Socket with safety protection

Abstract

The invention discloses a socket with safety protection. The socket comprises a multi-mode thermal sensing array, and a fusion array of a miniature infrared thermal imaging sensor array and a distributed fiber grating temperature sensor which are integrated at key points in the socket; according to the active locking mechanism based on the thermal deformation prediction model, a micro servo motor, a ball screw, a pre-tightening spring and a spring bolt form an electronic lock, a shape memory alloy wire is used for rapid and silent emergency unlocking, and the micro servo motor is used for high-precision conventional locking; the dynamic thermal safety boundary algorithm module is used for dynamically calculating the optimal temperature upper limit and the optimal power upper limit of each component in the current state according to factors such as a real-time temperature field, predicted thermal deformation, current charging power and historical charging data; the main controller is used for transmitting a large amount of real-time temperature field data. Through multi-mode sensing, predictive modeling and master-redundancy cooperative execution, thermal safety and mechanical reliability of the charging interface under extreme working conditions are guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of new energy technology, specifically, it relates to a socket with safety protection. Background Technology

[0002] Car charging sockets are dedicated interface devices used for charging electric vehicles. With the increasing popularity of electric vehicles, their safety has also become a major concern.

[0003] Existing charging socket (charging gun socket) systems for new energy vehicles have the following inherent defects in terms of thermal management and mechanical locking: Commonly, a single or a few NTC (negative temperature coefficient) thermistors are used, attached to the base of the metal pins. These can only sense the temperature at a single point and cannot capture the temperature distribution across the entire connector, resulting in a monitoring blind spot. Electronic locks are typically driven by simple DC motors or electromagnets and only receive "lock" or "unlock" commands. Their control logic is loosely coupled with the temperature monitoring system and usually only operates before and after charging begins. The temperature control and locking logic is simple, typically "if T > T_set, then reduce power or cut off power; if there is a fault, then alarm." The information from each subsystem (power supply, temperature control, locking) is isolated, lacking intelligent decision-making based on multi-parameter fusion. Emergency unlocking typically relies on a reverse drive motor or a backup mechanical pull rope. The former cannot function if the motor drive circuit fails, while the latter requires manual operation, which may be too late or impossible in an emergency. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide a socket with safety protection.

[0005] To achieve the aforementioned objectives, the technical solution adopted by this invention includes: A socket with safety protection includes a socket body, a vehicle BMS, a charging pile controller, a heat sink disposed within the socket body, and further includes: Multimodal thermal sensing array (MTPA array) is a fusion array that integrates a miniature infrared thermal imaging sensor array (non-contact absolute temperature measurement) and a distributed fiber optic grating temperature sensor (FBG, contact measurement point, anti-electromagnetic interference) at key points inside the socket body (such as the root of each pin and next to the latch drive mechanism). The active locking mechanism based on the thermal deformation prediction model (ALM-TDP actuator) includes a micro servo motor, a ball screw, a preload spring made of shape memory alloy (SMA) wire, and a locking tongue. The output end of the micro servo motor is connected to the ball screw. The preload spring is mounted on the ball screw slider, and the locking tongue is mounted on the preload spring. The micro servo motor, ball screw, preload spring, and locking tongue constitute an electronic lock. The shape memory alloy wire is used for fast and silent emergency unlocking, and the micro servo motor is used for high-precision conventional locking. The dynamic thermal safety boundary algorithm module dynamically calculates the optimal temperature and power limits for each component under the current state based on factors such as real-time temperature field, predicted thermal deformation, current charging power, and historical charging data. The main controller interacts with the vehicle BMS and charging pile controller via the charging communication CAN bus (following protocols such as GB / T27930) to send power reduction requests or receive charging status information. The main controller is connected to the active locking mechanism based on the thermal deformation prediction model via the CAN bus to transmit locking status, execution feedback, and receive precise locking commands from the main controller. The main controller is connected to the multimodal thermal sensing array via the high-speed SPI bus to transmit a large amount of real-time temperature field data.

