Electric heating cap temperature control system adopting fuzzy PID (Proportion Integration Differentiation) algorithm

Abstract

The invention relates to the technical field of automatic control, and particularly discloses an electric heating cap temperature control system adopting a fuzzy PID algorithm. The system comprises a temperature sensing module, a main control module integrated with a self-adaptive fuzzy PID controller, a power driving module, a multi-section heating execution module and a power management module. The system can automatically match the optimal PID control parameter initial value according to the actual environment temperature in the cold start stage, and the problem of parameter mismatch of a traditional fuzzy PID algorithm in the low-temperature environment is fundamentally solved. According to the method, the temperature rise response time of the system under severe cold and low-temperature working conditions is remarkably shortened, the temperature overshoot phenomenon is effectively restrained, a user can obtain comfortable warm-keeping experience more quickly, and meanwhile extra energy loss caused by adjustment lag and oscillation is reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of automatic control, and particularly relates to an electric heating cap temperature control system adopting a fuzzy PID algorithm. BACKGROUND

[0002] In the field of intelligent wearable devices and personal thermal management technology, the electric heating cap, as a direct user-oriented warm-keeping product, its temperature control accuracy, response speed and energy efficiency directly determine the user experience and device reliability. Among them, the temperature control algorithm is the core technical means to achieve the above performance indicators.

[0003] The electric heating cap temperature control system aims to monitor the temperature inside the cap in real time through sensors and dynamically adjust the power output of the heating element according to the set target value to maintain a comfortable and stable wearing temperature. The system has high requirements for rapid heating and sustained heating in cold environments.

[0004] The existing technology usually adopts a fuzzy PID control algorithm to deal with the nonlinearity and time-varying nature of the temperature system. However, this algorithm faces significant challenges in the low-temperature cold start scenario: its initial control parameters are set based on normal temperature working conditions, which mismatch the actual dynamic characteristics of the system in low-temperature environments, resulting in slow system response, obvious temperature overshoot and steady-state regulation lag during the heating stage, and the user will be in a cold state for a long time during this process. At the same time, the power output oscillation caused by the rigid parameters will intensify the frequent start-stop of the heating element, which not only shortens the service life of the element, but also significantly speeds up the power loss of the built-in battery due to invalid peak power consumption, seriously restricting the endurance of the device in scenes without stable power supply such as outdoors.

[0005] Therefore, how to design an electric heating cap temperature control system that can adapt to changes in environmental temperature, has rapid response and smooth regulation ability, has become a technical problem to be solved in the field. SUMMARY

[0006] The purpose of the present application is to provide an electric heating cap temperature control system adopting a fuzzy PID algorithm to solve the problems of slow system response, temperature overshoot, steady-state regulation lag, power output oscillation and battery endurance decline caused by the mismatch between control parameters and environmental dynamic characteristics in the low-temperature cold start scenario of the prior art.

[0007] The technical solution of the present application is that the system includes a temperature sensing module, a main control module, a power driving module, a heating execution module and a power management module. The temperature sensing module is responsible for real-time acquisition of temperature data of the wearing area inside the electric heating cap. The main control module integrates an adaptive fuzzy PID controller, which receives real-time temperature data from the temperature sensing module and the target temperature value preset by the user.

[0008] The power driving module precisely adjusts the electrical power applied to the heating execution module according to the control signal output by the master control module. The heating execution module is composed of multi-section heating fabric distributed inside the cap body. The power management module is responsible for monitoring and distributing the output of the built-in battery.

[0009] Further, the adaptive fuzzy PID controller internally contains an ambient temperature identification unit, a parameter fuzzy inference unit, and a PID control law operation unit. The ambient temperature identification unit analyzes the initial temperature value collected by the temperature sensing module at the system startup moment, and divides the current environment into a specific working condition level according to a plurality of preset temperature interval thresholds.

[0010] The parameter fuzzy inference unit pre-stores a plurality of PID parameter fuzzy rule bases corresponding to each working condition level, and activates the corresponding fuzzy rule base according to the identified working condition level. The PID control law operation unit loads the proportional coefficient, integral coefficient, and derivative coefficient initial values output by the parameter fuzzy inference unit, and calculates the control quantity based on the real-time temperature deviation and its change rate.

[0011] As an embodiment of the present application, the temperature interval thresholds set by the ambient temperature identification unit include a severe cold working condition below minus 10 degrees Celsius, a low temperature working condition from minus 10 degrees Celsius to 5 degrees Celsius, and a normal temperature working condition above 5 degrees Celsius.

