Temperature control method of liquid cooling charging gun for charging electric vehicle

Abstract

The invention discloses a liquid cooling charging gun temperature control method for charging an electric vehicle, and relates to the technical field of liquid cooling control, and the method comprises the steps: carrying out the liquid cooling initialization, detecting the integrity of a bionic fractal microchannel and the distribution of phase change microcapsules, calibrating a magnetic field parameter, loading a pre-trained prediction model and a thermal resistance network model, and outputting a ready signal to a charging pile; after the charging pile communicates with a vehicle, according to charging power and environmental parameters, matching an initial flow, a magnetic field intensity threshold value and a piezoelectric vibration frequency, and activating a phase change microcapsule latent heat mode and pulse cooling; and executing pulse cooling and magnetic field fluctuation in a low-flow latent heat mode to remove sediments, recovering a full-flow mode when the temperature falls back, and monitoring an abnormal event to trigger grading protection at the same time. The dynamic heat dissipation performance and working condition adaptability of the liquid cooling charging gun are improved by constructing bionic fractal flow channel-phase change microcapsule-magnetic field regulation and control.

Description

Technical Field

[0001] The present invention relates to the field of liquid cooling control technology, and in particular to a temperature control method for a liquid cooling charging gun used for charging electric vehicles. Background Art

[0002] With the rapid advancement of electric vehicle fast-charging power, liquid-cooled charging guns face the dual challenges of dramatically increasing transient heat flux density and dynamic control under complex operating conditions. Currently, fixed-geometry flow channels and conventional coolants are used, with flow rate adjusted by a PID algorithm to achieve basic temperature control. However, due to issues such as low laminar heat transfer efficiency and delayed thermal response, this makes it difficult to adapt to the nonlinear heat conduction characteristics of high-power charging scenarios. In recent years, phase-change microcapsules and magnetic field collaborative heat dissipation technology have been gradually applied to the field of thermal management. The idea is to disperse magnetic particles in the coolant to enhance heat transfer. However, this approach does not address the issues of decreased latent heat utilization due to uneven microcapsule distribution and long-term performance degradation caused by the accumulation of flow channel sediment.

[0003] The lack of joint modeling capabilities for the structural integrity of bionic fractal flow channels, the distribution state of phase change materials, and the dynamic matching of magnetic field parameters makes it difficult to maintain precise temperature gradient control under high dynamic heat loads, and is unable to compensate for nonlinear disturbances such as microcapsule rupture and sediment adhesion in real time, resulting in local overheating risks and energy waste, seriously restricting the reliability and service life of liquid-cooled charging guns. Summary of the Invention

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

[0005] Therefore, the present invention provides a temperature control method for a liquid-cooled charging gun for charging electric vehicles to solve the problems of dynamic thermal resistance matching mismatch and multi-physical field collaborative heat dissipation efficiency optimization of high-power liquid-cooled charging guns under complex working conditions.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a temperature control method for a liquid-cooled charging gun for electric vehicle charging, which includes: liquid cooling initialization, detecting the integrity of bionic 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; After the charging pile communicates with the vehicle, it matches the initial flow rate, magnetic field intensity threshold, and piezoelectric vibration frequency according to the charging power and environmental parameters, activating the phase change microcapsule latent heat mode and pulse cooling; Real-time collection of charging gun temperature and coolant dielectric constant signals, input into the prediction model, and calculate the temperature rise trend prediction value; Update the thermal resistance network model and adjust the flow rate, magnetic field strength and piezoelectric vibration parameters through optimization algorithms. When the temperature reaches the preset threshold, it switches to low-flow latent heat mode and directionally controls the migration of magnetic particles. In low-flow latent heat mode, pulse cooling and magnetic field fluctuations 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.

[0007] As a preferred solution of the temperature control method of the liquid-cooled charging gun for electric vehicle charging according to the present invention, the liquid cooling is initialized, the integrity of the bionic fractal microchannel and the distribution of the phase change microcapsules are detected, the magnetic field parameters are calibrated and the pre-trained prediction model and thermal resistance network model are loaded, and the ready signal is output to the charging pile. The specific steps are as follows: Detect the integrity of the bionic fractal microchannel in the liquid-cooled charging gun and output the structural status signal; Detect the distribution state of phase change microcapsules in the coolant and generate microcapsule uniformity index; Calibrate the parameter range of the magnetic field generator and simultaneously load the pre-trained prediction model and thermal resistance network model; After loading is completed and the magnetic field parameter calibration is passed, a ready signal is output to the charging pile.

[0008] As a preferred solution of the temperature control method of the liquid-cooled charging gun for electric vehicle charging according to the present invention, the charging pile communicates with the vehicle, matches the initial flow rate, magnetic field intensity threshold and piezoelectric vibration frequency according to the charging power and environmental parameters, activates the latent heat mode of the phase change microcapsule and pulse cooling, and the specific steps are as follows: After the charging pile establishes communication with the vehicle, it obtains the vehicle battery's maximum charging power, current remaining power status, and battery cell temperature data; The system simultaneously collects ambient temperature and humidity data, combines the vehicle battery data to calculate the initial flow rate and magnetic field strength threshold, and uses the built-in impedance analysis of the piezoelectric ceramic to measure the piezoelectric vibration frequency in real time. Activate the latent heat mode of the phase change microcapsules and start the duty cycle control of pulse cooling.

[0009] As a preferred solution of the temperature control method of the liquid-cooled charging gun for electric vehicle charging according to the present invention, the real-time acquisition of the charging gun temperature and the coolant dielectric constant signal is input into the prediction model to calculate the temperature rise trend prediction value. The specific steps are as follows: Real-time acquisition of charging gun surface temperature signals and coolant dielectric constant signals, combined time-frequency domain analysis of the two signals, and extraction of frequency domain energy characteristics; Perform time domain difference operation on the coolant dielectric constant signal to calculate the coolant dielectric constant change rate; Calculate the dynamic confidence weight based on the frequency domain energy characteristics and the coolant dielectric constant change rate; The charging gun surface temperature signal, coolant dielectric constant signal and dynamic confidence weight are input into the prediction model to calculate the predicted value of future temperature rise trend.