[0006] Furthermore, the main controller communicates and interacts with the vehicle BMS, charging pile controller, active locking mechanism based on thermal deformation prediction model, and multimodal thermal sensing array through the following methods: S1. Initialization and handshake: The charging gun is inserted, and the active locking mechanism based on the thermal deformation prediction model executes the standard locking procedure. The main controller starts up, and the multimodal thermal sensing array begins scanning to establish an initial temperature field baseline; The main controller establishes communication with the vehicle BMS and the charging pile controller to obtain the parameters of this charging request (rated voltage, current, etc.). S2, Real-time Monitoring and Modeling Cycle: S2.1, The multimodal thermal sensing array acquires global temperature data T_array(x,y,z,t) at a high frequency (e.g., 10Hz). S2.2 The main controller runs the thermal field reconstruction algorithm to reconstruct the discrete point data into a 3D temperature field model. A gradient calculation algorithm is used to identify regions where the temperature change rate exceeds the threshold ▽T_threshold, marking potential heat. S2.3 The main controller inputs the predicted deformation data ΔL_predicted of the key components into the built-in finite element thermo-mechanical coupling model. This model quickly calculates and outputs the prediction results: the predicted deformation ΔL_predicted of the key components and the predicted stress distribution S_max, resulting in: ΔL_predicted = α * L0 * (T_current - T_initial) + β* (dT / dt). Where α is the coefficient of thermal expansion of the material, L0 is the initial length, β is the model correction coefficient (obtained from historical data training), dT / dt is the rate of temperature change, T_current is the current temperature, which is a characteristic temperature value most relevant to the thermal deformation of the latch mechanism, obtained by the multimodal thermal sensing array (MTPA), and T_initial is the initial temperature, which is the temperature of the charging socket latch mechanism when it is in an ambient temperature equilibrium state before the charging process begins. = max(S_predicted_1, S_predicted_2, ..., S_predicted_n), where S_predicted_n is the predicted stress value of n key points. This formula is obtained by the following method: For each pre-stored key risk point j, Find its corresponding position in the real-time temperature field T_fused.

[0007] Extract the temperature data around the point and quickly calculate its feature vector X_j_realTime = [T_avg_j, T_gradient_j, dT_dt_j].

[0008] Stress prediction: Input the feature vector X_j_realTime into the prediction model g_j corresponding to this point.

[0009] Model g_j outputs a predicted value S_predicted_j, which is the current predicted stress value at that point. S_predicted_j = g_j(T_avg_j, T_gradient_j, dT_dt_j) Determine the maximum predicted stress S_max: Iterate through the predicted stress values ​​S_predicted_j of all key points.

[0010] Find the maximum value among them, which is the predicted stress at the most dangerous point in the entire structure at the current moment. S_max = max(S_predicted_1, S_predicted_2, ..., S_predicted_n) This S_max represents the maximum mechanical risk under the current thermal state.

[0011] Security Decisions: Compare S_max with the material's allowable stress S_allowable (a preset safety threshold).

[0012] If S_max > S_allowable, it means that the structure is at risk of yielding or failure, and safety measures such as power reduction or emergency unlocking should be triggered immediately.

[0013] S2.4 Dynamic safety boundary calculation: P_max_safe(t) = f( T_max(t), ▽T_max(t), ΔL_predicted(t), S_max(t), P_rated ), This is a multivariate function that is calculated in real time using a pre-trained machine learning model (such as random forest regression). Its inputs are the current maximum temperature, maximum temperature gradient, predicted deformation, predicted stress, and rated power, and its output is the maximum safe power allowed at the current moment. S3. Collaborative Decision-Making and Execution: Scenario A (Normal): P_max_safe >= current power. The active locking mechanism based on the thermal deformation prediction model performs micron-level position compensation according to the predicted deformation data ΔL_predicted to prevent future jamming. The system does not take any additional actions. Scenario B (Warning): When P_max_safe < current power, the main controller immediately sends instructions to the charging pile controller and vehicle BMS to smoothly reduce the charging power to P_max_safe. At the same time, the active locking mechanism based on the thermal deformation prediction model strengthens position compensation and may activate micro air cooling (if designed) for local cooling. Scenario C (Emergency): If a sudden temperature change is detected (e.g., ΔT exceeds the emergency threshold) or an immediate risk of jamming is predicted (ΔL_predicted exceeds the physical limit), the main controller issues the highest priority command: Send a hard-wire power-off signal to the charging pile controller (at the fastest speed). An emergency unlocking signal is sent to the active locking mechanism based on a thermal deformation prediction model, triggering the shape memory alloy wire circuit. The shape memory alloy wire contracts within milliseconds, causing the mechanical structure to forcibly disengage the locking tongue, achieving active mechanical release and ensuring the ultimate safety that the gun cable can be pulled out under any circumstances. S4. Charging complete: Upon receiving the BMS charging completion signal, the main controller instructs the active locking mechanism based on the thermal deformation prediction model to unlock normally.

[0014] Store the thermal field data and algorithm decision logs for the entire charging process to optimize the dynamic safety boundary algorithm model.

[0015] Furthermore, a miniature infrared thermal imaging sensor array based on MEMS thermopile technology (such as the MLX90640) is integrated into the inner wall of the socket insulation shell to non-contactly scan and measure the temperature distribution of the pin metal parts and the latch mechanism.

