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CN-121995768-A - Multi-mode self-adaptive switching control optimization method and system for thermal management of capacitor module

CN121995768ACN 121995768 ACN121995768 ACN 121995768ACN-121995768-A

Abstract

The invention provides a multimode self-adaptive switching control optimization method and a system for thermal management of a capacitor module, which relate to the technical field of thermal management of the capacitor module and comprise the steps of constructing a state evolution space and a thermal state track by acquiring temperature field and current data, and identifying the energy aggregation and diffusion areas through curvature tensor and divergence field analysis, establishing a boundary of an equipotential surface of a cooling mode, generating a mode switching signal based on a space geometrical relationship, calculating an optimal cooling path, and inverting to obtain a cooling regulation sequence. The intelligent switching device and the intelligent switching method realize intelligent switching of the cooling mode of the capacitor module, improve the cooling efficiency and prolong the service life of the capacitor module.

Inventors

  • ZHANG RUI

Assignees

  • 北京鸿信德宝新能源科技有限公司

Dates

Publication Date
20260508
Application Date
20260311

Claims (9)

  1. 1. The multi-mode self-adaptive switching control optimization method for the thermal management of the capacitor module is characterized by comprising the following steps of: acquiring temperature field data, current data and operation parameters of each cooling mode of the capacitor module; based on the temperature field data and the current data, mapping to obtain a temperature state variable and a heat flow state variable, constructing a state evolution space and drawing a thermal state track; Extracting equipotential surface boundaries corresponding to all cooling modes according to the energy gathering area and the energy diffusion area, calculating the space geometric relation quantity of the track points corresponding to the current thermal state and the equipotential surface boundaries, and generating a mode switching trigger signal when the space geometric relation quantity meets preset switching conditions; Starting path optimization by adopting a mode switching trigger signal, calculating path integral potential energy and an accumulated risk value of a state space path corresponding to each candidate cooling mode, determining a comprehensive index, and selecting the candidate cooling mode with the minimum comprehensive index as a target cooling mode; And inverting according to a state space path corresponding to the target cooling mode to obtain a cooling intensity regulation sequence, and controlling a cooling execution unit to regulate the cooling intensity according to the cooling intensity regulation sequence.
  2. 2. The method of claim 1, wherein mapping temperature state variables and heat flow state variables based on the temperature field data and the current data, constructing a state evolution space and drawing a thermal state trajectory comprises: dividing temperature field data into a plurality of subareas according to space positions, calculating a temperature average value and a temperature variance of each subarea, taking the temperature average value as a first temperature state component, taking the temperature variance as a second temperature state component, and combining to form a temperature state variable; Carrying out time difference on the current data to obtain a current change rate, calculating the heat power of each subarea according to the current change rate and the heat capacity of each subarea, carrying out space summation to obtain total heat power, carrying out space standard deviation calculation on the heat power of each subarea to obtain heat power distribution unevenness, taking the total heat power as a first heat flow state component, and combining the heat power distribution unevenness as a second heat flow state component to form a heat flow state variable; Establishing a temperature subspace based on the first temperature state component and the second temperature state component, establishing a heat flow subspace based on the first heat flow state component and the second heat flow state component, and combining the temperature subspace and the heat flow subspace to form a four-dimensional state evolution space; and mapping the temperature state variable and the heat flow state variable at each moment into state points in a state evolution space according to time sequence, and sequentially connecting the state points at adjacent moments to form a thermal state track.
  3. 3. The method of claim 1, wherein identifying the energy concentration region and the energy diffusion region by calculating a curvature tensor and a divergence field distribution of the thermal state trajectory comprises: Performing tangential space projection on the thermal state track, decomposing the track motion into a thermal potential component along the temperature state direction and an energy flow component along the heat flow state direction, calculating the change rate of an included angle between the thermal potential component and the energy flow component as track torsion, and combining the track torsion and track curvature to construct a curvature tensor; Performing divergence calculation on a heat flow state variable in a state evolution space to obtain a divergence field, performing multi-scale wavelet decomposition on the divergence field, extracting a first-scale divergence component and a second-scale divergence component, calculating corresponding spatial correlation coefficients, and performing weighted fusion on the divergence components based on the spatial correlation coefficients to obtain a reconstructed divergence field; identifying torsion mutation points with torsion degree exceeding a preset torsion threshold value in the curvature tensor, identifying divergence inversion points with the divergence value changing from negative to positive or from positive to negative in the reconstructed divergence field, calculating space-time matching degree between the torsion mutation points and the divergence inversion points, and determining a region with the space-time matching degree exceeding the preset matching threshold value as an energy phase change region; and performing cross-correlation analysis on the temperature state variable and the heat flow state variable in the energy phase change region, calculating the phase lag quantity of the temperature state variable relative to the heat flow state variable, and comparing the phase lag quantity with a preset lag threshold value to determine an energy accumulation region or an energy diffusion region.
