A thermal management control method and system for energy storage power supply

Abstract

The present invention relates to a thermal management control method and system for an energy storage power supply, the method comprising: obtaining real-time temperature data of the energy storage power supply, and performing heat source distribution analysis to obtain corresponding temperature distribution information; obtaining heat dissipation operation parameters of the energy storage power supply, and performing adjustment analysis in combination with the temperature distribution information to obtain a corresponding initial heat dissipation control strategy; obtaining real-time monitoring data of the energy storage power supply, and performing dynamic charge and discharge analysis in combination with the temperature distribution information and the initial heat dissipation control strategy to obtain corresponding charge and discharge adjustment information; performing hot spot identification based on the real-time temperature data and the thermal management strategy to obtain corresponding potential hot spot information, and performing early warning processing to obtain corresponding hot spot early warning results; performing comprehensive control analysis on the thermal management strategy, the initial heat dissipation control strategy and the hot spot early warning results to obtain a corresponding global thermal management control scheme. The present invention can comprehensively and intelligently control the thermal management of the energy storage power supply.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage power supplies, and in particular to a thermal management control method and system for an energy storage power supply. Background Art

[0002] As an important energy storage and supply device, energy storage power supplies play a key role in modern power systems and new energy applications. Among them, thermal management, as one of the key factors affecting the performance and life of energy storage power supplies, has become a key research topic in the industry. At present, the thermal management control method of energy storage power supplies usually adopts a single or static control strategy, which often ignores the dynamic characteristics and complexity of the energy storage system during actual operation. For example, only the overall temperature is considered while ignoring local hot spots, or only the heat dissipation is focused while ignoring the impact of the charging and discharging process on the temperature distribution. This simplified thermal management method may lead to poor control effects and cannot effectively respond to the thermal management needs of energy storage power supplies under different working conditions, thereby affecting the performance and life of the system. Summary of the invention

[0003] The main purpose of the present invention is to provide a thermal management control method and system for an energy storage power supply, which can comprehensively and intelligently control the thermal management of the energy storage power supply.

[0004] To achieve the above object, the present invention provides a thermal management control method for an energy storage power supply, comprising:

[0005] Obtain real-time temperature data of the energy storage power source, perform heat source distribution analysis, and obtain corresponding temperature distribution information;

[0006] Acquire the heat dissipation operation parameters of the energy storage power supply, perform adjustment analysis in combination with the temperature distribution information, and obtain a corresponding initial heat dissipation control strategy;

[0007] Acquire real-time monitoring data of the energy storage power supply, perform dynamic analysis of charging and discharging in combination with the temperature distribution information and the initial heat dissipation control strategy, and obtain corresponding charging and discharging adjustment information;

[0008] Performing a thermal balance optimization analysis according to the temperature distribution information, the initial heat dissipation control strategy and the charge and discharge adjustment information to obtain a corresponding thermal management strategy;

[0009] Based on the real-time temperature data and the thermal management strategy, hotspot identification is performed to obtain corresponding potential hotspot information, and early warning processing is performed to obtain corresponding hotspot early warning results;

[0010] A comprehensive control analysis is performed on the thermal management strategy, the initial heat dissipation control strategy and the hotspot warning result to obtain a corresponding global thermal management control solution.

[0011] Furthermore, the real-time temperature data of the energy storage power source is obtained, and the heat source distribution analysis is performed to obtain the corresponding temperature distribution information, including:

[0012] Acquire the real-time temperature data collected by the module temperature sensor deployed in the energy storage power supply;

[0013] Performing abnormal temperature fluctuation identification on the real-time temperature data to obtain a temperature abnormality point set;

[0014] Performing density cluster analysis on the temperature anomaly point set and identifying potential heat source areas to obtain a heat source distribution map;

[0015] Calculating heat source heat flux distribution based on the heat source distribution diagram to obtain heat flux distribution data;

[0016] constructing a heat transfer structure according to the heat flux distribution data to obtain a corresponding heat transfer topological structure;

[0017] Performing heat node analysis on the heat transfer topology structure according to a preset PageRank algorithm to obtain heat accumulation location information;

[0018] Performing finite difference thermal stress calculation based on the heat accumulation position information to obtain a thermal stress distribution map of each area;

[0019] Combining the heat source distribution map, the heat transfer topology structure and the thermal stress distribution map to perform short-term heat distribution trend prediction to obtain a heat distribution prediction result;

[0020] The heat source distribution map, the heat accumulation position information, the thermal stress distribution map and the heat distribution prediction result are fused to obtain the temperature distribution information.

[0021] Furthermore, the heat transfer topology structure is subjected to heat node analysis according to a preset PageRank algorithm to obtain heat accumulation location information, including the following steps:

[0022] Quantifying the heat transfer relationship between nodes in the heat transfer topology structure to obtain a heat transfer relationship matrix;

[0023] Calculating the node PageRank value of the heat transfer relationship matrix to obtain the heat node importance index;

[0024] Performing nonlinear threshold segmentation according to the heat node importance index to obtain a candidate heat accumulation node set;

[0025] Performing heat accumulation intensity evaluation on the candidate heat accumulation node set to obtain a heat accumulation intensity distribution map;

[0026] Positioning the heat accumulation position based on the heat accumulation intensity distribution map to obtain a heat accumulation position coordinate set;

[0027] The heat accumulation position coordinate set is mapped to the energy storage power source in a three-dimensional physical structure space to obtain the heat accumulation position information.

[0028] Furthermore, the obtaining of the heat dissipation operation parameters of the energy storage power supply and the adjustment analysis combined with the temperature distribution information to obtain the corresponding initial heat dissipation control strategy include:

[0029] Collecting heat dissipation parameters of the energy storage power supply to obtain corresponding heat dissipation operation parameters;

[0030] Calculate the heat dissipation efficiency according to the heat dissipation operation parameters to obtain corresponding heat dissipation efficiency data;

[0031] Performing a heat dissipation demand analysis on the heat dissipation efficiency data according to the temperature distribution information to obtain corresponding heat dissipation demand data;

[0032] Performing regional heat dissipation analysis on the heat dissipation demand data to obtain corresponding regional heat dissipation control parameters;

[0033] Perform heat dissipation power allocation according to the regional heat dissipation control parameters to obtain a corresponding heat dissipation power allocation plan;

[0034] Simulating the heat dissipation effect of the heat dissipation power allocation scheme to obtain corresponding heat dissipation effect prediction data;

[0035] The heat dissipation power allocation scheme is optimized and adjusted according to the heat dissipation effect prediction data to obtain the initial heat dissipation control strategy.

[0036] Furthermore, the real-time monitoring data of the energy storage power source is acquired, and the charge and discharge dynamic analysis is performed in combination with the temperature distribution information and the initial heat dissipation control strategy to obtain corresponding charge and discharge adjustment information, including:

[0037] The voltage, current and power of the energy storage power supply are collected and integrated in real time to obtain the real-time monitoring data;

[0038] Perform charge and discharge identification on the energy storage power source according to the real-time monitoring data to obtain corresponding charge and discharge status information;

[0039] Based on the charge and discharge state information and the temperature distribution information, a temperature change prediction is performed on the energy storage power supply to obtain a corresponding temperature change prediction result;

[0040] Performing a heat dissipation effect evaluation on the energy storage power source according to the temperature change prediction result and the initial heat dissipation control strategy to obtain a corresponding heat dissipation effect evaluation result;

[0041] Dynamically adjusting the charge and discharge power of the energy storage power source based on the heat dissipation effect evaluation result to obtain corresponding charge and discharge power adjustment information;

[0042] According to the heat dissipation effect evaluation result, a charge and discharge current of the energy storage power supply is limited and analyzed to obtain a corresponding charge and discharge current limit value;

[0043] Optimizing the charging and discharging time of the energy storage power source according to the heat dissipation effect evaluation result to obtain corresponding charging and discharging time optimization information;

[0044] The charge and discharge adjustment information is obtained by performing a comprehensive analysis based on the charge and discharge power adjustment information, the charge and discharge current limit value and the charge and discharge time optimization information.