[0016] Furthermore, the distributed fiber Bragg grating temperature sensor consists of 4-6 FBG sensors directly inscribed or soldered onto the root of the current pin (the point of highest temperature) and the base of the locking mechanism. The FBG sensors are connected in series on a thin optical fiber using wavelength division multiplexing (WDM) technology and led out to the demodulator.

[0017] Furthermore, step 2.3 can be replaced with: Extensive thermo-mechanical coupling simulations can be performed using finite element software (such as ANSYS). Different charging power curves (generating thermal loads) are input to obtain a large amount of data on the temperature field and corresponding deformation. Using this data, a linear time-varying (LTV) system model or a simple neural network model is trained for each key component (such as the latch and latch hole wall). The input to this model is the temperature of the component's surroundings over a current and past period, and the output is the predicted deformation of the component. = Online prediction in, It is a pre-trained simplified prediction model for component i. It has low computational cost and is suitable for running on the main controller.

[0018] : At time t, the predicted thermal deformation of the i-th critical component (such as the latch). (...): A prediction function / model pre-trained specifically for the i-th component. The input to this model is a time series of temperature data. At time tn, the representative temperature value (unit: °C) of the area near the i-th component is obtained and fused by the multimodal thermal sensing array (MTPA). This value can be the highest temperature, average temperature or weighted average temperature of the area. n: The time backtracking step size of the model. For example, n=5 means that the model will consider the temperature at the current time and the previous 5 time points to predict the deformation.

[0019] This formula shows that the predictive deformation is a function related to both current and historical temperatures. This expression intuitively demonstrates that the system's "predictive" capability depends on time series data.

[0020] Furthermore, it also includes the coordinated control of active locking mechanisms based on thermal deformation prediction models: The main controller receives ΔL_predicted from the prediction model.

[0021] If ΔL_predicted is lower than the fine-tuning threshold, the main controller sends a command to the active locking mechanism based on the thermal deformation prediction model via the CAN bus: "Fine-tuning mode, target position: L0 + ΔL_predicted * γ" (γ is the safety factor, <1).

[0022] The miniature servo motor in the active locking mechanism based on a thermal deformation prediction model receives commands and precisely controls the position of the locking tongue to compensate for displacement caused by thermal expansion, maintaining the optimal fit clearance and actively preventing jamming. At this point, the triggering condition for scenario C should be: Condition 1 (thermal failure): The temperature value of any FBG sensor > T_critical (e.g., 70°C); Condition 2 (Prediction Failure): Predicted deformation ΔL_predicted > ΔL_max (95% of the physical limit); Condition 3 (Communication Failure): Communication interruption with the main controller exceeds 50ms; Where ΔL_max is the maximum allowable deformation, which refers to the maximum effective fit clearance that the charging socket latch mechanism can be allowed in mechanical design; T_critical is the critical temperature, which refers to the highest safe temperature that all non-metallic material components in the charging socket (such as the insulating shell, plastic components in the latch mechanism, etc.) can withstand over a long period of time. As long as any condition is met, the local watchdog circuit on the active locking mechanism board based on the thermal deformation prediction model will directly trigger the SMA drive circuit, achieving the final release of local autonomous decision-making, without relying on the main controller, and with extremely high security.

[0023] The temperature field measurement utilizes the high absolute accuracy (±0.5°C) of FBG contact measurement to correct the problem of high relative accuracy but low absolute accuracy (±1.5°C) of infrared measurement, ultimately generating a reliable temperature field with both spatial resolution and high absolute accuracy. This is the foundation for achieving accurate prediction. (Includes:) Compared with the prior art, the advantages of the present invention include: (1) The present invention provides a socket with safety protection, which actively fine-tunes the position of the locking tongue based on the thermal deformation prediction model, compensates for thermal expansion, and physically prevents jamming, resulting in a qualitative leap in reliability. (2) The present invention provides a socket with safety protection. The SMA emergency unlocking mechanism does not rely on any electronic control system and is a pure mechanical-material level backup, ensuring automatic release under any extreme conditions.

[0024] (3) The present invention provides a socket with safety protection that deeply integrates technologies from multiple fields such as heat, force, electricity, and control to form an intelligent organism with sensing, prediction, decision-making and execution capabilities. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is an overall schematic diagram of a socket with safety protection according to the present invention; Figure 2 This is a schematic diagram of a socket with safety protection according to the present invention.

[0027] Figure label: 11. Socket body; 12. Miniature servo motor; 13. Ball screw; 14. Preload spring; 15. Locking tongue; 16. ; 17. Charging status indicator light. Detailed Implementation

[0028] In view of the shortcomings of the prior art, the inventors of this invention, through long-term research and extensive practice, have proposed the technical solution of this invention. The technical solution, its implementation process, and principles will be further explained below with reference to the accompanying drawings and specific implementation examples in the embodiments of this application.

