Intelligent temperature control method and system for liquid silicone gel heat-conducting material for new energy automobile battery pack

Abstract

The present application relates to the technical field of battery thermal management, in particular to a liquid silicone gel heat-conducting material intelligent temperature control method and system for new energy vehicle battery packs. The present application combines thermodynamics, electromagnetic field and multi-agent theory, estimates the contact thermal resistance change rate through high-frequency impedance spectrum analysis, and constructs a dynamic directed heat network topology model; generates a thermal management instruction and reconstructs the cooling liquid flow direction through multi-level judgment, and verifies the effectiveness of the execution through heat flow response comparison; temperature collaborative tracking and thermal consensus verification are realized by using the virtual leader-follower consistency algorithm; the process characteristics are stored in the aging memory library to correct the model weight. The present application realizes the control from state perception, decision execution to model self-evolution, effectively improves the precision, robustness and self-adaptive ability of the battery pack thermal management.

Description

Technical Field

[0001] This invention relates to the field of battery thermal management technology, specifically to an intelligent temperature control method and system for liquid silicone thermal conductive material used in new energy vehicle battery packs. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the energy density and charging / discharging power of power batteries are constantly increasing, placing more stringent demands on the performance of battery pack thermal management systems. Uneven heat distribution or localized overheating within the battery pack not only affects the battery's charging / discharging efficiency and cycle life, but can also lead to thermal runaway in severe cases, endangering driving safety. Liquid silicone, as a novel thermally conductive interface material, is widely used for heat conduction between battery modules and cooling plates due to its excellent flexibility, electrical insulation, and thermal conductivity, filling interface gaps and reducing contact thermal resistance. However, in practical applications, liquid silicone is subjected to complex conditions of long-term thermal cycling and mechanical vibration, causing its interface contact state to gradually change, resulting in dynamic fluctuations in contact thermal resistance. This affects the stability and predictability of heat conduction, posing a challenge to existing temperature control strategies.

[0003] Existing thermal management methods for battery packs primarily focus on coolant on / off control based on temperature thresholds or simple proportional-integral-derivative (PID) feedback regulation. These methods typically treat the battery pack as a whole or control individual modules independently, failing to fully consider the impact of dynamic changes in the properties of thermal interface materials on the heat transfer paths between modules. When the thermal conductivity of liquid silicone decreases due to aging or interface delamination, the preset control parameters and fixed cooling channel structures are difficult to adapt to actual thermal field changes, resulting in heat accumulation in some modules due to ineffective heat dissipation. Furthermore, traditional methods lack flexible adjustment mechanisms when facing complex temperature gradient distributions, often resorting to overcooling strategies to meet safety requirements under extreme conditions, leading to unnecessary energy waste. Summary of the Invention

[0004] To overcome the problems of inaccurate temperature control and low energy efficiency caused by neglecting the dynamic characteristics of thermal interface materials in existing technologies, this invention provides an intelligent temperature control method and system for liquid silicone thermal conductive materials used in new energy vehicle battery packs. By constructing a directed thermal network topology model based on the rate of change of contact thermal resistance, and combining a multi-level judgment mechanism to dynamically generate various topology commands such as emergency heat dissipation, compensation enhancement, and optimal energy efficiency, and then using a virtual navigator-follower mechanism to achieve collaborative tracking of temperature trajectory, and using actual heat transfer data to iteratively correct the model, this technical solution achieves accurate, efficient, and adaptive intelligent control of the thermal state of the battery pack.

[0005] The technical solution of this application specifically includes: According to one aspect of this application, a smart temperature control method for liquid silicone thermal conductive material used in new energy vehicle battery packs is provided, comprising: Real-time temperature data of each battery module in the battery pack and high-frequency impedance spectrum data of the liquid silicone thermal interface are collected. Based on the high-frequency impedance spectrum data, the contact thermal resistance change rate of the liquid silicone interface is estimated. Each battery module is mapped as a thermal control node, and the liquid silicone thermal conduction path between modules is mapped as a directed communication edge between nodes. The contact thermal resistance change rate is quantified as the initial weight coefficient of the directed communication edge to construct a directed thermal network topology model. Based on the real-time temperature data of each battery module and the dynamic weight coefficients of the directed thermal network topology model, multi-level judgments are performed to generate the thermal management topology instructions for the current moment. The thermal management topology instructions include emergency heat dissipation topology instructions, compensation enhancement topology instructions, and energy efficiency optimal topology instructions. According to the instructions of the thermal management topology, the thermal management system is driven to adjust the flow direction of the coolant and monitor the rate of change of heat flux density on the surface of the battery module. A virtual navigator-follower mechanism is adopted, with the battery module that has the highest current temperature or the fastest temperature rise as the virtual navigator and the other modules as followers. By adjusting the heat flux density, the temperature change trajectory of the follower nodes is made to track the virtual navigator, thereby achieving temperature synchronization. Once the temperature trajectories of all battery modules remain consistent with the virtual navigator within the allowable error range and are maintained for a preset duration, a thermal consensus state is determined to be reached. Information from this control process is recorded, and the initial weight coefficients of the directed thermal network topology model are corrected using eigenvectors.

[0006] As a further option of the method of the present invention, the acquisition of high-frequency impedance spectrum data of the liquid silicone thermal interface includes: A multi-frequency sweep excitation signal is applied to the liquid silicone thermal interface arranged between each battery module and the cooling plate, and the voltage response signal and current response signal at both ends of the liquid silicone interface are collected simultaneously. Fast Fourier transform is performed on the voltage response signal and the current response signal to obtain the complex impedance at each excitation frequency, resulting in high-frequency impedance spectrum data containing the impedance magnitude spectrum and the impedance phase angle spectrum.

[0007] As a further option of the method of the present invention, the estimation of the contact thermal resistance change rate of the liquid silicone interface based on high-frequency impedance spectroscopy data includes: The characteristic frequency can be determined by identifying the minimum point of the phase angle curve from the impedance phase angle spectrum or the inflection point from the impedance modulus spectrum. The impedance magnitude at the characteristic frequency is extracted as the core feature parameter, and the average phase angle within a preset bandwidth near the characteristic frequency is calculated as the auxiliary feature parameter. Calculate the rate of change of contact thermal resistance: ;in, This represents the rate of change of contact thermal resistance. The characteristic impedance magnitude in the initial state. The initial average phase angle, The current characteristic impedance magnitude, The current average phase angle, This represents the impedance modulus influence coefficient. This represents the phase angle influence coefficient.

[0008] As a further option of the method of the present invention, the construction of the directed heat network topology model includes: Map each battery module as a thermal control node; The liquid silicone thermal conduction path between adjacent battery modules is mapped as a directed communication edge between the connecting nodes, and the heat transfer direction is defined as from the high-temperature module to the low-temperature module. The rate of change of contact thermal resistance is quantified as the initial weighting coefficients for the directed communication edges: ;in, Representing an edge The initial weighting coefficients; Represents a node With nodes The rate of change of contact thermal resistance at the liquid-silicone interface; This represents the proportionality constant.

[0009] As a further option of the method of the present invention, the multi-level judgment includes a first-level judgment: The real-time temperature of each thermal control node is compared with the preset absolute safety threshold, and the temperature change rate of each thermal control node is calculated and compared with the preset temperature rise safety threshold. If any thermal control node satisfies the condition that the real-time temperature is greater than the absolute safety threshold or the temperature change rate is greater than the temperature rise safety threshold, the battery pack is determined to be in a thermally unsafe state, and an emergency heat dissipation topology command is forcibly generated to control all cooling branches to be fully open.

[0010] As a further option of the method of the present invention, the multi-level judgment also includes a second-level judgment: When the real-time temperature and temperature change rate of all thermal control nodes are within a safe range, calculate the average value of the dynamic weight coefficients of all directed communication edges. The average value of the dynamic weighting coefficients is compared with the baseline weighting coefficients to calculate the percentage decrease in thermal conductivity. If the decrease in thermal conductivity exceeds the preset compensation threshold, the health of the liquid silicone material is determined to be deteriorated. A compensation enhancement topology command is generated to control the coolant flow channel to switch to series or partial series to increase the flow rate through the area with deteriorated thermal conductivity.

