A liquid cooling charging gun temperature control method for electric vehicle charging

Abstract

The application discloses a liquid cooling charging gun temperature control method for electric vehicle charging, and relates to the technical field of liquid cooling control, which comprises the following steps: liquid cooling initialization, detection of bionic fractal microchannel integrity and phase change microcapsule distribution, calibration of magnetic field parameters and loading of a pre-trained prediction model and thermal resistance network model, and output of a ready signal to a charging pile; after the charging pile communicates with a vehicle, initial flow, magnetic field strength threshold and piezoelectric vibration frequency are matched according to charging power and environmental parameters, a phase change microcapsule latent heat mode and pulse cooling are activated; pulse cooling and magnetic field fluctuation deposition removal are performed in a low-flow latent heat mode, a full-flow mode is restored when the temperature falls back, and abnormal event monitoring triggers graded protection. Through the construction of a bionic fractal flow channel-phase change microcapsule-magnetic field regulation and control, the dynamic heat dissipation performance and working condition adaptability of the liquid cooling charging gun are improved.

Description

Technical Field

[0001] This invention relates to the field of liquid cooling control technology, and in particular to a method for temperature control of a liquid-cooled charging gun for electric vehicle charging. Background Technology

[0002] With the breakthrough in fast charging power for electric vehicles, liquid-cooled charging guns face the dual challenges of a surge in transient heat flux density and dynamic control under complex operating conditions. Current methods employ fixed geometric flow channels and conventional coolants, using PID algorithms to adjust flow rate and achieve basic temperature control. However, limitations such as low laminar heat transfer efficiency and sluggish thermal response make them unsuitable for the nonlinear heat conduction characteristics of high-power charging scenarios. In recent years, phase change microcapsule and magnetic field-assisted heat dissipation technologies have been gradually applied to the thermal management field, proposing the dispersion of magnetic particles in the coolant to enhance heat transfer. However, these methods do not address the issues of decreased latent heat utilization due to uneven microcapsule distribution and long-term performance degradation caused by deposit accumulation in the flow channels.

[0003] The lack of joint modeling capabilities for the integrity of biomimetic fractal flow channel structure, distribution of phase change materials, and dynamic matching of magnetic field parameters makes it difficult to maintain precise temperature gradient control under high dynamic heat loads. It also makes it impossible to compensate for nonlinear disturbances such as microcapsule rupture and deposit adhesion in real time, resulting in local overheating risks and energy waste, which seriously restricts the reliability and service life of liquid-cooled charging guns. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a temperature control method for liquid-cooled charging guns used in electric vehicle charging to solve the problems of dynamic thermal resistance mismatch and multi-physics field collaborative heat dissipation efficiency optimization in high-power liquid-cooled charging guns under complex operating conditions.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a liquid-cooled charging gun temperature control method for electric vehicle charging, which includes liquid cooling initialization, detecting the integrity of biomimetic fractal microchannels and the distribution of phase change microcapsules, calibrating magnetic field parameters and loading a pre-trained prediction model and thermal resistance network model, and outputting a ready signal to the charging pile.

[0008] After the charging pile communicates with the vehicle, it matches the initial flow rate, magnetic field strength threshold and piezoelectric vibration frequency according to the charging power and environmental parameters, and activates the latent heat mode of phase change microcapsules and pulse cooling.

[0009] Real-time acquisition of charging gun temperature and coolant dielectric constant signals, input into prediction model, and calculation of temperature rise trend prediction value;

[0010] The thermal resistance network model was updated, and the flow rate, magnetic field strength, and piezoelectric vibration parameters were adjusted by optimizing the algorithm. When the temperature reached the preset threshold, the mode was switched to low flow rate latent heat mode and the migration of magnetic particles was controlled in a directional manner.

[0011] In low-flow latent heat mode, pulse cooling and magnetic field fluctuations are used to remove deposits. When the temperature drops, the full-flow mode is restored, and abnormal events are monitored to trigger graded protection.

[0012] As a preferred embodiment of the liquid-cooled charging gun temperature control method for electric vehicle charging described in this invention, the liquid-cooled initialization, detection of the integrity of the biomimetic fractal microchannel and the distribution of phase change microcapsules, calibration of magnetic field parameters and loading of a pre-trained prediction model and thermal resistance network model, and output of a ready signal to the charging pile are as follows:

[0013] The integrity of the biomimetic fractal microchannel inside the liquid-cooled charging gun is detected, and the structural status signal is output.

[0014] The distribution state of phase change microcapsules in coolant is detected to generate microcapsule uniformity index;

[0015] The parameter range of the magnetic field generator is calibrated, and the pre-trained prediction model and thermal resistance network model are loaded simultaneously.

[0016] Once loading is complete and the magnetic field parameters have been calibrated, a ready signal is output to the charging station.

[0017] In a preferred embodiment of the liquid-cooled charging gun temperature control method for electric vehicle charging described in this invention, after the charging pile communicates with the vehicle, it matches the initial flow rate, magnetic field strength threshold, and piezoelectric vibration frequency according to the charging power and environmental parameters, and activates the latent heat mode of phase change microcapsules and pulse cooling. The specific steps are as follows.

[0018] After establishing communication between the charging pile and the vehicle, the maximum charging power of the vehicle battery, the current remaining power status, and the temperature data of individual battery cells are obtained.

[0019] Simultaneously collect ambient temperature and humidity data, combine vehicle battery data to calculate initial flow rate and magnetic field strength thresholds, and measure piezoelectric vibration frequency in real time through impedance analysis built into the piezoelectric ceramic sheet.

[0020] The latent heat mode of the phase change microcapsule is activated, and the duty cycle control of the pulse cooling is initiated.

[0021] As a preferred embodiment of the liquid-cooled charging gun temperature control method for electric vehicle charging described in this invention, the steps of real-time acquisition of charging gun temperature and coolant dielectric constant signals, inputting them into a prediction model, and calculating the predicted temperature rise trend are as follows.

[0022] The surface temperature signal of the charging gun and the dielectric constant signal of the coolant are acquired in real time. The two signals are then analyzed in the time and frequency domains to extract the frequency domain energy characteristics.

[0023] Perform time-domain difference operation on the coolant dielectric constant signal to calculate the rate of change of coolant dielectric constant;

[0024] The dynamic confidence weights are calculated based on the frequency domain energy characteristics and the rate of change of the coolant dielectric constant.