[0012] The fuzzy rule base configured for the severe cold working condition by the parameter fuzzy inference unit focuses on enhancing the proportional action and moderately weakening the integral action, with the proportional coefficient initial value set to 1.8 times the reference value of the normal temperature working condition, and the integral coefficient initial value set to 0.6 times the reference value. The fuzzy rule base configured for the low temperature working condition adopts a strategy of balanced strengthening of proportional and integral actions, with the proportional coefficient and integral coefficient initial values set to 1.3 times and 0.9 times the reference value, respectively.

[0013] Further, the parameter fuzzy inference unit also integrates an online learning mechanism. This mechanism continuously monitors the dynamic response characteristics of the system during the warming-up phase, including the time required for the system to reach 90% of the target temperature value, the maximum overshoot, and the temperature fluctuation range after entering the steady state.

[0014] When the actual response characteristics deviate from the expected performance indicators of the corresponding working condition level, the online learning mechanism will start the parameter fine-tuning process, and make a small correction to the membership function in the currently activated fuzzy rule base according to the direction and magnitude of the deviation, with the correction amplitude not exceeding 5% of the original parameter value.

[0015] As an embodiment of the present application, the power driving module uses pulse width modulation technology to realize power regulation of the heating execution module. It receives the control quantity output by the master control module, which is a duty cycle command from 0 to 100%.

[0016] The power drive module converts the instruction into a high-frequency switching signal with a corresponding pulse width to drive the power switching device connected in series in the heating loop.

[0017] Further, the power management module is integrated with a dynamic power consumption evaluation unit and a discharge strategy optimization unit. The dynamic power consumption evaluation unit monitors the system total current and battery terminal voltage in real time, calculates the instantaneous power consumption and estimates the remaining available endurance time.

[0018] The adaptive fuzzy PID controller responds to this signal and automatically switches the system control target from accurate temperature tracking to maintaining the basic temperature maintenance mode, in which the upper limit of the control output is limited to 60% of the rated power.

[0019] As an embodiment of the present application, the adaptive fuzzy PID controller also performs parameter smoothing switching function during system operation. When the environmental temperature recognition unit detects a change in working condition level, the parameter fuzzy reasoning unit does not immediately load the parameter set corresponding to the new working condition, but starts a linear transition process with a duration of 30 seconds.

[0020] During this process, the proportional coefficient, integral coefficient and derivative coefficient used by the PID control law operation unit gradually change linearly from the current value to the target value to ensure the continuity of the control output and avoid temperature disturbance caused by parameter mutation.

[0021] Further, the temperature sensing module uses two high-precision digital temperature sensors distributed in the forehead area and the top area of the cap to realize redundant measurement. The main control module performs weighted average processing on the readings of the two sensors, with the forehead area sensor data weight being 0.7 and the top area sensor data weight being 0.3, to more accurately reflect the user's core body temperature.

[0022] Compared with the prior art, the present application has the following advantages: By introducing the environmental temperature recognition unit and the multi-working condition parameter fuzzy rule library, the system can automatically match the optimal PID control parameter initial value according to the actual environmental temperature during the cold start stage, which fundamentally solves the problem of parameter mismatch of the traditional fuzzy PID algorithm in low temperature environment. This significantly shortens the system's warm-up response time in cold and low temperature working conditions, effectively suppresses the temperature overshoot phenomenon, enables users to obtain a comfortable warm experience faster, and reduces the additional energy loss caused by adjustment lag and oscillation.

[0023] The integrated online learning mechanism of the application endows the system parameter with self-tuning capability, which can fine-tune the preset fuzzy rules according to the actual operation data, so as to adapt to the changes in system dynamic characteristics caused by different individual wearing habits, cap structure and battery performance differences. This continuous self-optimization ensures the long-term stability and robustness of the control performance, and improves the consistent performance of the product in different use scenarios.

[0024] The application realizes intelligent management of energy consumption through the dynamic power consumption evaluation and discharge strategy optimization unit in the power management module. The system can automatically switch to a low-power heat preservation mode when the endurance is tight, thereby maximizing the use time of the device while ensuring basic warmth needs. This feature has crucial practical value for application scenarios such as outdoor activities and emergency support without stable power supply.