[0045] Further, the heat balance optimization analysis is performed based on the temperature distribution information, the initial heat dissipation control strategy and the charge and discharge adjustment information to obtain a corresponding thermal management strategy, including:

[0046] Performing thermal diffusion characteristic analysis on the temperature distribution information to obtain a thermal diffusion coefficient matrix;

[0047] Performing heat conduction parameterization processing on the initial heat dissipation control strategy according to the thermal diffusion coefficient matrix to obtain a heat conduction parameter set;

[0048] Dynamically compensating the heat conduction parameter set based on the charge and discharge adjustment information to obtain a compensation parameter set;

[0049] Performing thermal field numerical calculation on the heat conduction parameter set to obtain a temperature field distribution prediction result;

[0050] The temperature field distribution prediction result is used to iteratively optimize the heat conduction parameter set to obtain an optimized heat conduction parameter set;

[0051] A comprehensive strategy analysis is performed on the optimized heat conduction parameter set and the compensation parameter set to obtain the thermal management strategy.

[0052] Further, the hotspot identification is performed based on the real-time temperature data and the thermal management strategy to obtain corresponding potential hotspot information, and early warning processing is performed to obtain corresponding hotspot early warning results, including:

[0053] Performing temperature anomaly analysis on the real-time temperature data according to a preset adaptive threshold segmentation algorithm to obtain an initial hotspot candidate area;

[0054] Performing finite element heat conduction calculation on the initial hotspot candidate area according to the thermal management strategy to obtain a hotspot development prediction result;

[0055] Dynamically clustering the hotspot development prediction results to obtain dynamic hotspot clustering;

[0056] Performing heat accumulation hotspot analysis based on the dynamic hotspot clustering to obtain the potential hotspot information;

[0057] Quantifying the warning level of the potential hotspot information to obtain a corresponding hotspot warning index;

[0058] Perform a warning analysis on the potential hotspot information and the hotspot warning index to obtain the hotspot warning result.

[0059] Furthermore, the thermal management strategy, the initial heat dissipation control strategy and the hotspot warning result are subjected to comprehensive control analysis to obtain a corresponding global thermal management control solution.

[0060] Performing dynamic response characteristic analysis on the thermal management strategy to obtain a corresponding strategy response curve;

[0061] Calculating the heat flux density distribution of the initial heat dissipation control strategy to obtain a corresponding heat flux density distribution diagram;

[0062] Performing hotspot diffusion simulation on the hotspot warning result to obtain the corresponding hotspot diffusion trend;

[0063] Perform coupling processing according to the strategy response curve, the heat flux density distribution diagram and the hot spot diffusion trend to obtain a corresponding multi-physics field coupling equation group;

[0064] Performing risk numerical solution on the multi-physics field coupling equation group to obtain a corresponding thermal failure risk point set;

[0065] Constructing a thermal management matrix according to the thermal failure risk point set to obtain a corresponding control parameter matrix;

[0066] Performing thermal stability analysis on the control parameter matrix to obtain corresponding stability boundary conditions;

[0067] A strategy collaborative analysis is performed based on the stability boundary conditions and the control parameter matrix to obtain the global thermal management control solution.

[0068] The present invention further provides a thermal management control system for an energy storage power supply, which is applied to any one of the above-mentioned thermal management control methods for an energy storage power supply, comprising:

[0069] An acquisition module, which is used to obtain real-time temperature data of the energy storage power supply, and perform heat source distribution analysis to obtain corresponding temperature distribution information;

[0070] An analysis module, the analysis module is used to obtain the heat dissipation operation parameters of the energy storage power supply, and perform adjustment analysis in combination with the temperature distribution information to obtain a corresponding initial heat dissipation control strategy;

[0071] An association module, the association module is used to obtain real-time monitoring data of the energy storage power supply, perform dynamic analysis of charging and discharging in combination with the temperature distribution information and the initial heat dissipation control strategy, and obtain corresponding charging and discharging adjustment information;

[0072] A processing module, the processing module is used to perform a thermal balance optimization analysis according to the temperature distribution information, the initial heat dissipation control strategy and the charge and discharge adjustment information to obtain a corresponding thermal management strategy;

[0073] A control module, the control module is used to identify hot spots based on the real-time temperature data and the thermal management strategy, obtain corresponding potential hot spot information, and perform early warning processing to obtain corresponding hot spot early warning results;

[0074] An execution module is used to perform comprehensive control analysis on the thermal management strategy, the initial heat dissipation control strategy and the hotspot warning result to obtain a corresponding global thermal management control solution.

[0075] The present invention provides a thermal management control method and system for an energy storage power supply, which has the following beneficial effects:

[0076] By acquiring real-time temperature data and performing heat source distribution analysis, the temperature distribution of the energy storage power supply can be fully and accurately grasped, providing a reliable data basis for subsequent thermal management control, and effectively solving the problem of poor thermal management effect caused by single temperature monitoring. Combined with temperature distribution information and heat dissipation operation parameters for adjustment analysis, the initial heat dissipation control strategy is obtained, which realizes the refined control of the energy storage power supply heat dissipation system, improves the heat dissipation efficiency, and overcomes the defect of ignoring local hot spots in traditional methods. By performing dynamic charge and discharge analysis on real-time monitoring data, and combining temperature distribution and initial heat dissipation control strategy, the charge and discharge adjustment information is obtained, and the dynamic thermal management of the energy storage power supply under different working conditions is realized, solving the problem that the static control strategy is difficult to adapt to complex operating environments. Based on temperature distribution, heat dissipation control and charge and discharge adjustment information, a comprehensive thermal management strategy is obtained, which realizes the systematic optimization of the thermal management of the energy storage power supply and improves the overall operating efficiency and performance. Through hot spot identification and early warning processing, potential hot spot problems are discovered and early warnings are issued in a timely manner, which effectively improves the safety of the energy storage power supply and solves the problem of lack of effective hot spot early warning mechanism in existing methods. Finally, by comprehensively processing the thermal management strategy, heat dissipation control strategy and hotspot warning results, a global thermal management control solution was obtained, which realized comprehensive and intelligent control of the thermal management of the energy storage power supply, significantly improved the reliability and life of the system, and reduced safety risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a flow chart of a thermal management control method of an energy storage power supply provided by the present invention;

[0078] Figure 2 It is a structural diagram of a thermal management control system of an energy storage power supply provided by the present invention.

[0079] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0080] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0081] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0082] Reference Figure 1 As shown, the present invention provides 1. A thermal management control method for an energy storage power supply, characterized in that it includes:

[0083] Step S1: Acquire real-time temperature data of the energy storage power source, and perform heat source distribution analysis to obtain corresponding temperature distribution information;

[0084] Step S2: Obtain the heat dissipation operation parameters of the energy storage power source, perform adjustment analysis in combination with the temperature distribution information, and obtain the corresponding initial heat dissipation control strategy;

[0085] Step S3: Acquire real-time monitoring data of the energy storage power source, perform dynamic analysis of charging and discharging in combination with temperature distribution information and initial heat dissipation control strategy, and obtain corresponding charging and discharging adjustment information;

[0086] Step S4: performing heat balance optimization analysis according to the temperature distribution information, the initial heat dissipation control strategy and the charge and discharge adjustment information to obtain a corresponding heat management strategy;

[0087] Step S5: performing hotspot identification based on real-time temperature data and thermal management strategies, obtaining corresponding potential hotspot information, and performing early warning processing to obtain corresponding hotspot early warning results;

[0088] Step S6: Perform comprehensive control analysis on the thermal management strategy, the initial heat dissipation control strategy and the hotspot warning results to obtain a corresponding global thermal management control solution.