[0025] The surface temperature signal of the charging gun, the dielectric constant signal of the coolant, and the dynamic confidence weight are input into the prediction model to calculate the predicted value of the future temperature rise trend.

[0026] As a preferred embodiment of the liquid-cooled charging gun temperature control method for electric vehicle charging described in this invention, the steps of updating the thermal resistance network model and adjusting the flow rate, magnetic field strength, and piezoelectric vibration frequency through optimization algorithms are as follows.

[0027] Input the predicted temperature rise trend into the thermal resistance network model, update the dynamic thermal resistance parameters, and generate a heat dissipation efficiency evaluation index.

[0028] Based on the heat dissipation efficiency evaluation index, current coolant flow rate, magnetic field strength and piezoelectric vibration frequency, a multi-objective chaotic optimization function is constructed.

[0029] The optimal combination of parameters, namely flow rate, magnetic field strength and piezoelectric vibration frequency, is obtained by solving the multi-objective chaotic optimization function using a chaotic particle swarm optimization-simulated annealing hybrid algorithm.

[0030] The optimal combination of flow rate, magnetic field strength, and piezoelectric vibration frequency parameters is converted into a pulse width modulation signal and dynamically applied to the liquid-cooled pump, magnetic field generator, and piezoelectric ceramic sheet.

[0031] In a preferred embodiment of the liquid-cooled charging gun temperature control method for electric vehicle charging described in this invention, the step of switching to a low-flow latent heat mode and directionally controlling the migration of magnetic particles when the temperature reaches a preset threshold is as follows:

[0032] The charging gun head temperature is monitored in real time. When the current temperature reaches the preset temperature threshold, a low flow switching command is generated.

[0033] Upon receiving a low flow switching command, the coolant flow rate is reduced from the initial flow rate by a preset flow rate ratio, activating the latent heat endothermic mode of the phase change microcapsules.

[0034] Based on the deviation between the current temperature and the preset temperature threshold, the magnetic field gradient control parameters are calculated to drive the magnetic particles to migrate directionally to the high-temperature region.

[0035] As a preferred embodiment of the liquid-cooled charging gun temperature control method for electric vehicle charging described in this invention, the steps include: performing pulse cooling and magnetic field fluctuation to remove deposits in low-flow latent heat mode, resuming full-flow mode when the temperature drops, and simultaneously monitoring for abnormal events to trigger graded protection. The specific steps are as follows:

[0036] In low flow latent heat mode, the pulse cooling start-stop cycle is generated based on the real-time coolant flow rate, and the coolant pump is periodically started and stopped.

[0037] Based on the distribution of sediments in the channel and the current magnetic field strength, the magnetic field strength fluctuation and direction are adaptively adjusted to drive magnetic particles to remove sediments along the fractal channel branches.

[0038] The charging gun head temperature is continuously monitored. When the temperature drops to the preset temperature threshold, it is determined to be in a temperature drop state and a full flow recovery trigger signal is generated.

[0039] Upon receiving a trigger signal, the coolant flow rate is gradually restored from the low flow rate mode to the initial flow rate, and an abnormal event scanning thread is started simultaneously.

[0040] It detects abnormal events in coolant flow, magnetic field strength, and piezoelectric vibration frequency, classifies them into severity levels, and triggers a three-level protection mechanism.

[0041] In a preferred embodiment of the liquid-cooled charging gun temperature control method for electric vehicle charging described in this invention, the steps for classifying severity levels and triggering a three-level protection mechanism are as follows:

[0042] A single parameter briefly deviates from the normal range, and the temperature rise trend initially appears but is controllable as a mild abnormality, triggering the first-level protection, reducing the charging power and increasing the coolant flow rate;

[0043] The dual-parameter coordinated deviation and continuous abnormality, but without causing physical damage, constitutes a moderate abnormality. This triggers the secondary protection, switches to the backup liquid cooling circuit, and starts the redundant magnetic field generator.

[0044] The three parameters continued to deteriorate irreversibly, and an extreme safety hazard was detected as a serious anomaly, triggering the three-level protection, severing the electrical connection of the charging gun, and sending an emergency stop signal.

[0045] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the liquid-cooled charging gun temperature control method for electric vehicle charging as described in the first aspect of the present invention.

[0046] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the liquid-cooled charging gun temperature control method for electric vehicle charging as described in the first aspect of the present invention.

[0047] The beneficial effects of this invention are as follows: By constructing a biomimetic fractal flow channel, phase change microcapsules, and magnetic field control, the dynamic heat dissipation performance and operational adaptability of the liquid-cooled charging gun are improved. Based on the pressure fluctuation characteristics of the fractal flow channel and the quantitative detection of microcapsule distribution, the effective range of the magnetic field is dynamically calibrated, and a pre-trained thermal resistance network model is loaded to ensure that the system starts with optimal parameters. Through the joint drive of the predictive model and real-time dielectric constant feedback, the synergistic optimization of coolant flow rate, magnetic field strength, and piezoelectric vibration parameters is achieved, effectively suppressing transient temperature rise and accelerating latent heat release. The turbulence effect excited by pulse cooling and the magnetic field gradient control form a synergistic sweeping mechanism, improving the particle migration efficiency within the fractal flow channel, while simultaneously rapidly isolating abnormal thermal shocks through a graded protection mechanism. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is an overall flowchart of the temperature control method for the liquid-cooled charging gun used for charging electric vehicles in Example 1.

[0050] Figure 2 This is a flowchart of the liquid cooling initialization process for the liquid-cooled charging gun temperature control method used for electric vehicle charging in Example 1.

[0051] Figure 3 This is a flowchart illustrating the real-time temperature control parameter optimization of the liquid-cooled charging gun temperature control method for electric vehicle charging in Example 1.

[0052] Figure 4 This is a flowchart of the thermal protection mode switching of the liquid-cooled charging gun temperature control method for electric vehicle charging in Example 1. Detailed Implementation

[0053] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0054] Example 1, referring to Figures 1-4 This embodiment provides a method for temperature control of a liquid-cooled charging gun for electric vehicle charging, including the following steps:

[0055] S1. Liquid cooling initialization, detection of biomimetic fractal microchannel integrity and phase change microcapsule distribution, calibration of magnetic field parameters and loading of pre-trained prediction model and thermal resistance network model, outputting a ready signal to the charging pile.

