A wireless charging device operation process temperature control method and system

Abstract

The application relates to the technical field of process control, and provides a wireless charging equipment operation process temperature control method and system, which comprises the following steps: acquiring internal temperature information, external environment information and energy conversion efficiency information in a wireless charging equipment operation process; uniformly converting the acquired internal temperature information, external environment information and energy conversion efficiency information; evaluating the thermal state of the wireless charging equipment based on the uniformly converted internal temperature information, external environment information and energy conversion efficiency information, and in combination with deviation information of actual output power and set output power of the wireless charging equipment and high-power continuous operation time information; and executing a hierarchical intervention measure according to the evaluated thermal state. The application can guarantee the safe operation of the charging equipment, and also takes into account energy efficiency and user experience.

Description

Technical Field

[0001] This application relates to the field of process control technology, and more specifically, to a method and system for temperature control during the operation of a wireless charging device. Background Technology

[0002] In real-world usage environments, users often place wireless charging devices in confined spaces, such as corners of desks, which obstructs the air intake or exhaust vents of cooling fans, severely limiting the effective airflow path. This airflow restriction directly leads to reduced heat dissipation efficiency, making the problem of heat accumulation inside the device more prominent. Even when the fan is running at maximum speed, the cooling effect is greatly reduced.

[0003] In environments with limited airflow, the cooling system of wireless charging devices, even with fans, often prioritizes localized cooling. Fans typically target core heat-generating components, such as the transmitter coil and power MOSFETs, to directly remove the heat generated by these primary heat sources. However, due to limited airflow paths and the conductive properties of heat within the device, heat transferred from core components to the motherboard, power management module, control chip, and other peripheral components is difficult to dissipate effectively and quickly. These non-core areas possess a certain heat storage capacity, causing their temperatures to rise as well. Even if the core components experience a temperature reduction under the direct action of the fan, these peripheral areas, due to their own heat storage characteristics and distance from the main airflow path, will cool down much more slowly than the core components.

[0004] Faced with continuously high-temperature reports from the auxiliary temperature sensor, the wireless charging device's control system, based on its preset safety logic, determines that the overall heat dissipation risk has not yet been eliminated. Although the temperature of the core heat-generating components may drop after a short-term power adjustment or fan acceleration, the persistently high overall ambient temperature reflected by the auxiliary sensor prevents the control system from confirming that the device is completely out of the overheating risk. Under this conservative design principle, the control system is forced to maintain a higher level of protection. While this overly conservative strategy ensures the safe operation of the device, it severely impacts the user experience. Users will find the charging speed abnormally slow or the charging process frequently interrupted, preventing the device from fully charging within the expected time. This contradicts users' expectations for fast and stable charging, significantly reducing the product's practicality and user satisfaction. Summary of the Invention

[0005] This application provides a method and system for temperature control during the operation of a wireless charging device to solve at least one of the above-mentioned technical problems.

[0006] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, this application discloses a method for temperature control during the operation of a wireless charging device, comprising: Acquire internal temperature information, external environmental information, and energy conversion efficiency information during the operation of wireless charging devices; The acquired internal temperature information, external environment information, and energy conversion efficiency information are uniformly converted into numerical values. Based on the internal temperature information, external environmental information, and energy conversion efficiency information after unified value conversion, and combined with the deviation information between the actual output power and the set output power of the wireless charging device and the high-power continuous operation time information, the thermal state of the wireless charging device is evaluated. Based on the assessed thermal state, tiered intervention measures are implemented.

[0007] Secondly, this application also discloses a temperature control system for the operation of a wireless charging device, comprising: The information acquisition module is used to acquire internal temperature information, external environmental information, and energy conversion efficiency information during the operation of the wireless charging device. The quantity conversion module is used to perform unified quantity conversion on the acquired internal temperature information, external environmental information, and energy conversion efficiency information. The thermal state assessment module is used to assess the thermal state of a wireless charging device based on internal temperature information, external environmental information, and energy conversion efficiency information after unified value conversion, combined with the deviation information between the actual output power and the set output power of the wireless charging device and the high-power continuous operation time information. The intervention implementation module is used to implement tiered interventions based on the assessed thermal state.

[0008] Compared with the prior art, this application has at least the following beneficial effects: This application effectively solves the technical problems of inaccurate temperature control, untimely response, and poor user experience caused by excessive conservatism in existing wireless charging devices under complex operating environments by acquiring and converting refined multi-dimensional information, comprehensive thermal state assessment, and intelligent hierarchical intervention measures.

