index, evaluates the potential hidden dangers of the heat dissipation control system at the current stage, and performs early warning management of the heat dissipation control system based on the evaluation results, and promptly detects potential faults of the heat dissipation system. It can also monitor the operating performance of the heat dissipation system in real time, and promptly discover problems such as abnormal fan speed and poor coolant circulation, providing a scientific basis for early warning management of the heat dissipation system, avoiding heat dissipation failure or system performance degradation, and preventing safety hazards caused by failure or performance degradation of the heat dissipation system, thereby effectively improving the safety, reliability and service life of the power battery, and avoiding battery accidents and performance degradation caused by overheating.

[0097] Example 2: This example is an introduction to a power battery thermal monitoring and management system based on big data analysis. Figure 2 As shown, it includes a regional abnormal temperature rise module, a fan abnormal speed module, a timing synchronization module, a coolant circulation module, and an early warning management module;

[0098] The regional abnormal temperature rise module is used to collect the temperature distribution data of the power battery in real time by arranging multiple temperature sensors on the power battery cells, and analyze the regional abnormal temperature rise behavior of the power battery temperature distribution data;

[0099] The fan abnormal speed module is used to obtain the fan speed information of the heat dissipation control system when there is abnormal temperature rise in a certain area of the power battery, and calculate the fan speed abnormality coefficient based on the fan speed information of the heat dissipation control system;

[0100] The timing synchronization module is used to obtain the timing synchronization information between the heat dissipation control system and the battery charging and discharging when there is abnormal temperature rise in the power battery area, and calculate the timing synchronization coefficient based on the timing synchronization information between the heat dissipation control system and the battery charging and discharging;

[0101] A coolant circulation module is used to obtain coolant circulation information from the heat dissipation control system when abnormal temperature rise occurs in a certain area of the power battery, and calculate the coolant circulation blockage coefficient based on the coolant circulation information from the heat dissipation control system;

[0102] The early warning management module is used to build a self-check model of the heat dissipation control system based on the fan speed abnormality coefficient, timing synchronization coefficient, and coolant circulation blockage coefficient, output the heat dissipation control system self-check index, evaluate the potential hidden dangers of the heat dissipation control system at the current stage, and perform early warning management of the heat dissipation control system based on the evaluation results.

[0103] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0104] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0105] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0106] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process of the system and method described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways.