[0056] Furthermore, the integrity of the biomimetic fractal microchannels inside the liquid-cooled charging gun is detected, and the structural status signal is output.

[0057] Specifically, the biomimetic fractal microchannel integrity quantification detection uses a high-frequency pressure sensor array (sampling rate 10kHz) to collect fluid pressure fluctuation data within the microchannel, and detects the structural integrity quantification index of the biomimetic fractal microchannel, expressed as:

[0058] ;

[0059] ;

[0060] In the formula, This is expressed as a quantitative index of the structural integrity of biomimetic fractal microchannels. This represents the total number of branches in the biomimetic fractal microchannel. Indicates the microchannel branch index. This indicates that the summation is performed on all branches of the microchannel. This represents the start time of the time window. This represents the end time of the time window. Represented as within the time interval The instantaneous rate of change of the pressure fluctuation signal is integrated internally. Represents partial derivatives, Represented as the current time point, Represented as the first Each microchannel branch in the time interval Fluid pressure fluctuation signal inside, Represented as the current time point The differential, Represented as the first Fluid pressure fluctuation signal of each microchannel branch At the point of time The absolute value of the instantaneous rate of change, Represented as the first Each microchannel branch at a specific time point Fluid pressure fluctuation signal, Expressed as the variance suppression coefficient, , Represented as the first Microchannel branch fluid pressure fluctuation signal In time interval within variance, Represented as a variance function, Time differential unit, This is expressed as the total duration of the time window. Represented as the first Microchannel branch fluid pressure fluctuation signal In time interval The mean within, It is expressed as the mean.

[0061] It should be noted that, A higher value indicates a higher risk of microchannel structure deformation or blockage.

[0062] Specifically, pressure fluctuation signals of each branch of the biomimetic fractal microchannel within a time window are collected using a high-frequency pressure sensor array. The instantaneous rate of change of the pressure fluctuation signal and the variance of the pressure signal are jointly analyzed using the calculation method of the fractal structure integrity index to generate a quantitative value of the fractal structure integrity index. The instantaneous rate of change is dynamically weighted by the variance suppression weight of the pressure fluctuation signal, and the integral mean of the pressure fluctuation signals of each branch is combined to comprehensively assess the deformation or blockage risk of the biomimetic fractal microchannel. The structural state signal containing the value of the fractal structure integrity index is output.

[0063] The distribution state of phase change microcapsules in coolant is detected to generate microcapsule uniformity index;

[0064] Specifically, laser scattering imaging is used to capture the distribution of phase change microcapsules in the coolant and detect non-uniformity indicators, expressed as:

[0065] ;

[0066] In the formula, A quantitative index representing the non-uniformity of phase change microcapsule distribution. This represents the total number of spatially discrete grids in laser scattering imaging. Represented as an index of a spatial discrete grid. This indicates that the computation is performed by traversing all discrete spatial grids. Represented as the gradient operator, Indicates the first Gray-level gradient vector of a spatial discrete grid Indicates the first The gray intensity of a spatial discrete grid. Represented as the Euclidean norm, Represented as the first Local variance of a spatial discrete grid, This is a minimal constant used to prevent division by zero. , Represents the hyperbolic tangent function. Represents the density sensitivity coefficient. , Represented as the first Phase transition microcapsule density of a spatially discrete grid This represents the global average density.

[0067] It should be noted that, , The microcapsules are evenly distributed and operating normally; Localized, slight clustering requires observation or triggering of Level 3 protection. The distribution is severely uneven, triggering magnetic field intervention or shutdown for maintenance.

[0068] Specifically, a grayscale image of the spatial distribution of phase change microcapsules within the coolant is captured using laser scattering imaging. This image is then divided into several discrete grids. The L2 norm of the grayscale gradient vector for each grid is calculated, and a local normalization factor is generated by combining the local variance of the grayscale values ​​within the grid with the elimination of the zero constant. Based on the ratio of the microcapsule density in each grid to the global average density, a hyperbolic tangent function is used to perform a nonlinear mapping of density differences, suppressing the influence of extreme density deviations. A weighted summation operation is performed on the normalized gradient intensity and density nonlinear mapping results for all grids to generate a quantitative index characterizing the uniformity of the phase change microcapsule distribution. Finally, a distribution state signal containing the microcapsule uniformity index value is output.

[0069] The parameter range of the magnetic field generator is calibrated, and the pre-trained prediction model and thermal resistance network model are loaded simultaneously.

[0070] Specifically, based on the real-time values ​​of the biomimetic fractal microchannel integrity index and the microcapsule uniformity index, the upper and lower limits of the dynamic adjustment range of the magnetic field strength are calculated using calibrated fractal integrity weighting coefficients, microcapsule distribution weighting coefficients, and logarithmic smoothing coefficients. Simultaneously, the parameter matrices of the pre-trained prediction model and the thermal resistance network model are loaded from the embedded memory into the control unit. The pre-trained prediction model includes weighting parameters for the frequency domain linear term and the time domain nonlinear term of temperature prediction, and the thermal resistance network model includes the dynamic conduction coefficient between equivalent thermal resistance nodes. When the dynamic adjustment range of the magnetic field strength is verified and the check codes of the pre-trained prediction model and the thermal resistance network model match, a magnetic field parameter calibration completion identifier is generated, and the parameter calibration result containing the boundary values ​​of the dynamic adjustment range of the magnetic field strength and the calibration completion identifier is output.

[0071] Integrity based on biomimetic fractal microchannels Distribution of phase change microcapsules The dynamic range of the generated magnetic field strength is expressed as:

[0072] ;

[0073] In the formula, This is represented as the calibrated magnetic field strength. , Represented as the basic magnetic field strength, , This is represented as the structural integrity weighting coefficient of the biomimetic fractal microchannel. , This is represented by the weighting coefficient for the non-uniformity of the phase change microcapsule distribution. , Represented as log-smoothing coefficients, , Represents the natural logarithm function. This is represented by the combined effect of the structural integrity of the biomimetic fractal microchannel and the non-uniformity of the phase change microcapsule distribution.

[0074] It should be noted that the structural integrity weighting coefficient of the biomimetic fractal microchannel was obtained by optimizing and calibrating the fractal flow channel damage and heat dissipation efficiency, the non-uniformity weighting coefficient of the phase change microcapsule distribution was determined by optimizing the microcapsule distribution and magnetic field energy consumption balance, and the logarithmic smoothing coefficient was determined by numerical stability analysis and extreme working condition verification.