[0009] This application can not only accurately sense and respond to complex temperature changes, but also ensure good heat dissipation while taking into account efficient energy utilization and user experience, significantly improving the safety, reliability and practicality of wireless charging devices. Attached Figure Description

[0010] Figure 1 A flowchart illustrating a method for temperature control during the operation of a wireless charging device provided in this application; Figure 2This application provides a schematic diagram of the structure of a temperature control system for the operation of a wireless charging device. Detailed Implementation

[0011] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0012] like Figure 1 As shown in the figure, this application embodiment proposes a temperature control method for the operation of a wireless charging device, including: Acquire internal temperature information, external environmental information, and energy conversion efficiency information during the operation of the wireless charging device. The external environmental information includes the temperature of the wireless charging device casing and the ambient air temperature. The acquired internal temperature information, external environment information, and energy conversion efficiency information are uniformly converted into numerical values. Based on the unified converted internal temperature information, external environmental information, and energy conversion efficiency information, and combined with the deviation information between the actual output power and the set output power of the wireless charging device, as well as the high-power continuous operation time information, the thermal state of the wireless charging device is evaluated. Specifically, the unified converted internal temperature information, external environmental information, energy conversion efficiency information, power deviation information, and operation time information are weighted and summed to obtain a comprehensive thermal state index. The comprehensive thermal state index is used to quantitatively characterize the thermal state of the wireless charging device. The evaluation may also include judging the degree of deviation between the energy conversion efficiency and the preset efficiency. Based on the assessed thermal state, tiered intervention measures are implemented, including: improving the heat dissipation capacity of the wireless charging device when the thermal state reaches a preset warning level; and reducing the energy generation of the wireless charging device when the thermal state reaches a preset intervention level.

[0013] This application comprehensively considers multiple dimensions of information, including internal temperature, external environment, energy conversion efficiency, power deviation, and continuous high-power operation time. By introducing a unified value conversion and comprehensive thermal state index calculation, it can more comprehensively and accurately assess the thermal state of wireless charging devices. Furthermore, by judging the degree of deviation between the energy conversion efficiency and the preset efficiency, the assessment dimensions of thermal state are further refined. Based on this assessment result, this application can implement graded intervention measures, that is, adopt different intervention strategies at different thermal state levels, thereby achieving refined and intelligent control of the temperature during the operation of wireless charging devices, effectively avoiding overheating risks, and optimizing device performance and user experience.

[0014] The temperature control method for wireless charging devices proposed in this application aims to achieve accurate perception and dynamic management of the thermal state of wireless charging devices through multi-dimensional information fusion and intelligent hierarchical intervention.

[0015] The key terms in the embodiments of this application are explained as follows: Internal temperature information refers to the real-time temperature data of various key components or areas inside a wireless charging device. For example, it can be collected through built-in temperature sensors (such as thermistors, thermocouples, or infrared sensors).

[0016] External environmental information includes the temperature conditions outside the device, specifically the temperature of the wireless charging device's casing and the ambient air temperature. The casing temperature can be obtained through a temperature sensor attached to the surface of the device casing, while the ambient air temperature can be obtained through a separate ambient temperature sensor around the device or a sensor integrated inside the device but exposed to the external environment.

[0017] Energy conversion efficiency information refers to the efficiency data of a wireless charging device in converting input electrical energy into output electrical energy during the energy transmission process. This is usually obtained by monitoring input power and output power and performing calculations.

[0018] Standardized conversion refers to converting data such as temperature, efficiency, and power from different sources and with different dimensions into comparable standardized values ​​through standardization, normalization, or other mathematical transformations, so as to facilitate subsequent comprehensive evaluation.

[0019] The deviation information between the actual output power and the set output power reflects the difference between the actual charging power provided by the device and the charging power preset by the user or system.

[0020] High-power continuous operation time information records the duration for which the device operates at a higher power level, which is crucial for assessing the heat accumulation effect.

[0021] The comprehensive thermal state index is a quantitative indicator, calculated by weighting and summing the multi-dimensional information after the above unified value conversion. It is used to comprehensively reflect the overall heat load and potential overheating risk of the equipment.

[0022] The preset warning level and preset intervention level are pre-set thermal state thresholds. When the comprehensive thermal state index reaches these thresholds, the system will trigger corresponding intervention measures.