[0010] As a preferred solution of the temperature control method of the liquid-cooled charging gun for electric vehicle charging according to the present invention, wherein: the updating of the thermal resistance network model, adjusting the flow rate, magnetic field strength and piezoelectric vibration frequency by an optimization algorithm, the specific steps are as follows: Input the thermal resistance network model according to the temperature rise trend prediction value, update the dynamic thermal resistance parameters, and generate the heat dissipation efficiency evaluation index; A multi-objective chaos optimization function is constructed based on the heat dissipation efficiency evaluation index, current coolant flow, magnetic field intensity, and piezoelectric vibration frequency. The multi-objective chaos optimization function is solved by using a chaotic particle swarm optimization-simulated annealing hybrid algorithm to obtain the optimal combination of flow rate, magnetic field intensity and piezoelectric vibration frequency parameters. The optimal flow rate, magnetic field strength and piezoelectric vibration frequency parameter combination is converted into a pulse width modulation signal and dynamically loaded to the liquid cooling pump, magnetic field generator and piezoelectric ceramic piece.

[0011] As a preferred solution of the temperature control method of the liquid-cooled charging gun for electric vehicle charging according to the present invention, when the temperature reaches a preset threshold, the method switches to a low-flow latent heat mode and directionally controls the migration of magnetic particles. The specific steps are as follows: Monitor the current temperature of the charging gun head in real time. When the current temperature reaches the preset temperature threshold, a low flow switching instruction is generated. Receiving a low flow switching instruction, the coolant flow rate is reduced from the initial flow rate according to a preset flow rate ratio, thereby activating the latent heat absorption 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 in a directional manner toward the high-temperature area.

[0012] As a preferred solution of the temperature control method of the liquid-cooled charging gun for electric vehicle charging according to the present invention, wherein: pulse cooling and magnetic field fluctuation are performed in the low-flow latent heat mode to remove deposits, and the full-flow mode is restored when the temperature drops. At the same time, abnormal events are monitored to trigger hierarchical protection. The specific steps are as follows: In low-flow latent heat mode, the start-stop cycle of pulse cooling is generated according to the real-time coolant flow, and the coolant pump is periodically started and stopped; According to the distribution of sediment in the flow channel and the current magnetic field strength, the magnetic field intensity fluctuation and direction are adaptively adjusted to drive the magnetic particles along the fractal flow channel branches to remove sediment; Continuously monitor the temperature changes of the charging gun head. 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; Receive the trigger signal, gradually restore the coolant flow from the low flow mode to the initial flow, and synchronously start the abnormal event scanning thread; Capture abnormal events of coolant flow, magnetic field strength and piezoelectric vibration frequency, classify them into severity levels, and trigger a three-level protection mechanism.

[0013] As a preferred solution of the temperature control method of the liquid-cooled charging gun for electric vehicle charging according to the present invention, the severity level is divided and the three-level protection mechanism is triggered. The specific steps are as follows: If a single parameter deviates from the normal range briefly, a temperature rise trend may appear, but it can be controlled as a mild abnormality, triggering the first-level protection, reducing charging power and increasing coolant flow; If the two parameters deviate in a coordinated and persistent manner but do not cause physical damage, it is considered a moderate anomaly and triggers secondary protection, switching to the backup liquid cooling circuit and activating the redundant magnetic field generator. The three parameters continue to deteriorate irreversibly, and an extreme safety hazard is detected as a serious abnormality, triggering the third-level protection, melting the electrical connection of the charging gun, and sending an emergency shutdown signal.

[0014] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the temperature control method for a liquid-cooled charging gun for charging an electric vehicle as described in the first aspect of the present invention is implemented.

[0015] In a third aspect, 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, any step of the temperature control method for a liquid-cooled charging gun for charging an electric vehicle as described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are: by constructing a bionic fractal flow channel-phase change microcapsule-magnetic field control, the dynamic heat dissipation performance and working condition adaptability of the liquid-cooled charging gun are improved. According to the pressure fluctuation characteristics of the fractal flow channel and the quantitative detection of the microcapsule distribution, the magnetic field action range is dynamically calibrated and the pre-trained thermal resistance network model is loaded to ensure that the system starts with the optimal parameters. Through the joint drive of the prediction model and real-time dielectric constant feedback, the coordinated optimization of the coolant flow rate, magnetic field intensity and piezoelectric vibration parameters is achieved, which effectively suppresses transient temperature rise and accelerates latent heat release. The turbulent effect stimulated by pulse cooling and the magnetic field gradient control form a collaborative sweeping mechanism, which improves the efficiency of particle migration in the fractal flow channel, and at the same time quickly isolates abnormal thermal shocks through a hierarchical protection mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is an overall flow chart of the temperature control method for the liquid-cooled charging gun used to charge electric vehicles in Example 1.

[0019] Figure 2 This is a flow chart of liquid cooling initialization of the temperature control method for a liquid-cooled charging gun for charging an electric vehicle in Example 1.

[0020] Figure 3 This is a flow chart for optimizing real-time temperature control parameters of the temperature control method for a liquid-cooled charging gun for charging electric vehicles in Example 1.

[0021] Figure 4 This is a flow chart of thermal protection mode switching of the temperature control method for a liquid-cooled charging gun for charging electric vehicles in Example 1. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0023] Example 1, reference Figures 1 to 4 This embodiment provides a temperature control method for a liquid-cooled charging gun for charging an electric vehicle, comprising the following steps: S1. Liquid cooling initialization, detection of bionic 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, output of ready signal to the charging pile.