[0011] As a further option of the method of the present invention, the multi-level judgment also includes a third-level judgment: When both thermal safety and material health are normal, calculate the variance of the real-time temperature of all thermal control nodes. Compare the variance with a preset temperature consistency threshold; If the variance is less than the temperature consistency threshold, then generate the energy-efficient topology instruction, which uses intermittent cooling or reduces the speed of the coolant circulation pump. If the variance is greater than or equal to the temperature consistency threshold, a compensation-enhanced topology instruction is generated.

[0012] As a further option of the method of the present invention, after monitoring the rate of change of heat flux density on the surface of the battery module, a topology adaptability verification step is also included: Based on the directed thermal network topology model, the predicted rate of change of heat flux density at the thermally controlled nodes is calculated according to the node heat balance equation. The formula is as follows: ; in, This indicates the predicted rate of change of heat flux density. This represents the proportionality coefficient. Represents nodes There exists a set of neighbor nodes for a directed communication edge. Indicates the dynamic weighting coefficient. and These represent the temperatures of the neighboring nodes and the current node, respectively. Calculate the average deviation between the measured rate of change of heat flux density and the predicted rate of change of heat flux density. If the average deviation is greater than the preset deviation threshold, it is determined that the current thermal management topology instruction is not compatible, and a reset signal is automatically triggered to re-execute the multi-level judgment.

[0013] As a further option of the method of the present invention, the adoption of the virtual navigator-follower mechanism includes: Select the node with the highest current temperature from all thermal control nodes as the virtual navigator. If multiple nodes have the same temperature and are all at the highest, select the node with the highest temperature rise rate as the virtual navigator. For follower nodes, the desired adjustment amount of the rate of change of heat flux density is calculated according to the consistency control law, using the following formula: ; in, This represents the desired adjustment amount for the rate of change of heat flux density. This represents the temperature trajectory of the virtual navigator. Represents a node The current temperature, Represents a node The current temperature, Indicates proportional control gain. This represents the differential control gain. Represents a node Temperature rise rate, Indicates the rate of temperature rise of the virtual navigator. Indicates the relationship with follower nodes There exists a set of neighbor nodes for a directed edge. Indicates from node Pointing to node The dynamic weight coefficients of directed edges.

[0014] As a further option of the method of the present invention, the temperature synchronization includes: Calculate the temperature synchronization error between all follower nodes and the virtual navigator node; Set the synchronization error convergence accuracy threshold and the continuous observation window length; If the maximum value of the temperature synchronization error within the continuous observation window is less than the synchronization error convergence accuracy threshold, then it is determined that the current thermal management topology command can achieve thermal consensus. If the temperature synchronization error shows a divergent trend within the continuous observation window, it is determined that the current thermal management topology command cannot achieve thermal consensus, and the multi-level judgment is re-executed.

[0015] As a further option of the method of the present invention, the step of correcting the initial weight coefficients of the directed heat network topology model using eigenvectors includes: Construct a feature vector, which includes the thermal management topology command switching frequency, total cooling energy consumption, and actual heat transfer efficiency of liquid silicone. Store the feature vectors in the material aging memory bank; Set a correction period, extract feature vectors from the material aging memory bank, use the feature vectors as input features, and use the actual dynamic weight coefficients as output labels to train the regression model. The initial weight coefficients of the directed heat network topology model are corrected using a regression model.

[0016] Another aspect of this application provides an intelligent temperature control system for liquid silicone thermal conductive material used in new energy vehicle battery packs, the system comprising: The data acquisition module is configured to acquire real-time temperature data of each battery module in the battery pack and high-frequency impedance spectrum data of the liquid silicone thermal interface. The thermal network topology construction module is configured to estimate the contact thermal resistance change rate of the liquid silicone interface based on high-frequency impedance spectrum data, map each battery module as a thermal control node, map the liquid silicone thermal conduction path between modules as a directed communication edge between nodes, and quantify the contact thermal resistance change rate as the initial weight coefficient of the directed communication edge to construct a directed thermal network topology model. The multi-level decision module is configured to perform multi-level judgments based on the real-time temperature data of each battery module and the dynamic weight coefficients of the directed thermal network topology model to generate the thermal management topology instructions for the current moment. The thermal management topology instructions include emergency heat dissipation topology instructions, compensation enhancement topology instructions, and energy efficiency optimal topology instructions. The execution and verification module is configured to drive the thermal management system to adjust the coolant flow direction according to the thermal management topology instructions, and monitor the rate of change of heat flux density on the surface of the battery module. The collaborative tracking control module is configured to use a virtual navigator-follower mechanism, with the battery module that has the highest current temperature or the fastest temperature rise as the virtual navigator and the other modules as followers. By adjusting the heat flux density, the temperature change trajectory of the follower nodes tracks the virtual navigator, thereby achieving temperature synchronization. The self-learning correction module is configured to determine that a thermal consensus state has been reached when the temperature trajectories of all battery modules are consistent with the virtual navigator within the allowable error range and remain so for a preset time. It then records the information from this control process and uses feature vectors to correct the initial weight coefficients of the directed thermal network topology model.

[0017] The beneficial effects of this application are as follows: This technology uses high-frequency impedance spectroscopy to perceive the contact state of the liquid silicone interface in real time. It quantifies the dynamic changes in contact thermal resistance and dynamically corrects them as weight coefficients in the thermal network model, thus accurately tracking the decline in thermal conductivity caused by material aging. This effectively solves the problem of unpredictable heat conduction due to interface state changes in the prior art. Furthermore, based on consistent collaborative control using a virtual leader-follower architecture, the temperature synchronization error of all battery modules can converge to a preset accuracy within multiple consecutive control cycles. Compared to traditional independent control modes, this reduces the maximum temperature difference between modules by more than 40%, significantly suppressing local heat accumulation and avoiding overcooling strategies required under extreme conditions.

[0018] Meanwhile, this method generates topology commands through a three-level progressive decision-making process and verifies the discrepancy between measured heat flow and model predictions, enabling on-demand reconfiguration of cooling channels and reducing cooling energy consumption by 20%-30%. More importantly, through a self-learning mechanism, it stores feature vectors such as topology switching frequency, energy consumption, and heat transfer efficiency in each control process into an aging memory bank, and uses machine learning to correct the initial model weights, so that the model prediction accuracy continues to improve as the battery life extends. Thus, while ensuring battery safety, it significantly improves the energy efficiency ratio and adaptability to material aging of the thermal management system. Attached Figure Description

[0019] Figure 1 A schematic diagram of an intelligent temperature control method for liquid silicone thermal conductive materials used in new energy vehicle battery packs. Figure 2 S100 Flowchart of Intelligent Temperature Control Method for Liquid Silicone Thermal Conductive Material in New Energy Vehicle Battery Packs; Figure 3 S200 Flowchart of Intelligent Temperature Control Method for Liquid Silicone Thermal Conductive Material in New Energy Vehicle Battery Packs; Figure 4 Flowchart of S300 intelligent temperature control method for liquid silicone thermal conductive material used in new energy vehicle battery packs; Figure 5 S400 Flowchart of Intelligent Temperature Control Method for Liquid Silicone Thermal Conductive Material in New Energy Vehicle Battery Packs; Figure 6 S500 Flowchart of Intelligent Temperature Control Method for Liquid Silicone Thermal Conductive Material in New Energy Vehicle Battery Packs; Figure 7 , Figure 8 , Figure 9 , Figure 10 , Figure 11 and Figure 12 This is a diagram of the operating interface of an intelligent temperature control system for liquid silicone thermal conductive materials used in new energy vehicle battery packs. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The core theoretical foundation of this invention is built upon thermodynamics, electromagnetic field theory, nonlinear system identification theory, and multi-agent cooperative control theory. By verifying the temperature field distribution law using the heat conduction equation, identifying changes in the contact state of the liquid silicone interface based on high-frequency impedance spectroscopy analysis, constructing a directed thermal network topology model using graph theory, and introducing a virtual leader-follower consensus algorithm to achieve cooperative tracking of the temperature trajectory, a complete method is formed, encompassing state perception, topology modeling, multi-level decision-making, execution verification, and correction. This enables high-precision, robust intelligent temperature control for the thermal management of new energy vehicle battery packs.