[0029] It should be noted that the embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, the present invention covers any substitutions, modifications, equivalent methods and solutions made on the spirit, principles and scope of the present invention as defined by the claims. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] In the description of this application, the terms "first," "second," "third," and similar words do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms "a" or "one," and similar words, do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms "comprising" or "including," and similar words, mean that the elements or objects preceding "comprising" or "including" encompass the elements or objects listed following "comprising" or "including," and their equivalents, but do not exclude other elements or objects. The terms "connected" or "linked," and similar words, are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0031] In the description of this application, the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used solely for the convenience of describing this application and for simplification, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, when using positional terms such as "both sides," "outer side," and "upper and lower," it should be understood that they are used only for ease of understanding and description, taking into account that the structure may be oriented to other positions.

[0032] In the description of this application, unless otherwise expressly specified and limited, the technical or scientific terms used shall have the ordinary meaning understood by a person with ordinary skills in the art to which this application pertains. Terms such as “installation,” “connection,” and “joining” shall be interpreted broadly, for example, as fixed connection, detachable connection, mating connection, or integral connection. For a person skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0033] The present invention aims to introduce and explain the structural composition of a socket with safety protection and the cooperation relationship between the various components. Unless otherwise specified, the size, material and manufacturing process of each component in the socket with safety protection in the present invention can be selected according to specific circumstances, and no special limitation or explanation is given here.

[0034] Furthermore, to provide the public with a better understanding of the present invention, certain specific details are described in detail in the following description of the invention. However, those skilled in the art will fully understand the invention even without these detailed descriptions.

[0035] Example 1 Please see Figure 1 and Figure 2A socket with safety protection includes a socket body 11, a vehicle BMS, a charging pile controller, a heat sink disposed within the socket body, and further includes: The multimodal thermal sensing array (MTPA array) integrates a miniature infrared thermal imaging sensor array (non-contact absolute temperature measurement) and a distributed fiber optic grating temperature sensor (FBG, contact measurement point, anti-electromagnetic interference) at key points inside the socket body 11 (such as the root of each pin and next to the latch drive mechanism). The active locking mechanism (ALM-TDP actuator) based on the thermal deformation prediction model includes a micro servo motor 12, a ball screw 13, a preload spring 14 made of shape memory alloy (SMA) wire, and a locking tongue 15. The output end of the micro servo motor 12 is connected to the lead screw of the ball screw 13. The preload spring 14 is mounted on the ball screw slider, and the locking tongue 15 is mounted on the preload spring 14. The micro servo motor 13, the ball screw 13, the preload spring 14, and the locking tongue 15 constitute an electronic lock 16. The electronic lock replaces the original mechanical lock or traditional electronic lock of the socket body 11. The shape memory alloy wire is used for fast and silent emergency unlocking, and the micro servo motor is used for high-precision conventional locking. The dynamic thermal safety boundary algorithm module dynamically calculates the optimal temperature and power limits for each component under the current state based on factors such as real-time temperature field, predicted thermal deformation, current charging power, and historical charging data. The main controller interacts with the vehicle BMS and charging pile controller via the charging communication CAN bus (following protocols such as GB / T27930) to send power reduction requests or receive charging status information. The main controller is connected to the active locking mechanism based on the thermal deformation prediction model via the CAN bus to transmit locking status, execution feedback, and receive precise locking commands from the main controller. The main controller is connected to the multimodal thermal sensing array via the high-speed SPI bus to transmit a large amount of real-time temperature field data.

[0036] Furthermore, the main controller communicates and interacts with the vehicle BMS, charging pile controller, active locking mechanism based on thermal deformation prediction model, and multimodal thermal sensing array through the following methods: S1. Initialization and handshake: The charging gun is inserted, and the active locking mechanism based on the thermal deformation prediction model executes the standard locking procedure. The main controller starts up, and the multimodal thermal sensing array begins scanning to establish an initial temperature field baseline; The main controller establishes communication with the vehicle BMS and the charging pile controller to obtain the parameters of this charging request (rated voltage, current, etc.). S2, Real-time Monitoring and Modeling Cycle: S2.1, The multimodal thermal sensing array acquires global temperature data T_array(x,y,z,t) at a high frequency (e.g., 10Hz). S2.2 The main controller runs the thermal field reconstruction algorithm to reconstruct the discrete point data into a 3D temperature field model. It uses a gradient calculation algorithm to identify areas where the temperature change rate exceeds the threshold ▽T_threshold and marks them as potential hot spots. S2.3 The main controller inputs the predicted deformation data ΔL_predicted of the key components into the built-in finite element thermo-mechanical coupling model. This model quickly calculates and outputs the prediction results: the predicted deformation ΔL_predicted of the key components and the predicted stress distribution S_max, resulting in: ΔL_predicted = α * L0 * (T_current - T_initial) + β* (dT / dt). Where α is the coefficient of thermal expansion of the material, L0 is the initial length, β is the model correction coefficient (obtained from historical data training), dT / dt is the rate of temperature change, T_current is the current temperature, which is a characteristic temperature value most relevant to the thermal deformation of the latch mechanism, obtained by the multimodal thermal sensing array (MTPA), and T_initial is the initial temperature, which is the temperature of the charging socket latch mechanism when it is in an ambient temperature equilibrium state before the charging process begins. = max(S_predicted_1, S_predicted_2, ..., S_predicted_n), where S_predicted_n is the predicted stress value of n key points. This formula is obtained by the following method: For each pre-stored key risk point j, Find its corresponding position in the real-time temperature field T_fused.