[0024] Furthermore, the integrity of the bionic fractal microchannel in the liquid-cooled charging gun is detected and a structural status signal is output; Specifically, the quantitative detection of bionic fractal microchannel integrity is carried out by collecting the pressure fluctuation data of the fluid in the microchannel through a high-frequency pressure sensor array (sampling rate 10kHz) to detect the quantitative index of the structural integrity of the bionic fractal microchannel. The expression is: ; ; Where, It is expressed as a quantitative index of the structural integrity of the bionic fractal microchannel, Expressed as the total number of branches of the bionic fractal microchannel, represents the microchannel branch index, Indicates traversing and summing all branches of the microchannel, Represents the starting time of the time window, Represents the end time of the time window, Expressed as a time interval The instantaneous rate of change of the pressure fluctuation signal is integrated. represents the partial derivative, Represents the current time point, Expressed as microchannel branches in the time interval Fluid pressure fluctuation signal inside, Represents the current time point The differential of Expressed as Fluid pressure fluctuation signal of a microchannel branch At the time point The absolute value of the instantaneous rate of change, Expressed as microchannel branches at time points Fluid pressure fluctuation signal, Expressed as the variance suppression coefficient, , Expressed as Microchannel branch fluid pressure fluctuation signal In the time interval The variance within Expressed as a variance function, Time differential unit, Expressed as the total duration of the time window, Expressed as Microchannel branch fluid pressure fluctuation signal In the time interval The mean within Expressed as mean.

[0025] It should be noted that , the larger the value, the higher the risk of microchannel structure deformation or clogging.

[0026] Specifically, the pressure fluctuation signals of each branch of the bionic fractal microchannel within the time window are collected through a high-frequency pressure sensor array, and the calculation method of the fractal structure integrity index is adopted to jointly analyze the instantaneous change rate of the pressure fluctuation signal and the pressure signal variance to generate a quantitative value of the fractal structure integrity index; the instantaneous change rate is dynamically weighted by the pressure fluctuation signal variance suppression weight, and combined with the integral mean operation of the pressure fluctuation signals of each branch, the deformation or blockage risk of the bionic fractal microchannel is comprehensively evaluated; and a structural status signal containing the fractal structure integrity index value is output.

[0027] Detect the distribution state of phase change microcapsules in the coolant and generate microcapsule uniformity index; Specifically, laser scattering imaging is used to capture the distribution of phase change microcapsules in the coolant and detect the heterogeneity index, which is expressed as: ; Where, A quantitative index that represents the heterogeneity of the distribution of phase change microcapsules, Represents the total number of spatial discrete grids for laser scattering imaging, Represented as an index into a spatially discrete grid, Indicates traversal calculation of all spatial discrete grids, Expressed as a gradient operator, Indicates the The gray gradient vector of a spatial discrete grid, Indicates the The grayscale intensity of a spatially discrete grid, Expressed as the Euclidean norm, Expressed as The local variance of a spatially discretized grid, represents a very small constant to prevent division by zero, , represents the hyperbolic tangent function, represents the density sensitivity coefficient, , Expressed as The density of phase-change microcapsules in a spatially discrete grid, represents the global average density.

[0028] It should be noted that , , microcapsules are evenly distributed and operate normally; , localized slight aggregation, requiring observation or triggering level 3 protection; , the distribution is seriously uneven, triggering magnetic field intervention or shutdown for maintenance.

[0029] Specifically, laser scattering imaging is used to capture the spatial distribution grayscale image of phase change microcapsules in the coolant, and the grayscale image is divided into several discrete grids. The L2 norm of the grayscale gradient vector of each grid is calculated, and the local normalization factor is generated by combining the local variance of the grayscale value in the grid and the anti-zero constant; based on the ratio of the microcapsule density of each grid to the global average density, the density difference is nonlinearly mapped by the hyperbolic tangent function to suppress the influence of extreme density deviation; the normalized gradient intensity of all grids and the density nonlinear mapping results are weighted summed to generate a quantitative index characterizing the uniformity of the phase change microcapsule distribution; and a distribution state signal containing the numerical value of the microcapsule uniformity index is output.

[0030] Calibrate the parameter range of the magnetic field generator and simultaneously load the pre-trained prediction model and thermal resistance network model; Specifically, based on the real-time numerical values ​​of the bionic fractal microchannel integrity index and the microcapsule uniformity index, the upper and lower limits of the dynamic adjustment range of the magnetic field intensity are calculated through the calibrated fractal integrity weight coefficient, the microcapsule distribution weight coefficient and the logarithmic smoothing coefficient; the parameter matrix of the pre-trained prediction model and the thermal resistance network model are synchronously loaded from the embedded memory to the control unit, the pre-trained prediction model includes the weight parameters of the frequency domain linear term and the time domain nonlinear term of the temperature prediction, and the thermal resistance network model includes the dynamic conduction coefficient between the equivalent thermal resistance nodes; when the dynamic adjustment range of the magnetic field intensity 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 mark is generated, and a parameter calibration result including the boundary value of the dynamic adjustment range of the magnetic field intensity and the calibration completion mark is output.

[0031] Integrity of microchannels based on bionic fractal and distribution of phase change microcapsules , generating the dynamic range of magnetic field intensity, the expression is: ; Where, Expressed as the calibrated magnetic field strength, , Expressed as the basic magnetic field strength, , Expressed as the structural integrity weight coefficient of the bionic fractal microchannel, , Expressed as the heterogeneity weight coefficient of phase change microcapsule distribution, , Expressed as a logarithmic smoothing coefficient, , represents the natural logarithm function, It is expressed as the combined effect of the structural integrity of the bionic fractal microchannel and the heterogeneity of the distribution of phase change microcapsules.