[0022] The specific embodiments of the present invention will be described in detail below.

[0023] Example 1: Please see Figure 1 The diagram illustrates an overall flowchart of a smart temperature control method for liquid silicone thermal conductive material used in new energy vehicle battery packs, provided by an embodiment of the present invention. The method includes: S100: Collects real-time temperature data of each battery module in the battery pack and high-frequency impedance spectrum data of the liquid silicone thermal interface. Based on the high-frequency impedance spectrum data, it estimates the contact thermal resistance change rate of the liquid silicone interface, maps each battery module as a thermal control node, maps the liquid silicone thermal conduction path between modules as a directed communication edge between nodes, quantifies the contact thermal resistance change rate as the initial weight coefficient of the communication edge, and constructs a directed thermal network topology model. S200: Based on the real-time temperature data and dynamic weighting coefficients of each battery module, perform multi-level judgments to generate a thermal management topology instruction that includes emergency heat dissipation topology instructions, compensation enhancement topology instructions, and energy efficiency optimal topology instructions at the current moment. S300: According to the thermal management topology instructions, drive the thermal management system to reconstruct the coolant flow direction to match the selected topology, and immediately monitor the rate of change of heat flux density on the surface of the battery module. If there is a significant deviation between the rate of change of heat flux density and the heat flux response predicted by the directed thermal network topology model, it is determined that the current topology is not suitable for the current thermal field characteristics, and a reset signal is automatically triggered to re-perform multi-level judgment. S400: Based on the verified thermal management topology instructions, implement temperature trajectory tracking control based on the virtual navigator-follower mechanism. Set the battery module with the highest current temperature or the fastest temperature rise as the virtual navigator, and the other modules as follower nodes. By adjusting the heat flux density change rate, the temperature change trajectory of the follower nodes tracks the navigator, and calculate the temperature synchronization error in real time. If the temperature synchronization error does not converge to the preset accuracy range within multiple consecutive control cycles, it is determined that the current thermal management topology instructions cannot achieve thermal consensus, and automatically return to S200 to reselect a thermal management topology instruction that can eliminate the temperature synchronization error. S500: When the temperature trajectories of all battery modules are consistent with the virtual navigator within the allowable error range and remain consistent for a preset duration, the system is determined to have reached a thermal consensus state. The system records the thermal management topology instruction switching frequency, cooling energy consumption, and actual heat transfer efficiency data of liquid silicone during this control process. These data are stored as new feature vectors in the material aging memory bank, and the feature vectors are used to correct the initial weight coefficients of the directed thermal network topology model in S100.

[0024] The specific plan is as follows: In a smart temperature control method for liquid silicone thermal conductive material used in new energy vehicle battery packs, S100 applies a high-frequency sweep signal to the liquid silicone interface and analyzes its impedance spectrum response to extract characteristic parameters related to the interface contact state, thereby accurately estimating the rate of change of contact thermal resistance, and based on this, constructing a directed thermal network topology model describing the heat transfer relationship between modules.

[0025] Please refer to Figure 2It illustrates a flowchart of an exemplary intelligent temperature control method S100 for liquid silicone thermal conductive material used in new energy vehicle battery packs, the contents of which include: S110: In this embodiment, a set of pre-configured multi-frequency sweep excitation signals are applied to the liquid silicone thermal interface arranged between each battery module and the cooling plate through the impedance analysis module.

[0026] In one possible implementation of this embodiment, the multi-frequency sweep excitation signal is synthesized by superimposing multiple sinusoidal wave components of different frequencies. The number of frequency points is greater than or equal to 10, and the frequency range covers a wide frequency band from 100Hz to 10MHz. The amplitude and initial phase of each frequency component are configured according to a preset frequency sweep parameter table. The signal generation module generates the above-mentioned multi-frequency sweep excitation signal through direct digital frequency synthesis technology, and applies it to the electrodes on both sides of the liquid silicone interface after isolation and amplification.

[0027] S120: In this embodiment, while applying the excitation signal, the voltage response signal and current response signal at both ends of the liquid silicone interface are simultaneously acquired by the high-speed data acquisition module.

[0028] In one possible implementation of this embodiment, the acquired time-domain voltage and current signals are subjected to a Fast Fourier Transform to convert them to the frequency domain, obtaining complex voltages and complex currents at each excitation frequency. Then, the complex impedance of the liquid silicone interface at each frequency point is calculated to obtain high-frequency impedance spectrum data describing the electrical characteristics of the liquid silicone interface. This data includes impedance modulus spectrum and impedance phase angle spectrum.

[0029] S130: In this embodiment, based on high-frequency impedance spectroscopy data, characteristic parameters highly correlated with the rate of change of contact thermal resistance at the liquid silicone interface are extracted. The rate of change of contact thermal resistance reflects the degree of decrease in the thermal conductivity of the liquid silicone due to factors such as aging and pressure changes.

[0030] In one possible implementation of this embodiment, the feature parameter extraction step includes: S131: Identify the characteristic frequencies most sensitive to interfacial contact pressure, thickness, and aging degree from the impedance spectrum. The characteristic frequency typically corresponds to the time constant of the relaxation process in the interface equivalent circuit model, which is determined by analyzing the phase angle. The minimum point of the curve or the impedance magnitude The inflection point of the curve is used to determine this.

[0031] S132: Feature extraction frequency The impedance characteristic values ​​in and around the frequency band. Specifically, the characteristic frequency is selected. impedance magnitude at This serves as the core feature parameter. Simultaneously, a narrow frequency band near the feature frequency is calculated. Average phase angle within As auxiliary feature parameters, where This represents bandwidth, typically taken as 10% of the characteristic frequency.

[0032] S133: Estimate the rate of change of contact thermal resistance using the change in impedance eigenvalues. Establish an impedance eigenvalue-contact thermal resistance mapping model beforehand through experimental calibration. Record the characteristic impedance magnitude in the initial state after initial battery pack assembly or after each recalibration. and initial average phase angle During subsequent operation, the current characteristic impedance magnitude is calculated in real time. and the current average phase angle Then the rate of change of contact thermal resistance Calculated using the following mapping relationship: ;in, This represents the rate of change of contact thermal resistance. It is dimensionless, and a positive value indicates that the contact thermal resistance increases and the thermal conductivity decreases. This represents the impedance modulus influence coefficient. This represents the phase angle influence coefficient. and The calibration data were determined by fitting experimental calibration data using the least squares method. The calibration data were obtained by simultaneously measuring impedance spectra and contact thermal resistance under different interfacial pressures and aging degrees.

[0033] S140: In this embodiment, the physical structure within the battery pack is abstracted as a directed weighted network in graph theory. Specifically, each battery module is mapped as a thermal control node. ,in This represents the node number, with a value ranging from 1 to... , This represents the total number of battery modules within the battery pack. The liquid silicone thermal conductivity paths between adjacent battery modules and between battery modules and the cooling plate are mapped as connection nodes. and nodes Directed communication edge .

[0034] In one possible implementation of this embodiment, the directed heat network topology model construction steps include: S141: Determine the adjacency relationship between nodes based on the physical arrangement of the battery modules. If there is a liquid silicone thermal conductive path between two battery modules, then at the corresponding node... and nodes An edge is established between them. Since heat transfer is directional, i.e., flows from the high-temperature module to the low-temperature module, the edge is defined as a directed edge.

[0035] S142: The rate of change of contact thermal resistance calculated in S130 Quantized into the initial weight coefficients of the corresponding directed edges Weighting coefficients This reflects the initial state of the nodes. Pointing to node The heat transfer efficiency of the heat conduction path. Higher heat transfer efficiency results in lower thermal resistance, and therefore a larger weighting coefficient should be applied. Thus, the weighting coefficient is inversely proportional to the rate of change of contact thermal resistance. ;in, Representing an edge The initial weighting coefficients; Represents a node With nodes The rate of change of contact thermal resistance at the liquid-silicone interface; This represents the proportionality constant, used to adjust the weighting coefficients to a suitable numerical range.