[0025] The multi-section independent heating design and weighted average temperature measurement strategy adopted by the application not only improve the uniformity of the temperature field, avoiding local overheating or overcooling, but also enable the control system to more accurately reflect the real heat demand of the user by focusing on monitoring the sensitive areas of the body, further optimizing the balance between comfort and energy efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 is the overall technical scheme architecture diagram of the temperature control system of the electric heating cap using the fuzzy PID algorithm proposed by the application; Figure 2 is the core principle framework diagram of the adaptive fuzzy PID controller in the application; Figure 3 is the logic flow framework diagram of the environmental temperature identification and parameter fuzzy reasoning in the application; Figure 4 is the multi-level interaction relationship and data flow diagram of power driving and heating execution in the application; Figure 5 is the dynamic power consumption evaluation and discharge strategy optimization logic framework diagram of the power management module in the application. DETAILED DESCRIPTION

[0027] Please refer to the attached Figures 1 to 5 The embodiment details a specific implementation of a temperature control system of an electric heating cap using a fuzzy PID algorithm. The system is composed of a temperature sensing module, a main control module, a power driving module, a heating execution module and a power management module, which are interconnected through electrical and data buses. The temperature sensing module is responsible for real-time acquisition of temperature data in the wearing area inside the electric heating cap. It uses two high-precision digital temperature sensors distributed in the forehead area and the top area of the cap to realize redundant measurement.

[0028] The forehead area sensor is installed on the inner lining of the cap body close to the user's forehead skin, and the top area sensor is fixed on the inner side of the center area of the cap top, both of which communicate with the main control module through the I2C digital interface. The main control module performs weighted average processing on the readings of the two sensors, with the forehead area sensor data weight being 0.7 and the top area sensor data weight being 0.3, so as to more accurately reflect the user's core body temperature.

[0029] The weighted average calculation is performed every 100 milliseconds, and the calculation formula is: ; Among them, represents the forehead area temperature sampling value, represents the top area temperature sampling value, is the effective temperature value finally sent to the main control module. The main control module integrates an adaptive fuzzy PID controller, which receives real-time temperature data from the temperature sensing module and target temperature values set by the user through touch buttons. The target temperature value is stored in the non-volatile memory, and the default setting is 37 degrees Celsius. The user can adjust it in the range of 35 to 45 degrees Celsius with a step of 0.5 degrees Celsius.

[0030] Combined with the attached Figure 2 , the adaptive fuzzy PID controller includes an environment temperature identification unit, a parameter fuzzy reasoning unit and a PID control law operation unit. The environment temperature identification unit reads the initial temperature value collected by the temperature sensing module within 100 milliseconds after the system is powered on.

[0031] The unit has pre-stored three temperature interval thresholds: below minus 10 degrees Celsius for severe cold working condition, minus 10 to 5 degrees Celsius for low temperature working condition, and above 5 degrees Celsius for normal temperature working condition. The environment temperature identification unit compares the initial temperature value with these thresholds to determine the current working condition level, and sends the level identifier to the parameter fuzzy reasoning unit.

[0032] The parameter fuzzy reasoning unit has three groups of PID parameter fuzzy rule bases corresponding to the working condition level, each group of rule base including the initial value mapping table of the proportional coefficient, the integral coefficient and the differential coefficient. The severe cold working condition rule base focuses on enhancing the proportional action and moderately weakening the integral action, with the initial value of the proportional coefficient set to 1.8 times the reference value of the normal temperature working condition, and the initial value of the integral coefficient set to 0.6 times the reference value.

[0033] The low-temperature working condition rule base adopts a proportional and integral action balanced reinforcement strategy, and the initial values of the proportional coefficient and the integral coefficient are set to 1.3 times and 0.9 times of the reference values respectively. The normal-temperature working condition rule base uses default PID parameters, and the reference values of the proportional coefficient, the integral coefficient and the differential coefficient are 2.5, 0.05 and 0.1 respectively. The parameter fuzzy reasoning unit extracts the initial values of the proportional coefficient Kp, the integral coefficient Ki and the differential coefficient Kd from the corresponding rule base according to the received working condition grade identifier, and loads them to the PID control law operation unit.

[0034] The PID control law operation unit performs control quantity calculation at a frequency of 200 Hz. Its input is the real-time temperature deviation e and its change rate ec, where e is the difference between the target temperature value and the weighted average temperature value , and ec is calculated by the difference between the current period e value and the last period e value. The calculation of the control quantity u adopts a positional PID algorithm: ; Where k is the current sampling time, and the sampling period is 0.005 seconds. The calculated control quantity is mapped to a duty cycle instruction of 0 to 100% after amplitude limiting processing, and is sent to the power driving module through the SPI interface. The amplitude limiting processing ensures that the u value is always within the range of 0% to 100%, and when exceeds 100%, 100% is forced to be output, and when is less than 0%, 0% is forced to be output.