[0023] Improving the heat dissipation capabilities of wireless charging devices may include increasing fan speed, activating additional heat dissipation modules, or optimizing heat dissipation paths.

[0024] Reducing the energy output of wireless charging devices may involve measures such as reducing charging power, adjusting charging frequency, or suspending charging.

[0025] This method ensures that wireless charging devices can maintain a safe and efficient temperature range under various operating conditions through comprehensive processing of this information and intelligent decision-making.

[0026] The temperature control method for the wireless charging device during operation of this application first requires acquiring internal temperature information, external environmental information, and energy conversion efficiency information of the wireless charging device during operation. The external environmental information includes the temperature of the wireless charging device's casing and the ambient air temperature.

[0027] There are several ways to acquire internal temperature information. For example, multiple high-precision thermistors or thermocouples can be deployed at key heat-generating components inside the wireless charging device, such as the transmitting coil, power management chip, and rectifier, to monitor the temperature at these points in real time. These sensors can be wired to the device's microcontroller unit (MCU) and convert analog temperature signals into digital signals via an analog-to-digital converter (ADC). Another approach is to use an infrared thermal imager to perform a non-contact scan of the device's interior, acquiring a temperature distribution map of the entire internal area, and then extracting temperature data from key areas using image processing techniques.

[0028] For acquiring information about the external environment, the temperature of the wireless charging device's casing can be achieved by attaching a thermistor or thermocouple sensor to the surface of the casing. These sensors are also connected to the MCU for data acquisition. The ambient air temperature can be obtained by placing a separate digital temperature sensor near the device, or by utilizing an integrated environmental sensor within the device (if its design allows it to sense the external ambient temperature).

[0029] Obtaining energy conversion efficiency information typically requires monitoring both the device's input and output power simultaneously. For example, voltage and current sensors can be installed at the power input and charging output terminals respectively to collect voltage and current data in real time. The input and output power can then be calculated, leading to the energy conversion efficiency. This data is also transmitted to the MCU for processing.

[0030] After acquiring internal temperature, external environmental, and energy conversion efficiency information, it is necessary to perform a standardized conversion of these data. This is because different types of sensors may output data with different dimensions or ranges; for example, temperature may be expressed in degrees Celsius, and efficiency may be expressed as a percentage. To comprehensively evaluate this heterogeneous data, a standardized conversion is required.

[0031] One approach is to use normalization. For example, all temperature data can be normalized to a range of 0 to 1, where 0 represents the minimum safe temperature and 1 represents the maximum permissible temperature. Energy conversion efficiency can be similarly normalized, for example, 0% normalized to 0 and 100% normalized to 1. Other information, such as power deviation, can also be normalized according to its maximum possible deviation range. This normalization can be achieved using a linear mapping function, for example: Normalized value = (Actual value - Minimum value) / (Maximum value - Minimum value).

[0032] Another approach is to use standardization. By calculating the Z-score for each data point, i.e., (actual value - mean) / standard deviation, the data is transformed into a distribution with a mean of 0 and a standard deviation of 1. This method is suitable for data distributions that approximate a normal distribution, effectively eliminating dimensional differences and reducing the impact of outliers.

[0033] After completing the unified value conversion, the thermal state of the wireless charging device is evaluated based on the internal temperature information, external environmental information, and energy conversion efficiency information after the unified value conversion, combined with the deviation information between the actual output power and the set output power of the wireless charging device and the high-power continuous operation time information.

[0034] A weighted summation model can be used to calculate the comprehensive thermal state index. For example, a weighting coefficient can be assigned to each type of information after its standardized value conversion (such as internal temperature, external ambient temperature, energy conversion efficiency, power deviation, and high-power continuous operating time). These weighting coefficients can be trained and optimized based on experience, expert knowledge, or through machine learning algorithms (such as regression analysis) to reflect the importance of different information on the thermal state of the equipment. The comprehensive thermal state index is calculated as follows: Comprehensive Thermal State Index = w1 × Internal Temperature Information (normalized) + w2 × External Ambient Information (normalized) + w3 × Energy Conversion Efficiency Information (normalized) + w4 × Power Deviation Information (normalized) + w5 × Operating Time Information (normalized). Here, w1 to w5 are the corresponding weighting coefficients, and their sum is 1.

