Battery cell thermal management system state intelligent diagnosis method
By establishing a thermal response topology network and extracting features, combined with flow field analysis, the problem of accurate diagnosis of thermal degradation status in the cell thermal management system was solved. This enabled precise positioning of the thermal degradation area and initial location, as well as prediction of the remaining safe operating cycle, thus improving the diagnostic accuracy and prediction accuracy of the system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUNAN WALTON NEW ENERGY TECH CO LTD
- Filing Date
- 2025-12-23
- Publication Date
- 2026-04-17
AI Technical Summary
Existing technologies are insufficient to accurately reveal the thermal degradation state and evolution of the battery cell thermal management system, and cannot promptly identify the initial location of thermal degradation and predict the remaining safe operating cycle of the system, resulting in insufficient diagnostic accuracy and timely early warning.
By establishing a thermal response topology network and utilizing the local thermal imbalance entropy production accumulation process to determine the thermal degradation region, combined with the extraction of temperature field asymmetry features and flow field disturbance propagation path analysis, the initial thermal degradation unit is accurately located, and its remaining safe operating cycle is predicted.
It enables rapid and accurate diagnosis of the battery cell thermal management system, accurately identifies thermal degradation areas and initial locations, significantly improves the accuracy of thermal anomaly identification and prediction, and provides a scientific basis for system maintenance and operation management.
Smart Images
Figure CN121878482A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of battery management technology, and more specifically, to a method for intelligent diagnosis of the status of a battery cell thermal management system. Background Technology
[0002] With the rapid development of new energy vehicle technology, the safety and operational reliability of power batteries have become crucial factors determining the overall vehicle performance. However, power battery cells are prone to localized thermal degradation under prolonged, high-load operation, which not only severely shortens the effective lifespan of the battery but may also lead to safety risks such as damage to the internal structure of the cell or even thermal runaway. Therefore, condition diagnosis and safety early warning technologies for battery cell thermal management systems have become one of the key technical challenges that the industry urgently needs to address.
[0003] In existing technologies, solutions for diagnosing the thermal state of battery cells generally rely on surface temperature monitoring or simple temperature anomaly threshold determination. These methods fail to effectively reveal the heat transfer mechanisms and thermal degradation patterns between functional units within the system, resulting in diagnostic accuracy and timely warnings that fall short of practical application requirements. Furthermore, current methods lack in-depth analysis of the propagation paths of thermal response characteristics and degradation evolution patterns, making it difficult to accurately determine the initial location and progression of thermal degradation. Consequently, they cannot effectively predict the system's safe operating cycle, posing significant challenges to practical maintenance and safety management.
[0004] Therefore, how to develop an intelligent diagnostic method that can accurately reveal the thermal degradation state and evolution of each functional unit within the battery cell thermal management system, and can promptly identify the initial location of the thermal degradation unit and predict the remaining safe operating cycle of the system has become an important technical issue that urgently needs to be addressed in the field of battery thermal management. Summary of the Invention
[0005] To overcome the above-mentioned deficiencies of the prior art, embodiments of the present invention provide an intelligent diagnostic method for the status of a battery cell thermal management system.
[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for intelligent diagnosis of the status of a battery cell thermal management system, the method comprising: Based on the topology of the cell thermal management system and historical operating temperature data, a thermal response topology network of the functional units is established, and the thermal degradation region of each functional unit is determined by the local thermal imbalance entropy production accumulation process. Based on real-time operating temperature data and the thermal degradation region, thermal response features characterizing the current system anomaly are generated through a temperature field asymmetric feature extraction process. Based on the thermal response characteristics and thermal response topology, the thermal response degradation region is determined by analyzing the propagation path of the flow field disturbance, and the location of the initial functional unit where performance degradation occurs within the thermal response degradation region is determined. Based on the location of the initial functional unit and historical operating temperature data, the remaining safe operating cycle of the initial functional unit is predicted through correlation analysis between the local temperature rise rate and the degradation rate.