[0035] Please refer to the attached Figure 3 , the parameter fuzzy reasoning unit also integrates an online learning mechanism. This mechanism continuously monitors three dynamic response characteristic indicators during the system warming-up phase: the time t90 required for the system to reach 90% of the target temperature value, the maximum overshoot , and the temperature fluctuation range after entering the steady state. The monitoring period starts from the start of heating and ends when the temperature first enters the target value ± 0.3 degrees Celsius range and maintains for 10 seconds. The online learning mechanism compares the actual measured t90, and with the expected performance indicators of the corresponding working condition grade: the expected t90 is less than 120 seconds, the expected maximum overshoot is less than 2 degrees Celsius, the expected temperature fluctuation range is less than 0.5 degrees Celsius; the expected t90 is less than 90 seconds, the expected maximum overshoot is less than 1.5 degrees Celsius, the expected temperature fluctuation range is less than 0.4 degrees Celsius; the expected t90 is less than 60 seconds, the expected maximum overshoot is less than 1 degree Celsius, Less than 0.3 degrees Celsius. When any actual indicator deviates from the expected value by more than 15%, the online learning mechanism initiates a parameter fine-tuning process.

[0036] The fine-tuning process targets the membership functions in the currently activated fuzzy rule base, with adjustments not exceeding 5% of the original parameter values. Specifically, the adjustment logic is as follows: if t90 exceeds the expected value, the membership function center value is increased by a step of 0.5% with the scaling factor Kp; if... If the value exceeds the expectation, the membership function width of the integral coefficient Ki is reduced in steps of 0.3%; if If the value exceeds the expected value, the membership function center value of the differential coefficient Kd is increased in steps of 0.4%. All correction operations must pass a consistency check to ensure that the corrected parameter combination still satisfies the system stability criterion.

[0037] Please refer to the attached document. Figure 4 The power drive module employs pulse width modulation (PWM) technology to regulate the power of the heating execution module. Its core is a 32-bit timer integrated circuit that receives the duty cycle command from the main control module and converts it into a 20 kHz, pulse-width-adjustable switching signal. This signal, after being amplified by optocoupler isolation and gate drive circuitry, controls the MOSFET power switching device connected in series in the heating circuit.

[0038] The heating module consists of multi-segment heating fabric distributed inside the cap. The heating fabric is woven from carbon fiber material, with each carbon fiber filament having a diameter of 8 micrometers and a weaving density of 12 filaments per square millimeter.

[0039] The cap is divided into four independent heating zones: the forehead zone, the left temporal zone, the right temporal zone, and the occipital zone. Each zone is controlled by an independent power drive channel.

[0040] Each section is physically isolated by a 0.5 mm thick silicone insulating thermally conductive layer to ensure uniform heat distribution and no electrical interference. The four channels of the power drive module adopt a synchronous modulation strategy, with the switching signals of all channels in phase to reduce electromagnetic interference noise.

[0041] The power management module is responsible for monitoring and distributing the output of the built-in lithium polymer battery, which has a nominal capacity of 2000 mAh and a rated voltage of 3.7V.

[0042] Please refer to the attached document. Figure 5 The power management module integrates a dynamic power consumption assessment unit and a discharge strategy optimization unit. The dynamic power consumption assessment unit monitors the total system current in real time through a 0.01-ohm precision sampling resistor, and simultaneously acquires the battery terminal voltage through an ADC channel.

[0043] This unit calculates the instantaneous power consumption P every 5 seconds, using the formula P equal to the product of current I and voltage U. Based on the current power consumption and remaining battery capacity, it estimates the remaining usable battery life Test. The estimation method is: Test equals the remaining battery capacity Q divided by the average power consumption Pavg, where Pavg is calculated as a moving average of the most recent 30 power consumption samples. The discharge strategy optimization unit communicates with the adaptive fuzzy PID controller via a UART interface. When the estimated remaining battery life Test is lower than a preset 2-hour threshold, the discharge strategy optimization unit sends a power reduction request signal to the adaptive fuzzy PID controller.