  4. 4. The method of claim 1, wherein extracting equipotential surface boundaries corresponding to each cooling mode according to the energy accumulation region and the energy diffusion region, calculating a spatial geometrical relation of trajectory points corresponding to the current thermal state and the equipotential surface boundaries, and generating the mode switching trigger signal when the spatial geometrical relation satisfies a preset switching condition comprises: Performing contour surface slicing on the thermal state distribution of each cooling mode in a state evolution space, extracting a contour surface at the junction position of an energy aggregation area and an energy diffusion area to obtain a conversion equipotential surface of the cooling mode at an aggregation diffusion conversion point, performing envelope fitting on a plurality of conversion equipotential surfaces of each cooling mode at different temperature equivalents to obtain an envelope surface, and taking the intersection line of the envelope surface and a state evolution space boundary as an equipotential surface boundary corresponding to the cooling mode; Calculating the shortest distance from the track point corresponding to the current thermal state to the equipotential surface boundary corresponding to each cooling mode, calculating the included angle between the motion direction of the track point and the equipotential surface boundary, and combining the shortest distance and the included angle to construct a space geometric relation quantity; And comparing the space geometric relation quantity with a preset relation threshold value, when the space geometric relation quantity is smaller than the relation threshold value, judging that the current thermal state track point passes through the equipotential surface boundary, identifying a cooling mode corresponding to the equipotential surface boundary with the minimum space geometric relation quantity as a target cooling mode, and generating a mode switching trigger signal.
  5. 5. The method of claim 1, wherein starting path optimization using a mode switch trigger signal, calculating path integral potential energy and an accumulated risk value of a state space path corresponding to each candidate cooling mode, determining a composite index, and selecting a candidate cooling mode with a minimum composite index as a target cooling mode comprises: Receiving a mode switching trigger signal, extracting position coordinates and motion speed vectors of track points corresponding to the current thermal state, constructing a path search domain taking the current track points as a starting point in a state evolution space, carrying out normal extension on equipotential surface boundaries corresponding to each candidate cooling mode to obtain an equipotential surface gradient field, and carrying out path tracking along the direction of the equipotential surface gradient field until reaching steady-state points of each candidate cooling mode to obtain a state space path corresponding to each candidate cooling mode; Calculating the energy density of a sampling point on a state space path, taking a line integral value of the energy density along the path as a path integral potential energy, calculating the shortest distance from the sampling point on the state space path to the boundary of an energy accumulation area, and taking a product of the shortest distance and a heat flow gradient modulus value of the sampling point along the line integral value of the path as an accumulated risk value; And multiplying the path integral potential energy by a preset potential energy weight coefficient and multiplying the accumulated risk value by a preset risk weight coefficient to obtain a comprehensive index, comparing the magnitude of the comprehensive index corresponding to each candidate cooling mode, and selecting the candidate cooling mode with the minimum comprehensive index as the target cooling mode.
  6. 6. The method of claim 1, wherein inverting the cooling intensity control sequence according to the state space path corresponding to the target cooling pattern, controlling the cooling execution unit to adjust the cooling intensity according to the cooling intensity control sequence comprises: Extracting energy density gradients and heat flow density vectors of sampling points on a state space path corresponding to a target cooling mode, constructing an energy backtracking path from the sampling points to a heat source of a capacitor module, marking energy transfer nodes on the energy backtracking path, reversely solving through a thermal resistance network at the energy transfer nodes to obtain source end cooling demand corresponding to the sampling points, and converting the source end cooling demand into a cooling intensity value which is output by a cooling execution unit at the moment corresponding to the sampling points; Performing pattern feature matching verification on cooling intensity values corresponding to all sampling points, substituting the cooling intensity values into a state evolution rule of a target cooling pattern, performing forward verification, identifying cooling intensity deviation points causing state evolution to deviate from a state space path, performing iterative correction on the cooling intensity values of the cooling intensity deviation points until the state evolution path converges to the state space path, and combining the corrected cooling intensity values according to time sequence to form a cooling intensity regulation sequence; and sending the cooling intensity control sequence to a cooling execution unit, and controlling the cooling execution unit to sequentially adjust the cooling intensity according to the cooling intensity values corresponding to all moments in the cooling intensity control sequence.
  7. 7. A multimode adaptive switching control optimization system for thermal management of a capacitive module, configured to implement the method of any one of the preceding claims 1-6, comprising: the data acquisition unit is used for acquiring temperature field data and current data of the capacitor module and operation parameters of each cooling mode; the state construction unit is used for mapping to obtain a temperature state variable and a heat flow state variable based on the temperature field data and the current data, constructing a state evolution space and drawing a heat state track; The area identification unit is used for identifying an energy gathering area and an energy diffusion area by calculating curvature tensor and divergence field distribution of the thermal state track; The boundary extraction unit is used for extracting the equipotential surface boundary corresponding to each cooling mode according to the energy gathering area and the energy diffusion area, calculating the space geometric relation quantity of the track point corresponding to the current thermal state and the equipotential surface boundary, and generating a mode switching trigger signal when the space geometric relation quantity meets the preset switching condition; The mode optimization unit is used for starting path optimization by adopting a mode switching trigger signal, calculating path integral potential energy and accumulated risk values of state space paths corresponding to each candidate cooling mode, determining a comprehensive index, and selecting the candidate cooling mode with the minimum comprehensive index as a target cooling mode; and the regulation and control execution unit is used for inverting the state space path corresponding to the target cooling mode to obtain a cooling intensity regulation and control sequence, and controlling the cooling execution unit to regulate the cooling intensity according to the cooling intensity regulation and control sequence.
  8. 8. An electronic device, comprising: A processor; A memory for storing processor-executable instructions; Wherein the processor is configured to invoke the instructions stored in the memory to perform the method of any of claims 1 to 6.
  9. 9. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of claims 1 to 6.