[0036] S143: Put all nodes and directed edges and its initial weight coefficients Combined together, they form a complete directed heat network topology model. .in, Represents the set of all thermal control nodes. Represents the set of all directed communication edges. This represents the set of all initial weight coefficients.

[0037] In a smart temperature control method for liquid silicone thermal conductive material used in new energy vehicle battery packs, S200, based on the directed thermal network topology model constructed by S100 and the real-time collected temperature data, dynamically selects and generates the thermal management topology structure instruction most suitable for the current operating conditions through a three-level progressive judgment logic.

[0038] Please refer to Figure 3 The document illustrates a flowchart of an exemplary intelligent temperature control method S200 for a liquid silicone thermal conductive material used in a new energy vehicle battery pack, the contents of which include: S210: In this embodiment, based on the initial directed thermal network topology model constructed in S100, the latest high-frequency impedance spectrum data of the liquid silicone interface is received in real time, and the contact thermal resistance change rate at the current moment is calculated using the method described in S130. The initial weighting coefficients are corrected using the real-time rate of change to obtain the dynamic weighting coefficients at the current moment. .

[0039] S220: In this embodiment, the first-level judgment focuses on the overall thermal safety of the battery pack. Temperature sensor data from all battery modules is read in real time to obtain data from each node. Real-time temperature .

[0040] In one possible implementation of this embodiment, the thermal safety status assessment step includes: S221: Move each node Real-time temperature Compared with the preset absolute safety threshold Comparison. Absolute safety threshold. It is the maximum permissible operating temperature determined based on the battery's electrochemical characteristics.

[0041] S222: Calculate each node Temperature change rate and compare it with the preset temperature rise safety threshold. Compare them.

[0042] S223: Comprehensive assessment of thermal safety status. If any node exists... satisfy or If the battery pack is found to be in a thermally unsafe state, then regardless of the results of subsequent level assessments, an emergency cooling topology command is forcibly generated. The purpose of this instruction is to rapidly reduce the battery pack temperature at all costs, prioritizing safety. If all nodes meet the requirements... and If the thermal safety status is normal, the process proceeds to the second level of judgment.

[0043] S230: In this embodiment, the second-level assessment evaluates the health of the liquid silicone material only under the premise that the thermal safety condition is normal. The system utilizes the dynamic weighting coefficients calculated in real time in S210. .

[0044] In one possible implementation of this embodiment, the material health assessment step includes: S231: Calculate the average of the dynamic weight coefficients of all directed edges at the current time. ,in This represents the total number of edges.

[0045] S232: Adjust the current average dynamic weighting coefficient With a benchmark weighting coefficient Comparison. Benchmark weighting coefficients. It can be the average of the initial weighting coefficients, or the historical average of the battery pack's health status.

[0046] S233: Calculate the percentage decrease in thermal conductivity .like Exceeding the preset compensation threshold If this happens, the liquid silicone material is deemed to have deteriorated, resulting in a significant decrease in overall thermal conductivity. At this point, the system generates a compensation-enhanced topology command. The purpose of the instructions is to increase the flow rate or velocity of the coolant flowing through areas of decreased thermal conductivity by changing the topology of the coolant flow channels, exemplarily switching the originally parallel cooling branches to series connections, thereby compensating for heat transfer losses caused by material aging. If the material is deemed to be in good condition, the process proceeds to the third level of assessment.

[0047] S240: In this embodiment, the third-level judgment evaluates the temperature gradient distribution between modules only when the thermal safety status and material health are both normal, in order to seek the optimal energy efficiency control strategy.

[0048] In one possible implementation of this embodiment, the temperature gradient distribution evaluation step includes: S241: Calculate the real-time temperature of all battery module nodes. variance .

[0049] S242: Temperature variance consistent with the preset temperature threshold Compare. If This indicates that the temperature distribution between modules is uniform, the temperature gradient is small, and the topology command with optimal energy efficiency is generated. The purpose of the instructions is to minimize the energy consumption of the cooling system while maintaining uniform temperature, exemplified by intermittent cooling or reducing the speed of the coolant circulation pump. If This indicates a significant temperature gradient between modules and poor temperature consistency, thus generating the same compensation-enhanced topology instructions as the second level. This allows for proactive intervention to eliminate temperature unevenness.

[0050] S250: In this embodiment, after the above three-level progressive judgment, a unique thermal management topology instruction is output at each moment. , The instructions are encapsulated in a standard format and sent to the execution layer to drive the physical reconfiguration of the cooling system.

[0051] In a smart temperature control method for liquid silicone thermal conductive material for new energy vehicle battery packs, S300 physically executes the thermal management topology instructions generated by S200, matches the instructions by controlling the coolant flow channel topology, and immediately verifies the execution effect. By comparing the measured heat flow response with the model predicted heat flow response, the effectiveness of the selected topology is determined.

[0052] Please refer to Figure 4 The document illustrates a flowchart of an exemplary intelligent temperature control method S300 for liquid silicone thermal conductive material used in new energy vehicle battery packs, the contents of which include: S310: In this embodiment, the thermal management system includes a coolant flow channel switching network composed of multiple solenoid valves or electronic proportional valves. The thermal management system receives a thermal management topology instruction. This generates the corresponding valve control signal.

[0053] In one possible implementation of this embodiment, the coolant flow direction topology reconfiguration step includes: S311: Establish a valve status table, mapping each possible topology instruction to a set of specific valve on / off states or opening values. For example, emergency cooling topology instructions. This corresponds to all cooling branches being fully open, with coolant flowing in parallel through all battery modules; compensation and enhancement topology instructions. The corresponding branch valve opening in the area where thermal conductivity decreases is adjusted to the maximum, forming a series or partially series flow channel; optimal energy efficiency topology command. The corresponding adjustment is to set the main valve opening to the minimum, or to use an intermittent switching pulse width modulation mode.

[0054] S312: The controller follows the instructions. The system looks up a table, generates a control signal, and outputs it to the valve drive circuit to drive the corresponding valve action. Within seconds, it completes the physical reconstruction of the coolant flow channel, making the coolant flow path match the desired path in the directed thermal network topology model.

[0055] S320: In this embodiment, high-precision heat flux density monitoring is initiated immediately upon completion of the coolant flow direction reconstruction. Thin-film heat flux sensors are arranged on the surface of each battery module in contact with the cooling plate.

[0056] In one possible implementation of this embodiment, the heat flux density monitoring step includes: S321: Collect heat flux density data of the surface of each battery module at a fixed high sampling frequency. ,in Indicates the module number.

[0057] S322: Perform differential calculations on the real-time acquired heat flux density data to obtain the measured rate of change of heat flux density. .

[0058] S323: For a time window The data is filtered and smoothed to remove high-frequency noise, yielding measured feature values ​​for verification.

[0059] S330: In this embodiment, the system is based on the current directed heat network topology model. and the latest temperature data Parallel computing of the theoretical predicted values ​​of the rate of change of heat flux density at each node under the current topology command. .

[0060] In one possible implementation of this embodiment, the model prediction of heat flux response calculation steps include: S331: Based on thermal network model Establish nodes The heat balance equation. Nodes From adjacent nodes The incoming heat equals the temperature difference multiplied by the corresponding dynamic weighting coefficient. Node The predicted rate of change of heat flux density is expressed as: ;in, This represents the proportionality coefficient that converts the product of temperature difference and weight into the rate of change of heat flux density. This coefficient is related to the heat capacity and structure of the battery module and is predetermined through experimental calibration. Represents nodes There exists a set of neighboring nodes for a directed edge.

[0061] S340: In this embodiment, the system will use the measured rate of change of heat flux density. Heat flow response predicted by the model Perform a node-by-node comparison and calculate the deviation.

[0062] In one possible implementation of this embodiment, the topology adaptability verification step includes: S341: Calculate the absolute value of the deviation for each node. .

[0063] S342: Calculate the average deviation of all nodes .

[0064] S343: Average deviation Deviation threshold from preset Compare. If If this is not observed, it indicates a significant deviation between the current thermal management topology command and the actual thermal field characteristics of the battery pack. This suggests that the currently selected topology may fail to achieve the expected thermal field control effect due to certain unmodeled dynamic factors.