[0089] Based on the above steps, the detailed process is as follows:

[0090] Step S1: By deploying multi-point temperature sensors at key locations such as battery modules, battery cells, connections, and battery boxes, all-round and high-precision temperature monitoring of energy storage power supplies can be achieved. Temperature sensors should have the characteristics of high precision, fast response, and anti-interference, and sensing technologies such as thermocouples and thermistors can be used. The data acquisition system needs to have functions such as high-speed real-time sampling, data filtering, and outlier processing to ensure the accuracy and reliability of temperature data. After obtaining temperature data, heat source distribution analysis is required, mainly including temperature gradient calculation, hot spot identification, and temperature uniformity assessment. By constructing a three-dimensional temperature field model, the temperature distribution characteristics of different locations and depths are analyzed to reveal the internal heat transfer and accumulation rules of energy storage power supplies. Focus on key indicators such as the temperature difference between battery cells, the temperature gradient inside the battery module, and the temperature distribution of the battery box, to provide basic data support for the subsequent formulation of thermal management strategies. Temperature distribution information not only reflects the current thermal state of the battery, but also indicates potential thermal safety risks.

[0091] Step S2: Based on the obtained temperature distribution information, comprehensively collect the heat dissipation operation parameters of the energy storage power supply, including cooling system parameters, ambient temperature, ventilation conditions, radiator performance, etc. By comprehensively evaluating the working efficiency and thermal management capabilities of the current heat dissipation system. Focus on analyzing the key parameters such as temperature, flow, and pressure of the cooling medium (such as liquid cooling and air cooling), and evaluate its real-time response capability to battery temperature regulation. Combined with the temperature distribution information, identify the key heat dissipation weak areas, and optimize the local heat dissipation strategy in a targeted manner. The initial heat dissipation control strategy should follow the principle of balancing battery safety, performance, and life, effectively suppressing the risk of excessive battery temperature and avoiding energy loss caused by excessive cooling. Strategy formulation needs to consider multi-dimensional factors such as battery charging and discharging status, ambient temperature changes, and load characteristics, and establish a dynamic and adaptive heat dissipation adjustment mechanism. Through numerical simulation and experimental verification, the heat dissipation control parameters are continuously iterated and optimized to construct a heat dissipation adjustment framework with real-time self-learning capabilities.

[0092] Step S3: Based on steps S1 and S2, the real-time operation monitoring data of the energy storage power supply is fully obtained, including key parameters such as voltage, current, state of charge (SOC), and state of health (SOH). By deeply exploring the inherent laws of battery thermal behavior during the charging and discharging process. Focus on analyzing the coupling effect of temperature distribution information and charging and discharging process, and identify the temperature change characteristics and thermal runaway risks of batteries under different charging and discharging conditions. Combined with the initial heat dissipation control strategy, a dynamic charging and discharging adjustment mechanism is constructed. The key is to establish a charging and discharging power adjustment model based on temperature constraints. When the local temperature of the battery is close to the safety threshold, the charging and discharging power is intelligently adjusted to achieve precise temperature control. The adjustment strategy should comprehensively consider battery performance, safety, and system efficiency, and effectively suppress local overheating of the battery through power peak shaving, segmented charging and discharging, and balanced charging. The charging and discharging adjustment information not only reflects the real-time adjustment results, but also provides an important basis for the subsequent optimization of thermal management strategies.

[0093] Step S4: Construct a comprehensive thermal balance optimization solution by deeply integrating temperature distribution information, initial heat dissipation control strategy and charge and discharge adjustment information. Focus on the temperature gradient and heat transfer mechanism between battery cells and within the battery pack to identify the root cause of thermal non-uniformity. The optimization analysis should focus on reducing the temperature difference inside the battery pack to achieve coordinated battery temperature. Through multi-dimensional means such as optimizing the heat dissipation structure, adjusting the cooling strategy, and improving the thermal management system design, an active and intelligent thermal balance control solution is constructed. The formulation of thermal management strategies requires balancing system energy efficiency, safety and economy, and adopting multi-objective optimization algorithms to maximize system performance while ensuring battery safety. The key is to establish a dynamic adjustment mechanism based on temperature balance, which can respond to changes in the thermal state of the battery in real time and actively prevent the risk of thermal runaway.

[0094] Step S5: Perform intelligent analysis and pattern recognition on real-time temperature data, establish a hotspot judgment system based on multiple dimensions such as temperature gradient, local temperature rise rate, and abnormal temperature characteristics, and realize accurate positioning and early warning of potential hotspots. Hotspot identification not only focuses on the absolute value of temperature, but also includes analyzing temperature change trends and abnormal characteristics. By constructing a dynamic threshold method and setting a multi-level early warning mechanism, the early warning program is immediately triggered when the local temperature of the battery exceeds the preset safety threshold or the temperature rise rate is abnormal. Early warning processing should include real-time data push, alarm signal output, emergency disposal suggestions, etc. The hotspot early warning results not only provide risk warnings, but also provide important references for subsequent thermal management decisions.

[0095] Step S6: The formulation of a global thermal management control scheme is the ultimate goal of thermal management of energy storage power sources. By integrating the analysis results of the above steps, a comprehensive and intelligent thermal management control framework is constructed. A collaborative control method integrating multiple strategies is established to achieve dynamic integration of thermal management strategies, heat dissipation control strategies and hotspot warning results. An intelligent regulation system based on predictive control is constructed, which can respond to the dynamic changes of battery thermal in real time and actively adjust the control strategy. The control scheme has the ability of adaptive learning and online optimization, and continuously improves the thermal management performance by establishing a closed-loop feedback mechanism. The global thermal management control scheme needs to balance safety, performance and cost, and comprehensively consider battery life, system efficiency and operating economy. The formulation of the control strategy should follow the principle of "prevention first, dynamic adjustment" to establish a multi-level and multi-dimensional intelligent early warning and regulation system. Finally, a set of global thermal management solutions that can monitor in real time, accurately warn and actively adjust are formed, providing strong guarantees for the safe and reliable operation of energy storage power sources.

[0096] The present invention provides a thermal management control method for an energy storage power supply. By acquiring real-time temperature data and performing heat source distribution analysis, the temperature distribution of the energy storage power supply can be fully and accurately grasped, providing a reliable data basis for subsequent thermal management control, and effectively solving the problem of poor thermal management effect caused by single temperature monitoring. The temperature distribution information and heat dissipation operation parameters are combined for adjustment analysis to obtain an initial heat dissipation control strategy, which realizes the refined control of the heat dissipation system of the energy storage power supply, improves the heat dissipation efficiency, and overcomes the defect of ignoring local hot spots in traditional methods. By performing dynamic charge and discharge analysis on real-time monitoring data, and combining temperature distribution and initial heat dissipation control strategy, charge and discharge adjustment information is obtained, and dynamic thermal management of the energy storage power supply under different working conditions is realized, solving the problem that the static control strategy is difficult to adapt to complex operating environments. Based on temperature distribution, heat dissipation control and charge and discharge adjustment information, a comprehensive thermal management strategy is obtained, which realizes the systematic optimization of thermal management of the energy storage power supply and improves the overall operating efficiency and performance. Through hot spot identification and early warning processing, potential hot spot problems are discovered and early warnings are issued in a timely manner, which effectively improves the safety of the energy storage power supply and solves the problem of lack of effective hot spot early warning mechanism in existing methods. Finally, by comprehensively processing the thermal management strategy, heat dissipation control strategy and hotspot warning results, a global thermal management control solution was obtained, which realized comprehensive and intelligent control of the thermal management of the energy storage power supply, significantly improved the reliability and life of the system, and reduced safety risks.

[0097] In one embodiment, real-time temperature data of the energy storage power source is obtained, and heat source distribution analysis is performed to obtain corresponding temperature distribution information, including:

[0098] The acquisition of temperature distribution information is achieved through multi-level and refined data analysis and processing. As a key device for data acquisition, temperature sensors are evenly deployed in different locations of the energy storage power module to capture temperature changes in each area in real time. These sensors can accurately obtain real-time temperature data inside the module through high-precision temperature measurement technology, with data accuracy up to 0.1°C.