[0075] Once loading is complete and the magnetic field parameters have been calibrated, a ready signal is output to the charging station.

[0076] Specifically, after loading is complete and the magnetic field parameters are calibrated, the structural status signal, microcapsule uniformity index, boundary values ​​of the dynamic adjustment range of magnetic field parameters, and loading status of the pre-trained prediction model and thermal resistance network model are encapsulated into a ready signal data packet via the CAN bus communication protocol. This ready signal data packet includes the values ​​of the biomimetic fractal microchannel integrity index, microcapsule uniformity index, upper limit of magnetic field strength, lower limit of magnetic field gradient switching frequency, check code of the pre-trained prediction model, check code of the thermal resistance network model, and status flag bits. The calibrated ready signal data packet is then encoded and transmitted to the charging pile control unit via the high-speed CAN channel at a baud rate of 500kbps, triggering the charging pile to start the charging handshake process.

[0077] S2. After the charging pile communicates with the vehicle, it matches the initial flow rate, magnetic field strength threshold and piezoelectric vibration frequency according to the charging power and environmental parameters, and activates the latent heat mode of phase change microcapsules and pulse cooling.

[0078] Furthermore, after establishing communication between the charging pile and the vehicle, it obtains the vehicle's maximum charging power, current remaining power status, and battery cell temperature data.

[0079] It should be noted that the maximum charging power refers to the maximum instantaneous charging rate that the vehicle battery can accept within a safe range, and the unit is kilowatt (kW). It is dynamically limited according to the characteristics of the cell material and thermal boundary conditions.

[0080] Current remaining power status refers to the percentage of the battery's current usable capacity relative to its total standard capacity (unit: %), used to characterize the battery's real-time energy reserve level;

[0081] Battery cell temperature data refers to the real-time temperature measurement value (unit: °C) of each individual cell in the battery pack, which is used to monitor the thermal state and heat dissipation requirements of the battery during operation.

[0082] Specifically, after establishing communication between the charging pile and the vehicle, the charging pile sends an extended diagnostic service request to the vehicle battery via power line communication or wireless communication protocols. The request message includes the maximum charging power parameter identifier, the current remaining charge status parameter identifier, and the battery cell temperature data parameter identifier. The vehicle battery returns a response message via the CAN FD bus in an 8-byte data frame format. The maximum charging power data occupies bytes 1-2 (unit: 0.1kW, big-endian), the current remaining charge status occupies byte 3 (unit: 1%), and the battery cell temperature data occupies bytes 4-8 (each cell temperature occupies 1 byte, unit: 1°C). The charging pile parses the hexadecimal data in the response message and converts it into decimal physical quantities. After verifying the validity of the data, it stores it in non-volatile memory, generating a battery status dataset containing a list of maximum charging power values, current remaining charge status values, and battery cell temperature values.

[0083] Simultaneously collect ambient temperature and humidity data, combine vehicle battery data to calculate initial flow rate and magnetic field strength thresholds, and measure piezoelectric vibration frequency in real time through impedance analysis built into the piezoelectric ceramic sheet.

[0084] It should be noted that the ambient temperature and humidity data refer to the air temperature (unit: °C) and relative humidity percentage (unit: %) of the external environment where the charging pile is located, which are collected in real time by temperature and humidity sensors and converted into digital signals.

[0085] The initial flow rate and magnetic field strength threshold refer to the coolant flow rate reference value and the magnetic field strength safety upper limit value generated by the heat load calculation formula and the magnetic field dynamic calibration formula based on the vehicle battery's maximum charging power value, current remaining charge value and ambient temperature data. These values ​​are used to constrain the activation conditions of the latent heat mode of phase change microcapsules.

[0086] The piezoelectric vibration frequency refers to the periodic fluctuation characteristics of the mechanical vibration generated by the piezoelectric ceramic sheet during pulse cooling. It is calculated by using equivalent stiffness and equivalent mass to obtain the reference frequency, and then optimized by combining the predicted value of temperature rise trend. It is used to control the turbulence intensity of coolant and the efficiency of bubble removal.

[0087] Specifically, ambient temperature data (in °C) and humidity data (in %) are simultaneously collected by temperature and humidity sensors at a sampling rate of 5 times per second. Combined with the maximum charging power value, current remaining charge status value, and battery cell temperature value list obtained from the vehicle battery, the ambient temperature data is substituted into the heat load calculation formula to calculate the initial flow rate value, and the maximum charging power value and current remaining charge status value are substituted into the magnetic field dynamic calibration formula to calculate the magnetic field strength threshold. The impedance analysis built into the piezoelectric ceramic sheet measures the piezoelectric vibration resonant frequency waveform at a sampling rate of 10kHz. The piezoelectric vibration frequency reference value is calculated through equivalent stiffness and equivalent mass. Combined with the temperature rise trend prediction value, the piezoelectric vibration frequency reference value is subjected to frequency domain constraint, generating the optimized piezoelectric vibration frequency execution value.

[0088] The latent heat mode of the phase change microcapsule is activated, and the duty cycle control of the pulse cooling is initiated.

[0089] Specifically, when the magnetic field strength threshold exceeds the preset lower limit of the safety threshold and the initial flow rate is within the permissible range of the output of the heat load calculation formula, the high-frequency alternating magnetic field generator is triggered to output a sinusoidal magnetic field with a frequency of 1kHz-5kHz. The magnetic field strength is dynamically adjusted to within ±5% of the magnetic field strength threshold error band. The duty cycle control of pulse cooling calculates the duty cycle reference value by inversely proportionally multiplying the piezoelectric vibration frequency execution value and the temperature rise trend prediction value. The duty cycle reference value is converted into the conduction time ratio of the pulse width modulation signal and loaded into the power drive circuit of the pulse cooling module. The frequency of the pulse width modulation signal is locked to an integer multiple of the piezoelectric vibration frequency execution value. At the same time, the duty cycle reference value is dynamically compensated according to the difference between the highest temperature in the battery cell temperature value list and the temperature rise trend prediction value.

[0090] S3. Real-time acquisition of charging gun temperature and coolant dielectric constant signals, input into the prediction model, and calculation of temperature rise trend prediction value.