[0032] It should be noted that the structural integrity weight coefficient of the bionic fractal microchannel is obtained through the optimization calibration of fractal flow channel damage and heat dissipation efficiency, the non-uniformity weight coefficient of the phase change microcapsule distribution is determined by optimizing the balance between microcapsule distribution and magnetic field energy consumption, and the logarithmic smoothing coefficient is determined through numerical stability analysis and extreme working conditions verification.

[0033] After loading is completed and the magnetic field parameter calibration is passed, a ready signal is output to the charging pile.

[0034] Specifically, after loading is completed and the magnetic field parameter calibration is passed, the structural status signal, microcapsule uniformity index, magnetic field parameter dynamic adjustment range boundary value and the loading status of the pre-trained prediction model and thermal resistance network model are encapsulated into a ready signal data packet through the CAN bus communication protocol, which includes the bionic fractal microchannel integrity index value, microcapsule uniformity index value, magnetic field intensity upper limit value, magnetic field gradient switching frequency lower limit value, pre-trained prediction model verification code, thermal resistance network model verification code and status identification bit; the ready signal data packet that passes the verification is encoded and transmitted to the charging pile control unit through the high-speed CAN channel at a baud rate of 500kbps, triggering the charging pile to start the charging handshake process.

[0035] S2. After the charging pile communicates with the vehicle, it matches the initial flow rate, magnetic field intensity threshold and piezoelectric vibration frequency according to the charging power and environmental parameters, activating the latent heat mode and pulse cooling of the phase change microcapsules.

[0036] Furthermore, after establishing communication between the charging pile and the vehicle, the maximum charging power, current remaining power status and battery cell temperature data of the vehicle battery are obtained; 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, measured in kilowatts (kW), and is dynamically limited based on the battery cell material characteristics and thermal boundary conditions; The current remaining power status refers to the percentage of the battery's current available capacity to the total rated capacity (unit: %), which is used to represent the battery's real-time energy reserve level; Battery cell temperature data refers to the real-time temperature measurement value (unit: °C) of each individual battery cell in the battery pack, which is used to monitor the thermal state and heat dissipation requirements of the battery during operation.

[0037] Specifically, after the charging pile establishes communication with the vehicle, it sends an extended diagnostic service request to the vehicle battery through power line communication or wireless communication protocol. The request message contains the maximum charging power parameter identifier, current remaining power status parameter identifier and battery cell temperature data parameter identifier of the vehicle battery; the vehicle battery returns a response message in an 8-byte data frame format through the CAN FD bus, with the maximum charging power data occupying bytes 1-2 (unit: 0.1kW, big endian), the current remaining power status occupying byte 3 (unit: 1%), and the battery cell temperature data occupying 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 a decimal physical quantity. After verifying the validity of the data, it is stored in a non-volatile memory to generate a battery status data set containing the maximum charging power value, the current remaining power status value and a list of battery cell temperature values.

[0038] The system simultaneously collects ambient temperature and humidity data, combines the vehicle battery data to calculate the initial flow rate and magnetic field strength threshold, and uses the built-in impedance analysis of the piezoelectric ceramic to measure the piezoelectric vibration frequency in real time. It should be noted that the ambient temperature and humidity data refer to the air temperature value (unit: °C) and relative humidity percentage value (unit: %) of the external environment where the charging pile is located. These data are collected in real time by temperature sensors and humidity sensors and converted into digital signals. The initial flow rate and magnetic field strength thresholds refer to the coolant flow rate baseline value and magnetic field strength safety upper limit value generated by the heat load calculation formula and magnetic field dynamic calibration formula based on the maximum charging power value of the vehicle battery, the current remaining power state value and the ambient temperature data. They are used to constrain the activation conditions of the phase change microcapsule latent heat mode; Piezoelectric vibration frequency refers to the periodic fluctuation characteristics of mechanical vibration generated by piezoelectric ceramic pieces during pulse cooling. After calculating the reference frequency through equivalent stiffness and equivalent mass, it is optimized and generated in combination with the predicted value of temperature rise trend. It is used to control the turbulence intensity of the coolant and the bubble removal efficiency.

[0039] Specifically, the ambient temperature data (unit: ℃) and humidity data (unit: %) are synchronously collected through the temperature sensor and the humidity sensor at a sampling rate of 5 times per second. Combined with the maximum charging power value, the current remaining power status value and the 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 value, and the maximum charging power value and the current remaining power status value are substituted into the magnetic field dynamic calibration formula to calculate the magnetic field strength threshold; the built-in impedance analysis of the piezoelectric ceramic piece measures the piezoelectric vibration resonant frequency waveform at a sampling rate of 10kHz, and the piezoelectric vibration frequency reference value is calculated by the equivalent stiffness and equivalent mass. The frequency domain constraint is imposed on the piezoelectric vibration frequency reference value in combination with the temperature rise trend prediction value to generate the optimized piezoelectric vibration frequency execution value.

[0040] Activate the latent heat mode of the phase change microcapsules and start the duty cycle control of pulse cooling.

[0041] Specifically, when the magnetic field strength threshold exceeds the preset safety threshold lower limit and the initial flow value is within the allowable range output by 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, and the magnetic field strength is dynamically adjusted to within the ±5% error band of the magnetic field strength threshold; the duty cycle control of the pulse cooling calculates the duty cycle reference value inversely proportionally by multiplying the piezoelectric vibration frequency execution value and the temperature rise trend prediction value, and converts the duty cycle reference value into the on-time ratio of the pulse width modulation signal, which is 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, and 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.

[0042] S3. Collect the charging gun temperature and coolant dielectric constant signals in real time, input them into the prediction model, and calculate the temperature rise trend prediction value.