[0035] To determine the degree of deviation between energy conversion efficiency and a preset efficiency, a preset efficiency threshold can be set. For example, when the energy conversion efficiency is lower than a certain preset value, or when the deviation from the ideal efficiency exceeds a certain percentage, a deviation is considered to exist. For instance, the percentage of (preset efficiency - actual efficiency) / preset efficiency can be calculated. If this percentage exceeds a certain threshold (e.g., 5%), a significant deviation is considered to exist. This determination of the degree of deviation can serve as a correction term for the comprehensive thermal state index or as an independent condition for triggering specific intervention measures.

[0036] Finally, based on the assessed thermal state, tiered intervention measures are implemented. These measures include: improving the heat dissipation capacity of the wireless charging device when the thermal state reaches a preset warning level; and reducing the energy generation of the wireless charging device when the thermal state reaches a preset intervention level.

[0037] When the thermal condition reaches a preset warning level, for example, when the comprehensive thermal state index exceeds the first threshold but has not yet reached a dangerous level, measures can be taken to improve heat dissipation. For example, if the equipment is equipped with a cooling fan, the fan speed can be gradually increased to increase airflow and accelerate heat dissipation. If the equipment has adjustable heat sinks or a liquid cooling system, its operating parameters can be adjusted to enhance the heat dissipation effect.

[0038] When the thermal state reaches a preset intervention level, for example, if the comprehensive thermal state index exceeds the second threshold (higher than the first threshold), it indicates a high risk of overheating. In this case, stronger intervention measures are needed to reduce the energy generation of the wireless charging device. For example, the output charging power can be gradually reduced to decrease internal heat generation. In extreme cases, if the thermal state index reaches the highest danger level, charging can even be suspended until the device temperature returns to a safe range. These intervention measures can be implemented by the MCU sending control commands to the corresponding actuators (such as fan controllers and power management units).

[0039] The temperature control method for wireless charging devices proposed in this application integrates multi-dimensional information to achieve a comprehensive and accurate assessment of the thermal state of the wireless charging device, and performs graded intervention based on this assessment, thereby effectively solving the problems of imprecise temperature control and untimely response in the prior art.

[0040] This application constructs a comprehensive thermal state index by introducing multi-dimensional information (internal temperature, external environment, energy conversion efficiency, power deviation, and high-power continuous operation time) and unifying the value conversion, thereby enabling a more comprehensive and accurate reflection of the equipment's true heat load. For example, even if the temperature of the internal core components is acceptable, if the external ambient temperature is too high or the energy conversion efficiency decreases significantly, the comprehensive thermal state index will increase accordingly, prompting the system to take intervention measures in advance.

[0041] Furthermore, the tiered intervention measures in this application demonstrate its advanced nature. Traditional methods may directly reduce power or stop charging when the temperature reaches a certain threshold, and this rigid intervention often severely impacts the user experience. This application, however, adopts differentiated intervention strategies based on different levels of thermal condition: prioritizing improved heat dissipation at warning levels to minimize the impact on charging performance; and only reducing energy generation when the thermal condition reaches a more severe intervention level. This flexible and intelligent intervention mechanism not only effectively ensures the safe operation of the device but also maximizes both user experience and charging efficiency.

[0042] In summary, this application overcomes the limitations of existing technologies in temperature control by integrating multi-dimensional information, unifying value conversion, comprehensively evaluating thermal state index, and employing a graded intervention strategy, providing a more intelligent, efficient, and user-friendly method for controlling the temperature during the operation of wireless charging devices.

[0043] In some embodiments, the temperature control method for the operation of the wireless charging device further includes: Obtain real-time relative humidity information of the operating environment of the wireless charging device; The humidity influence coefficient is calculated based on the difference between the real-time relative humidity information and the preset normal humidity baseline. The humidity influence coefficient increases as the real-time relative humidity deviates from the normal humidity baseline. Based on the humidity influence coefficient, the first weight corresponding to the internal temperature information, the second weight corresponding to the external environment information, and the third weight corresponding to the energy conversion efficiency information are adjusted respectively. When the humidity influence coefficient increases, the first and third weights increase, and the second weight decreases. The adjusted first weight, second weight, and third weight are normalized so that the sum of the adjusted first weight, second weight, and third weight is 1. The comprehensive thermal state index is recalculated based on the adjusted and normalized first, second, and third weights. Based on real-time relative humidity information, humidity risk items are determined, and the humidity risk items increase as the real-time relative humidity increases; The humidity risk factor is added to the recalculated comprehensive thermal state index to obtain the updated comprehensive thermal state index, which is used to quantitatively characterize the thermal state of wireless charging devices under the influence of real-time relative humidity.