[0007] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention establishes a thermal response topology network and utilizes the local thermal imbalance entropy accumulation process to quickly and accurately determine the thermal degradation region of each functional unit in the cell thermal management system. This effectively reveals the complex thermal degradation mechanism inside the system and overcomes the diagnostic ambiguity and lag problems caused by existing technologies that rely solely on temperature thresholds to determine the thermal degradation state.
[0008] This invention extracts asymmetric features of the temperature field and combines them with real-time operating data to precisely capture the asymmetric deviation features of the temperature field of each functional unit under the current abnormal state of the system. This makes the acquisition of thermal response features more targeted and accurate, significantly improves the accuracy of identifying system thermal anomalies, and avoids the risk of misdiagnosis caused by the lack of spatial thermal field benchmarks in traditional methods.
[0009] This invention determines the thermal response degradation region and the initial unit location of performance degradation by analyzing the propagation path of flow field disturbances. By utilizing the correlation analysis between the local temperature rise rate and the degradation rate, it accurately predicts the remaining safe operating cycle of the initial functional unit, realizing the active tracking and accurate prediction of the degradation process of the cell thermal management system, thereby providing timely and reliable scientific decision-making basis for system maintenance and operation management. Attached Figure Description
[0010] Figure 1 The flowchart illustrates an intelligent diagnostic method for the status of a battery cell thermal management system provided by this invention. Detailed Implementation
[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0012] Example 1 Please see Figure 1 As shown in the figure, this embodiment discloses an intelligent diagnostic method for the status of a battery cell thermal management system, the method comprising: S101: Establish the thermal response topology network of the functional unit based on the topology of the cell thermal management system and historical operating temperature data, and determine the thermal degradation region of each functional unit through the local thermal imbalance entropy production accumulation process. It should be understood that a "functional unit" refers to a sub-region that can independently describe changes in thermal state, based on the structural characteristics and heat conduction laws of the battery cell thermal management system. For example, specific functional units include, but are not limited to, the battery cell center region, the battery cell edge region, the region where the heat-conducting structure contacts the battery cell, the cooling channel inlet region, and the cooling channel outlet region.
[0013] Furthermore, the "thermal response topology network" is a topological structure diagram composed of functional units as nodes and heat transfer paths between functional units as connecting edges, used to reflect the spatial transfer relationship of thermal response within the system.
[0014] It should be noted that the specific method for establishing the thermal response topology network is as follows: determine the heat transfer relationship between functional units based on historical operating temperature data; if two functional units exhibit continuous temperature transfer during operation, then establish a connection relationship between the corresponding nodes in the topology network.
[0015] Specifically, the determination of the thermal degradation region of each functional unit through the local thermal imbalance entropy production accumulation process includes: Using historical operating temperature data of each functional unit as input, construct the curve of heat entropy production in each functional unit as a function of operating time; The construction of the thermal entropy production curves within each functional unit as a function of operating time includes: Based on the historical operating temperature data of each functional unit, the local temperature gradient of multiple temperature measurement points within each functional unit is calculated. In one specific embodiment, the formula for calculating the local temperature gradient is as follows: In the formula, This represents the local temperature gradient between temperature measurement point i and temperature measurement point j at time t; These are the temperature values measured at temperature measurement points i and j at time t, respectively. Let be the spatial distance between temperature measurement point i and temperature measurement point j.
[0016] It should be noted that the method of selecting temperature measurement points within each functional unit should ensure that the temperature measurement points can effectively cover the typical heat conduction path of the functional unit and the key areas where significant temperature differences may occur. For example, temperature measurement points can be set at the edge of the cell near the cooling channel to accurately capture local temperature gradient changes.
[0017] Based on the local temperature gradient and the thermal conductivity of the materials within the functional units, the local thermal entropy production within each functional unit is obtained using an irreversible thermodynamic method. Specifically, this embodiment uses the following formula to calculate local thermal entropy production: ; The rate of thermal entropy production in a local region within a functional unit at time t; This indicates the thermal conductivity of the material in the corresponding region; Represents a local temperature gradient; This represents the absolute temperature value at the corresponding temperature measurement point.
[0018] For example, in one specific embodiment, when the material within the functional unit is aluminum alloy, its thermal conductivity can be found in a material database and typically takes the value 205.