[0043] 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 jointly analyzed in the time and frequency domains to extract the frequency domain energy characteristics; 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 collected at a frequency of 1kHz through a capacitive sensor.

[0044] Specifically, the surface temperature signal of the charging gun is collected in real time through a platinum resistance temperature sensor at a sampling rate of 100Hz, and the coolant dielectric constant signal is collected in real time through an interdigital capacitance sensor at a sampling rate of 1kHz; the surface temperature signal of the charging gun is subjected to a Hanning window and a short-time Fourier transform is performed, and the spectrum energy integral value in the low-frequency band of 0.1-1kHz is extracted as the cooling pump vibration correlation feature, and the spectrum energy peak value in the high-frequency band of 1-5kHz is extracted as the contact resistance arc correlation feature; after performing a sliding average filter on the coolant dielectric constant signal, the differential absolute value sequence of adjacent sampling points is calculated to generate a dielectric constant fluctuation intensity index; the low-frequency band spectrum energy integral value, the high-frequency band spectrum energy peak value and the dielectric constant fluctuation intensity index are input into a pre-trained frequency domain energy feature mapping table, and the frequency domain energy feature characterizing the thermal fluctuation stability is output.

[0045] Perform time domain difference operation on the coolant dielectric constant signal to calculate the coolant dielectric constant change rate; It should be noted that the instantaneous rate of change is calculated based on the ratio of the absolute value of the difference in the coolant dielectric constant values ​​between two adjacent sampling points (time interval 0.1 seconds) to the time interval, and the instantaneous rate of change sequence is input into a sliding average filter (filter window length 0.5 seconds) for noise suppression, and the smoothed coolant dielectric constant rate of change value is output.

[0046] Calculate the dynamic confidence weight based on the frequency domain energy characteristics and the coolant dielectric constant change rate; Specifically, the frequency domain energy stability coefficient is calculated by the weighted sum of the low-frequency energy proportion value and the high-frequency energy sudden change number value, and the sum of the frequency domain energy stability coefficient and the absolute value of the coolant dielectric constant change rate is used as the denominator, and the frequency domain energy stability coefficient is used as the numerator to generate the initial value of the dynamic confidence weight; the product of the dielectric constant-energy coupling coefficient value and the coolant dielectric constant change rate value is used as the correction factor, and exponential smoothing is applied to the initial value of the dynamic confidence weight, and the output is normalized to the dynamic confidence weight value in the [0,1] interval.

[0047] The charging gun surface temperature signal, coolant dielectric constant signal and dynamic confidence weight are input into the prediction model to calculate the predicted value of future temperature rise trend.

[0048] It should be noted that the surface temperature signal of the charging gun is aligned with the coolant dielectric constant signal in the time-frequency domain after time series normalization, and the dynamic confidence weight is used as a weighting coefficient to dynamically scale the frequency domain energy characteristic components of the two signals; the scaled frequency domain energy characteristic components are converted into a multidimensional feature vector (including the proportion of low-frequency energy, the number of high-frequency energy sudden changes and the dielectric constant-energy coupling coefficient) through a pre-trained frequency domain energy feature mapping table. The multidimensional feature vector and the coolant dielectric constant change rate value are jointly input into the nonlinear regression layer of the prediction model to output the temperature rise trend prediction value.

[0049] Specifically, the predicted value of future temperature rise trend is calculated as follows: ; Where, Expressed as the predicted value of temperature rise trend, Expressed as traversing all frequency domain energy features, Expressed as the total number of frequency domain energy features, Represents the index of frequency domain energy feature, Expressed as The weight coefficient of the frequency domain energy feature, represents the real part of a complex number, Represents the charging gun surface temperature signal The Fourier transform of the frequency The complex value at Represented as the charging gun surface temperature signal, Expressed as The frequency of the frequency domain energy feature, Expressed as a dynamic confidence weight, , Expressed as the nonlinear gain coefficient, , Expressed as the coolant dielectric constant signal, Expressed as the rate of change of the coolant dielectric constant, Expressed as the temperature suppression factor, , Indicates the charging gun surface at the current time point Real-time temperature, Dynamic data fusion algorithm expressed as Bayesian theorem, Represented as a historical temperature data series, It is expressed as the prior temperature rise trend prediction value.

[0050] It should be noted that the The weight coefficients of the frequency domain energy features are obtained by fitting the training data (such as historical temperature rise data); the dynamic confidence weight is generated by the real-time data fusion calculation of the frequency domain energy ratio and the dielectric constant change rate; the nonlinear gain coefficient is calibrated by the dynamic response of the dielectric constant 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 to balance the attenuation intensity of the nonlinear term due to temperature.

[0051] S4. Update the thermal resistance network model and adjust the flow rate, magnetic field strength and piezoelectric vibration parameters through the optimization algorithm. When the temperature reaches the preset threshold, switch to the low flow latent heat mode and directionally control the migration of magnetic particles.

[0052] Input the thermal resistance network model according to the temperature rise trend prediction value, update the dynamic thermal resistance parameters, and generate the heat dissipation efficiency evaluation index; It should be noted that the dynamic thermal resistance parameter refers to the equivalent thermal resistance value between nodes in the thermal resistance network model (unit: K / W), which is dynamically adjusted with the coolant flow rate and the phase change state of the microcapsules.

[0053] The heat dissipation efficiency evaluation index refers to the ratio of the actual heat dissipation of the coolant to the theoretical maximum heat dissipation capacity.