[0099] Temperature anomaly identification is based on statistical methods. Specifically, the normal distribution outlier judgment criteria are adopted, that is, data points that exceed the range of plus or minus 3 standard deviations from the mean are regarded as temperature anomalies. Cluster analysis is performed on the identified temperature anomaly point set, and the temperature anomaly points are clustered according to spatial distribution and density characteristics to accurately locate potential heat source areas.

[0100] The heat flux distribution calculation of the heat source introduces Fourier's heat conduction law and calculates the heat flux of each area based on the temperature gradient and thermal conductivity coefficient. The heat flux distribution data reflects the transfer intensity and direction of heat in the energy storage power module. Based on the heat flux distribution, a heat transfer topology is constructed. The structure is essentially a weighted graph, where nodes represent different areas and the weight of the edge represents the heat transfer intensity.

[0101] The PageRank algorithm is introduced into the thermal node analysis, and the importance of each node is iteratively calculated to identify the location of heat accumulation. The algorithm calculates the heat transfer influence of each area based on the heat transfer topology, and the nodes with high importance are regarded as the locations of heat accumulation. The thermal stress distribution diagram is calculated by the finite difference method, considering parameters such as the thermal expansion coefficient and elastic modulus of the material, to evaluate the thermomechanical impact of temperature changes on the energy storage power structure.

[0102] The prediction of heat distribution trend adopts the time series analysis method, combines historical temperature data, heat source distribution characteristics and heat transfer laws to build a prediction model. The prediction model can simulate the changing trend of temperature distribution in the short term in the future. Through information fusion technology, the heat source distribution map, heat accumulation location information, thermal stress distribution map and heat distribution prediction results are integrated in multiple dimensions to generate comprehensive and accurate temperature distribution information. The information fusion process adopts the Bayesian network method, comprehensively considers the credibility and relevance of each data source, and obtains a more accurate description of temperature distribution through probabilistic inference.

[0103] This embodiment can accurately identify the heat source distribution and potential abnormal areas in the energy storage power module through a multi-level and refined temperature data analysis method. The normal distribution outlier judgment criterion can effectively filter random temperature fluctuations, accurately locate significant temperature anomalies, and improve the accuracy and reliability of temperature monitoring. The introduction of the PageRank algorithm for heat node analysis can dynamically evaluate the heat transfer influence of each region and identify key heat accumulation locations. Combined with finite difference thermal stress calculation and time series prediction technology, it can not only evaluate the thermal mechanical impact of temperature changes on the energy storage power structure, but also predict short-term temperature distribution trends, providing a scientific decision-making basis for active thermal management. Through multi-dimensional information fusion, comprehensive and accurate temperature distribution information is generated by comprehensively considering heat source distribution, heat accumulation location, thermal stress distribution and temperature prediction results. This systematic approach significantly improves the intelligent level of thermal management of energy storage power supplies, helps to optimize power performance, extend service life, and reduce safety risks.

[0104] In one embodiment, heat node analysis is performed on the heat transfer topology structure according to a preset PageRank algorithm to obtain heat accumulation location information, including the following steps:

[0105] In the heat transfer topology analysis phase, a heat transfer relationship matrix between nodes is constructed. This matrix quantifies the intensity and direction of heat exchange between nodes based on the thermal conductivity characteristics of battery cells, modules, and components inside the battery pack. The matrix elements reflect physical parameters such as temperature gradient, thermal conductivity, and contact area. Through normalization, the heat transfer relationship is quantifiable and comparable.

[0106] The PageRank value calculation phase introduces an importance assessment mechanism based on heat transfer. The web page link relationship in the traditional PageRank algorithm is replaced by a heat transfer path, and each node is regarded as a key node for heat propagation. During the calculation process, an attenuation factor (usually set to 0.85) is introduced to balance the effects of direct heat transfer and indirect heat diffusion. Nodes with high PageRank values ​​are regarded as key hubs for heat transfer, reflecting their central position in the thermal network.

[0107] Nonlinear threshold segmentation is a key step in screening candidate heat accumulation nodes. The threshold selection does not use a simple percentile method, but combines the distribution characteristics of PageRank values ​​and thermodynamic critical conditions. The threshold function comprehensively considers the node PageRank value, local temperature gradient, and heat transfer flux, and dynamically adjusts the node screening criteria. Nodes exceeding the threshold are included in the candidate heat accumulation node set, which have significant heat accumulation risks.

[0108] The heat accumulation intensity assessment introduces a multi-dimensional indicator system. In addition to the PageRank value, factors such as node temperature, heat flux, and local heat capacity are also considered. By constructing a weighted comprehensive assessment model, a heat accumulation intensity distribution map is generated. The distribution map not only shows the location of heat accumulation, but also quantifies the severity of heat accumulation, providing a basis for subsequent precise positioning.

[0109] The heat accumulation location is based on the heat accumulation intensity distribution map, and the spatial clustering algorithm is used to extract the geometric center coordinates of the heat accumulation area. The coordinate extraction process takes into account the spatial continuity and thermal intensity gradient to ensure that the positioning result accurately reflects the actual heat accumulation area.

[0110] Through three-dimensional physical structure spatial mapping, the abstract heat accumulation position coordinates are converted into the actual physical coordinates of the energy storage power source. The mapping process establishes the correspondence between the thermal distribution model and the battery geometric structure, accurately locates the specific location of heat accumulation, and provides direct guidance for subsequent thermal management strategies.

[0111] This embodiment realizes the accurate identification and location of heat accumulation through the innovative application of PageRank algorithm, which has significant technical advantages. Through comprehensive evaluation of multi-dimensional indicators, the specific location of heat accumulation can be accurately located, providing more accurate guidance for thermal management strategies. Compared with traditional methods, the present invention significantly improves the level of intelligence of thermal management of energy storage power sources, can dynamically and accurately identify potential thermal safety risks, and effectively improve the safety and reliability of energy storage systems. By introducing nonlinear threshold segmentation and spatial mapping technology, the thermal management strategy is more accurate and targeted.

[0112] In one embodiment, the heat dissipation operation parameters of the energy storage power supply are obtained, and adjustment analysis is performed in combination with the temperature distribution information to obtain the corresponding initial heat dissipation control strategy, including:

[0113] The energy storage power supply is comprehensively temperature parameterized through a multi-point temperature sensor array. Temperature sensors are placed at key locations of the battery assembly, including battery cells, module connections, and battery pack edges. The sampling frequency is set to 1-2 times per second to ensure real-time and accurate temperature distribution information. The collected heat dissipation operating parameters include key indicators such as battery cell temperature, ambient temperature, internal temperature gradient of the battery assembly, and charge and discharge current.

[0114] Based on the collected heat dissipation operating parameters, the heat dissipation efficiency will be calculated through a preset heat transfer mathematical model. The calculation model takes into account the thermal conductivity coefficient, convection heat transfer coefficient and radiation heat transfer coefficient to comprehensively evaluate the real-time heat dissipation capacity of the battery component. The heat dissipation efficiency data will be quantified into two core indicators: temperature uniformity index and heat transfer efficiency, reflecting the thermal management level of the battery component.

[0115] In the temperature distribution analysis phase, the heat dissipation efficiency data will be deeply analyzed according to the preset temperature threshold and temperature gradient standards. When the battery cell temperature exceeds 35°C or the temperature gradient is greater than 5°C, accurate heat dissipation demand analysis will be triggered. By building a temperature anomaly recognition algorithm, high-risk temperature areas can be accurately located and detailed heat dissipation demand data can be generated.