[0065] S350: In this embodiment, if S340 determines that there is a significant deviation, it will not continue to execute subsequent control steps, but will automatically generate a reset signal and return to S200. The reset signal will force the re-execution of multi-level judgments in order to select a topology instruction that is more suitable for the current actual thermal field characteristics. If the verification passes, i.e. If the system confirms that the current instruction is valid, the process enters S400.

[0066] In a smart temperature control method for liquid silicone thermal conductive material for new energy vehicle battery packs, S400 implements distributed temperature collaborative control based on a virtual navigator-follower mechanism after topology adaptability verification, so as to achieve synchronous tracking of the temperature of all battery modules and further verify whether the selected topology can achieve thermal consensus during dynamic control.

[0067] Please refer to Figure 5 The document illustrates a flowchart of an exemplary intelligent temperature control method S400 for liquid silicone thermal conductive material used in new energy vehicle battery packs, the contents of which include: S410: In this embodiment, at the beginning of each control cycle, a virtual leader node is elected based on the latest temperature status of all battery modules. The virtual leader node is the target that all other follower nodes need to track.

[0068] In one possible implementation of this embodiment, the virtual navigator node election rule is: [The rule applies to all nodes]. In the middle, select the current temperature. The node with the highest temperature is designated as the virtual leader. If multiple nodes have the same highest temperature, the node with the highest temperature rise rate is selected. The largest node acts as the virtual navigator. The virtual navigator's temperature trajectory. This represents the expected temperature change trajectory for the entire battery pack.

[0069] S420: In this embodiment, in addition to the virtual navigator node All nodes other than these are follower nodes. ( Each follower node The control objective is to reduce its own temperature Track the temperature of the virtual navigator as closely as possible. .

[0070] In one possible implementation of this embodiment, the temperature trajectory tracking control employs a consensus algorithm. For each follower node... The desired rate of change of heat flux density adjustment Calculated using the following control law: ;in, Indicates proportional control gain. This represents the differential control gain. Indicates the relationship with follower nodes There exists a set of neighbor nodes for a directed edge. Indicates from node Pointing to node The dynamic weight coefficients of the directed edges. The first and second terms of the control law are proportional-differential feedback terms, used to eliminate temperature and temperature rise rate deviations from the leader; the third term is a neighbor coupling term, which utilizes information from the directed thermal network model to promote temperature convergence between nodes and their neighbors, thereby achieving overall network coordination. The calculated... This is achieved by adjusting the valve opening or coolant flow rate of the corresponding cooling branch.

[0071] S430: In this embodiment, after each control cycle, the system calculates the temperature synchronization error between all follower nodes and the virtual navigator node.

[0072] In one possible implementation of this embodiment, the temperature synchronization error is taken from the temperature synchronization errors of all follower nodes: The temperature synchronization error It quantitatively describes the degree of temperature uniformity within the battery pack.

[0073] S440: In this embodiment, temperature synchronization error is monitored. The trend of change over multiple consecutive control cycles.

[0074] In one possible implementation of this embodiment, the hot consensus determination step includes: S441: Set a synchronization error convergence accuracy threshold. and continuous observation window length .

[0075] S442: Continuous monitoring In the recent The value within a period. If in At the end of each cycle, there exists If the error has converged to the preset accuracy, the current topology can achieve thermal consensus, and the process is ready to enter S500.

[0076] S443: If in At the end of the cycle If the error shows a divergent trend within the observation window, it is determined that the current thermal management topology command cannot achieve thermal consensus.

[0077] S450: In this embodiment, if S440 determines that thermal consensus cannot be achieved, a reset signal is automatically generated, returning to S200 to request the re-execution of multi-level judgments in order to select a new topology instruction that can eliminate the current temperature synchronization error. If thermal consensus is successfully achieved, the final state confirmation and self-learning phase begins.

[0078] In an intelligent temperature control method for liquid silicone thermal conductive material used in new energy vehicle battery packs, after the system reaches a thermal consensus state, the S500 summarizes and learns the complete control process, extracts key feature data and stores it in the memory bank, and uses this data to correct the initial weight coefficients of the most basic directed thermal network topology model, thereby achieving self-evolution.

[0079] Please refer to Figure 6 The document illustrates a flowchart of an exemplary intelligent temperature control method S500 for liquid silicone thermal conductive material used in new energy vehicle battery packs, the contents of which include: S510: In this embodiment, when S440 determines the temperature synchronization error... Continuously below the accuracy threshold After maintaining a preset stable duration, the battery pack was determined to have reached a stable thermal consensus state. At this point, the temperature of all battery modules synchronously followed the trajectory of the virtual navigator with an acceptable small error, indicating that the thermal management effect achieved the expected goal.

[0080] S520: In this embodiment, the system records the key performance data of the entire control process from entering S200 to achieving thermal consensus, forming a feature vector. .

[0081] In one possible implementation of this embodiment, the feature vector Includes the following data: ;in, This indicates the switching frequency of thermal management topology commands during this control process, reflecting the activity level of the control system. This represents the total cooling energy consumption during the control process, obtained by integrating the power of the coolant pump and the running time. The actual heat transfer efficiency of liquid silicone is estimated by the ratio of the total heat actually transferred in this process to the theoretical maximum heat transfer efficiency.

[0082] S530: In this embodiment, the system will use the feature vector generated in S520. As a new sample, it is stored in a specially established material aging memory bank. The memory bank continuously accumulates data as the battery pack's lifespan, recording the performance of the liquid silicone thermal conductive material under different operating conditions and different aging stages.

[0083] S540: In this embodiment, using the data accumulated in the material aging memory bank, the initial weight coefficients of the directed thermal network topology model in S100 are adjusted using a machine learning algorithm. Make adjustments online or offline to better reflect the actual aging and operating characteristics of the battery pack.

[0084] In one possible implementation of this embodiment, the initial weight coefficient correction step includes: S541: Set a correction period, or trigger whenever the number of new samples added to the memory reaches a certain amount.

[0085] S542: Extract all feature vectors from the memory within the most recent period. And its corresponding control effect labels.

[0086] S543: Using feature vectors Using the actual dynamic weight coefficients as output labels, the regression model is trained, and the model learns the mapping relationship between different feature combinations and the true values ​​of the weight coefficients.

[0087] S544: Using the trained model, adjust the initial weight coefficients in S100. Make corrections. The corrected new initial weight coefficients. This allows for a better reflection of the general characteristics of the current material aging stage, enabling the directed thermal network topology model to better reflect these characteristics at the start of the next control cycle. This makes the system more closely resemble real-world conditions, thereby improving the response speed and control accuracy of the entire intelligent temperature control system.

[0088] In summary, this method, based on thermodynamics, electromagnetic fields, graph theory, and multi-agent cooperative control theory, achieves intelligent control of thermal management for new energy vehicle battery packs. It senses material states and constructs a dynamic thermal network model through high-frequency impedance spectroscopy analysis; generates topology commands adapted to different operating conditions using multi-level progressive judgment; verifies the physical validity of the commands through thermal flow response comparison; employs a virtual leader-follower mechanism to achieve collaborative temperature tracking and thermal consensus verification; and finally, uses a self-learning mechanism to revise the basic model using historical data. This method significantly improves the safety, energy efficiency, and adaptability of the battery pack thermal management system under complex dynamic conditions.

[0089] Example 2: This invention has been comprehensively deployed and verified in a thermal management system for a new energy vehicle battery pack consisting of 24 square lithium-ion battery modules. The battery pack employs a liquid cooling solution, with a 2mm thick liquid silicone thermal pad filling the space between the battery modules and the bottom aluminum cooling plate. The cooling plate has a reconfigurable flow channel, with the flow direction of the coolant controlled by eight high-speed solenoid valves. A PT100 temperature sensor is positioned at the center of the surface of each battery module, and a thin-film heat flow sensor is embedded at the interface between the module and the cooling plate. Flexible thin-film electrodes are pre-installed on both sides of the liquid silicone interface for connecting an impedance analysis module.

[0090] I. Implementation and Configuration; 1. Hardware system configuration: Battery modules: 24 in total, rated voltage 3.2V, capacity 100Ah, arranged in 4 rows and 6 columns.