[0044] Specifically, real-time relative humidity information can be acquired through environmental sensors integrated inside or outside the wireless charging device. This sensor continuously monitors the relative humidity of the air surrounding the device and transmits the data to the control unit for processing. A preset normal humidity baseline can be determined based on the wireless charging device's design specifications, typical operating environment conditions, or industry standards; for example, it can be set to the humidity range where the device achieves optimal heat dissipation performance. The humidity impact coefficient is calculated to quantify the degree to which the current real-time relative humidity deviates from the normal baseline and its potential impact on the device's thermal performance. For example, when the real-time relative humidity is higher than the normal baseline, the humidity impact coefficient may be positive, indicating a potential decrease in heat dissipation efficiency; conversely, it may be negative or close to zero.

[0045] In high-humidity environments, the thermal conductivity and convective heat dissipation capacity of air are affected, leading to a decrease in the contribution of the external environment to heat dissipation. In such cases, the weight of external environmental information can be appropriately reduced, while the weight of internal temperature information or energy conversion efficiency information can be increased to more sensitively reflect the accumulation of internal heat. The humidity risk term is determined based on real-time relative humidity information, aiming to directly reflect the additional risks that high humidity may bring, such as decreased insulation performance, accelerated component corrosion, or localized condensation. This risk term can be a non-linear function; when humidity exceeds a certain threshold, the risk term will increase significantly. Adding this humidity risk term to the comprehensive thermal state index ensures that the comprehensive thermal state index not only reflects direct thermal parameters such as temperature and efficiency but also reflects the potential risks brought by environmental humidity, thus making the thermal state assessment more comprehensive and accurate.

[0046] This application optimizes the thermal state assessment mechanism for wireless charging devices by introducing real-time relative humidity information and converting it into a humidity influence coefficient and a humidity risk term. Specifically, the humidity influence coefficient quantifies the potential impact of ambient humidity on the device's heat dissipation performance. For example, high humidity may reduce air convection heat dissipation efficiency or increase the hygroscopicity of materials, thus affecting their thermal conductivity. By dynamically adjusting the weights of internal temperature information, external environmental information, and energy conversion efficiency information in the comprehensive thermal state index based on this humidity influence coefficient, the thermal state assessment model can adapt to different humidity environments, avoiding assessment bias caused by fixed weights under extreme humidity conditions. For example, in a humid environment, the contribution of the external environment to heat dissipation may be weakened. In this case, reducing the weight of external environmental information can more accurately reflect the true situation of internal heat accumulation. In addition, the introduction of the humidity risk term directly incorporates the additional risks that high humidity may cause (such as decreased electrical insulation performance and accelerated component corrosion), so that the comprehensive thermal state index not only focuses on direct temperature and efficiency parameters but also provides early warning of potential failure risks caused by humidity. Therefore, this solution can more comprehensively and accurately assess the thermal state of wireless charging devices, overcoming the limitations of traditional methods in assessing inaccurate conditions under complex humidity environments.

[0047] In some embodiments, the steps of obtaining internal temperature information, external environmental information, and energy conversion efficiency information during the operation of the wireless charging device include: Obtain temperature information of key components inside wireless charging devices; Based on the temperature information of key components, the temperature of unmonitored areas inside the wireless charging device is estimated; Based on the temperature information and estimated temperature of key components, determine the rate of temperature rise or the temperature difference between the local peak temperature and the adjacent area inside the wireless charging device. Based on the assessment results, implement tiered intervention measures to improve the heat dissipation capabilities of wireless charging devices.

[0048] Specifically, obtaining temperature information of key components inside wireless charging devices refers to deploying temperature sensors in hotspot areas such as power transmission coils, rectifier circuits, control chips, and power switching devices within the wireless charging device to monitor the operating temperature of these key components in real time. These key components are usually the main sources of heat generation inside the device, and accurate monitoring of their temperature is the foundation for effective thermal management.

[0049] Estimate the temperature of unmonitored areas inside a wireless charging device based on temperature information from key components. This can be understood as using methods such as thermal conduction models, finite element analysis, or machine learning algorithms, combined with monitored temperature data from key components, to predict the temperature of areas inside the device where temperature sensors are not installed. The goal is to construct a more comprehensive temperature distribution map inside the device, compensating for the limitations of sensor deployment and ensuring coverage of all potential thermal risk areas.