[0019] By accumulating the local thermal entropy production within each functional unit along the historical operating time series, the curve of thermal entropy production as a function of operating time is obtained. Specifically, in this embodiment, the cumulative calculation method for thermal entropy production is as follows: In the formula, Accumulated from the start time to time The cumulative value of local thermal entropy production; Let be the rate of thermal entropy production at time i. A fixed time interval for sampling historical operational data.
[0020] It should be noted that the thermal entropy production curve obtained through the above cumulative calculation can clearly show the progression trend of thermal aging and degradation inside the functional unit, so as to support subsequent diagnostic analysis.
[0021] The inflection point of the curvature of the thermal entropy production change curve is used to determine the initial operating time of thermal instability within the functional unit. Specifically, the curvature calculation formula used in the implementation process is as follows: In the formula, This represents the curvature of the cumulative thermal entropy production curve at time t; and Let represent the first and second derivatives of the curve at time t, respectively.
[0022] For example, when the curvature of the cumulative thermal entropy production curve of a functional unit first reaches a set significant change threshold (e.g., 0.01) at a certain moment, that moment is defined as the onset moment of thermal instability within that functional unit.
[0023] Based on the cumulative operating interval between the start time of thermal instability and the current time, the thermal degradation region corresponding to each functional unit is determined; Specifically, the method for determining the thermal degradation region in this embodiment is as follows: taking the start time of thermal instability as a benchmark, the cumulative value of thermal entropy production at each temperature measuring point from that time to the current time is calculated. If the cumulative value of thermal entropy production at a certain temperature measuring point is greater than or equal to a preset degradation threshold, then a local area centered on that temperature measuring point is defined as the thermal degradation region.
[0024] For example, the preset degradation threshold can be determined in the following way: In the formula, The threshold for determining the thermal degradation region; This represents the cumulative average thermal entropy production during the historical stable operation phase of the functional unit. To determine the coefficients empirically, for example in one specific embodiment A value of 1.5 indicates that when the cumulative heat entropy production at the temperature measurement point reaches 1.5 times or more of the average value during the stable operation phase, thermal degradation is determined to have occurred in the area.
[0025] S102: Based on the real-time operating temperature data and the thermal degradation region, generate thermal response features characterizing the current system anomaly through a temperature field asymmetric feature extraction process; It should be understood that the "temperature field asymmetry characteristic" in this embodiment refers to the degree of deviation of the real-time temperature field of each functional unit under the current operating state of the system from the symmetrical temperature distribution under the historical stable operating state, which can be used to characterize the current thermal anomaly state of the system.
[0026] Furthermore, the "real-time operating temperature data" refers to the actual temperature value collected by each temperature measuring point within each functional unit at the current moment; the "thermal degradation region" refers to the functional unit region that has been determined to have undergone thermal degradation through the aforementioned thermal entropy accumulation process.
[0027] Specifically, the process of generating thermal response features characterizing the current system anomalies through the temperature field asymmetry feature extraction includes: Based on real-time operating temperature data and thermal degradation regions, a symmetrical temperature field reference benchmark is constructed within the cell thermal management system. Specifically, the "temperature field symmetry reference benchmark" refers to a spatial temperature distribution benchmark established based on temperature data during the historical stable operation of functional units, used to compare whether the current real-time temperature field has undergone asymmetrical changes.
[0028] The construction of the temperature field symmetric reference datum includes: Based on the temperature data of the functional units in the thermal degradation area during the historical stable operation phase, a spatial symmetry distribution pattern of the temperature field of the cell thermal management system is established. Specifically, the spatial symmetry distribution pattern is constructed by extracting the temperature values of each temperature measuring point during the period of temperature stability of the functional unit in the thermal degradation region during the historical operation phase, averaging the temperature values of each temperature measuring point over time, and obtaining the spatial temperature baseline distribution data under stable operating conditions.
[0029] For example, assuming a functional unit collects 100 consecutive hours of temperature data during normal operation, the average value of these 100 hours of temperature data for each measurement point is calculated to obtain the stable temperature field reference value for each measurement point. The specific calculation formula is as follows: In the formula, Temperature measurement point The corresponding temperature field symmetrical reference value, For the temperature measurement point at the i-th acquisition time The temperature value, where N is the total number of samples of historical stable operating temperature data used.