[0091] Furthermore, the surface temperature signal of the charging gun and the dielectric constant signal of the coolant are collected in real time, and the two signals are analyzed in the time and frequency domains to extract the frequency domain energy characteristics.

[0092] It should be noted that the charging gun surface temperature signal refers to the continuous analog signal of the charging gun surface temperature changing over time; the coolant dielectric constant signal refers to the dynamic measurement value of the coolant dielectric constant, which is acquired by a capacitive sensor at a frequency of 1kHz.

[0093] Specifically, the surface temperature signal of the charging gun is acquired in real time using a platinum resistance temperature sensor at a sampling rate of 100Hz, and the dielectric constant signal of the coolant is acquired in real time using an interdigitated capacitive sensor at a sampling rate of 1kHz. After applying a Hanning window to the surface temperature signal of the charging gun, a short-time Fourier transform is performed to extract the spectral energy integral value of the low-frequency band (0.1-1kHz) as the vibration correlation feature of the cooling pump, and the spectral energy peak value of the high-frequency band (1-5kHz) as the contact resistance arc correlation feature. After applying a moving average filter to the dielectric constant signal of the coolant, the absolute value sequence of the difference between adjacent sampling points is calculated to generate a dielectric constant fluctuation intensity index. The low-frequency spectral energy integral value, the high-frequency spectral energy peak value, and the dielectric constant fluctuation intensity index are input into a pre-trained frequency domain energy feature mapping table to output a frequency domain energy feature characterizing the thermal fluctuation stability.

[0094] Perform time-domain difference operation on the coolant dielectric constant signal to calculate the rate of change of coolant dielectric constant;

[0095] It should be noted that the instantaneous rate of change is calculated based on the ratio of the absolute value of the difference between the dielectric constant values ​​of two adjacent sampling points (with a time interval of 0.1 seconds) to the time interval. The instantaneous rate of change sequence is then input into a moving average filter (with a filter window length of 0.5 seconds) for noise suppression, and the smoothed value of the dielectric constant change of the coolant is output.

[0096] The dynamic confidence weights are calculated based on the frequency domain energy characteristics and the rate of change of the coolant dielectric constant.

[0097] Specifically, the frequency domain energy stability coefficient is calculated by weighting the low-frequency energy ratio and the number of high-frequency energy abrupt changes. The sum of the frequency domain energy stability coefficient and the absolute value of the rate of change of the coolant dielectric constant is used as the denominator and the frequency domain energy stability coefficient as the numerator to generate the initial value of the dynamic confidence weight. The product of the dielectric constant-energy coupling coefficient and the rate of change of the coolant dielectric constant is used as a correction factor to apply exponential smoothing to the initial value of the dynamic confidence weight, and the dynamic confidence weight value is output to be normalized to the [0,1] interval.

[0098] The surface temperature signal of the charging gun, the dielectric constant signal of the coolant, and the dynamic confidence weight are input into the prediction model to calculate the predicted value of the future temperature rise trend.

[0099] It should be noted that the charging gun surface temperature signal is time-series standardized and then aligned with the coolant dielectric constant signal in the time and frequency domains. The dynamic confidence weight is used as a weighting coefficient to dynamically scale the frequency domain energy feature components of the two signals. The scaled frequency domain energy feature components are converted into multi-dimensional feature vectors (including the proportion of low-frequency energy, the number of high-frequency energy abrupt changes, and the dielectric constant-energy coupling coefficient) through a pre-trained frequency domain energy feature mapping table. The multi-dimensional feature vectors and the coolant dielectric constant change rate are input together into the nonlinear regression layer of the prediction model to output the predicted value of the temperature rise trend.

[0100] Specifically, the formula for calculating the predicted future temperature rise trend is:

[0101] ;

[0102] In the formula, This is expressed as a predicted value for the temperature rise trend. This is represented as traversing all frequency domain energy features. This is represented as the total number of frequency domain energy characteristics. An index representing the energy characteristics in the frequency domain. Represented as the first Weighting coefficients for each frequency domain energy feature. Represents the real part of a complex number. This is represented as the surface temperature signal of the charging gun. Fourier transform at frequency Complex values ​​at that location, This is represented as the surface temperature signal of the charging gun. Represented as the first The frequency of the energy characteristics in the frequency domain. Represented as dynamic confidence weights, , Represented as nonlinear gain coefficient, , This is represented as the coolant dielectric constant signal. Expressed as the rate of change of the dielectric constant of the coolant, Represented as temperature inhibition factor, , This represents the surface of the charging gun at the current time point. Real-time temperature, Represented as a dynamic data fusion algorithm based on Bayes' theorem. Represented as a historical temperature data sequence, This is represented as a priori prediction of the temperature rise trend.

[0103] It should be noted that the first The weighting coefficients of each frequency domain energy feature are obtained by fitting training data (such as historical temperature rise data); the dynamic confidence weights are generated by fusing real-time data of frequency domain energy proportion and dielectric constant change rate; the nonlinear gain coefficient is calibrated by the dynamic response of dielectric constant and is used to amplify the contribution of transient anomalies to temperature rise; the temperature suppression factor is calibrated by the heat dissipation efficiency under high temperature conditions and is used to balance the attenuation intensity of temperature on nonlinear terms.

[0104] S4. Update the thermal resistance network model, adjust the flow rate, magnetic field strength and piezoelectric vibration parameters through optimization algorithm, and switch to low flow rate latent heat mode when the temperature reaches the preset threshold and directionally control the migration of magnetic particles.

[0105] Input the predicted temperature rise trend into the thermal resistance network model, update the dynamic thermal resistance parameters, and generate a heat dissipation efficiency evaluation index.

[0106] It should be noted that the dynamic thermal resistance parameter refers to the equivalent thermal resistance value (unit: K / W) between nodes in the thermal resistance network model, which is dynamically adjusted with the coolant flow rate and the phase change state of the microcapsules.

[0107] The heat dissipation efficiency evaluation index refers to the ratio of the actual heat dissipation of the coolant to its theoretical maximum heat dissipation capacity.

[0108] Based on the heat dissipation efficiency evaluation index, current coolant flow rate, magnetic field strength and piezoelectric vibration frequency, a multi-objective chaotic optimization function is constructed.