[0054] A multi-objective chaos optimization function is constructed based on the heat dissipation efficiency evaluation index, current coolant flow, magnetic field intensity, and piezoelectric vibration frequency. Specifically, a multi-objective chaos optimization function is constructed, and the expression is: ; Where, Expressed as a multi-objective optimization function, Indicates the coolant flow rate, Expressed as the piezoelectric vibration frequency, Expressed as time The predicted value of temperature rise trend is Expressed as time Dynamic thermal resistance parameters, Time-differentiated variable, represents the energy consumption weight coefficient, , represents the square term of flow rate, represents the cubic term of the calibrated magnetic field strength, Expressed as an exponential decay term of the piezoelectric vibration frequency, Expressed as the vibration attenuation coefficient, , Expressed as the noise suppression weight coefficient, , Represents the temperature monitoring point index, Expressed as the total number of temperature monitoring points, Indicates traversing all temperature monitoring points. Expressed as The second-order derivative of temperature space at each temperature monitoring point.

[0055] It should be noted that the energy consumption weight coefficient is calibrated through the Pareto frontier optimization of energy consumption and heat dissipation efficiency to balance the flow and magnetic field energy consumption; the vibration attenuation coefficient is calibrated through the exponential attenuation of piezoelectric vibration frequency and energy consumption to suppress high-frequency vibration loss; the noise suppression weight coefficient is calibrated through the uniformity of infrared thermal imaging temperature field.

[0056] The multi-objective chaos optimization function is solved by using a chaotic particle swarm optimization-simulated annealing hybrid algorithm to obtain the optimal combination of flow rate, magnetic field intensity and piezoelectric vibration frequency parameters. Specifically, the particle positions of the particle swarm algorithm are initialized to a three-dimensional vector combination of the current coolant flow value, magnetic field intensity value and piezoelectric vibration frequency value, the initial value of the inertia weight, the individual learning factor and the group learning factor are set, and a perturbation sequence of the inertia weight is generated based on the chaotic mapping; the simulated annealing algorithm sets the initial value of the annealing temperature and the annealing temperature scheduling coefficient, and adopts the Metropolis criterion as the acceptance criterion; in each round of iteration, the particle swarm algorithm calculates the fitness value according to the multi-objective chaotic optimization function and updates the particle velocity and position, then the simulated annealing algorithm applies random perturbations to the particle positions and calculates the improvement rate of the fitness value after the perturbation; and outputs the optimal parameter combination of flow value, magnetic field intensity value and piezoelectric vibration frequency value.

[0057] The optimal flow rate, magnetic field strength and piezoelectric vibration frequency parameter combination is converted into a pulse width modulation signal and dynamically loaded to the liquid cooling pump, magnetic field generator and piezoelectric ceramic piece.

[0058] Specifically, the optimal flow rate is converted into the duty cycle value of the pulse width modulation signal of the liquid cooling pump through a predefined flow rate-duty cycle mapping table, the optimal magnetic field strength is generated by the voltage-frequency conversion formula of the magnetic field generator to generate the pulse width modulation signal frequency value and amplitude value of the magnetic field generator, and the optimal piezoelectric vibration frequency is generated by the resonant frequency-drive signal frequency matching algorithm of the piezoelectric ceramic piece to generate the pulse width modulation signal frequency value of the piezoelectric ceramic piece; during the loading process, the timer interrupt is used to ensure the phase synchronization of the pulse width modulation signals of the liquid cooling pump, magnetic field generator and piezoelectric ceramic piece, and after the loading is completed, the register value is read back to verify the consistency of the pulse width modulation signal parameters with the optimal flow rate, magnetic field strength and piezoelectric vibration frequency.

[0059] Monitor the current temperature of the charging gun head in real time. When the current temperature reaches the preset temperature threshold, a low flow switching instruction is generated. Specifically, a platinum resistance temperature sensor is used to continuously collect the temperature analog signal of the contact area of ​​the charging gun head at a fixed sampling rate, and the temperature analog signal is subjected to sliding average filtering and converted into a digital temperature value; the digital temperature value is compared with a preset temperature threshold, and when the digital temperature value continuously exceeds the preset temperature threshold for a duration reaching the anti-shake delay time, a low-flow switching instruction is generated.

[0060] Receiving a low flow switching instruction, the coolant flow rate is reduced from the initial flow rate according to a preset flow rate ratio, thereby activating the latent heat absorption mode of the phase change microcapsules; Specifically, after receiving the low flow switching instruction, the initial flow value and the preset flow ratio value are input into the flow ratio attenuation function to generate a target flow value that decreases according to an exponential curve; 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, and the high-frequency alternating magnetic field generator is synchronously triggered to output a sinusoidal wave magnetic field with a frequency of 1kHz-5kHz, and the magnetic field strength is adjusted to activate the latent heat absorption mode of the phase change microcapsules.

[0061] 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 in a directional manner toward the high-temperature area.

[0062] 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; the magnetic particles are driven to migrate directionally along the magnetic field gradient direction toward the high-temperature area of ​​the charging gun head.

[0063] S5. Perform pulse cooling and magnetic field fluctuations in low-flow latent heat mode to remove sediments, and restore full-flow mode when the temperature drops. At the same time, monitor abnormal events to trigger graded protection.

[0064] Furthermore, in low-flow latent heat mode, a pulse cooling start-stop cycle is generated according to the real-time coolant flow rate, and the coolant pump is periodically started and stopped; 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 determined by the ratio of the real-time flow rate to the temperature drop rate in the low-flow mode.

[0065] Specifically, the start and stop time interval of the coolant pump is set through the timer interrupt controller to generate a periodic start and stop control signal; the periodic start and stop control signal is converted into a pulse width modulation signal duty cycle value of the liquid cooling pump. When the duty cycle value is 0, the coolant pump is turned off, and when the duty cycle value is 100%, the coolant pump is operated at full power; the pulse width modulation signal duty cycle value is loaded into the power drive circuit of the liquid cooling pump through the digital-to-analog converter to perform periodic start and stop operations of the coolant pump.