Description

Multi-mode self-adaptive switching control optimization method and system for thermal management of capacitor module Technical Field The invention relates to the technical field of thermal management of a capacitor module, in particular to a multi-mode self-adaptive switching control optimization method and system for thermal management of a capacitor module. Background In the field of thermal management of capacitive modules, the prior art generally relies on preset fixed cooling strategies or simple switching logic based on a single temperature threshold. It is common practice to configure the capacitive module with one or more cooling modes, such as forced air cooling, liquid cooling, or phase change cooling. The system switches from the current cooling mode to another preset cooling mode or linearly adjusts the intensity of a single cooling mode by monitoring the temperature of the key points of the modules when the temperature exceeds a certain preset threshold. The core of the control logic is to establish a direct and static mapping relationship between temperature and cooling action, and the decision basis is relatively single, and mainly focuses on whether the instant temperature is out of range. However, such conventional thermal management methods have significant limitations. Because the temperature field inside the capacitor module is unevenly distributed and has severe dynamic change, the temperature is judged by only depending on a few measuring points or a single threshold value, and the evolution trend of the whole thermal state and the local heat flow characteristic of the module cannot be accurately perceived. This results in a lag in system response, often taking action after thermal problems have accumulated significantly, making it difficult to prevent localized overheating. Meanwhile, the switching strategy of the fixed threshold lacks the adaptability to the dynamic change of the operation working condition of the module, for example, the heat generation rate under different current loads has huge difference, the same cooling strategy can be insufficient in cooling under certain working conditions, and excessive cooling is performed under other working conditions, so that energy waste is caused, unnecessary thermal stress circulation can be introduced, and the service life and reliability of the capacitor module are influenced. Disclosure of Invention The embodiment of the invention provides a multi-mode self-adaptive switching control optimization method and system for thermal management of a capacitor module, which can solve the problems in the prior art. In a first aspect of the embodiments of the present invention, a method for optimizing multi-mode adaptive switching control for thermal management of a capacitor module is provided, including: acquiring temperature field data, current data and operation parameters of each cooling mode of the capacitor module; based on the temperature field data and the current data, mapping to obtain a temperature state variable and a heat flow state variable, constructing a state evolution space and drawing a thermal state track; Extracting equipotential surface boundaries corresponding to all cooling modes according to the energy gathering area and the energy diffusion area, calculating the space geometric relation quantity of the track points corresponding to the current thermal state and the equipotential surface boundaries, and generating a mode switching trigger signal when the space geometric relation quantity meets preset switching conditions; Starting path optimization by adopting a mode switching trigger signal, calculating path integral potential energy and an accumulated risk value of a state space path corresponding to each candidate cooling mode, determining a comprehensive index, and selecting the candidate cooling mode with the minimum comprehensive index as a target cooling mode; And inverting according to a state space path corresponding to the target cooling mode to obtain a cooling intensity regulation sequence, and controlling a cooling execution unit to regulate the cooling intensity according to the cooling intensity regulation sequence. In an alternative embodiment, mapping the temperature state variable and the heat flow state variable based on the temperature field data and the current data, constructing a state evolution space and drawing a heat state trajectory includes: dividing temperature field data into a plurality of subareas according to space positions, calculating a temperature average value and a temperature variance of each subarea, taking the temperature average value as a first temperature state component, taking the temperature variance as a second temperature state component, and combining to form a temperature state variable; Carrying out time difference on the current data to obtain a current change rate, calculating the heat power of each subarea according to the current change rate and the heat capaci