[0044] In response to this signal, the adaptive fuzzy PID controller automatically switches the system control target from precise temperature tracking to maintaining a basic temperature level. In this mode, the upper limit of its output control quantity is limited to 60% of the rated power, while the target temperature value is automatically lowered by 2 degrees Celsius. The basic temperature maintenance mode continues until the remaining runtime (Test) recovers to more than 3 hours, at which point the system returns to normal control mode.

[0045] The adaptive fuzzy PID controller also performs a parameter smoothing function during system operation. When the ambient temperature identification unit detects a change in the operating condition level, the parameter fuzzy inference unit does not immediately load the parameter set corresponding to the new operating condition, but instead initiates a linear transition process lasting 30 seconds. During this process, the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd used by the PID control law calculation unit gradually and linearly change from their current values ​​to the target values.

[0046] The transition algorithm updates the parameters every 0.1 seconds in a proportional step, with the step size equal to 1 / 300 of the difference between the target value and the current value. For example, if the current Kp is 4.0 and the target Kp is 2.5, then the update step size is 0.005, and the transition is completed after 300 updates. This mechanism ensures the continuity of the control output and avoids temperature disturbances caused by sudden parameter changes.

[0047] The temperature sensing module uses two high-precision digital temperature sensors with DS18B20 chips, providing a measurement accuracy of ±0.1 degrees Celsius and a measurement range of -20 degrees Celsius to 85 degrees Celsius. Sensor data is transmitted to the main control module via a 1-Wire bus. The main control module has a built-in CRC check mechanism to ensure data integrity. If any sensor reads three times consecutively outside the reasonable range or the CRC check fails, the system automatically switches to single-sensor operation mode and prompts the user to check the sensor status via LED indicators.

[0048] The main control module uses a 32-bit microcontroller with an ARM Cortex-M4 core, operating at a frequency of 100 MHz, and has 128KB of flash memory and 32KB of RAM. All algorithms of the adaptive fuzzy PID controller are stored in flash memory as firmware and loaded into RAM for execution after power-on.

[0049] The microcontroller also manages the user interface unit, which includes three touch buttons, a 0.96-inch OLED display, and a buzzer. The OLED display shows the current temperature, target temperature, remaining battery life, and operating mode icon in real time, with a refresh rate of 2 Hz.

[0050] The MOSFET power switch used in the power drive module is the IRF7416, with an on-resistance of 8 milliohms and a maximum drain current of 60 amps. Each heating channel is independently equipped with a current detection circuit, which immediately shuts down the corresponding channel when the detected current exceeds 5 amps to prevent overcurrent damage to the heating fabric. All power channels are equipped with thermal fuses with a melting temperature of 85 degrees Celsius, providing dual overheat protection.

[0051] The power management module uses the TI BQ25895 chip for battery charge and discharge management, supporting a maximum charging current of 2 amps and a discharging current of 3 amps. The module integrates a coulomb counter to accurately track battery charge and discharge capacity with an accuracy of ±1%. Battery level is displayed via a 4-level LED indicator, updating the status every 25% of the battery level.

[0052] The carbon fiber heating fabric of the heating module is covered with a 0.3 mm thick flame-retardant polyester fiber layer, achieving a flame retardancy rating of UL94 V-0. A 0.8 mm thick aerogel insulation layer is installed between the fabric and the inner lining of the cap to reduce heat loss and improve thermal efficiency. The resistance of each section of the heating fabric is 12 ohms ± 5%, and at a rated voltage of 3.7 volts, the maximum heating power of a single section is 1.14 watts.

[0053] The system startup process consists of four stages: hardware self-test, sensor initialization, parameter loading, and control loop startup. The hardware self-test verifies that all modules communicate normally; sensor initialization calibrates the zero-point offset; parameter loading reads user settings and system parameters from non-volatile memory; and after the control loop startup, the system enters normal operating mode. The entire startup process takes no more than 500 milliseconds.

[0054] The fuzzy inference unit of the adaptive fuzzy PID controller also implements rule base version management. The system automatically backs up the current rule base to a backup area every 24 hours. If rule base corruption is detected, the latest valid version is restored from the backup area. Version information is verified using a 16-bit checksum to ensure data integrity.

[0055] The dynamic power consumption assessment unit of the power management module also implements power consumption trend prediction. Based on the power consumption data of the most recent hour, it builds an ARIMA model to predict the power consumption trend for the next 30 minutes. If the prediction shows that the power consumption will rise sharply, the system will start pre-cooling measures 10 minutes in advance, slightly reducing the target temperature by 0.5 degrees Celsius to smooth the power consumption curve.