[0037] Extract the temperature data around the point and quickly calculate its feature vector X_j_realTime = [T_avg_j, T_gradient_j, dT_dt_j].

[0038] Stress prediction: Input the feature vector X_j_realTime into the prediction model g_j corresponding to this point.

[0039] Model g_j outputs a predicted value S_predicted_j, which is the current predicted stress value at that point. S_predicted_j = g_j(T_avg_j, T_gradient_j, dT_dt_j) Determine the maximum predicted stress S_max: Iterate through the predicted stress values ​​S_predicted_j of all key points, including the predicted stress value of point j.

[0040] Find the maximum value among them, which is the predicted stress at the most dangerous point in the entire structure at the current moment. S_max = max(S_predicted_1, S_predicted_2, ..., S_predicted_n), This S_max represents the maximum mechanical risk under the current thermal state.

[0041] Security Decisions: Compare S_max with the material's allowable stress S_allowable (a preset safety threshold).

[0042] If S_max > S_allowable, it means that the structure is at risk of yielding or failure, and safety measures such as power reduction or emergency unlocking should be triggered immediately.

[0043] S2.4 Dynamic safety boundary calculation: P_max_safe(t) = f( T_max(t), ▽T_max(t), ΔL_predicted(t), S_max(t), P_rated ), This is a multivariate function that is calculated in real time using a pre-trained machine learning model (such as random forest regression). Its inputs are the current maximum temperature, maximum temperature gradient, predicted deformation, predicted stress, and rated power, and its output is the maximum safe power allowed at the current moment. Dynamic safety boundary calculation employs a fuzzy logic control algorithm. It is well-suited for handling multi-input, nonlinear systems that require expert experience, and its computational efficiency surpasses that of complex machine learning models.

[0044] Clearly define the inputs and outputs: Input variables: max_temp: The maximum value in the current fused temperature field.

[0045] max_gradient: The maximum spatial temperature gradient in the current fused temperature field.

[0046] predicted_stress: The maximum stress value (normalized value) output by the prediction model.

[0047] Output variable: power_adjustment (power adjustment amount, percentage).

[0048] Explicitly define the membership function and fuzzy rules: Define fuzzy sets such as "low", "medium", and "high" for each input and output variable.

[0049] Example of a fuzzy rule base (partial): If max_temp is medium and max_gradient is high, then power_adjustment is negative (meaning: the absolute temperature value is not high but the temperature rises rapidly, indicating a potential fault, and power should be significantly reduced). If max_temp is high and max_gradient is low, then power_adjustment is negative (meaning: the temperature is high but stable, so power should be reduced appropriately). If predicted_stress is high, then power_adjustment is negative (meaning: mechanical risks are predicted, power is significantly reduced, or even a power outage is prepared). Decision-making process: The MCU fuzzifies the clear input values ​​(max_temp, max_gradient, predicted_stress) in real time, substitutes them into the fuzzy rule base for inference, and finally defuzzifies the inference result to obtain a clear power_adjustment value, and then calculates P_max_safe = P_current * (1 + power_adjustment).

[0050] S3. Collaborative Decision-Making and Execution: Scenario A (Normal): P_max_safe >= current power. The active locking mechanism based on the thermal deformation prediction model performs micron-level position compensation according to the predicted deformation data ΔL_predicted to prevent future jamming. The system does not take any additional actions. Scenario B (Warning): When P_max_safe < current power, the main controller immediately sends instructions to the charging pile controller and vehicle BMS to smoothly reduce the charging power to P_max_safe. At the same time, the active locking mechanism based on the thermal deformation prediction model strengthens position compensation and may activate micro air cooling (if designed) for local cooling. Scenario C (Emergency): If a sudden temperature change is detected (e.g., ΔT exceeds the emergency threshold) or an immediate risk of jamming is predicted (ΔL_predicted exceeds the physical limit), the main controller issues the highest priority command: Send a hard-wire power-off signal to the charging pile controller (at the fastest speed). An emergency unlocking signal is sent to the active locking mechanism based on a thermal deformation prediction model, triggering the shape memory alloy wire circuit. The shape memory alloy wire contracts within milliseconds, causing the mechanical structure to forcibly disengage the locking tongue, achieving active mechanical release and ensuring the ultimate safety that the gun cable can be pulled out under any circumstances. S4. Charging complete: Upon receiving the BMS charging completion signal, the main controller instructs the active locking mechanism based on the thermal deformation prediction model to unlock normally.