[0109] Specifically, a multi-objective chaotic optimization function is constructed, with the following expression:

[0110] ;

[0111] In the formula, Represented as a multi-objective optimization function, Indicates coolant flow rate. It is expressed as the piezoelectric vibration frequency. Represented as time The predicted value of the temperature rise trend, Represented as time The dynamic thermal resistance parameters, Time differential variable, This represents the energy consumption weighting coefficient. , Represents the squared flow term. This represents the cubic term of the calibrated magnetic field strength. This is expressed as the exponential decay term of the piezoelectric vibration frequency. Expressed as the vibration attenuation coefficient, , Represented as noise suppression weighting coefficient, , Represented as an index of temperature monitoring points, This represents the total number of temperature monitoring points. This indicates that all temperature monitoring points will be traversed. Represented as the first The second spatial derivative of temperature at each temperature monitoring point.

[0112] It should be noted that the energy consumption weighting coefficient is optimized and calibrated by the Pareto front of energy consumption and heat dissipation efficiency to balance flow rate and magnetic field energy consumption; the vibration attenuation coefficient is calibrated by the exponential attenuation of piezoelectric vibration frequency and energy consumption to suppress high-frequency vibration loss; and the noise suppression weighting coefficient is calibrated by the uniformity of the temperature field in infrared thermal imaging.

[0113] The optimal combination of parameters, namely flow rate, magnetic field strength and piezoelectric vibration frequency, is obtained by solving the multi-objective chaotic optimization function using a chaotic particle swarm optimization-simulated annealing hybrid algorithm.

[0114] Specifically, the particle swarm optimization algorithm initializes the particle positions as a three-dimensional vector combination of the current coolant flow rate, magnetic field strength, and piezoelectric vibration frequency. It sets initial values ​​for inertia weights, individual learning factors, and group learning factors, and generates a perturbation sequence for the inertia weights based on chaotic mapping. The simulated annealing algorithm sets initial values ​​for the annealing temperature and annealing temperature scheduling coefficients, using the Metropolis criterion as the acceptance criterion. In each iteration, the particle swarm optimization algorithm calculates the fitness value and updates the particle velocity and position according to the multi-objective chaotic optimization function. The simulated annealing algorithm then applies random perturbations to the particle positions and calculates the improvement rate of the fitness value after the perturbation. Finally, it outputs the optimal combination of flow rate, magnetic field strength, and piezoelectric vibration frequency parameters.

[0115] The optimal combination of flow rate, magnetic field strength, and piezoelectric vibration frequency parameters is converted into a pulse width modulation signal and dynamically applied to the liquid-cooled pump, magnetic field generator, and piezoelectric ceramic sheet.

[0116] Specifically, the optimal flow rate is converted into the duty cycle value of the liquid cooling pump's pulse width modulation signal using a predefined flow-duty cycle mapping table. The optimal magnetic field strength is generated by the voltage-frequency conversion formula of the magnetic field generator to produce the frequency and amplitude values ​​of the magnetic field generator's pulse width modulation signal. The optimal piezoelectric vibration frequency is generated by the resonant frequency of the piezoelectric ceramic sheet and the frequency matching algorithm of the drive signal to produce the frequency value of the piezoelectric ceramic sheet's pulse width modulation signal. During the loading process, a timer interrupt is used to ensure that the phase synchronization of the pulse width modulation signals of the liquid cooling pump, the magnetic field generator, and the piezoelectric ceramic sheet is achieved. After loading is completed, the consistency of the pulse width modulation signal parameters with the optimal flow rate, magnetic field strength, and piezoelectric vibration frequency is verified by reading back the register values.

[0117] The charging gun head temperature is monitored in real time. When the current temperature reaches the preset temperature threshold, a low flow switching command is generated.

[0118] Specifically, a platinum resistance temperature sensor continuously collects the temperature simulation signal of the contact area of ​​the charging gun head at a fixed sampling rate. The temperature simulation signal is then filtered by moving average and converted into a digital temperature value. The digital temperature value is compared with a preset temperature threshold. When the duration of the digital temperature value exceeding the preset temperature threshold reaches the anti-shake delay time, a low flow switching command is generated.

[0119] Upon receiving a low flow switching command, the coolant flow rate is reduced from the initial flow rate by a preset flow rate ratio, activating the latent heat endothermic mode of the phase change microcapsules.

[0120] Specifically, after receiving the low flow switching command, the initial flow value and the preset flow ratio value are input into the flow ratio decay function to generate a target flow value that decreases exponentially. The target flow value is converted into the pulse width modulation signal duty cycle value of the liquid cooling pump through a predefined flow-duty cycle mapping table, which synchronously triggers the high-frequency alternating magnetic field generator to output a sinusoidal magnetic field with a frequency of 1kHz-5kHz. The magnetic field strength is adjusted to activate the latent heat endothermic mode of phase change microcapsules.

[0121] Based on the deviation between the current temperature and the preset temperature threshold, the magnetic field gradient control parameters are calculated to drive the magnetic particles to migrate directionally to the high-temperature region.

[0122] Specifically, based on the deviation between the current temperature value and the preset temperature threshold value, the magnetic field gradient control parameter value is calculated through the deviation-magnetic field gradient mapping table (the mapping table parameters are calibrated based on the magnetic susceptibility and thermophoretic migration coefficient of the magnetic particles). The magnetic field gradient control parameter value includes the magnetic field intensity gradient value and the magnetic field direction angle value; driving the magnetic particles to migrate directionally towards the high-temperature region of the charging gun head along the magnetic field gradient direction.

[0123] S5. In low flow latent heat mode, pulse cooling and magnetic field fluctuation are performed to remove deposits. When the temperature drops, the full flow mode is restored. At the same time, abnormal events are monitored to trigger graded protection.

[0124] Furthermore, in low flow latent heat mode, the pulse cooling start-stop cycle is generated based on the real-time coolant flow rate, and the coolant pump is periodically started and stopped.

[0125] It should be noted that the start-stop cycle of pulse cooling refers to the time interval between the periodic start and stop of the coolant pump, which is the ratio of the real-time flow rate to the temperature drop rate in low flow mode.

[0126] Specifically, the start-stop interval of the coolant pump is set by the timer interrupt controller, generating a periodic start-stop control signal. The periodic start-stop control signal is converted into the duty cycle value of the pulse width modulation signal of the liquid coolant pump. When the duty cycle value is 0, the coolant pump is turned off. When the duty cycle value is 100%, the coolant pump runs at full power. The duty cycle value of the pulse width modulation signal is loaded into the power drive circuit of the liquid coolant pump through the digital-to-analog converter to execute the periodic start-stop operation of the coolant pump.