[0030] The temperature field of the region unaffected by anomalies in the real-time operating temperature data is fitted by the spatially symmetrical distribution pattern to generate the temperature field symmetrical reference benchmark. Specifically, during implementation, in order to eliminate the influence of abnormal temperatures in the thermal degradation region, only the real-time temperature data of the temperature measurement points of functional units in the system that are not identified as thermally degraded are selected for spatial interpolation fitting to obtain a complete temperature field symmetric reference benchmark under the current system operating state.
[0031] In specific embodiments, Kriging space interpolation or radial basis function (RBF) space interpolation can be used for fitting. For example, when using RBF interpolation, the specific interpolation calculation formula is as follows: In the formula, Indicates the location The interpolation calculation results; This indicates the position of the j-th temperature measurement point. Let be the interpolation coefficients to be determined. M is the interpolation radial basis function (e.g., Gaussian function or multiple quadratic function), and M is the number of temperature measurement points used for interpolation.
[0032] It should be noted that the interpolation coefficients The determination method can be determined by the least squares method using measured temperature data from the undegraded region and interpolated values: In the formula, This refers to the temperature of the measuring point, which is measured in real time.
[0033] By using the temperature field symmetric reference standard to perform differential calculations on the real-time operating temperature data, thermal response characteristics characterizing the current system anomaly are obtained; Specifically, during implementation, the thermal response characteristics are constructed by comparing the real-time temperature values of the corresponding temperature measurement points of each functional unit with the aforementioned symmetrical reference values of the temperature field. The calculation formula is as follows: In the formula, Temperature measurement point Thermal response characteristics; This is the current real-time temperature value. This is the temperature value of the temperature measurement point in the symmetrical reference datum of the temperature field.
[0034] It is understandable that the thermal response characteristics obtained through the above calculations can clearly reflect the degree of thermal anomaly in local areas of the current battery cell thermal management system relative to the historical stable state, providing a valid basis for subsequent diagnostic analysis. S103: Based on the thermal response characteristics and thermal response topology network, determine the thermal response degradation region through flow field disturbance propagation path analysis, and determine the location of the initial functional unit where performance degradation occurs within the thermal response degradation region; It should be understood that "flow field disturbance propagation path analysis" refers to using the propagation trajectory and propagation time sequence of the thermal response characteristics within the thermal response topology network to determine the evolution relationship of thermal anomalies within each functional unit, and further locate the initial functional unit that caused the thermal anomaly.
[0035] Specifically, determining the location of the initial functional unit where performance degradation occurs within the thermal response degradation region includes: The thermal response features are mapped to the corresponding functional unit nodes in the thermal response topology network, and the set of nodes where the thermal response features first appear is marked. Specifically, in this embodiment, the "set of nodes where thermal response characteristics first appear" refers to the set of node locations where the temperature measurement points corresponding to each functional unit node first show abnormal temperature differences, wherein the threshold for judging abnormal temperature differences is, for example, a set value of 3°C. In specific implementation, the thermal response characteristics of the real-time temperature measurement points are compared with the set threshold one by one. If the temperature measurement point corresponding to a certain functional unit node shows an abnormal temperature difference, then the functional unit node is marked as an initial abnormal node.
[0036] In the thermal response topology network, the propagation order of thermal response characteristics in the network is reconstructed by combining the heat transfer sequence between functional units in historical operating temperature data. During implementation, the starting point when the temperature of a functional unit transitions from stable to continuously fluctuating is determined by analyzing the temperature change trend.
[0037] The propagation order of the reconstructed thermal response features in the network includes: In historical operating temperature data, identify the starting point when the temperature of each functional unit changes from a stable change to a continuous deviation change; The step of identifying the starting moment when the temperature of each functional unit transitions from a stable change to a continuous shift includes: In the historical operating temperature data corresponding to the functional unit, a continuous evolution sequence of temperature change amplitude over time is constructed; In one specific embodiment, the method for constructing the continuous evolution sequence of temperature change amplitude is as follows: The average temperature of each functional unit during a historical stable operation phase is set as a stable benchmark value. The temperature difference variation amplitude at each consecutive time point is calculated using this benchmark value, thereby obtaining the temperature change amplitude sequence. For example, the formula for calculating the magnitude of temperature change is as follows: In the formula, The temperature change of the functional unit at time t; This is the actual temperature measurement value of the functional unit at time t; This is the average temperature baseline value of the functional unit during its historical stable operation phase.