[0051] Store the thermal field data and algorithm decision logs for the entire charging process to optimize the dynamic safety boundary algorithm model.

[0052] As an embodiment of the present invention, in addition to the above structure, the miniature infrared thermal imaging sensor array is based on a miniature infrared sensor array (such as MLX90640) using MEMS thermopile technology, and is integrated on the inner wall of the insulating shell of the socket body to non-contactly scan and measure the temperature distribution of the pin metal parts and the latch mechanism.

[0053] In one embodiment of the present invention, in addition to the above structure, the distributed fiber grating temperature sensor consists of 4-6 FBG sensors directly inscribed or soldered onto the root of the current pin (the point of highest temperature) and the base of the locking tongue mechanism. The FBG sensors are connected in series on a thin optical fiber using wavelength division multiplexing (WDM) technology and led out to the demodulator.

[0054] As an embodiment of the present invention, in addition to the above structure, step 2.3 is replaced by: performing extensive thermo-mechanical coupling simulations using finite element software (such as ANSYS). Different charging power curves (generating thermal loads) are input to obtain a large amount of data on the temperature field and corresponding deformation. Using this data, a linear time-varying (LTV) system model or a simple neural network model is trained for each key component (such as the latch and latch hole wall). The input of this model is the temperature of the component's accessories over a current and past period, and the output is the predicted deformation of the component. = Online prediction in, It is a pre-trained simplified prediction model for component i. It has low computational cost and is suitable for running on the main controller.

[0055] (...): A prediction function / model pre-trained specifically for the i-th component. The input to this model is a time series of temperature data. At time tn, the representative temperature value (unit: °C) of the area near the i-th component is obtained and fused by the multimodal thermal sensing array (MTPA). This value can be the highest temperature, average temperature or weighted average temperature of the area. n: The time backtracking step size of the model. For example, n=5 means that the model will consider the temperature at the current time and the previous 5 time points to predict the deformation.

[0056] This formula shows that the predictive deformation is a function related to both current and historical temperatures. This expression intuitively demonstrates that the system's "predictive" capability depends on time series data.

[0057] As one embodiment of the present invention, in addition to the above-described structure, it also includes cooperative control of an active locking mechanism based on a thermal deformation prediction model: The main controller receives ΔL_predicted from the prediction model.

[0058] If ΔL_predicted is lower than the fine-tuning threshold, the main controller sends a command to the active locking mechanism based on the thermal deformation prediction model via the CAN bus: "Fine-tuning mode, target position: L0 + ΔL_predicted * γ" (γ is the safety factor, <1).

[0059] The miniature servo motor in the active locking mechanism based on a thermal deformation prediction model receives commands and precisely controls the position of the locking tongue to compensate for displacement caused by thermal expansion, maintaining the optimal fit clearance and actively preventing jamming. At this point, the triggering condition for scenario C should be: Condition 1 (thermal failure): The temperature value of any FBG sensor > T_critical (e.g., 70°C); Condition 2 (Prediction Failure): Predicted deformation ΔL_predicted > ΔL_max (95% of the physical limit); Condition 3 (Communication Failure): Communication interruption with the main controller exceeds 50ms; where ΔL_max is the maximum allowable deformation, which refers to the maximum effective fit clearance that the charging socket locking tongue mechanism can be allowed in mechanical design; T_critical is the critical temperature, which refers to the highest safe temperature that all non-metallic material components in the charging socket (such as the insulating shell, plastic parts in the locking tongue mechanism, etc.) can withstand for a long time. As long as any condition is met, the local watchdog circuit on the active locking mechanism board based on the thermal deformation prediction model will directly trigger the SMA drive circuit, achieving the final release of local autonomous decision-making, without relying on the main controller, and with extremely high security.