[0127] Based on the distribution of sediments in the channel and the current magnetic field strength, the magnetic field strength fluctuation and direction are adaptively adjusted to drive magnetic particles to remove sediments along the fractal channel branches.

[0128] Specifically, the distribution data of sediments in the channel (including sediment density values ​​and location coordinates) is collected in real time by a Hall sensor array, and the amplitude of magnetic field strength fluctuation and the adjustment amount of magnetic field direction angle are calculated. The amplitude of magnetic field strength fluctuation is converted into the current fluctuation amplitude parameter of each coil by the current amplitude control algorithm of the multi-coil array drive circuit, and the adjustment amount of magnetic field direction angle is generated into the current direction correction parameter of each coil by the polar coordinate to Cartesian coordinate transformation algorithm. The magnetic particles are driven to migrate directionally along the sediment enrichment area of ​​the fractal channel branch.

[0129] The charging gun head temperature is continuously monitored. When the temperature drops to the preset temperature threshold, it is determined to be in a temperature drop state and a full flow recovery trigger signal is generated.

[0130] Specifically, a platinum resistance temperature sensor continuously collects the temperature simulation signal of the contact area of ​​the charging gun head at a fixed sampling rate. The temperature simulation signal is then filtered by moving average and converted into a digital temperature value. The digital temperature value is compared with a preset temperature threshold. When the duration of the digital temperature value being continuously lower than the preset temperature threshold reaches the anti-shake delay time, it is determined to be a temperature drop state, and a full flow recovery trigger signal containing the initial flow value, full flow recovery command, and timestamp is generated.

[0131] Upon receiving a trigger signal, the coolant flow rate is gradually restored from the low flow rate mode to the initial flow rate, and an abnormal event scanning thread is started simultaneously.

[0132] Specifically, after receiving the full flow recovery trigger signal, the initial flow value and the full flow recovery command are read. After verifying the validity of the command CRC check code and timestamp, the coolant flow value is increased in stages according to the flow recovery curve. The flow increase in each stage does not exceed 20% of the initial flow value. At the same time, the abnormal event scanning thread is started through the timer interrupt controller.

[0133] It should be noted that the abnormal event scanning thread refers to the monitoring process that continuously polls the real-time parameters of coolant flow rate, magnetic field strength, and piezoelectric vibration frequency, and compares them item by item with the preset safety threshold range; when the parameters are detected to exceed the safety threshold range, the thread generates an abnormal event code and triggers a graded protection mechanism.

[0134] It detects abnormal events in coolant flow, magnetic field strength, and piezoelectric vibration frequency, classifies them into severity levels, and triggers a three-level protection mechanism.

[0135] It should be noted that by comparing the real-time parameters of coolant flow rate, magnetic field strength, and piezoelectric vibration frequency with the preset safety range, if the flow rate deviation exceeds 10%, the magnetic field strength deviation exceeds ±15%, or the frequency deviation exceeds 5%, an abnormality type is marked; according to the abnormality type, it is divided into three levels: mild, moderate, and severe abnormality, triggering a three-level protection mechanism.

[0136] A single parameter briefly deviates from the normal range, and the temperature rise trend initially appears but is controllable as a mild abnormality, triggering the first-level protection, reducing the charging power and increasing the coolant flow rate;

[0137] It should be noted that if any one of the parameters—coolant flow rate, magnetic field strength, or piezoelectric vibration frequency—experiences an abnormality for more than 2 seconds, the first-level protection will be triggered.

[0138] The dual-parameter coordinated deviation and continuous abnormality, but without causing physical damage, constitutes a moderate abnormality. This triggers the secondary protection, switches to the backup liquid cooling circuit, and starts the redundant magnetic field generator.

[0139] It should be noted that when any two of the parameters of coolant flow rate, magnetic field strength and piezoelectric vibration frequency show abnormality or a single parameter lasts for more than 5 seconds, the secondary protection will be triggered.

[0140] The three parameters continued to deteriorate irreversibly, and an extreme safety hazard was detected as a serious anomaly, triggering the three-level protection, severing the electrical connection of the charging gun, and sending an emergency stop signal.

[0141] It should be noted that when the coolant flow rate, magnetic field strength, and piezoelectric vibration frequency are abnormal or the voltage drops by more than 30%, the three-level protection is triggered.

[0142] This embodiment also provides a computer device applicable to the temperature control method of a liquid-cooled charging gun for electric vehicle charging, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the temperature control method of a liquid-cooled charging gun for electric vehicle charging as proposed in the above embodiment.

[0143] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0144] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the liquid-cooled charging gun temperature control method for electric vehicle charging as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0145] In summary, this invention improves the dynamic heat dissipation performance and operational adaptability of a liquid-cooled charging gun by constructing a biomimetic fractal flow channel, phase change microcapsules, and magnetic field control. Based on the pressure fluctuation characteristics of the fractal flow channel and the quantitative detection of microcapsule distribution, the magnetic field's effective range is dynamically calibrated, and a pre-trained thermal resistance network model is loaded to ensure the system starts with optimal parameters. Through the joint drive of the predictive model and real-time dielectric constant feedback, the coolant flow rate, magnetic field strength, and piezoelectric vibration parameters are synergistically optimized, effectively suppressing transient temperature rise and accelerating latent heat release. The turbulence effect excited by pulsed cooling and the magnetic field gradient control form a synergistic sweeping mechanism, improving particle migration efficiency within the fractal flow channel, while simultaneously providing a graded protection mechanism to quickly isolate abnormal thermal shocks.