[0091] Temperature sensor: PT100, accuracy ±0.1℃, sampling frequency 1Hz.

[0092] Heat flux sensor: thin film type, range 0-10kW / m 2 The response time is 10ms and the sampling frequency is 100Hz.

[0093] Impedance analysis module: frequency range 100Hz-10MHz, 64 sweep points, excitation voltage amplitude 1V.

[0094] High-speed data acquisition and control system: FPGA-based NIPXIe-1073 chassis, including analog input modules for acquiring temperature and heat flux data, analog output modules for controlling solenoid valves, and a dedicated impedance measurement module.

[0095] Computing and Control Unit: PXI embedded controller, Intel i7 processor, 16GB memory, running LabVIEW Real-Time operating system.

[0096] Cooling system: includes a variable frequency water pump, 8 high-speed solenoid valves, and a plate heat exchanger.

[0097] 2. Software and Algorithm Configuration: The underlying driver layer: The LabVIEW FPGA module implements the hardware timing logic for high-speed data acquisition and valve control.

[0098] Real-time control layer: LabVIEW Real-Time enables real-time execution of the S100-S500 core algorithms, with a control cycle set to 1 second.

[0099] Algorithm Implementation Layer: The core algorithm is written in C++ and encapsulated as a dynamic link library for LabVIEW to call; some parts are implemented using the LabVIEW graphical language. Key parameter settings are as follows: The impedance modulus influence coefficient is 0.6, and the phase angle influence coefficient is 0.4, which were obtained by fitting the calibration data from previous experiments.

[0100] Initial weighting coefficient calculation parameter: proportional constant 1.0.

[0101] The absolute safety threshold is 55℃, and the temperature rise safety threshold is 2℃ / m.

[0102] The thermal conductivity reduction compensation threshold is 15%.

[0103] Temperature consistency threshold 2℃ 2 .

[0104] Deviation threshold 0.5KW / m 2 / s.

[0105] The synchronization error convergence accuracy threshold is 1.5℃, and the observation window length is K = 10 control cycles.

[0106] The hot consensus was maintained stably for 300 seconds.

[0107] Data storage: The TDengine time series database is used to store all sensor raw data, intermediate calculation results, control commands and final operation logs.

[0108] II. Experimental Procedures and Data; 1. Initial system calibration and modeling: With the battery pack at rest and all modules at an initial temperature of 25°C, a multi-frequency sweep excitation (64 frequency points, 100Hz-10MHz) was applied to each liquid silicone interface. Initial impedance spectra were collected, and the initial characteristic impedance magnitude and initial average phase angle were recorded. Simultaneously, individual modules were locally heated using heating elements, and the heat flow transferred through the liquid silicone interface to the cooling plate was measured. Combined with the interface temperature difference measured by thermocouples, the initial contact thermal resistance at each location was calculated to calibrate the coefficients in the impedance-thermal resistance mapping model. Based on this, an initial directed thermal network topology model was constructed.

[0109] 2. Urban operating condition cycle simulation test: To simulate real-world driving conditions, the battery pack was placed in a constant-temperature chamber and charged and discharged according to the power requirements of the Worldwide Light Vehicles Test Cycle (WLTC) using a charging and discharging device. The entire test lasted for 2 hours. The system automatically ran the intelligent temperature control method described in this invention. During the test, the switching of control commands, temperature response, and energy consumption were closely monitored.

[0110] 3. Verification of the complete S100-S500 process (taking a typical time period during testing as an example): The timeline extends from t=1200 seconds to t=3600 seconds: Initial stage (t=1200-1800 seconds): The battery pack discharges at a low power, and the temperature of each module is uniformly distributed between 36-38℃, with a temperature variance of 1.5℃. 2 The S200 system generates an optimal energy-efficient topology command at the third level. S300 verification is successful. The S400 virtual navigator selects module #12 with a temperature of 38℃. The follower temperature closely tracks the system under control, with a stable temperature synchronization error of approximately 0.5℃. The system is operating in a high-efficiency, energy-saving mode.

[0111] Power ramp-up phase (t=1800-2100 seconds): The charging and discharging equipment simulates rapid acceleration, and the discharge power increases sharply. The temperature of module #5 rapidly rises to 51℃, with a temperature rise rate of 2.5℃ / min, exceeding the threshold. The first-level judgment of S220 is triggered, forcibly generating an emergency cooling topology command. S300 drives all cooling branch valves to fully open, and the coolant flows in parallel through all modules at maximum flow rate. In S400, the virtual navigator becomes the module #5 with the highest temperature. All follower nodes strongly track the navigator with a large temperature difference. The temperature synchronization error is widened in a short time and then quickly converges. Thermal consensus is successfully achieved.

[0112] During the high-temperature cruise phase (t=2100-3000 seconds): the battery pack discharges at a constant current with medium power, and the temperature stabilizes at 48-50℃. At this time, the second-level judgment of S230 detects that due to prolonged high-temperature operation, the dynamic weight coefficient of some liquid silicone interfaces in the central region (such as between modules #5, #6, #11, and #12) has decreased by 18% compared to the initial value, exceeding the compensation threshold of 15%. The system generates a compensation enhancement topology command. S300 adjusts the opening of the cooling branch valves in the central region to the maximum, changing the flow topology to allow more coolant to flow through this region. Under the control of S400, the temperature synchronization error converges again to 1.2℃.

[0113] S500 Self-Learning Trigger: After the test, the system statistically analyzes the feature data from this 2-hour cycle to form a feature vector F, which includes the command switching frequency, total cooling energy consumption, and average heat transfer efficiency. This feature vector is stored in the material aging memory library for subsequent model correction.

[0114] III. Experimental Results and Data Analysis; 1. Temperature control accuracy and consistency: Throughout the 2-hour test period, the system continuously recorded the temperature of each module. To quantitatively evaluate the control effect, five representative time points were selected for data analysis and compared with a traditional on / off cooling strategy based on maximum temperature. The results are shown in Table 1.

[0115] Table 1 Comparison of battery module temperature control performance As shown in Table 1, the method of the present invention can strictly control the maximum temperature below the safety threshold of 55℃ under all operating conditions, and the maximum temperature difference between modules is much smaller than that of the traditional method. In particular, the maximum temperature difference is less than 2℃ under steady-state conditions, and the temperature consistency is significantly improved.

[0116] 2. Energy efficiency performance: After completing the entire WLTC cycle test, the total energy consumption of the cooling system was calculated. Simultaneously, the percentage of time the system operated under the optimal energy-efficient topology command under different conditions was recorded. The results are shown in Table 2.

[0117] Table 2 Comparison of Cooling System Energy Consumption The method of this invention reduces energy consumption by 22.2% compared to traditional methods, mainly due to the system automatically selecting the energy-efficient topology under non-extreme operating conditions, thus avoiding over-cooling.

[0118] 3. Verification of self-learning effectiveness: After the battery pack underwent 200 complete charge-discharge cycles for aging, the same WLTC cycle test was performed again. The system control performance was tested under two conditions: with and without the self-learning function enabled. Key performance indicators are compared in Table 3.

[0119] Table 3. Impact of self-learning function on control performance (after aging) Data shows that the self-learning mechanism significantly compensates for model bias caused by material aging, enabling the system to maintain good control performance.

[0120] 4. Robustness and fault tolerance: In one test, the control valve #3 was artificially simulated to be stuck in a half-open state, and the system response was observed. During the S300 verification, a sustained and significant deviation was found between the measured heat flux density change rate and the model prediction, immediately triggering a reset signal. The S200 re-performed multi-level judgments and generated a compensation enhancement topology command. This command, by adjusting the opening of other related valves, bypassed the stuck valve #3 and still achieved compensated cooling of the target area. The temperature changes of the key modules before and after the fault are shown in Table 4.

[0121] Table 4 Fault tolerance test results (valve #3 stuck) Throughout the entire fault and recovery process, the battery pack temperature did not overshoot abnormally, with a maximum temperature of only 50.1℃, which is still within the safe range, demonstrating the system's strong robustness to actuator failures.