[0060] The temperature field measurement utilizes the high absolute accuracy (±0.5°C) of FBG contact measurement to correct the problem of high relative accuracy but low absolute accuracy (±1.5°C) of infrared measurement, ultimately generating a reliable temperature field with both spatial resolution and high absolute accuracy. This is the foundation for achieving accurate prediction. (Includes:) Input: IR_image (from infrared array), FBG_temps[] (from FBG sensor) Output: T_fused (fused temperature field matrix) Step 1: Infrared data preprocessing (calibration and alignment) FOR each pixel in IR_image: T_ir(x,y) = calibrate(IR_image(x,y)) converts the raw values ​​to temperature values ​​using factory calibration parameters. END FOR Step 2: Accuracy correction based on FBG data The FBG sensor provides several absolutely accurate temperature reference points. FOR each FBG sensor i at known physical position (x_i, y_i): Find the pixel at position (x_i, y_i) in the infrared image. ir_pixel_value = IR_image(x_i, y_i) Calculate the deviation between the infrared measurement value and the true FBG value at this point. error_i = FBG_temps[i] - calibrate(ir_pixel_value) END FOR Step 3: Based on the reference point deviation, construct the temperature error correction field for the entire infrared image (using bilinear interpolation). error_field = interpolate_errors(error_i, positions_i, IR_image.size) Step 4: Generate the fusion temperature field T_fused = T_ir + error_field Step 5: Output T_fused to the main controller MCU.

[0061] In one embodiment of the present invention, in addition to the above structure, a charging status indicator light 17 is provided at the charging port of the charging socket body, indicating three lighting states (off, constant, and flashing) and three colors (blue, green, and red). Color adjustment via RGB is also possible as needed. Multiple RGB displays are achieved through an LED control chip, including constant, flashing, and brightness requirements at different times.

[0062] This invention addresses the core contradiction between thermal safety and mechanical reliability in existing charging interfaces under extreme conditions through a novel technological approach: multimodal sensing, predictive modeling, and master-redundant collaborative execution. Its innovation lies not in simply appropriating technologies from adjacent fields (such as using infrared imaging in charging guns), but in the deep integration of multiple technologies to create a novel system-level solution with inherent synergistic effects, effectively achieving a dual improvement in safety and charging efficiency.

[0063] It should be understood that the above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. It should not be considered that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, several simple deductions or substitutions can be made without departing from the concept of the present invention. All equivalent changes or modifications made in accordance with the spirit and essence of the present invention should be covered within the protection scope of the present invention.

Claims

1. A socket with safety protection, comprising a socket body, a vehicle BMS, and a charging pile controller, characterized in that: Also includes: A multimodal thermal sensing array is a fusion array that integrates a miniature infrared thermal imaging sensor array and a distributed fiber optic temperature sensor at key points inside the socket body. The active locking mechanism based on the thermal deformation prediction model includes a micro servo motor, a ball screw, a preload spring made of shape memory alloy wire, and a locking tongue. The output end of the micro servo motor is connected to the lead screw of the ball screw. The preload spring is mounted on the ball screw slider, and the locking tongue is mounted on the preload spring. The micro servo motor, ball screw, preload spring, and locking tongue constitute an electronic lock. The shape memory alloy wire is used for fast and silent emergency unlocking, and the micro servo motor is used for high-precision conventional locking. The dynamic thermal safety boundary algorithm module dynamically calculates the optimal temperature and power limits for each component under the current state based on factors such as real-time temperature field, predicted thermal deformation, current charging power, and historical charging data. The main controller interacts with the vehicle BMS and charging pile controller via a charging communication CAN bus, sending power reduction requests or receiving charging status information. The main controller is connected to the active locking mechanism based on a thermal deformation prediction model via the CAN bus, transmitting locking status and execution feedback, and receiving precise locking commands from the main controller. The main controller is connected to a multimodal thermal sensing array via a high-speed SPI bus to transmit a large amount of real-time temperature field data.