[0116] Regional heat dissipation analysis will formulate differentiated heat dissipation control parameters based on temperature distribution characteristics. For battery cells or modules with higher temperatures, local cooling intensity will be increased; for areas with relatively stable temperatures, basic heat dissipation levels will be maintained. Heat dissipation control parameters include cooling fan speed, liquid cooling system flow, and refrigerant expansion valve opening, etc., to form a refined regional adjustment plan.

[0117] In the heat dissipation power allocation phase, a multi-dimensional power allocation plan will be formulated based on the regional heat dissipation control parameters. Power allocation takes into account the thermodynamic characteristics of battery components, energy consumption of the cooling system, and heat dissipation uniformity, and achieves optimal power configuration through intelligent algorithms. The allocation plan accurately directs cooling resources to areas with abnormal temperatures to avoid energy waste.

[0118] In order to verify the effectiveness of the heat dissipation power allocation scheme, a thermodynamic simulation model will be used to predict the heat dissipation effect. The prediction model is based on the finite element analysis method to simulate the temperature change trend of battery components under different heat dissipation strategies. By comparing and analyzing the prediction data, the potential defects of the scheme can be identified and the scheme optimization based on simulation can be achieved.

[0119] According to the heat dissipation effect prediction data, the heat dissipation power allocation scheme is iteratively optimized. The optimization process follows the design principles of minimum temperature difference and minimum energy consumption to obtain the initial heat dissipation control strategy.

[0120] This embodiment uses a multi-point temperature sensor array to accurately collect the temperature distribution of battery components, realize comprehensive and real-time monitoring of the thermal state of the energy storage power supply, and significantly improve the accuracy and reliability of temperature management. The use of intelligent heat transfer mathematical models and temperature anomaly recognition algorithms can accurately locate high-temperature risk areas and formulate differentiated regional heat dissipation strategies to effectively prevent local overheating of battery components. The heat dissipation effect prediction technology based on thermodynamic simulation models allows the heat dissipation control strategy to be simulated and optimized before actual execution, greatly reducing the performance degradation and safety risks of battery components caused by improper heat dissipation.

[0121] In one embodiment, real-time monitoring data of the energy storage power source is obtained, and a dynamic analysis of charging and discharging is performed in combination with temperature distribution information and an initial heat dissipation control strategy to obtain corresponding charging and discharging adjustment information, including:

[0122] In the real-time monitoring stage, the voltage sensor collects the voltage data of the energy storage power supply every 0.1 seconds, and the current sensor monitors the charge and discharge current changes in real time. Through the data fusion algorithm, the raw data collected by multiple sensors are standardized to generate a complete real-time monitoring data set.

[0123] The charge and discharge status recognition is based on the voltage-current-power correlation analysis. When the current direction is positive and the power is greater than the preset threshold of 10kW, it is judged as the charging state; when the current direction is negative and the power is greater than the preset threshold of 10kW, it is judged as the discharging state. The status recognition is based on the preset judgment rules to ensure accurate status recognition.

[0124] Based on the temperature distribution information and charge and discharge status information, the temperature change trend analysis is performed. By comparing the current temperature distribution with the historical temperature change law, the temperature development trend of the energy storage power supply under different charge and discharge states is predicted. The analysis process focuses on the uniformity of temperature distribution and extreme value changes.

[0125] The heat dissipation effect evaluation is based on the preset heat dissipation control strategy and temperature distribution information. The evaluation indicators include the maximum temperature, the minimum temperature and the uniformity of temperature distribution. When the maximum temperature exceeds 70°C or the unevenness of temperature distribution exceeds 15°C, the advanced heat dissipation strategy is triggered. The evaluation is based on the pre-set temperature control threshold and judgment criteria.

[0126] The dynamic adjustment of charge and discharge power follows the temperature control principle. When the heat dissipation effect evaluation results show that the temperature risk is high, the charge and discharge power is reduced in a linear decreasing manner. The power adjustment range is set to 50%-80% of the original power to ensure the safe operation of the battery pack. The adjustment range is positively correlated with the degree of temperature risk.

[0127] Current limit analysis is based on temperature distribution information. When the local temperature exceeds 65°C, the charge and discharge current of the battery cells in the corresponding area will be reduced proportionally. The current limit value is queried and determined through the preset temperature-current correspondence table to ensure the overall safety of the battery pack.

[0128] The optimization of charge and discharge time takes into account temperature uniformity and energy efficiency. When the temperature distribution is uneven, the charge and discharge frequency is reduced, the single charge and discharge time is extended, and the heat is evenly diffused. The optimization basis includes temperature gradient, charge and discharge rate, and preset time adjustment rules.

[0129] Integrate the charge and discharge power adjustment information, current limit value and charge and discharge time optimization information to form a comprehensive charge and discharge adjustment strategy. Through preset rules and threshold control, safe and efficient thermal management of energy storage power supply is achieved. Strategy adjustment follows the principle of prevention and always puts the safe operation of battery packs in the first place.

[0130] This embodiment realizes accurate dynamic monitoring and early warning of the temperature of the energy storage power supply through multi-dimensional data collection and intelligent analysis, and significantly improves the reliability of safe operation of the battery pack. The introduction of intelligent control mechanism of charging and discharging power, current and time based on temperature distribution can effectively alleviate the problem of local overheating of the battery, reduce the risk of thermal runaway of the battery pack, and extend the service life of the energy storage power supply. The dynamic adjustment strategy of preset rules and thresholds is adopted to make the thermal management of the energy storage power supply highly adaptive and preventive, and can flexibly adjust the charging and discharging parameters according to real-time temperature changes to ensure the safe and stable operation of the battery pack under different working conditions. Through a multi-level, dynamically adaptive temperature control method, the thermal management performance of the energy storage power supply is optimized, while ensuring the safety of the battery, the energy utilization efficiency of the energy storage system is maximized.

[0131] In one embodiment, a thermal balance optimization analysis is performed based on the temperature distribution information, the initial heat dissipation control strategy, and the charge and discharge adjustment information to obtain a corresponding thermal management strategy, including:

[0132] The thermal diffusion characteristics of the temperature distribution information are analyzed to obtain the thermal diffusion coefficient matrix. The thermal diffusion characteristics analysis is based on the Fourier heat conduction equation, which deeply analyzes the temperature distribution matrix and calculates the heat transfer characteristics between battery cells. Through eigenvalue decomposition and eigenvector calculation, the thermal diffusion coefficient matrix is ​​constructed. This matrix quantitatively describes the speed, path and intensity of heat transfer inside the battery pack, providing a theoretical basis for subsequent thermal management strategies.

[0133] The initial heat dissipation control strategy is formulated based on the thermal diffusion coefficient matrix, and the battery pack cooling system is parameterized. The cooling system includes a liquid cooling plate, forced convection air cooling, and a phase change material thermal management module. By accurately adjusting the cooling medium flow, temperature difference, and heat exchange area, a comprehensive heat conduction parameter set is formed. The construction of the parameter set takes into account multi-dimensional factors such as the thermal physical properties of the battery pack, the thermal conductivity of the battery material, the geometric structure of the battery pack, and the ambient temperature to ensure that the thermal stability of the battery pack is maintained under different operating conditions.

[0134] As the key input of dynamic compensation, the charge and discharge adjustment information mainly includes key parameters such as current intensity, charge and discharge rate and duration. Based on this information, the heat conduction parameter set is dynamically compensated in real time to generate a compensation parameter set. The compensation process deeply analyzes the internal Joule heat of the battery, the exothermic process of the electrochemical reaction, and the external heat exchange characteristics, constructs a multi-dimensional compensation relationship, and ensures that the compensation parameter set can accurately reflect the instantaneous and dynamic thermodynamic changes of the battery pack.

[0135] The temperature field distribution prediction adopts the numerical simulation method, specifically the finite element analysis technology is used to accurately calculate the heat conduction parameter set. The calculation process also involves key factors such as the complex geometric structure of the battery pack, the thermal physical properties of the battery material, and the boundary heat exchange conditions. Through numerical iteration and boundary condition constraints, the internal and surface temperature distribution of the battery pack is simulated to obtain high-precision temperature field distribution prediction results.