[0038] In the continuous evolution sequence, the turning point when the temperature change amplitude changes from periodic fluctuation to unidirectional shift is determined as the starting point; Specifically, during implementation, the method for determining the turning point is as follows: perform trend analysis on the temperature change amplitude data of several consecutive time steps (e.g., 10 consecutive time steps). If the temperature change amplitude shows a monotonically increasing trend within 10 consecutive time steps, and the temperature change amplitude within each time step is greater than the previous moment, then the first moment when the continuous increasing trend begins is defined as the turning point.
[0039] For example, if from a certain moment If the temperature changes over the first 10 consecutive time steps (e.g., 1 hour per time step) are 0.5℃, 0.7℃, 0.9℃, 1.2℃, 1.5℃, 1.8℃, 2.2℃, 2.7℃, 3.1℃, and 3.6℃, then the time step is determined. This is the starting point of the continuous temperature shift within the functional unit.
[0040] Based on the time sequence of the start time of each functional unit, the connection relationship of the functional units in the thermal response topology network is ordered in a directed manner to form the propagation order of thermal response characteristics. Specifically, during implementation, the start times determined by each functional unit are sorted from early to late, and the connection relationships between nodes in the thermal response topology network are directional according to the sorting order, thereby determining the propagation order of thermal response characteristics in the topology network.
[0041] For example, if the start time of functional unit A is earlier than the start time of functional unit B, and there is a connection between functional unit A and functional unit B in the topology network, then the direction of thermal response characteristic propagation is from functional unit A to functional unit B.
[0042] Based on the propagation order, the functional unit that appears earliest in the propagation link and continues to participate in the propagation of thermal response characteristics is determined as the initial functional unit position. Specifically, during implementation, the functional unit node at the forefront of the link is identified through the directed propagation link in the aforementioned topological network with a clear propagation order. This node has the earliest anomaly start time, and the propagation of thermal anomaly characteristics of subsequent functional unit nodes can all be traced back to this node.
[0043] For example, suppose there is a propagation path in the thermal response topology network consisting of functional unit A → functional unit B → functional unit C. If the starting time of functional unit A is the 100th hour, functional unit B is the 110th hour, and functional unit C is the 120th hour, then functional unit A is determined to be the initial functional unit position.
[0044] S104: Based on the location of the initial functional unit and historical operating temperature data, predict the remaining safe operating cycle of the initial functional unit through correlation analysis between the local temperature rise rate and the degradation rate; Specifically, it should be understood that the "remaining safe operating cycle" in this embodiment refers to the remaining operating time required from the current operating state until the thermal degradation state of the initial functional unit reaches the preset failure judgment threshold; wherein, the "initial functional unit position" is the aforementioned determined thermal degradation starting unit position that leads to system abnormality.
[0045] Specifically, predicting the remaining safe operating cycles of the initial functional unit includes: In historical operating temperature data, the local temperature rise rate sequence of the initial functional unit in multiple continuous operating intervals is extracted, and the degradation evolution trajectory of the corresponding interval is constructed simultaneously. Specifically, the method for extracting the local temperature rise rate sequence during implementation is as follows: Typical temperature measurement points are selected at the initial functional unit location, and the temperature rise rate is calculated for each of the multiple consecutive operating time intervals. The specific formula for calculating the local temperature rise rate is as follows: In the formula, Let be the local temperature rise rate of the k-th operating range; These are the actual temperature measurements taken at adjacent times; It represents the length of the time interval between two adjacent moments.