[0056] The multi-segment independent control of the heating execution module supports an intelligent thermal field allocation algorithm. The main control module dynamically adjusts the power distribution ratio of each segment based on the temperature readings of each segment from the temperature sensing module. If the temperature of a certain segment is detected to be more than 1 degree Celsius lower than the average temperature, the power distribution weight of that segment is increased by 20% until the temperature difference between the segments is less than 0.5 degrees Celsius.

[0057] The data acquisition link of the temperature sensing module includes digital filtering. The raw temperature data is filtered by a 5-point moving average before being fed into the weighted average calculation stage. The filtering window size is 250 milliseconds, effectively suppressing high-frequency noise interference and improving temperature sampling stability.

[0058] This embodiment provides an alternative implementation scheme for an electric heating cap temperature control system using a fuzzy PID algorithm. Its core feature is that the collaborative working mode of the ambient temperature identification unit and the parameter fuzzy inference unit is different.

[0059] Please refer to the attached document. Figure 1 The overall system architecture remains unchanged, but the temperature range threshold division of the ambient temperature identification unit is more refined. In this embodiment, the ambient temperature is divided into 5 operating conditions: below -15 degrees Celsius is extremely cold, -15 degrees Celsius to -5 degrees Celsius is severe cold, -5 degrees Celsius to 5 degrees Celsius is low temperature, 5 degrees Celsius to 15 degrees Celsius is normal temperature, and above 15 degrees Celsius is warm temperature.

[0060] The parameter fuzzy inference unit stores five sets of PID parameter fuzzy rule bases. Each rule base contains the initial values ​​of the proportional coefficient, integral coefficient, and derivative coefficient, as well as the adaptive adjustment curve.

[0061] The ambient temperature identification unit not only collects the initial temperature value upon system startup but also analyzes the temperature change trend. It calculates the slope of temperature change using temperature readings over 10 consecutive sampling periods (a total of 50 milliseconds). If the slope is positive and the absolute value is greater than 0.1 degrees Celsius per second, it determines that the environment is in a rapid temperature rise phase and automatically activates the anti-saturation integral strategy. This strategy introduces an integral separation mechanism into the PID control law calculation unit. When the absolute value of the temperature deviation e is greater than 3 degrees Celsius, the accumulation of the integral term is paused to prevent integral saturation from causing increased overshoot.

[0062] In this embodiment, the online learning mechanism of the parameter fuzzy inference unit is enhanced to a two-level adjustment structure. The first level maintains the parameter fine-tuning function described in Embodiment 1, while the second level adds a rule base structure optimization function. When the system's cumulative running time exceeds 100 hours and the performance indicators continuously deviate from the expected value by more than 20%, the second-level optimization function is activated.

[0063] It re-clusters and generates new fuzzy rule base center points by analyzing temperature deviations, control outputs, and ambient temperature records from historical operating data. The optimization process uses an incremental learning algorithm, with each adjustment not exceeding 8% of the original rule base structure to ensure that control stability is not affected. After a 72-hour trial run, if the new rule base shows a performance improvement of more than 5%, it will officially replace the old rule base.

[0064] Please refer to the attached document. Figure 4 In this embodiment, the power drive module employs multi-phase interleaved modulation technology. The switching signals of the four heating channels are sequentially shifted by 90 degrees. This design increases the input current ripple frequency to 80 kHz, significantly reducing electromagnetic interference.

[0065] The power drive module also integrates an adaptive dead-time adjustment circuit, which monitors the voltage and current waveforms during MOSFET switching in real time and dynamically adjusts the dead time to a range of 100 nanoseconds to 500 nanoseconds to optimize the balance between switching losses and electromagnetic compatibility.

[0066] In this embodiment, the carbon fiber heating fabric of the heating execution module adopts a double-layer woven structure. The inner layer consists of carbon fiber filaments with a diameter of 6 micrometers and a weaving density of 16 filaments per square millimeter; the outer layer consists of carbon fiber filaments with a diameter of 10 micrometers and a weaving density of 8 filaments per square millimeter.

[0067] This structure enables a more uniform temperature distribution in the heating element under the same voltage, reducing thermal inertia by 15%. The thickness of the insulating thermally conductive layer between sections is increased to 0.7 mm, and the material is changed to aluminum nitride ceramic filled with silicone, improving the thermal conductivity to 3.5 watts per meter Kelvin.