[0066] According to the distribution of sediment in the flow channel and the current magnetic field strength, the magnetic field intensity fluctuation and direction are adaptively adjusted to drive the magnetic particles along the fractal flow channel branches to remove sediment; Specifically, the Hall sensor array is used to collect the sediment distribution status data in the flow channel (including the sediment density value and position coordinates) in real time, and the magnetic field intensity fluctuation amplitude and the magnetic field direction angle adjustment are calculated; the magnetic field intensity fluctuation amplitude value is converted into the current fluctuation amplitude parameter of each coil through the current amplitude control algorithm of the multi-coil array drive circuit, and the magnetic field direction angle adjustment is generated into the current direction correction parameter of each coil through the polar coordinate-Cartesian coordinate conversion algorithm; the magnetic particles are driven to migrate in a directional manner along the sediment-enriched area of ​​the fractal flow channel branch.

[0067] Continuously monitor the temperature changes of the charging gun head. 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; Specifically, a platinum resistance temperature sensor is used to continuously collect the temperature analog signal of the contact area of ​​the charging gun head at a fixed sampling rate, and the temperature analog signal is subjected to sliding average filtering and converted into a digital temperature value; the digital temperature value is compared with a preset temperature threshold. When the digital temperature value is continuously lower than the preset temperature threshold for a duration reaching the anti-shake delay time, it is determined to be a temperature drop state, and a full flow recovery trigger signal is generated including the initial flow value, full flow recovery instruction and timestamp.

[0068] Receive the trigger signal, gradually restore the coolant flow from the low flow mode to the initial flow, and synchronously start the abnormal event scanning thread; Specifically, after receiving the full flow recovery trigger signal, the initial flow value and the full flow recovery instruction are read, and after verifying the validity of the instruction 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; It should be noted that the abnormal event scanning thread refers to a monitoring process that continuously polls the real-time parameters of coolant flow, magnetic field strength and piezoelectric vibration frequency, and compares them item by item with the preset safety threshold range; when it is detected that the parameters exceed the safety threshold range, the thread generates an abnormal event code and triggers the hierarchical protection mechanism.

[0069] Capture abnormal events of coolant flow, magnetic field strength and piezoelectric vibration frequency, classify them into severity levels, and trigger a three-level protection mechanism.

[0070] It should be noted that by comparing the real-time parameters of coolant flow, magnetic field strength and piezoelectric vibration frequency with the preset safety range, if the flow deviation exceeds 10%, the magnetic field strength deviation exceeds ±15% or the frequency offset exceeds 5%, the abnormality type will be marked; according to the abnormality type, it is divided into three levels: mild, moderate and severe abnormalities, triggering the three-level protection mechanism.

[0071] If a single parameter deviates from the normal range briefly, a temperature rise trend may appear, but it can be controlled as a mild abnormality, triggering the first-level protection, reducing charging power and increasing coolant flow; It should be noted that when any of the parameters, including coolant flow, magnetic field strength and piezoelectric vibration frequency, shows a single parameter abnormality and lasts for more than 2 seconds, the first-level protection is triggered.

[0072] If the two parameters deviate in a coordinated and persistent manner but do not cause physical damage, it is considered a moderate anomaly and triggers secondary protection, switching to the backup liquid cooling circuit and activating the redundant magnetic field generator. It should be noted that when any two parameters of coolant flow, magnetic field strength and piezoelectric vibration frequency show dual parameter abnormality or a single parameter lasts for more than 5 seconds, the second-level protection is triggered.

[0073] The three parameters continue to deteriorate irreversibly, and an extreme safety hazard is detected as a serious abnormality, triggering the third-level protection, melting the electrical connection of the charging gun, and sending an emergency shutdown signal.

[0074] It should be noted that when the three parameters of coolant flow, magnetic field strength and piezoelectric vibration frequency are abnormal or the voltage drops by more than 30%, the third level protection is triggered.

[0075] This embodiment also provides a computer device, which is suitable for the liquid-cooled charging gun temperature control method for charging electric vehicles, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the liquid-cooled charging gun temperature control method for charging electric vehicles proposed in the above embodiment.

[0076] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device 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 an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0077] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the temperature control method for a liquid-cooled charging gun for charging an electric vehicle as proposed in the above embodiment; 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 read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0078] In summary, the present invention improves the dynamic heat dissipation performance and working condition adaptability of the liquid-cooled charging gun by: constructing a bionic fractal flow channel-phase change microcapsule-magnetic field control. According to the pressure fluctuation characteristics of the fractal flow channel and the quantitative detection of the microcapsule distribution, the magnetic field action range is dynamically calibrated and the pre-trained thermal resistance network model is loaded to ensure that the system starts with the optimal parameters. Through the joint drive of the prediction model and real-time dielectric constant feedback, the coordinated optimization of the coolant flow rate, magnetic field intensity and piezoelectric vibration parameters is achieved, which effectively suppresses transient temperature rise and accelerates latent heat release. The turbulent effect stimulated by pulse cooling and the magnetic field gradient control form a collaborative sweeping mechanism, which improves the efficiency of particle migration in the fractal flow channel, and at the same time quickly isolates abnormal thermal shocks through a hierarchical protection mechanism.

[0079] 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 the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A temperature control method for a liquid-cooled charging gun for electric vehicle charging, characterized in that: include, Liquid cooling is initialized, the integrity of the bionic fractal microchannel and the distribution of phase-change microcapsules are detected, magnetic field parameters are calibrated, and pre-trained prediction models and thermal resistance network models are loaded, and 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 intensity threshold, and piezoelectric vibration frequency according to the charging power and environmental parameters, activating the phase change microcapsule latent heat mode and pulse cooling; Real-time collection of charging gun temperature and coolant dielectric constant signals, input into the prediction model, and calculate the temperature rise trend prediction value; Update the thermal resistance network model and adjust the flow rate, magnetic field strength and piezoelectric vibration parameters through optimization algorithms. When the temperature reaches the preset threshold, it switches to low-flow latent heat mode and directionally controls the migration of magnetic particles. In low-flow latent heat mode, pulse cooling and magnetic field fluctuations 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.