[0050] Based on temperature information and estimated temperatures of key components, the system determines the rate of temperature rise or the temperature difference between local peak temperatures and adjacent areas within the wireless charging device. Specifically, the system continuously tracks the temperature change trend of a specific local area (such as a key component or an estimated hotspot), calculates its temperature rise per unit time, and compares it to a preset threshold. Simultaneously, the system compares the peak temperature of this local area with the temperature difference of adjacent areas to identify any localized heat buildup or uneven heat dissipation. For example, if the temperature rise rate of a power switching device exceeds 2 degrees Celsius per second, or its peak temperature is more than 10 degrees Celsius higher than the surrounding area, it is considered abnormal. The aim is to more sensitively detect early signs of localized overheating through dynamic and relative indicators.

[0051] Therefore, based on the assessment results, implementing tiered intervention measures to enhance the heat dissipation capacity of wireless charging devices means that once an abnormal rate of temperature rise or a significant local peak temperature difference is detected in a local area, the system will immediately trigger the corresponding heat dissipation enhancement mechanism. For example, the speed of the cooling fan can be increased, the liquid cooling system can be activated, or the working mode of the heat sink can be adjusted to quickly dissipate local heat and prevent further heat accumulation.

[0052] This application constructs a more comprehensive and detailed temperature field information for the internal components of a wireless charging device by performing refined temperature monitoring on multiple key components and combining this with temperature estimation of unmonitored areas. It is precisely this high-granularity temperature sensing capability that enables the system to not only focus on the overall thermal state but also to deeply identify temperature anomalies in localized areas. By further judging the rate of temperature rise in localized areas and the temperature difference between local peak temperatures and adjacent areas, this solution can capture early signs of localized overheating more accurately and earlier, such as rapid temperature rise of a component due to sudden load changes or poor heat dissipation, or heat accumulation in specific areas. This sensitive detection of localized thermal anomalies allows the system to implement targeted and timely graded intervention measures to improve heat dissipation capabilities, thereby preventing the continued deterioration of localized hotspots and effectively preventing component damage or device performance degradation caused by localized overheating.

[0053] The following is a specific example to illustrate this.

[0054] Consider a wireless charging device whose key internal components include a transmitting coil, a power MOSFET, a rectifier diode, and a main control chip. Temperature sensors are deployed on each of these components. The system continuously collects temperature data from these sensors. Simultaneously, using a pre-established thermal conductivity model and combining the temperatures at these monitoring points, the system estimates the temperature of the PCB area between the transmitting coil and the MOSFET (where no sensors are directly mounted). During high-power operation, the system detects that the temperature of the power MOSFET rises from 60 degrees Celsius to 75 degrees Celsius within 10 seconds, at a rate of 1.5 degrees Celsius per second, exceeding the preset threshold of 1 degree Celsius per second. Furthermore, the peak temperature of this MOSFET is 8 degrees Celsius higher than that of its adjacent rectifier diode, also exceeding the preset temperature difference threshold of 5 degrees Celsius. Based on these findings, the system immediately triggers tiered intervention measures to enhance heat dissipation, such as increasing the internal cooling fan speed from 2000 RPM to 4000 RPM and simultaneously activating an auxiliary cooling module to rapidly reduce the temperature of the MOSFET and its surrounding area, preventing damage due to overheating.

[0055] In some embodiments, the temperature control method for the operation of the wireless charging device further includes: Obtain temperature information of key components inside wireless charging devices; Based on the temperature information of key components, combined with the preset temperature sensitivity curve and accelerated aging factor of key components, the thermal stress index of key components is calculated. The thermal stress index is used to quantify the cumulative thermal damage risk of key components due to long-term thermal exposure at the current temperature. The thermal stress index is used as an independent risk factor and superimposed on the comprehensive thermal state index to obtain the updated comprehensive thermal state index. The updated comprehensive thermal state index is used to quantitatively characterize the thermal state of wireless charging devices considering the impact of thermal accumulation damage to key components.

[0056] Specifically, obtaining temperature information of key components inside a wireless charging device refers to deploying high-precision temperature sensors at critical locations within the device, such as power semiconductor devices, inductors, capacitors, or control chips—components susceptible to heat—to monitor and acquire the surface or internal temperature of these key components in real time. These key components are typically those that generate a large amount of heat, are temperature-sensitive, or have a significant impact on the device's performance and lifespan.