[0108] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A power battery thermal monitoring and management method based on big data analysis, characterized by: The steps include: Step S1, by arranging multiple temperature sensors on the power battery cells, collecting temperature distribution data of the power battery in real time, and analyzing the temperature distribution data of the power battery for abnormal temperature rise behavior in different regions; Step S2: When the power battery has abnormal temperature rise in a certain area, obtain the fan speed information of the heat dissipation control system, and calculate the fan speed abnormality coefficient based on the fan speed information of the heat dissipation control system; Step S3: When the power battery has abnormal regional temperature rise behavior, obtain the timing synchronization information between the heat dissipation control system and the battery charging and discharging, and calculate the timing synchronization coefficient based on the timing synchronization information between the heat dissipation control system and the battery charging and discharging; Step S4: when the power battery has abnormal temperature rise in a certain area, obtain coolant flow information of the heat dissipation control system, and calculate the coolant flow blockage coefficient based on the coolant flow information of the heat dissipation control system; Step S5: constructing a heat dissipation control system self-check model based on the fan speed abnormality coefficient, the timing synchronization coefficient, and the coolant flow obstruction coefficient, outputting a heat dissipation control system self-check index, evaluating potential hidden dangers of the heat dissipation control system at the current stage, and performing early warning management of the heat dissipation control system based on the evaluation results; The temperature distribution data of the power battery includes temperature data of different sub-areas; Identify regional abnormal temperature rise behavior based on the temperature distribution data of the power battery, as follows: Step A1: Create a temperature clustering dataset based on the temperature data of different sub-regions ,in represents the temperature of the ith sub-region, ; Step A2: Use the elbow rule to determine the initial number of clusters K, randomly select the temperatures of K sub-regions from the temperature clustering dataset as the initial clusters, and use the temperatures of the sub-regions as the cluster centers of the initial clusters; Step A3: using the Euclidean distance calculation method to calculate the distance between the temperature of each sub-region in the temperature clustering dataset and the cluster center of each cluster, and assigning it to the cluster with the smallest distance; Step A4, calculating the average temperature of the sub-regions in each cluster and using it as the cluster center of the cluster; Step A4: repeat steps A3 and A4 until the cluster center no longer changes, then the clustering process ends and the final temperature distribution cluster is obtained; Calculate temperature distribution clusters separately and temperature distribution clusters mean and standard deviation of interior temperature; Calculate the nth temperature distribution cluster Cluster with the mth temperature distribution The difference value , the expression is as follows ,in Represents the nth temperature distribution cluster The standard deviation of the internal temperature, Represents the mth temperature distribution cluster The standard deviation of the internal temperature, Represents the nth temperature distribution cluster The mean internal temperature, Represents the mth temperature distribution cluster The mean internal temperature, Represents a minimum constant factor to avoid the denominator being zero; Calculate the temperature distribution difference coefficient , the expression is as follows ,in , N is the total number of final temperature distribution clusters; The temperature distribution difference coefficient is compared with the preset temperature distribution difference coefficient threshold to identify the abnormal temperature rise behavior in the area, as follows: If the temperature distribution difference coefficient is greater than the temperature distribution difference coefficient threshold, a regional abnormal temperature rise behavior signal is generated; if the temperature distribution difference coefficient is less than or equal to the temperature distribution difference coefficient threshold, no regional abnormal temperature rise behavior signal needs to be generated; By acquiring fan speed information of the heat dissipation control system, analyzing the abnormality of the fan speed of the heat dissipation control system, and obtaining a fan speed abnormality coefficient, the abnormality of the fan speed of the heat dissipation control system is measured; The logic for obtaining the fan speed abnormality coefficient is as follows: Obtain the actual fan speed through real-time monitoring system , and get the normal speed range of the fan ,in The fan's minimum speed under normal circumstances. is the maximum speed of the fan under normal conditions; calculate the expected average speed of the fan , the expression is as follows ; Calculate the standard deviation of the actual fan speed , the expression is as follows ,in represents the actual speed of the fan at the jth sampling point, Indicates the average actual fan speed. The calculation expression is as follows ,in , is the total number of sampling points; calculate the fan speed abnormality coefficient , the expression is as follows ,in Indicates the fan speed change rate. The sensitivity factor controls the degree of influence of the fan speed change rate on the fan speed abnormality coefficient; By acquiring the timing synchronization information between the heat dissipation control system and the battery charging and discharging, the timing synchronization between the heat dissipation control system and the battery charging and discharging is analyzed, and the timing synchronization coefficient is obtained to measure the timing synchronization between the heat dissipation control system and the battery charging and discharging; The logic for obtaining the timing synchronization coefficient is as follows: Get the response time of the cooling system to start controlling the battery temperature and the generation time of regional abnormal warming behavior signal , calculate the heat dissipation response delay , the expression is as follows ; Get the battery charging process duration and discharge process duration , calculate the total charge and discharge time of the battery , the expression is as follows ; Calculate the timing synchronization coefficient , the expression is as follows ; By acquiring the coolant flow information of the heat dissipation control system, analyzing the smoothness of the coolant flow, and obtaining the coolant flow blockage coefficient, the smoothness of the coolant flow is measured; The logic for obtaining the coolant flow blockage coefficient is as follows: Get the coolant density of the coolant through the monitoring system , coolant flow rate , pipe inner diameter , coolant viscosity , calculate the coolant Reynolds coefficient , the expression is as follows ; The coolant flow type is classified according to the coolant Reynolds coefficient, as follows ; Calculate the coolant flow blockage coefficient , the expression is as follows ,in Indicates the length of the pipe, is the volume flow rate of the coolant, represents the friction factor of turbulence, represents the friction factor of laminar flow; According to the fan speed abnormality coefficient, timing synchronization coefficient, and coolant flow blockage coefficient, a heat dissipation control system self-check model is constructed, and the heat dissipation control system self-check index is output. The model is based on the following formula , where Indicates the temperature distribution difference coefficient corresponding to the generation of regional abnormal temperature rise behavior signal, They represent the fan speed abnormality coefficient, timing synchronization coefficient, and coolant flow blockage coefficient respectively. They represent the preset proportional coefficients of the fan speed abnormality coefficient, timing synchronization coefficient, and coolant flow blockage coefficient, respectively, and Both are greater than 0.

2. The power battery thermal monitoring and management method based on big data analysis according to claim 1, characterized in that: Compare the heat dissipation control system self-check index with the preset heat dissipation control system self-check index threshold to perform early warning management of the heat dissipation control system; If the heat dissipation control system self-test index is greater than the heat dissipation control system self-test index threshold, a high-risk warning is immediately triggered; if the heat dissipation control system self-test index is less than or equal to the heat dissipation control system self-test index threshold, there is no need to trigger a high-risk warning.

3. A power battery thermal monitoring and management system based on big data analysis, used to implement the power battery thermal monitoring and management method based on big data analysis as described in any one of claims 1-2, characterized in that: Including regional abnormal temperature rise module, fan abnormal speed module, timing synchronization module, coolant circulation module, and early warning management module; The regional abnormal temperature rise module is used to collect the temperature distribution data of the power battery in real time by arranging multiple temperature sensors on the power battery cells, and analyze the regional abnormal temperature rise behavior of the power battery temperature distribution data; The fan abnormal speed module is used to obtain the fan speed information of the heat dissipation control system when there is abnormal temperature rise in a certain area of the power battery, and calculate the fan speed abnormality coefficient based on the fan speed information of the heat dissipation control system; A timing synchronization module is used to obtain timing synchronization information between the heat dissipation control system and the battery charging and discharging when there is abnormal regional temperature rise in the power battery, and calculate the timing synchronization coefficient based on the timing synchronization information between the heat dissipation control system and the battery charging and discharging; A coolant circulation module is used to obtain coolant circulation information from the heat dissipation control system when abnormal temperature rise occurs in a certain area of the power battery, and calculate the coolant circulation blockage coefficient based on the coolant circulation information from the heat dissipation control system; The early warning management module is used to build a self-check model of the heat dissipation control system based on the fan speed abnormality coefficient, timing synchronization coefficient, and coolant circulation blockage coefficient, output the heat dissipation control system self-check index, evaluate the potential hidden dangers of the heat dissipation control system at the current stage, and perform early warning management of the heat dissipation control system based on the evaluation results.

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