[0122] IV. Conclusion; This embodiment fully implements the intelligent temperature control method for liquid silicone thermal conductive material used in new energy vehicle battery packs, as described in this invention, within a thermal management system for a new energy vehicle battery pack comprising 24 modules. Experimental data shows that: 1. Under all operating conditions, the maximum temperature of all battery modules is strictly controlled below the safety threshold of 55℃. The maximum temperature difference between modules is less than 2℃ under steady state and less than 5℃ under transient state, with high temperature synchronization control accuracy.

[0123] 2. Compared with the traditional on / off cooling method, the method of the present invention reduces the energy consumption of the cooling system by 22.2% after completing the same WLTC test, which significantly improves energy efficiency.

[0124] 3. Through the self-learning mechanism of S500, the system can effectively adapt to the aging of liquid silicone materials, reducing the failure rate of thermal flow response verification after aging from 15% to 5% and shortening the thermal consensus conditioning time by 35.4%, thus verifying the adaptability and long-term reliability of the method.

[0125] 4. When a partial failure occurs in the cooling system, the present invention can automatically adjust the control strategy through real-time verification in S300 and re-decision in S200, so as to maintain temperature stability under fault conditions and achieve fault tolerance.

[0126] 5. The control cycle of the entire method is 1 second, and all calculations are completed in real time within the PXI controller, meeting the real-time requirements of the vehicle-mounted embedded system.

[0127] This invention deeply integrates material state perception, thermal network topology modeling, multi-level decision-making, physical verification, collaborative control, and self-learning mechanisms, achieving a leap in thermal management of new energy vehicle battery packs from passive response to active prediction, from single-point control to network collaboration, and from static parameters to dynamic evolution. It significantly improves the safety, energy efficiency, and long-term reliability of battery packs under complex operating conditions, and has extremely high engineering application value and industrialization prospects.

[0128] Example 3: like Figure 7 , Figure 8 , Figure 9 , Figure 10 , Figure 11 and Figure 12The image shows a navigation guide to the six core interfaces of an intelligent temperature control system for liquid silicone thermal conductive materials used in new energy vehicle battery packs. Each interface corresponds to a key step in the patented method, including: Comprehensive monitoring: Overview of battery module temperature field, virtual navigator node, current topology commands and key statuses.

[0129] Impedance Spectrum & Thermal Resistance: Demonstrates the process and parameters of high-frequency sweep excitation, characteristic frequency extraction, and estimation of the rate of change of contact thermal resistance.

[0130] Multi-level decision-making: Simulates three-level progressive judgment logic and displays the currently generated topology instructions.

[0131] Execution verification: Presents the reconstructed state of the cooling channel, a comparison between measured and predicted heat flux density, and the calculation results of topology adaptability deviation.

[0132] Collaborative control: Focusing on virtual leader election, follower control law parameters, temperature synchronization error, and thermal consensus achievement status.

[0133] Self-learning memory: Shows the data records of the material aging memory bank, the self-learning correction model, and the trend of heat transfer efficiency decline.

[0134] A smart temperature control system for liquid silicone thermal conductive material in new energy vehicle battery packs includes: The data acquisition module is configured to acquire real-time temperature data of each battery module in the battery pack and high-frequency impedance spectrum data of the liquid silicone thermal interface. The thermal network topology construction module is configured to estimate the contact thermal resistance change rate of the liquid silicone interface based on high-frequency impedance spectrum data, map each battery module as a thermal control node, map the liquid silicone thermal conduction path between modules as a directed communication edge between nodes, and quantify the contact thermal resistance change rate as the initial weight coefficient of the directed communication edge to construct a directed thermal network topology model. The multi-level decision module is configured to perform multi-level judgments based on the real-time temperature data of each battery module and the dynamic weight coefficients of the directed thermal network topology model to generate the thermal management topology instructions for the current moment. The thermal management topology instructions include emergency heat dissipation topology instructions, compensation enhancement topology instructions, and energy efficiency optimal topology instructions. The execution and verification module is configured to drive the thermal management system to adjust the coolant flow direction according to the thermal management topology instructions, and monitor the rate of change of heat flux density on the surface of the battery module. The collaborative tracking control module is configured to use a virtual navigator-follower mechanism, with the battery module that has the highest current temperature or the fastest temperature rise as the virtual navigator and the other modules as followers. By adjusting the heat flux density, the temperature change trajectory of the follower nodes tracks the virtual navigator, thereby achieving temperature synchronization. The self-learning correction module is configured to determine that a thermal consensus state has been reached when the temperature trajectories of all battery modules are consistent with the virtual navigator within the allowable error range and remain so for a preset time. It then records the information from this control process and uses feature vectors to correct the initial weight coefficients of the directed thermal network topology model.

[0135] Those skilled in the art will understand that the embodiments of this application are provided as methods, systems, or computer program products. Therefore, this application takes the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application takes the form of a computer program product implemented on one or more computer storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer program code. The solutions in the embodiments of this application are implemented using various computer languages, exemplified by the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0136] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, are implemented by computer program instructions. These computer program instructions are provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams.

[0137] These computer program instructions are also stored in a computer read-memory that can direct a computer or other programmed data processing device to operate in a particular manner, such that the instructions stored in the computer read-memory produce an article of manufacture including instruction means that implement the functions specified in the flowchart or multiple flowcharts and / or block diagram blocks or multiple block diagrams.

[0138] These computer program instructions are also loaded onto a computer or other programming data processing device to cause a series of operational steps to be performed on the computer or other programming device to produce a computer-implemented process, such that the instructions, which execute on the computer or other programming device, provide steps for implementing the functions specified in the flowchart flow or multiple flows and / or the block diagram blocks or multiple blocks.

[0139] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0140] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for intelligent temperature control of liquid silicone thermal conductive material for new energy vehicle battery packs, characterized in that, include: Real-time temperature data of each battery module in the battery pack and high-frequency impedance spectrum data of the liquid silicone thermal interface are collected. Based on the high-frequency impedance spectrum data, the contact thermal resistance change rate of the liquid silicone interface is estimated. Each battery module is mapped as a thermal control node, and the liquid silicone thermal conduction path between modules is mapped as a directed communication edge between nodes. The contact thermal resistance change rate is quantified as the initial weight coefficient of the directed communication edge to construct a directed thermal network topology model. Based on the real-time temperature data of each battery module and the dynamic weight coefficients of the directed thermal network topology model, multi-level judgments are performed to generate the thermal management topology instructions for the current moment. The thermal management topology instructions include emergency heat dissipation topology instructions, compensation enhancement topology instructions, and energy efficiency optimal topology instructions. According to the instructions of the thermal management topology, the thermal management system is driven to adjust the flow direction of the coolant and monitor the rate of change of heat flux density on the surface of the battery module. A virtual navigator-follower mechanism is adopted, with the battery module that has the highest current temperature or the fastest temperature rise as the virtual navigator and the other modules as followers. By adjusting the heat flux density, the temperature change trajectory of the follower nodes is made to track the virtual navigator, thereby achieving temperature synchronization. Once the temperature trajectories of all battery modules remain consistent with the virtual navigator within the allowable error range and are maintained for a preset duration, a thermal consensus state is determined to be reached. Information from this control process is recorded, and the initial weight coefficients of the directed thermal network topology model are corrected using eigenvectors.

2. The intelligent temperature control method for liquid silicone thermal conductive material used in new energy vehicle battery packs according to claim 1, characterized in that, The high-frequency impedance spectral data of the liquid silicone thermal interface were collected, including: A multi-frequency sweep excitation signal is applied to the liquid silicone thermal interface arranged between each battery module and the cooling plate, and the voltage response signal and current response signal at both ends of the liquid silicone interface are collected simultaneously. Fast Fourier transform is performed on the voltage response signal and the current response signal to obtain the complex impedance at each excitation frequency, resulting in high-frequency impedance spectrum data containing the impedance magnitude spectrum and the impedance phase angle spectrum.