[0136] The temperature field distribution prediction results serve as the key basis for iterative optimization of the heat conduction parameter set. By comparing the predicted temperature with the actual measured temperature, an error evaluation model is established to iteratively correct the heat conduction parameter set. An adaptive algorithm is introduced into the optimization process to adjust the parameter weights according to the temperature distribution deviation, gradually approaching the optimal parameter set, and improving the accuracy and reliability of the temperature field prediction.

[0137] The thermal management strategy is determined by the optimized heat conduction parameter set and compensation parameter set. The strategy formulation follows strict battery safe operating temperature range (usually 15-45°C) and temperature uniformity requirements (cell temperature difference is controlled within ±3°C). Specific measures include precise adjustment of cooling system parameters, intelligent control of charge and discharge rates, local thermal management module linkage, etc., to achieve precise control of battery pack temperature and long-term stable operation.

[0138] This embodiment achieves accurate quantification of the heat transfer characteristics of the battery pack by introducing a thermal diffusion coefficient matrix based on the Fourier heat conduction equation, significantly improving the scientificity and accuracy of temperature control. This method can maintain the thermal stability of the battery pack under complex and changeable working conditions and effectively extend the battery life by multi-dimensional parameterized processing of the cooling system. By constructing a multi-dimensional compensation relationship, it is possible to dynamically capture the thermodynamic changes inside the battery pack and achieve real-time and accurate tracking of thermal characteristics during the charging and discharging process. The introduction of an adaptive iterative optimization algorithm not only improves the accuracy of temperature field prediction, but also dynamically adjusts thermal management parameters according to actual temperature distribution, overcoming the limitations of traditional static thermal management methods.

[0139] In one embodiment, hotspot identification is performed based on real-time temperature data and thermal management strategies to obtain corresponding potential hotspot information, and early warning processing is performed to obtain corresponding hotspot early warning results, including:

[0140] Through temperature anomaly analysis and hotspot identification technology, accurate early warning of potential hotspots in the power system can be achieved. The temperature data acquisition module obtains the temperature information of the battery components in real time, and applies the adaptive threshold segmentation algorithm for preliminary anomaly identification. The algorithm dynamically adjusts the temperature anomaly judgment threshold based on the battery's historical operating data and the current working environment, significantly improving the sensitivity and accuracy of hotspot identification.

[0141] The adaptive threshold segmentation algorithm uses statistical methods to comprehensively consider factors such as battery cell temperature, ambient temperature, and charge and discharge status to establish a dynamic threshold for temperature anomaly judgment, denoise and smooth the temperature data, eliminate the interference of instantaneous measurement noise, and then calculate the local temperature anomaly area based on the data distribution characteristics to obtain the initial hot spot candidate area. This process can effectively filter out occasional temperature fluctuations and accurately locate potential hot spots.

[0142] The heat conduction calculation module introduces numerical simulation methods to conduct in-depth thermodynamic analysis of the initial hotspot candidate areas. By constructing the thermophysical characteristics of battery components, simulating the heat transfer and accumulation mechanism under different working conditions, and predicting the temperature evolution trend of the hotspot area, the calculation process takes into account multiple factors such as the internal heat source distribution of the battery, the thermal conductivity characteristics of the material, and the convection heat transfer conditions, and establishes dynamic heat conduction numerical calculations to obtain accurate prediction results of hotspot development.

[0143] Cluster analysis technology uses data grouping methods to intelligently cluster the hotspot development prediction results. The clustering method groups similar hotspot areas based on characteristics such as temperature gradient, heat accumulation rate, and spatial distribution. Through feature extraction, complex thermodynamic data is classified to reveal the potential laws of hotspot evolution and construct dynamic hotspot clustering results.

[0144] The hotspot accumulation analysis module deeply mines dynamic hotspot clustering information and evaluates key indicators of heat accumulation. The analysis module establishes a heat accumulation evaluation method to comprehensively evaluate the temperature accumulation intensity, duration and diffusion range of the hotspot area. By comparing and analyzing historical thermal runaway cases, a hotspot risk assessment system is constructed to accurately locate potential thermal safety hazards of the battery system.

[0145] According to the results of hotspot accumulation analysis, a multi-level hotspot warning index is constructed. The warning index is based on key parameters such as heat accumulation intensity, diffusion rate, and impact range, and is processed using a quantitative evaluation method. The warning index is set up with a four-level warning mechanism: the first-level warning is a low-risk state, the second-level warning is a slight abnormality, the third-level warning indicates that there is a significant risk of hotspot development, and the fourth-level warning means a very high possibility of thermal runaway.

[0146] Comprehensively evaluate potential hotspot information and warning indexes to generate accurate hotspot warning results.

[0147] This embodiment uses an adaptive threshold segmentation algorithm to dynamically adjust the temperature anomaly judgment standard, significantly improve the accuracy and sensitivity of hotspot identification, and effectively filter out occasional temperature fluctuations. This method comprehensively considers multi-dimensional factors to establish a more intelligent and accurate temperature anomaly detection mechanism, overcoming the limitations of traditional static threshold methods. The introduction of numerical simulation of heat conduction calculation methods can accurately predict the temperature evolution trend of hotspot areas, and by considering the complex thermodynamic characteristics inside the battery, in-depth analysis and accurate prediction of hotspot development can be achieved. This method can comprehensively evaluate the thermal safety risks of battery systems and provide more comprehensive and proactive prevention and control strategies for thermal management. The use of intelligent clustering and hotspot accumulation analysis technology can reveal the potential laws of hotspot evolution and build a multi-dimensional hotspot risk assessment system.

[0148] In one embodiment, a comprehensive control analysis is performed on the thermal management strategy, the initial heat dissipation control strategy, and the hotspot warning result to obtain a corresponding global thermal management control solution.

[0149] The initial heat dissipation control strategy is to obtain the heat flux density distribution of battery cells and battery packs through experimental measurement and data collection. The heat flux density distribution uses color gradients to intuitively present temperature gradients and heat transfer characteristics, identifying heat concentration areas and potential thermal failure risk points. The distribution map is based on actual measurement data, taking into account battery material properties, geometric structure and cooling conditions, and accurately describes the heat distribution.

[0150] The hotspot warning results are based on real-time monitoring data from temperature sensors, tracking and analyzing the hotspot generation, diffusion, and evolution process. Through continuous temperature acquisition and data comparison, a hotspot diffusion trend chart is drawn. The analysis process takes into account factors such as the internal chemical reaction of the battery and changes in ambient temperature to predict the potential diffusion path and impact range of the hotspot.

[0151] Multi-physics analysis comprehensively considers complex factors such as battery pack electrochemical reactions, heat transfer, and current distribution. Through experimental data and empirical formulas, qualitative and quantitative correlations between various physical fields are established. The analysis process focuses on the mutual influence and coupling characteristics of key parameters such as temperature field, current field, and stress field.

[0152] Risk assessment uses statistical analysis methods to identify thermal failure risk points based on historical operating data and experimental test results. The assessment process considers uncertain factors such as battery aging, internal resistance changes, and ambient temperature, and establishes a comprehensive risk assessment indicator system. Through data accumulation and trend analysis, the probability and severity of thermal failure under different operating conditions are quantified.

[0153] The thermal management matrix is ​​constructed to integrate temperature control, cooling strategy and battery status parameters. The control parameter matrix includes key control parameters such as coolant flow, heat transfer coefficient, thermal management system response time, etc. The matrix achieves precise control and dynamic optimization of thermal management strategy through parameter mapping and correlation analysis.

[0154] Thermal stability analysis determines the stability boundary conditions of thermal management control from the perspective of system dynamic characteristics and steady-state performance. The analysis method is based on experimental data and operating experience to evaluate the temperature change and response characteristics of the system under different operating conditions. The stability boundary conditions define the safe operating range of the thermal management system to prevent the system from entering an uncontrollable state.