[0068] In this embodiment, the dynamic power consumption assessment unit of the power management module adds a battery health monitoring function. It estimates the degree of battery capacity degradation by analyzing the battery's internal resistance change trend and charge / discharge efficiency curves. When the battery capacity is detected to have degraded to below 80% of its initial value, the system automatically adjusts the power consumption model parameters and increases the safety margin for estimated battery life from 15% to 25%. The power reduction trigger threshold of the discharge strategy optimization unit is now dynamically adjusted based on battery health: maintaining a 2-hour threshold when health is above 90%, adjusting to 2.5 hours when health is between 70% and 90%, and adjusting to 3 hours when health is below 70%.

[0069] Please refer to the attached document. Figure 2In this embodiment, the PID control law operation unit of the adaptive fuzzy PID controller achieves dual enhancements in anti-integral saturation and differential impulse suppression. The anti-integral saturation mechanism is implemented by limiting the accumulation rate of the integral term; when the control quantity u remains in a limited state for 5 seconds, the integral term decays at a rate of 0.5% every 0.1 seconds. Differential impulse suppression is achieved by processing the differential term input through a 4-point weighted average filter with filter coefficients of 0.4, 0.3, 0.2, and 0.1, effectively suppressing sudden changes in the control quantity caused by measurement noise.

[0070] In this embodiment, the temperature sensing module incorporates an ambient temperature compensation function. The main control module integrates an auxiliary temperature sensor to monitor the PCB board temperature in real time. When a temperature change exceeding 10 degrees Celsius is detected, the system performs software compensation on the readings of the two main temperature sensors, with a compensation coefficient of 0.05 degrees Celsius per degree Celsius, eliminating the influence of ambient temperature on measurement accuracy.

[0071] In this embodiment, the user interface of the main control module is enhanced to an intelligent interaction mode. In addition to displaying basic parameters, the OLED display also plots temperature change curves and power consumption distribution diagrams in real time. The touch buttons support multiple gesture operations such as long press, short press, and double press, allowing users to quickly switch operating modes or set parameters through combined gestures. The system also includes a vibration alert function; when the temperature reaches a set value or the battery level drops below 10%, tactile feedback is provided via a miniature vibration motor built into the cap.

[0072] In this embodiment, the system firmware enables remote upgrades. The main control module has a reserved Bluetooth Low Energy interface, allowing firmware update packages to be received via a mobile application. The upgrade process employs a dual-zone backup mechanism to ensure automatic revert to the older version in case of upgrade failure. All user settings and learning parameters remain intact during the upgrade process, eliminating the need for recalibration.

[0073] In this embodiment, the power management module supports fast charging protocols. When a compatible 9V 2A charger is detected, the system automatically switches to fast charging mode, increasing the charging current to 1.5A and reducing charging time by 40%. During charging, the parameters of the fuzzy PID controller are dynamically adjusted to reduce the priority of heating power, ensuring a balance between charging safety and efficiency.

[0074] In this embodiment, the multi-segment control of the heating execution module achieves adaptive thermal field mapping. The system learns the user's wearing habits to establish a three-dimensional thermal field model of the head. The model infers the heat loss distribution based on the temperature readings of each segment and dynamically optimizes the power distribution algorithm. If the forehead area is detected to be continuously hot while the occipital area is relatively cold, the power distribution center is automatically shifted backward to improve overall comfort.

[0075] In this embodiment, differential signal transmission technology is used for data transmission in the temperature sensing module. Two main temperature sensors communicate with the main control module via an RS-485 bus, improving interference immunity by 20dB. Each sensor address is independently configurable, supporting up to eight temperature sensors connected to the bus, with interfaces reserved for future functional expansion.

[0076] In this embodiment, a new safety monitoring mechanism has been added to the system. The main control module continuously monitors the operating status of each module. If an abnormal temperature spike, excessive current, or communication timeout is detected, the system immediately enters a safety mode. In safety mode, the system shuts down all heating channels, maintaining only basic monitoring functions, and prompts the user to check the equipment via audible and visual alarms. Safety event records are stored in an independent non-volatile memory, with a maximum of 100 historical records saved for subsequent analysis.