3. The intelligent temperature control method for liquid silicone thermally conductive material used in new energy vehicle battery packs according to claim 2, characterized in that, The estimation of the contact thermal resistance change rate of the liquid silicone interface based on high-frequency impedance spectroscopy data includes: The characteristic frequency can be determined by identifying the minimum point of the phase angle curve from the impedance phase angle spectrum or the inflection point from the impedance modulus spectrum. The impedance magnitude at the characteristic frequency is extracted as the core feature parameter, and the average phase angle within a preset bandwidth near the characteristic frequency is calculated as the auxiliary feature parameter. Calculate the rate of change of contact thermal resistance: ;in, This represents the rate of change of contact thermal resistance. The characteristic impedance magnitude in the initial state. The initial average phase angle, The current characteristic impedance magnitude, The current average phase angle, This represents the impedance modulus influence coefficient. This represents the phase angle influence coefficient.

4. The intelligent temperature control method for liquid silicone thermally conductive material for new energy vehicle battery packs according to claim 3, characterized in that, The construction of the directed heat network topology model includes: Map each battery module as a thermal control node; The liquid silicone thermal conduction path between adjacent battery modules is mapped as a directed communication edge between the connecting nodes, and the heat transfer direction is defined as from the high-temperature module to the low-temperature module. The rate of change of contact thermal resistance is quantified as the initial weighting coefficient of the directed communication edge: ;in, Representing an edge The initial weighting coefficients; Represents a node With nodes The rate of change of contact thermal resistance at the liquid-silicone interface; This represents the proportionality constant.

5. The intelligent temperature control method for liquid silicone thermal conductive material used in new energy vehicle battery packs according to claim 1, characterized in that, The multi-level judgment includes a first-level judgment: The real-time temperature of each thermal control node is compared with the preset absolute safety threshold, and the temperature change rate of each thermal control node is calculated and compared with the preset temperature rise safety threshold. If any thermal control node satisfies the condition that the real-time temperature is greater than the absolute safety threshold or the temperature change rate is greater than the temperature rise safety threshold, the battery pack is determined to be in a thermally unsafe state, and an emergency heat dissipation topology command is forcibly generated to control all cooling branches to be fully open.

6. The intelligent temperature control method for liquid silicone thermally conductive material for new energy vehicle battery packs according to claim 5, characterized in that, The multi-level judgment process also includes a second-level judgment: When the real-time temperature and temperature change rate of all thermal control nodes are within a safe range, calculate the average value of the dynamic weight coefficients of all directed communication edges. The average value of the dynamic weighting coefficients is compared with the baseline weighting coefficients to calculate the percentage decrease in thermal conductivity. If the decrease in thermal conductivity exceeds the preset compensation threshold, the health of the liquid silicone material is determined to be deteriorated. A compensation enhancement topology command is generated to control the coolant flow channel to switch to series or partial series to increase the flow rate through the area with deteriorated thermal conductivity.

7. The intelligent temperature control method for liquid silicone thermally conductive material for new energy vehicle battery packs according to claim 6, characterized in that, The multi-level judgment process also includes a third level of judgment: When both thermal safety and material health are normal, calculate the variance of the real-time temperature of all thermal control nodes. Compare the variance with a preset temperature consistency threshold; If the variance is less than the temperature consistency threshold, then generate the energy-efficient topology instruction, which uses intermittent cooling or reduces the speed of the coolant circulation pump. If the variance is greater than or equal to the temperature consistency threshold, a compensation-enhanced topology instruction is generated.

8. The intelligent temperature control method for liquid silicone thermally conductive material for new energy vehicle battery packs according to claim 1, characterized in that, Following the monitoring of the rate of change of heat flux density on the surface of the battery module, a topology compatibility verification step is also included: Based on the directed thermal network topology model, the predicted rate of change of heat flux density at the thermally controlled nodes is calculated according to the node heat balance equation. The formula is as follows: ; in, This indicates the predicted rate of change of heat flux density. Represents the proportionality coefficient. Represents nodes There exists a set of neighbor nodes for a directed communication edge. Indicates dynamic weighting coefficients. and These represent the temperatures of the neighboring nodes and the current node, respectively. Calculate the average deviation between the measured rate of change of heat flux density and the predicted rate of change of heat flux density. If the average deviation is greater than the preset deviation threshold, it is determined that the current thermal management topology instruction is not compatible, and a reset signal is automatically triggered to re-execute the multi-level judgment.

9. The intelligent temperature control method for liquid silicone thermally conductive material for new energy vehicle battery packs according to claim 1, characterized in that, The adoption of the virtual leader-follower mechanism includes: Select the node with the highest current temperature from all thermal control nodes as the virtual navigator. If multiple nodes have the same temperature and are all at the highest, select the node with the highest temperature rise rate as the virtual navigator. For follower nodes, the desired adjustment amount of the rate of change of heat flux density is calculated according to the consistency control law, using the following formula: ; in, This represents the desired adjustment amount for the rate of change of heat flux density. This represents the temperature trajectory of the virtual navigator. Represents a node The current temperature, Represents a node The current temperature, Indicates proportional control gain. This represents the differential control gain. Represents a node Temperature rise rate, Indicates the rate of temperature rise of the virtual navigator. Indicates the relationship with follower nodes There exists a set of neighbor nodes for a directed edge. Indicates from node Pointing to node The dynamic weight coefficients of directed edges.

10. The intelligent temperature control method for liquid silicone thermally conductive material for new energy vehicle battery packs according to claim 9, characterized in that, The temperature synchronization is achieved by: Calculate the temperature synchronization error between all follower nodes and the virtual navigator node; Set the synchronization error convergence accuracy threshold and the continuous observation window length; If the maximum value of the temperature synchronization error within the continuous observation window is less than the synchronization error convergence accuracy threshold, then it is determined that the current thermal management topology command can achieve thermal consensus. If the temperature synchronization error shows a divergent trend within the continuous observation window, it is determined that the current thermal management topology command cannot achieve thermal consensus, and the multi-level judgment is re-executed.

11. The intelligent temperature control method for liquid silicone thermally conductive material for new energy vehicle battery packs according to claim 1, characterized in that, The initial weight coefficients of the directed heat network topology model modified using eigenvectors include: Construct a feature vector, which includes the thermal management topology command switching frequency, total cooling energy consumption, and actual heat transfer efficiency of liquid silicone. Store the feature vectors in the material aging memory bank; Set a correction period, extract feature vectors from the material aging memory bank, use the feature vectors as input features, and use the actual dynamic weight coefficients as output labels to train the regression model. The initial weight coefficients of the directed heat network topology model are corrected using a regression model.

12. A smart temperature control system for liquid silicone thermal conductive material in a new energy vehicle battery pack according to any one of claims 1-11, characterized in that the system... include: The data acquisition module is configured to acquire real-time temperature data of each battery module in the battery pack and high-frequency impedance spectrum data of the liquid silicone thermal interface. The thermal network topology construction module is configured to estimate the contact thermal resistance change rate of the liquid silicone interface based on high-frequency impedance spectrum data, map each battery module as a thermal control node, map the liquid silicone thermal conduction path between modules as a directed communication edge between nodes, and quantify the contact thermal resistance change rate as the initial weight coefficient of the directed communication edge to construct a directed thermal network topology model. The multi-level decision module is configured to perform multi-level judgments based on the real-time temperature data of each battery module and the dynamic weight coefficients of the directed thermal network topology model to generate the thermal management topology instructions for the current moment. The thermal management topology instructions include emergency heat dissipation topology instructions, compensation enhancement topology instructions, and energy efficiency optimal topology instructions. The execution and verification module is configured to drive the thermal management system to adjust the coolant flow direction according to the thermal management topology instructions, and monitor the rate of change of heat flux density on the surface of the battery module. The collaborative tracking control module is configured to use a virtual navigator-follower mechanism, with the battery module that has the highest current temperature or the fastest temperature rise as the virtual navigator and the other modules as followers. By adjusting the heat flux density, the temperature change trajectory of the follower nodes tracks the virtual navigator, thereby achieving temperature synchronization. The self-learning correction module is configured to determine that a thermal consensus state has been reached when the temperature trajectories of all battery modules are consistent with the virtual navigator within the allowable error range and remain so for a preset time. It then records the information from this control process and uses feature vectors to correct the initial weight coefficients of the directed thermal network topology model.

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