[0046] Specifically, the method for synchronously constructing the degradation evolution trajectory is as follows: corresponding to the temperature rise rate of each of the above operating intervals, the change in the cumulative thermal entropy production value of the initial functional unit within the same time period is statistically analyzed to obtain the degradation evolution trajectory of the corresponding operating interval; the degradation evolution trajectory is represented by the cumulative thermal entropy production value, and the specific calculation formula is as follows: In the formula, This represents the cumulative value of thermal entropy up to the nth time. Let be the local thermal entropy production rate at time i. The time interval for sampling historical data.
[0047] Based on the evolutionary coupling relationship between the local temperature rise rate sequence and the degradation trajectory, the degradation propagation rate of the initial functional unit in different operating stages is determined; Specifically, in the implementation process, this embodiment uses correlation analysis methods (such as linear regression analysis) to establish the coupling relationship between the local temperature rise rate and the degradation evolution trajectory in order to determine the degradation propagation rate.
[0048] For example, the specific calculation formula for the linear regression model in this embodiment is as follows: In the formula, The degradation propagation rate represents the rate of change of the cumulative value of thermal entropy production per unit time. The corresponding local temperature rise rate is given by the model parameters a and b, which are obtained through regression analysis of historical operating data. For example, in a specific implementation, the model parameters a = 0.02 and b = 0.1 are obtained by performing linear regression analysis on historical data.
[0049] Based on the degradation rate corresponding to the current operating stage, the remaining operating time required for the initial functional unit to evolve from the current state to the failure boundary is extrapolated and used as the remaining safe operating cycle; Specifically, it should be understood that the "failure boundary" in this embodiment refers to the degradation state corresponding to the accumulation of thermal entropy of the initial functional unit reaching the set critical failure threshold, indicating the critical point at which the functional unit no longer has the ability to operate safely.
[0050] In the implementation process, the specific method for extrapolating and predicting the remaining safe operating period using the degradation propulsion rate is as follows: First, in a specific embodiment, the current cumulative thermal entropy production value of the initial functional unit is used as the starting point for extrapolation prediction. The evolution trend of the cumulative thermal entropy production value of the functional unit is predicted using the degradation propagation rate determined in the current operating stage, and the operating time required to reach the failure boundary is determined.
[0051] The specific calculation method is as follows: In the formula, The remaining safe operating cycle of the initial functional unit; It is the failure boundary threshold of the functional unit, that is, the critical failure value of the cumulative thermal entropy production; This represents the cumulative thermal entropy production value of the functional unit at the current moment. This represents the rate of degradation advancement of the functional unit at the current stage.
[0052] For example, in one specific embodiment, if the current cumulative thermal entropy production of a functional unit is 3500, the failure boundary threshold is set to 5000, and the degradation propagation rate determined for the current operating phase is 50 per hour, then the corresponding calculated remaining safe operating cycle is: This means that the remaining safe operating cycle of this functional unit is 30 hours.
[0053] Understandably, the methods disclosed above can be used to quickly and accurately determine the remaining safe operating cycle of an initial functional unit based on its real-time degradation status, thereby providing a scientific basis for decision-making regarding the maintenance and risk management of the battery cell thermal management system.
[0054] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters, weights, and thresholds in the formulas are set by those skilled in the art according to the actual situation.
[0055] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
[0056] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for intelligent diagnosis of the status of a battery cell thermal management system, characterized in that, The method includes: Based on the topology of the cell thermal management system and historical operating temperature data, a thermal response topology network of functional units is established, and the thermal degradation region of each functional unit is determined through the local thermal imbalance entropy production accumulation process. Based on real-time operating temperature data and the thermal degradation region, thermal response features characterizing the current system anomaly are generated through a temperature field asymmetric feature extraction process. Based on the thermal response characteristics and thermal response topology, the thermal response degradation region is determined by analyzing the propagation path of the flow field disturbance, and the location of the initial functional unit where performance degradation occurs within the thermal response degradation region is determined. Based on the location of the initial functional unit and historical operating temperature data, the remaining safe operating cycle of the initial functional unit is predicted through correlation analysis between the local temperature rise rate and the degradation rate.