[0077] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0078] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An electric heating cap temperature control system using a fuzzy PID algorithm, characterized by, The application relates to a temperature control system for electrically heated headwear, comprising: a temperature sensing module for collecting temperature data of the wearing area inside the electrically heated headwear in real time; a main control module integrated with an adaptive fuzzy PID controller for receiving real-time temperature data from the temperature sensing module and a target temperature value preset by a user; a power driving module for accurately adjusting the electrical power applied to a heating execution module according to the control signal output by the main control module; the heating execution module is composed of multi-section heating fabric distributed inside the headwear; a power management module for monitoring and distributing the output of the built-in battery; wherein the adaptive fuzzy PID controller comprises an ambient temperature identification unit, a parameter fuzzy reasoning unit and a PID control law operation unit; the ambient temperature identification unit analyzes the initial temperature value collected by the temperature sensing module at the moment of system startup, and divides the current environment into a specific working condition level according to a plurality of preset temperature interval thresholds; the parameter fuzzy reasoning unit pre-stores a plurality of PID parameter fuzzy rule bases corresponding to each working condition level, and activates the corresponding fuzzy rule base according to the identified working condition level; the PID control law operation unit loads the proportional coefficient, integral coefficient and initial value of the differential coefficient output by the parameter fuzzy reasoning unit, and calculates the control amount based on the real-time temperature deviation and its change rate.

2. The electric heating cap temperature control system employing a fuzzy PID algorithm according to claim 1, characterized in that, The temperature interval thresholds set by the ambient temperature identification unit include a severe cold working condition below minus 10 degrees Celsius, a low temperature working condition from minus 10 degrees Celsius to 5 degrees Celsius and a normal temperature working condition above 5 degrees Celsius.

3. The electric heating cap temperature control system employing a fuzzy PID algorithm according to claim 2, characterized in that, The fuzzy rule base configured for the severe cold working condition by the parameter fuzzy reasoning unit focuses on enhancing the proportional action and moderately weakening the integral action, the initial value of the proportional coefficient is set to 1.8 times the reference value of the normal temperature working condition, and the initial value of the integral coefficient is set to 0.6 times the reference value.

4. The electric heating cap temperature control system employing a fuzzy PID algorithm according to claim 2, characterized in that, The fuzzy rule base configured for the low temperature working condition by the parameter fuzzy reasoning unit adopts a strategy of balanced strengthening of proportional and integral actions, and the initial values of the proportional coefficient and the integral coefficient are set to 1.3 times and 0.9 times the reference value respectively.

5. The electric heating cap temperature control system employing a fuzzy PID algorithm according to claim 1, characterized in that, The parameter fuzzy reasoning unit is also integrated with an online learning mechanism, which continuously monitors the dynamic response characteristics of the system during the heating stage, specifically including the time required for the system to reach 90% of the target temperature value, the maximum overshoot and the temperature fluctuation range after entering the steady state.

6. The electric heating cap temperature control system employing a fuzzy PID algorithm according to claim 5, characterized in that, When the online learning mechanism monitors that the actual response characteristics deviate from the expected performance indicators of the corresponding working condition level, the parameter fine-tuning process is started, the membership function in the currently activated fuzzy rule base is slightly corrected according to the direction and amplitude of the deviation, and the correction amplitude does not exceed 5% of the original parameter value.

7. The electric heating cap temperature control system employing a fuzzy PID algorithm according to claim 1, wherein, The power driving module realizes power regulation of the heating execution module by using pulse width modulation technology, receives the control amount output by the main control module, and the control amount is a duty cycle instruction of 0 to 100%.

8. The electric heating cap temperature control system employing a fuzzy PID algorithm according to claim 7, characterized in that, The power driving module converts the duty cycle instruction into a high-frequency switching signal with a corresponding pulse width to drive the power switching device connected in series in the heating loop.

9. The electric heating cap temperature control system employing a fuzzy PID algorithm according to claim 1, wherein, The power management module is integrated with a dynamic power consumption evaluation unit and a discharge strategy optimization unit, the dynamic power consumption evaluation unit monitors the system total current and battery terminal voltage in real time, calculates the instantaneous power consumption and estimates the remaining available endurance time.

10. The electric heating cap temperature control system employing a fuzzy PID algorithm according to claim 9, wherein, The discharge strategy optimization unit is in communication connection with the adaptive fuzzy PID controller, and when the estimated remaining endurance time is lower than the preset 2-hour threshold, the discharge strategy optimization unit sends a power reduction request signal to the adaptive fuzzy PID controller, and the adaptive fuzzy PID controller responds to the signal and automatically switches the system control target from accurate temperature tracking to a basic temperature maintenance mode, in which the upper limit of the control amount output by the adaptive fuzzy PID controller is limited to 60% of the rated power.

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