[0057] The thermal stress index of key components is calculated based on their temperature information, combined with their pre-defined temperature sensitivity curves and accelerated aging factors. The temperature sensitivity curve describes the variation of component performance parameters (such as resistance, capacitance, and switching speed) with temperature, reflecting the component's responsiveness to temperature changes. The accelerated aging factor quantifies the rate of aging of the component under high temperature or high thermal stress conditions relative to normal operating conditions, typically derived from the Arrhenius equation or other reliability models. The calculation of the thermal stress index aims to comprehensively consider the component's current temperature, its inherent temperature sensitivity, and the degree of acceleration of its aging process at the current temperature, thereby deriving a quantitative index to characterize the thermal load borne by the component and its impact on its lifespan. For example, the thermal stress index can be calculated by comparing the current temperature with the component's rated operating temperature and weighting it using the temperature sensitivity curve and accelerated aging factor to reflect the component's actual stress level.

[0058] The purpose of overlaying the thermal stress index of critical components into the comprehensive thermal state index is to integrate the micro-level assessment of component health status into the macro-level assessment of the overall equipment thermal state. Through this overlay, the comprehensive thermal state index not only reflects the overall temperature, environmental, and efficiency conditions of the equipment but also includes risk considerations for the most vulnerable or thermally stressed critical components. This overlay can be achieved through simple addition, weighted averaging, or other more complex fusion algorithms, ensuring that the thermal stress index has an appropriate influence on the final comprehensive thermal state assessment.

[0059] This application effectively addresses the problem of neglecting the thermal stress of local components in the macroscopic thermal state assessment of the aforementioned basic solutions by introducing thermal stress assessment of key components inside the wireless charging device. Specifically, by acquiring the temperature information of key components in real time and combining it with their unique temperature sensitivity curves and accelerated aging factors, the thermal stress index of each key component can be accurately calculated. This thermal stress index directly reflects the risk of performance degradation and lifespan loss caused by increased temperature. Subsequently, the thermal stress indices of these key components are superimposed on the original comprehensive thermal state index, so that the updated comprehensive thermal state index not only includes the overall operating status of the device but also incorporates the health assessment of the most vulnerable internal components. Therefore, when a key component experiences excessive thermal stress due to its own characteristics or poor local heat dissipation, even when the overall device temperature is not high, the increase in its thermal stress index will directly lead to an increase in the comprehensive thermal state index. This allows for earlier and more accurate identification of potential local overheating or aging risks, avoiding missing the optimal intervention opportunity due to the lag in macroscopic assessment.

[0060] The following is a specific example to illustrate this.

[0061] Assuming a power MOSFET in a wireless charging device is a key component with a rated operating temperature of 85°C, its temperature sensitivity curve shows that its on-resistance increases significantly when the temperature exceeds 70°C, and the accelerated aging factor increases exponentially above 70°C. During a single charge, although the casing temperature of the wireless charging device and the ambient air temperature are within normal ranges, the local temperature of the power MOSFET reaches 80°C due to prolonged high-power output.

[0062] At this point, according to the above scheme, the temperature information (80℃) of the power MOSFET is first obtained. Then, combining its temperature sensitivity curve (e.g., its performance decreases by 10% at 80℃) and accelerated aging factor (e.g., the aging rate at 80℃ is twice that at 70℃), the thermal stress index of the power MOSFET is calculated. The calculated result is assumed to be 0.7 (a dimensionless risk value, where 0 represents no risk and 1 represents high risk).

[0063] Without considering the thermal stress index, the comprehensive thermal state index calculated based on internal temperature information, external environmental information, energy conversion efficiency information, power deviation information, and operating time information might be 0.4, which does not yet reach the preset warning level. However, when the thermal stress index of the power MOSFET (0.7) is added to the comprehensive thermal state index (e.g., through weighted averaging with weights of 0.8 and 0.2), the updated comprehensive thermal state index might become 0.4 × 0.8 + 0.7 × 0.2 = 0.32 + 0.14 = 0.46. If the preset warning level threshold is 0.45, then the updated comprehensive thermal state index of 0.46 exceeds the warning threshold.

[0064] Therefore, based on the updated comprehensive thermal state index, the system will promptly trigger warnings and implement tiered intervention measures. These measures may include enhancing local heat dissipation (such as activating additional fans or adjusting the heatsink's operating mode), or, if necessary, moderately reducing the energy production of the wireless charging device even if the overall system temperature appears normal, to protect the critical power MOSFET and prevent accelerated aging or damage due to localized overheating. This example clearly demonstrates that by introducing a thermal stress index for key components, localized thermal risks can be identified and addressed earlier and more accurately, effectively improving device reliability and lifespan.