[0146] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for temperature control of a liquid-cooled charging gun for electric vehicle charging, characterized in that: include, Liquid cooling initialization, detection of biomimetic fractal microchannel integrity and phase transition microcapsule distribution, calibration of magnetic field parameters and loading of pre-trained prediction and thermal resistance network models, and output of a ready signal to the charging pile are detailed below. The integrity of the biomimetic fractal microchannel inside the liquid-cooled charging gun is detected, and the structural status signal is output. The distribution state of phase change microcapsules in coolant is detected to generate microcapsule uniformity index; The parameter range of the magnetic field generator is calibrated, and the pre-trained prediction model and thermal resistance network model are loaded simultaneously. Once loading is complete and the magnetic field parameters have been calibrated, a ready signal is output to the charging station. After the charging pile communicates with the vehicle, it matches the initial flow rate, magnetic field strength threshold and piezoelectric vibration frequency according to the charging power and environmental parameters, and activates the latent heat mode of phase change microcapsules and pulse cooling. Real-time acquisition of charging gun temperature and coolant dielectric constant signals, input into prediction model, and calculation of temperature rise trend prediction value; The thermal resistance network model was updated, and the flow rate, magnetic field strength, and piezoelectric vibration parameters were adjusted through an optimization algorithm. The specific steps are as follows. Input the predicted temperature rise trend into the thermal resistance network model, update the dynamic thermal resistance parameters, and generate a heat dissipation efficiency evaluation index. Based on the heat dissipation efficiency evaluation index, current coolant flow rate, magnetic field strength and piezoelectric vibration frequency, a multi-objective chaotic optimization function is constructed. The optimal combination of parameters, namely flow rate, magnetic field strength and piezoelectric vibration frequency, is obtained by solving the multi-objective chaotic optimization function using a chaotic particle swarm optimization-simulated annealing hybrid algorithm. The optimal combination of flow rate, magnetic field strength and piezoelectric vibration frequency parameters is converted into a pulse width modulation signal and dynamically applied to the liquid cooling pump, magnetic field generator and piezoelectric ceramic sheet. When the temperature reaches the preset threshold, switch to low flow latent heat mode and directionally control the migration of magnetic particles; In low-flow latent heat mode, pulse cooling and magnetic field fluctuations are used to remove deposits. When the temperature drops, the full-flow mode is restored, and abnormal events are monitored to trigger graded protection.

2. The temperature control method for a liquid-cooled charging gun for electric vehicle charging as described in claim 1, characterized in that: After the charging pile communicates with the vehicle, it matches the initial flow rate, magnetic field strength threshold, and piezoelectric vibration frequency according to the charging power and environmental parameters, and activates the latent heat mode of the phase change microcapsule and pulse cooling. The specific steps are as follows. After establishing communication between the charging pile and the vehicle, the maximum charging power of the vehicle battery, the current remaining power status, and the temperature data of individual battery cells are obtained. Simultaneously collect ambient temperature and humidity data, combine vehicle battery data to calculate initial flow rate and magnetic field strength thresholds, and measure piezoelectric vibration frequency in real time through impedance analysis built into the piezoelectric ceramic sheet. The latent heat mode of the phase change microcapsule is activated, and the duty cycle control of the pulse cooling is initiated.

3. The temperature control method for a liquid-cooled charging gun for electric vehicle charging as described in claim 1, characterized in that: The real-time acquisition of charging gun temperature and coolant dielectric constant signals is input into the prediction model to calculate the predicted temperature rise trend. The specific steps are as follows: The surface temperature signal of the charging gun and the dielectric constant signal of the coolant are acquired in real time. The two signals are then analyzed in the time and frequency domains to extract the frequency domain energy characteristics. Perform time-domain difference operation on the coolant dielectric constant signal to calculate the rate of change of coolant dielectric constant; The dynamic confidence weights are calculated based on the frequency domain energy characteristics and the rate of change of the coolant dielectric constant. The surface temperature signal of the charging gun, the dielectric constant signal of the coolant, and the dynamic confidence weight are input into the prediction model to calculate the predicted value of the future temperature rise trend.

4. The temperature control method for a liquid-cooled charging gun for electric vehicle charging as described in claim 1, characterized in that: When the temperature reaches a preset threshold, the system switches to a low-flow latent heat mode and directionally controls the migration of magnetic particles. The specific steps are as follows: The charging gun head temperature is monitored in real time. When the current temperature reaches the preset temperature threshold, a low flow switching command is generated. Upon receiving a low flow switching command, the coolant flow rate is reduced from the initial flow rate by a preset flow rate ratio, activating the latent heat endothermic mode of the phase change microcapsules. Based on the deviation between the current temperature and the preset temperature threshold, the magnetic field gradient control parameters are calculated to drive the magnetic particles to migrate directionally to the high-temperature region.

5. The temperature control method for a liquid-cooled charging gun for electric vehicle charging as described in claim 1, characterized in that: The process involves pulse cooling and magnetic field fluctuations to remove deposits in low-flow latent heat mode, resuming full-flow mode when the temperature drops, and simultaneously monitoring for abnormal events to trigger tiered protection. The specific steps are as follows. In low flow latent heat mode, the pulse cooling start-stop cycle is generated based on the real-time coolant flow rate, and the coolant pump is periodically started and stopped. Based on the distribution of sediments in the channel and the current magnetic field strength, the magnetic field strength fluctuation and direction are adaptively adjusted to drive magnetic particles to remove sediments along the fractal channel branches. The charging gun head temperature is continuously monitored. When the temperature drops to the preset temperature threshold, it is determined to be in a temperature drop state and a full flow recovery trigger signal is generated. Upon receiving a trigger signal, the coolant flow rate is gradually restored from the low flow rate mode to the initial flow rate, and an abnormal event scanning thread is started simultaneously. It detects abnormal events in coolant flow, magnetic field strength, and piezoelectric vibration frequency, classifies them into severity levels, and triggers a three-level protection mechanism.

6. The temperature control method for a liquid-cooled charging gun for electric vehicle charging as described in claim 5, characterized in that: The severity level classification triggers a three-tier protection mechanism, and the specific steps are as follows: A single parameter briefly deviates from the normal range, and the temperature rise trend initially appears but is controllable as a mild abnormality, triggering the first-level protection, reducing the charging power and increasing the coolant flow rate; The dual-parameter coordinated deviation and continuous abnormality, but without causing physical damage, constitutes a moderate abnormality. This triggers the secondary protection, switches to the backup liquid cooling circuit, and starts the redundant magnetic field generator. The three parameters continued to deteriorate irreversibly, and an extreme safety hazard was detected as a serious anomaly, triggering the three-level protection, severing the electrical connection of the charging gun, and sending an emergency stop signal.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the liquid-cooled charging gun temperature control method for electric vehicle charging as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the liquid-cooled charging gun temperature control method for electric vehicle charging as described in any one of claims 1 to 6.

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