[0155] Based on the stability boundary conditions and control parameter matrix, a strategy collaborative analysis is performed to form a global thermal management control solution. The collaborative analysis formulates the optimal thermal management strategy by comprehensively weighing battery performance, safety and energy efficiency. The control solution includes precise temperature control strategy, intelligent early warning mechanism and dynamic response process to ensure the safe and reliable operation of energy storage power supply under complex working conditions.

[0156] This embodiment can accurately capture the temperature change law of the battery pack through multi-dimensional dynamic response characteristic analysis, and improve the accuracy and real-time performance of the thermal management strategy. The heat flux density distribution and hot spot diffusion trend analysis can accurately identify the potential thermal failure risk points of the battery pack and effectively prevent safety hazards. The multi-physics field coupling analysis technology comprehensively considers complex factors such as electrochemical reactions and heat transfer to establish a more accurate thermal management model. The innovative risk assessment method can quantify the probability of thermal failure under different working conditions through statistical analysis and data accumulation, and provide a scientific basis for the formulation of thermal management strategies. The construction of the thermal management matrix realizes the precise mapping and dynamic optimization of temperature control parameters, and significantly improves the regulation and control capabilities of the thermal management system. The stability boundary condition analysis effectively defines the safe operating range of the thermal management system to prevent the system from entering an uncontrollable state. The final global thermal management control scheme ensures the safe and reliable operation of the energy storage power supply under complex working conditions by comprehensively balancing battery performance, safety and energy efficiency, and significantly improves the overall performance and service life of the energy storage system.

[0157] Reference Figure 2 The present invention also provides a thermal management control system for an energy storage power supply, which is applied to any one of the above-mentioned thermal management control methods for an energy storage power supply, comprising:

[0158] The acquisition module is used to obtain the real-time temperature data of the energy storage power supply, and perform heat source distribution analysis to obtain the corresponding temperature distribution information;

[0159] An analysis module is used to obtain the heat dissipation operation parameters of the energy storage power supply, and to perform adjustment analysis in combination with the temperature distribution information to obtain the corresponding initial heat dissipation control strategy;

[0160] The association module is used to obtain real-time monitoring data of the energy storage power supply, perform dynamic analysis of charging and discharging in combination with temperature distribution information and initial heat dissipation control strategy, and obtain corresponding charging and discharging adjustment information;

[0161] A processing module, which is used to perform heat balance optimization analysis based on temperature distribution information, initial heat dissipation control strategy and charge and discharge adjustment information to obtain a corresponding heat management strategy;

[0162] A control module, which is used to identify hot spots based on real-time temperature data and thermal management strategies, obtain corresponding potential hot spot information, and perform early warning processing to obtain corresponding hot spot early warning results;

[0163] The execution module is used to perform comprehensive control analysis on the thermal management strategy, initial heat dissipation control strategy and hot spot warning results to obtain the corresponding global thermal management control plan.

[0164] The present invention provides a thermal management control system for an energy storage power supply. By acquiring real-time temperature data and performing heat source distribution analysis, the temperature distribution of the energy storage power supply can be fully and accurately grasped, providing a reliable data basis for subsequent thermal management control, and effectively solving the problem of poor thermal management effect caused by single temperature monitoring. The temperature distribution information and heat dissipation operation parameters are combined for adjustment analysis to obtain the initial heat dissipation control strategy, which realizes the refined control of the heat dissipation system of the energy storage power supply, improves the heat dissipation efficiency, and overcomes the defect of ignoring local hot spots in the traditional method. By performing dynamic charge and discharge analysis on the real-time monitoring data, and combining the temperature distribution and the initial heat dissipation control strategy, the charge and discharge adjustment information is obtained, and the dynamic thermal management of the energy storage power supply under different working conditions is realized, solving the problem that the static control strategy is difficult to adapt to the complex operating environment. According to the temperature distribution, heat dissipation control and charge and discharge adjustment information, a comprehensive thermal management strategy is obtained, which realizes the systematic optimization of the thermal management of the energy storage power supply and improves the overall operating efficiency and performance. Through hot spot identification and early warning processing, potential hot spot problems are discovered and early warnings are issued in a timely manner, which effectively improves the safety of the energy storage power supply and solves the problem of lack of effective hot spot early warning mechanism in the existing method. Finally, by comprehensively processing the thermal management strategy, heat dissipation control strategy and hotspot warning results, a global thermal management control solution was obtained, which realized comprehensive and intelligent control of the thermal management of the energy storage power supply, significantly improved the reliability and life of the system, and reduced safety risks.

[0165] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0166] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A thermal management control method for an energy storage power supply, characterized in that: include: Obtain real-time temperature data of the energy storage power source, perform heat source distribution analysis, and obtain corresponding temperature distribution information; Acquire the heat dissipation operation parameters of the energy storage power supply, perform adjustment analysis in combination with the temperature distribution information, and obtain a corresponding initial heat dissipation control strategy; Acquire real-time monitoring data of the energy storage power supply, perform dynamic analysis of charging and discharging in combination with the temperature distribution information and the initial heat dissipation control strategy, and obtain corresponding charging and discharging adjustment information; Performing a thermal balance optimization analysis according to the temperature distribution information, the initial heat dissipation control strategy and the charge and discharge adjustment information to obtain a corresponding thermal management strategy; Based on the real-time temperature data and the thermal management strategy, hotspot identification is performed to obtain corresponding potential hotspot information, and early warning processing is performed to obtain corresponding hotspot early warning results; Performing comprehensive control analysis on the thermal management strategy, the initial heat dissipation control strategy and the hotspot warning result to obtain a corresponding global thermal management control solution; The real-time temperature data of the energy storage power source is obtained, and the heat source distribution analysis is performed to obtain the corresponding temperature distribution information, including: Acquire the real-time temperature data collected by the module temperature sensor deployed in the energy storage power supply; Performing abnormal temperature fluctuation identification on the real-time temperature data to obtain a temperature abnormality point set; Performing density cluster analysis on the temperature anomaly point set and identifying potential heat source areas to obtain a heat source distribution map; Calculating heat source heat flux distribution based on the heat source distribution diagram to obtain heat flux distribution data; constructing a heat transfer structure according to the heat flux distribution data to obtain a corresponding heat transfer topological structure; Performing heat node analysis on the heat transfer topology structure according to a preset PageRank algorithm to obtain heat accumulation location information; Performing finite difference thermal stress calculation based on the heat accumulation position information to obtain a thermal stress distribution map of each area; Combining the heat source distribution map, the heat transfer topology structure and the thermal stress distribution map to perform short-term heat distribution trend prediction to obtain a heat distribution prediction result; The heat source distribution map, the heat accumulation position information, the thermal stress distribution map and the heat distribution prediction result are fused to obtain the temperature distribution information.

2. The thermal management control method of the energy storage power supply according to claim 1, characterized in that: The heat transfer topology structure is subjected to heat node analysis according to a preset PageRank algorithm to obtain heat accumulation location information, including the following steps: Quantifying the heat transfer relationship between nodes in the heat transfer topology structure to obtain a heat transfer relationship matrix; Calculating the node PageRank value of the heat transfer relationship matrix to obtain the heat node importance index; Performing nonlinear threshold segmentation according to the heat node importance index to obtain a candidate heat accumulation node set; Performing heat accumulation intensity evaluation on the candidate heat accumulation node set to obtain a heat accumulation intensity distribution map; Positioning the heat accumulation position based on the heat accumulation intensity distribution map to obtain a heat accumulation position coordinate set; The heat accumulation position coordinate set is mapped to the energy storage power source in a three-dimensional physical structure space to obtain the heat accumulation position information.