2. The method of claim 1, wherein, The process of determining the thermal degradation region of each functional unit through the accumulation of entropy production in local thermal imbalance includes: Using historical operating temperature data of each functional unit as input, construct the curve of heat entropy production in each functional unit as a function of operating time; The inflection point of the curvature of the thermal entropy production change curve is used to determine the initial operating time of thermal instability within the functional unit. Based on the cumulative operating interval between the start time of thermal instability and the current time, the thermal degradation region corresponding to each functional unit is determined.
3. The intelligent diagnostic method for the status of the battery cell thermal management system according to claim 2, characterized in that, The construction of the thermal entropy production curves within each functional unit as a function of operating time includes: Based on the historical operating temperature data of each functional unit, the local temperature gradient of multiple temperature measurement points within each functional unit is calculated. Based on the local temperature gradient and the thermal conductivity of the materials within the functional units, the local thermal entropy production within each functional unit is obtained using an irreversible thermodynamic method. By accumulating the local thermal entropy production within each functional unit along the historical operating time sequence, the curve of thermal entropy production changing with operating time is obtained.
4. The intelligent diagnostic method for the status of the battery cell thermal management system according to claim 3, characterized in that, The process of generating thermal response features characterizing the current system anomalies through temperature field asymmetry feature extraction includes: Based on real-time operating temperature data and thermal degradation regions, a symmetrical temperature field reference benchmark is constructed within the cell thermal management system. By using the temperature field symmetric reference benchmark to perform differential calculations on the real-time operating temperature data, thermal response characteristics characterizing the current system anomaly are obtained.
5. The intelligent diagnostic method for the status of the battery cell thermal management system according to claim 4, characterized in that, The construction of the temperature field symmetric reference standard includes: Based on the temperature data of the functional units in the thermal degradation area during the historical stable operation phase, a spatial symmetry distribution pattern of the temperature field of the cell thermal management system is established. The temperature field of the region unaffected by anomalies in the real-time operating temperature data is fitted by the spatially symmetrical distribution pattern to generate the temperature field symmetrical reference benchmark.
6. The intelligent diagnostic method for the status of the battery cell thermal management system according to claim 5, characterized in that, Determining the location of the initial functional unit where performance degradation occurs within the thermal response degradation region includes: The thermal response features are mapped to the corresponding functional unit nodes in the thermal response topology network, and the set of nodes where the thermal response features first appear is marked. In the thermal response topology network, the propagation order of thermal response characteristics in the network is reconstructed by combining the heat transfer sequence between functional units in historical operating temperature data. Based on the propagation order, the functional unit that appears earliest in the propagation chain and continuously participates in the propagation of thermal response characteristics is determined as the initial function.
7. The intelligent diagnostic method for the status of the battery cell thermal management system according to claim 6, characterized in that, The propagation order of the reconstructed thermal response features in the network includes: In historical operating temperature data, identify the starting point when the temperature of each functional unit changes from a stable change to a continuous deviation change; Based on the time sequence of the start time of each functional unit, the connection relationships of the functional units in the thermal response topology network are ordered in a directed manner to form the propagation order of thermal response characteristics.
8. The intelligent diagnostic method for the status of the battery cell thermal management system according to claim 7, characterized in that, The identification of the starting moment when the temperature of each functional unit transitions from a stable change to a continuous shift includes: In the historical operating temperature data corresponding to the functional unit, a continuous evolution sequence of temperature change amplitude over time is constructed; In the continuous evolution sequence, the turning point when the temperature change amplitude changes from periodic fluctuation to unidirectional shift is determined as the starting point.
9. The intelligent diagnostic method for the status of the battery cell thermal management system according to claim 8, characterized in that, Predicting the remaining safe operating cycles of the initial functional unit includes: In historical operating temperature data, the local temperature rise rate sequence of the initial functional unit in multiple continuous operating intervals is extracted, and the degradation evolution trajectory of the corresponding interval is constructed simultaneously. Based on the evolutionary coupling relationship between the local temperature rise rate sequence and the degradation trajectory, the degradation propagation rate of the initial functional unit in different operating stages is determined; Based on the degradation rate corresponding to the current operating stage, the remaining operating time required for the initial functional unit to evolve from the current state to the failure boundary is extrapolated and used as the remaining safe operating cycle.