2. The temperature control method for a liquid-cooled charging gun for electric vehicle charging according to claim 1, wherein: The liquid cooling initialization, detection of bionic 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, and output of ready signal to the charging pile are as follows: Detect the integrity of the bionic fractal microchannel in the liquid-cooled charging gun and output the structural status signal; Detect the distribution state of phase change microcapsules in the coolant and generate microcapsule uniformity index; Calibrate the parameter range of the magnetic field generator and simultaneously load the pre-trained prediction model and thermal resistance network model; After loading is completed and the magnetic field parameter calibration is passed, a ready signal is output to the charging pile.

3. The temperature control method for a liquid-cooled charging gun for charging an electric vehicle according to claim 2, wherein: After the charging pile communicates with the vehicle, the initial flow rate, magnetic field intensity threshold and piezoelectric vibration frequency are matched according to the charging power and environmental parameters to activate the phase change microcapsule latent heat mode and pulse cooling. The specific steps are as follows: After the charging pile establishes communication with the vehicle, it obtains the vehicle battery's maximum charging power, current remaining power status, and battery cell temperature data; The system simultaneously collects ambient temperature and humidity data, combines the vehicle battery data to calculate the initial flow rate and magnetic field strength threshold, and uses the built-in impedance analysis of the piezoelectric ceramic to measure the piezoelectric vibration frequency in real time. Activate the latent heat mode of the phase change microcapsules and start the duty cycle control of pulse cooling.

4. The temperature control method for a liquid-cooled charging gun for electric vehicle charging according to claim 3, wherein: The real-time collection of charging gun temperature and coolant dielectric constant signals, input into the prediction model, and calculation of the temperature rise trend prediction value are as follows: Real-time acquisition of charging gun surface temperature signals and coolant dielectric constant signals, combined time-frequency domain analysis of the two signals, and extraction of frequency domain energy characteristics; Perform time domain difference operation on the coolant dielectric constant signal to calculate the coolant dielectric constant change rate; Calculate the dynamic confidence weight based on the frequency domain energy characteristics and the coolant dielectric constant change rate; The charging gun surface temperature signal, coolant dielectric constant signal and dynamic confidence weight are input into the prediction model to calculate the predicted value of future temperature rise trend.

5. The temperature control method for a liquid-cooled charging gun for charging an electric vehicle according to claim 4, wherein: The thermal resistance network model is updated to adjust the flow rate, magnetic field strength and piezoelectric vibration frequency through the optimization algorithm. The specific steps are as follows: Input the thermal resistance network model according to the temperature rise trend prediction value, update the dynamic thermal resistance parameters, and generate the heat dissipation efficiency evaluation index; A multi-objective chaos optimization function is constructed based on the heat dissipation efficiency evaluation index, current coolant flow, magnetic field intensity, and piezoelectric vibration frequency. The multi-objective chaos optimization function is solved by using a chaotic particle swarm optimization-simulated annealing hybrid algorithm to obtain the optimal combination of flow rate, magnetic field intensity and piezoelectric vibration frequency parameters. The optimal flow rate, magnetic field strength and piezoelectric vibration frequency parameter combination is converted into a pulse width modulation signal and dynamically loaded to the liquid cooling pump, magnetic field generator and piezoelectric ceramic piece.

6. The temperature control method for a liquid-cooled charging gun for charging an electric vehicle according to claim 5, wherein: 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: Monitor the current temperature of the charging gun head in real time. When the current temperature reaches the preset temperature threshold, a low flow switching instruction is generated. Receiving a low flow switching instruction, the coolant flow rate is reduced from the initial flow rate according to a preset flow rate ratio, thereby activating the latent heat absorption 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 in a directional manner toward the high-temperature area.

7. The temperature control method for a liquid-cooled charging gun for electric vehicle charging according to claim 6, characterized in that: The method of performing pulse cooling and magnetic field fluctuation to remove sediments in low-flow latent heat mode and resuming full-flow mode when the temperature drops, while monitoring abnormal events to trigger graded protection, is as follows: In low-flow latent heat mode, the start-stop cycle of pulse cooling is generated according to the real-time coolant flow, and the coolant pump is periodically started and stopped; According to the distribution of sediment in the flow channel and the current magnetic field strength, the magnetic field intensity fluctuation and direction are adaptively adjusted to drive the magnetic particles along the fractal flow channel branches to remove sediment; Continuously monitor the temperature changes of the charging gun head. 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; Receive the trigger signal, gradually restore the coolant flow from the low flow mode to the initial flow, and synchronously start the abnormal event scanning thread; Capture abnormal events of coolant flow, magnetic field strength and piezoelectric vibration frequency, classify them into severity levels, and trigger a three-level protection mechanism.

8. The temperature control method for a liquid-cooled charging gun for electric vehicle charging according to claim 7, wherein: The classification of severity levels triggers the three-level protection mechanism. The specific steps are as follows: If a single parameter deviates from the normal range briefly, a temperature rise trend may appear, but it can be controlled as a mild abnormality, triggering the first-level protection, reducing charging power and increasing coolant flow; If the two parameters deviate in a coordinated and persistent manner but do not cause physical damage, it is considered a moderate anomaly and triggers secondary protection, switching to the backup liquid cooling circuit and activating the redundant magnetic field generator. The three parameters continue to deteriorate irreversibly, and an extreme safety hazard is detected as a serious abnormality, triggering the third-level protection, melting the electrical connection of the charging gun, and sending an emergency shutdown signal.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the temperature control method of the liquid-cooled charging gun for charging an electric vehicle are implemented as described in any one of claims 1 to 8.

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

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