3. The thermal management control method of the energy storage power supply according to claim 1, characterized in that: The obtaining of the heat dissipation operation parameters of the energy storage power supply and the adjustment analysis combined with the temperature distribution information to obtain the corresponding initial heat dissipation control strategy include: Collecting heat dissipation parameters of the energy storage power supply to obtain corresponding heat dissipation operation parameters; Calculate the heat dissipation efficiency according to the heat dissipation operation parameters to obtain corresponding heat dissipation efficiency data; Performing a heat dissipation demand analysis on the heat dissipation efficiency data according to the temperature distribution information to obtain corresponding heat dissipation demand data; Performing regional heat dissipation analysis on the heat dissipation demand data to obtain corresponding regional heat dissipation control parameters; Perform heat dissipation power allocation according to the regional heat dissipation control parameters to obtain a corresponding heat dissipation power allocation plan; Simulating the heat dissipation effect of the heat dissipation power allocation scheme to obtain corresponding heat dissipation effect prediction data; The heat dissipation power allocation scheme is optimized and adjusted according to the heat dissipation effect prediction data to obtain the initial heat dissipation control strategy.

4. The thermal management control method of the energy storage power supply according to claim 1, characterized in that: The real-time monitoring data of the energy storage power source is acquired, and the charge and discharge dynamic analysis is performed in combination with the temperature distribution information and the initial heat dissipation control strategy to obtain corresponding charge and discharge adjustment information, including: The voltage, current and power of the energy storage power supply are collected and integrated in real time to obtain the real-time monitoring data; Perform charge and discharge identification on the energy storage power source according to the real-time monitoring data to obtain corresponding charge and discharge status information; Based on the charge and discharge state information and the temperature distribution information, a temperature change prediction is performed on the energy storage power supply to obtain a corresponding temperature change prediction result; Performing a heat dissipation effect evaluation on the energy storage power source according to the temperature change prediction result and the initial heat dissipation control strategy to obtain a corresponding heat dissipation effect evaluation result; Dynamically adjusting the charge and discharge power of the energy storage power source based on the heat dissipation effect evaluation result to obtain corresponding charge and discharge power adjustment information; According to the heat dissipation effect evaluation result, a charge and discharge current of the energy storage power supply is limited and analyzed to obtain a corresponding charge and discharge current limit value; Optimizing the charging and discharging time of the energy storage power source according to the heat dissipation effect evaluation result to obtain corresponding charging and discharging time optimization information; The charge and discharge adjustment information is obtained by performing a comprehensive analysis based on the charge and discharge power adjustment information, the charge and discharge current limit value and the charge and discharge time optimization information.

5. The thermal management control method of the energy storage power supply according to claim 1, characterized in that: The heat balance optimization analysis is performed according to the temperature distribution information, the initial heat dissipation control strategy and the charge and discharge adjustment information to obtain a corresponding thermal management strategy, including: Performing thermal diffusion characteristic analysis on the temperature distribution information to obtain a thermal diffusion coefficient matrix; Performing heat conduction parameterization processing on the initial heat dissipation control strategy according to the thermal diffusion coefficient matrix to obtain a heat conduction parameter set; Dynamically compensating the heat conduction parameter set based on the charge and discharge adjustment information to obtain a compensation parameter set; Performing thermal field numerical calculation on the heat conduction parameter set to obtain a temperature field distribution prediction result; The temperature field distribution prediction result is used to iteratively optimize the heat conduction parameter set to obtain an optimized heat conduction parameter set; A comprehensive strategy analysis is performed on the optimized heat conduction parameter set and the compensation parameter set to obtain the thermal management strategy.

6. The thermal management control method of the energy storage power supply according to claim 1, characterized in that: The hotspot identification based on the real-time temperature data and the thermal management strategy is performed to obtain corresponding potential hotspot information, and early warning processing is performed to obtain corresponding hotspot early warning results, including: Performing temperature anomaly analysis on the real-time temperature data according to a preset adaptive threshold segmentation algorithm to obtain an initial hotspot candidate area; Performing finite element heat conduction calculation on the initial hotspot candidate area according to the thermal management strategy to obtain a hotspot development prediction result; Dynamically clustering the hotspot development prediction results to obtain dynamic hotspot clustering; Performing heat accumulation hotspot analysis based on the dynamic hotspot clustering to obtain the potential hotspot information; Quantifying the warning level of the potential hotspot information to obtain a corresponding hotspot warning index; Perform a warning analysis on the potential hotspot information and the hotspot warning index to obtain the hotspot warning result.

7. The thermal management control method of the energy storage power supply according to claim 1, characterized in that: The thermal management strategy, the initial heat dissipation control strategy and the hotspot warning result are subjected to comprehensive control analysis to obtain a corresponding global thermal management control solution; Performing dynamic response characteristic analysis on the thermal management strategy to obtain a corresponding strategy response curve; Calculating the heat flux density distribution of the initial heat dissipation control strategy to obtain a corresponding heat flux density distribution diagram; Performing hotspot diffusion simulation on the hotspot warning result to obtain the corresponding hotspot diffusion trend; Perform coupling processing according to the strategy response curve, the heat flux density distribution diagram and the hot spot diffusion trend to obtain a corresponding multi-physics field coupling equation group; Performing risk numerical solution on the multi-physics field coupling equation group to obtain a corresponding thermal failure risk point set; Constructing a thermal management matrix according to the thermal failure risk point set to obtain a corresponding control parameter matrix; Performing thermal stability analysis on the control parameter matrix to obtain corresponding stability boundary conditions; A strategy collaborative analysis is performed based on the stability boundary conditions and the control parameter matrix to obtain the global thermal management control solution.

8. A thermal management control system for an energy storage power source, characterized in that: The thermal management control method of the energy storage power supply applied to any one of claims 1 to 7 above comprises: An acquisition module, which is used to obtain real-time temperature data of the energy storage power supply, and perform heat source distribution analysis to obtain corresponding temperature distribution information; An analysis module, the analysis module is used to obtain the heat dissipation operation parameters of the energy storage power supply, and perform adjustment analysis in combination with the temperature distribution information to obtain a corresponding initial heat dissipation control strategy; An association module, the association module is used to obtain real-time monitoring data of the energy storage power supply, perform dynamic analysis of charging and discharging in combination with the temperature distribution information and the initial heat dissipation control strategy, and obtain corresponding charging and discharging adjustment information; A processing module, the processing module is used to perform a thermal balance optimization analysis according to the temperature distribution information, the initial heat dissipation control strategy and the charge and discharge adjustment information to obtain a corresponding thermal management strategy; A control module, the control module is used to identify hot spots based on the real-time temperature data and the thermal management strategy, obtain corresponding potential hot spot information, and perform early warning processing to obtain corresponding hot spot early warning results; An execution module, the execution module is used to perform comprehensive control analysis on the thermal management strategy, the initial heat dissipation control strategy and the hotspot warning result to obtain a corresponding global thermal management control solution; The real-time temperature data of the energy storage power source is obtained, and the heat source distribution analysis is performed to obtain the corresponding temperature distribution information, including: Acquire the real-time temperature data collected by the module temperature sensor deployed in the energy storage power supply; Performing abnormal temperature fluctuation identification on the real-time temperature data to obtain a temperature abnormality point set; Performing density cluster analysis on the temperature anomaly point set and identifying potential heat source areas to obtain a heat source distribution map; Calculating heat source heat flux distribution based on the heat source distribution diagram to obtain heat flux distribution data; constructing a heat transfer structure according to the heat flux distribution data to obtain a corresponding heat transfer topological structure; Performing heat node analysis on the heat transfer topology structure according to a preset PageRank algorithm to obtain heat accumulation location information; Performing finite difference thermal stress calculation based on the heat accumulation position information to obtain a thermal stress distribution map of each area; Combining the heat source distribution map, the heat transfer topology structure and the thermal stress distribution map to perform short-term heat distribution trend prediction to obtain a heat distribution prediction result; The heat source distribution map, the heat accumulation position information, the thermal stress distribution map and the heat distribution prediction result are fused to obtain the temperature distribution information.

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