2. A socket with safety protection according to claim 1, characterized in that: The main controller communicates and interacts with the vehicle BMS, charging pile controller, active locking mechanism based on thermal deformation prediction model, and multimodal thermal sensing array through the following methods: S1. Initialization and handshake: The charging gun is inserted, and the active locking mechanism based on the thermal deformation prediction model executes the standard locking procedure. The main controller starts up, and the multimodal thermal sensing array begins scanning to establish an initial temperature field baseline; The main controller establishes communication with the vehicle BMS and the charging pile controller to obtain the parameters of this charging request. S2, Real-time Monitoring and Modeling Cycle: S2.1, The multimodal thermal sensing array acquires global temperature data T_array(x,y,z,t) at high frequency. S2.2 The main controller runs the thermal field reconstruction algorithm to reconstruct the discrete point data into a 3D temperature field model. A gradient calculation algorithm is used to identify regions where the temperature change rate exceeds the threshold ▽T_threshold, marking them as potential hotspots. S2.3, The main controller will predict the deformation of key components. The data is input into the built-in finite element thermo-mechanical coupling model, which quickly calculates and outputs prediction results: the predicted deformation of key components. and predict stress distribution ,get: = α * L0 * ( - ) + β * (dT / dt), Where α is the coefficient of thermal expansion of the material, L0 is the initial length, β is the model correction coefficient (obtained from historical data training), and dT / dt is the rate of temperature change. This is the current temperature. It is the initial temperature; = max(S_predicted_1, S_predicted_2, ..., S_predicted_n), where S_predicted_n are the predicted stress values ​​for n key points. S2.4 Dynamic safety boundary calculation: P_max_safe(t) = f( T_max(t), ▽T_max(t), ΔL_predicted(t), S_max(t), P_rated ), The inputs are the current maximum temperature, maximum temperature gradient, predicted deformation, predicted stress, and rated power; the output is the maximum safe power allowed at the current moment. S3. Collaborative Decision-Making and Execution: Scenario A is normal: P_max_safe >= current power. The active locking mechanism based on the thermal deformation prediction model performs micron-level position compensation according to the predicted deformation data ΔL_predicted to prevent future jamming without any additional action. Scenario B warning: When P_max_safe < current power, the main controller immediately sends instructions to the charging pile controller and vehicle BMS to smoothly reduce the charging power to P_max_safe. At the same time, the active locking mechanism based on the thermal deformation prediction model strengthens position compensation. Scenario C Emergency: When a sudden temperature change is detected or an immediate risk of jamming is predicted, the main controller issues the highest priority command: Send a hard-wire power-off signal to the charging pile controller; An emergency unlocking signal is sent to the active locking mechanism based on a thermal deformation prediction model, triggering the shape memory alloy wire circuit. The shape memory alloy wire contracts within milliseconds, causing the mechanical structure to forcibly disengage the locking tongue. S4. Charging complete: After receiving the BMS charging completion signal, the main controller instructs the active locking mechanism based on the thermal deformation prediction model to unlock normally; Store the thermal field data and algorithm decision logs for the entire charging process to optimize the dynamic safety boundary algorithm model.

3. A socket with safety protection according to any one of claims 1 or 2, characterized in that: The miniature infrared thermal imaging sensor array is a miniature infrared sensor array based on MEMS thermopile technology. It is integrated on the inner wall of the insulating shell of the socket body to non-contactly scan and measure the temperature distribution of the metal parts of the pins and the locking mechanism.

4. A socket with safety protection according to any one of claims 1 or 2, characterized in that: The distributed fiber grating temperature sensor consists of 4-6 FBG sensors directly inscribed or soldered onto the root of the current pin and the base of the locking tongue mechanism. The FBG sensors are connected in series on a thin optical fiber using wavelength division multiplexing technology and led out to the demodulator.

5. A socket with safety protection according to claim 2, characterized in that: Step 2.3 can also involve using finite element software to perform extensive thermo-mechanical coupling simulations; inputting different charging power curves to obtain a large amount of data on the temperature field and corresponding deformation; Using the above data, train a linear time-varying system model or a simple neural network model for each key component. The input of the model is the temperature of the component's surrounding area over a current and past period, and the output is the predicted deformation of the component. = Online prediction in, It is a pre-trained simplified prediction model for component i. : At time t, let be the predicted thermal deformation of the i-th critical component. (...): A prediction function / model pre-trained specifically for the i-th component; At time tn, the representative temperature value of the region near the i-th component is obtained by acquiring and fusing the data through a multimodal thermal sensing array. This value can be the highest temperature, average temperature, or weighted average temperature of the region. n: The time backtracking step size of the model.

6. A socket with safety protection according to claim 2, characterized in that: It also includes coordinated control of active locking mechanisms based on thermal deformation prediction models: The main controller receives ΔL_predicted from the prediction model. If ΔL_predicted is lower than the fine-tuning threshold, the main controller sends a command via the CAN bus to the active locking mechanism based on the thermal deformation prediction model: Fine-tuning mode, target position: L0 + ΔL_predicted * γ", where γ is the safety factor, γ < 1. In the active locking mechanism based on a thermal deformation prediction model, a miniature servo motor receives commands and precisely controls the position of the locking tongue to compensate for displacement caused by thermal expansion. At this point, the triggering condition for scenario C should be: Condition 1: Thermal failure: Temperature value of any FBG sensor > T_critical; Condition 2 prediction fails: the predicted deformation ΔL_predicted > ΔL_max; Condition 3: Communication failure: Communication interruption with the main controller exceeds 50ms; Where ΔL_max is the maximum allowable deformation, and T_critica l is the critical temperature. The active locking mechanism based on the thermal deformation prediction model will be triggered directly if any condition is met.

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