[0065] In some embodiments, the step of calculating the thermal stress index of a key component based on its temperature information, combined with a preset temperature sensitivity curve and an accelerated aging factor, includes: Obtain current workload information, historical operating data, and environmental exposure information of key components inside wireless charging devices; Based on current workload information, historical operating data, and environmental exposure information, the temperature sensitivity curves and accelerated aging factors of key components are corrected. Based on the temperature information of key components, combined with the corrected temperature sensitivity curve of key components and the accelerated aging factor, the thermal stress index of key components is calculated.

[0066] Specifically, obtaining the current workload information of key components inside a wireless charging device refers to real-time monitoring of parameters such as voltage, current, switching frequency, and duty cycle of key components (e.g., power semiconductors, inductors, capacitors). These parameters directly reflect the instantaneous operating intensity of the components. Historical operating data can be understood as the cumulative operating time, average workload, peak load duration, and overload event records of key components over a period of time. This data can reveal the long-term stress accumulation of the components. Environmental exposure information specifically refers to external influencing factors such as temperature, humidity, vibration, and dust in the microenvironment in which the key components are located. This can be collected, for example, through miniature sensors integrated inside the device, with the aim of comprehensively capturing external conditions that affect component performance and lifespan.

[0067] Correcting the temperature sensitivity curves and accelerated aging factors of critical components involves adjusting the parameters of preset or initial temperature sensitivity curves and accelerated aging factors based on real-time acquired current workload information, historical operating data, and environmental exposure information. The temperature sensitivity curve describes the relationship between component performance parameters and temperature, while the accelerated aging factor quantifies the rate of increase in component aging under specific stress conditions. Dynamic correction allows these curves and factors to more closely reflect the actual performance of components in real-world operating environments. For example, under high loads or harsh conditions, the accelerated aging factor may be increased to reflect faster performance degradation.

[0068] Based on the corrected temperature sensitivity curve and accelerated aging factor, the thermal stress index of key components is calculated. For example, a specific mathematical model or algorithm can be used to substitute the real-time temperature information of the key components into the corrected temperature sensitivity curve and combine it with the corrected accelerated aging factor to comprehensively assess the thermal stress level of the components in their current state. The aim is to obtain a more accurate and representative thermal stress index, thereby providing a more reliable basis for the thermal status assessment of wireless charging devices.

[0069] This application addresses the inaccuracy issues caused by static or generalized parameters in basic solutions by incorporating current workload information, historical operating data, and environmental exposure information, and using this information to dynamically correct the temperature sensitivity curves and accelerated aging factors of key components. Specifically, current workload information reflects the instantaneous stress of the component, historical operating data reveals the cumulative damage, and environmental exposure information considers the impact of the external environment on component aging. By integrating this dynamic, real-time information into the correction process of curves and factors, the model can more accurately simulate the thermal behavior and aging process of components under actual operating conditions. Therefore, the calculated thermal stress index more realistically reflects the health status and potential risks of the component, avoiding misjudgments or omissions caused by discrepancies between the model and reality, and significantly improving the accuracy and reliability of thermal condition assessment.

[0070] The following is a specific example to illustrate this.

[0071] Assume the power MOSFET is a key component in a wireless charging device. First, the system continuously acquires the MOSFET's current operating load information, such as its drain current, source voltage, and switching frequency. Simultaneously, the system records and stores the MOSFET's historical operating data, including its cumulative operating time, operating time distribution at different power levels, and any historical overheating or overload events. Furthermore, sensors inside the device monitor the MOSFET's environmental exposure information in real time, such as local ambient temperature and humidity.

[0072] When calculating the thermal stress index of a MOSFET, the system first modifies the preset MOSFET temperature sensitivity curve and accelerated aging factor based on real-time and historical data. For example, if historical operating data shows that the MOSFET has been operating at high power and high frequency for a long time, and the ambient temperature is high, the system may increase its accelerated aging factor to reflect its faster performance degradation trend. The corrected temperature sensitivity curve will more accurately reflect the temperature-performance relationship of the specific MOSFET under current operating conditions. Subsequently, the real-time temperature information of the MOSFET is substituted into the corrected temperature sensitivity curve and accelerated aging factor model to calculate the current thermal stress index. For example, if the corrected model shows that the stress level of the MOSFET is close to its life limit at the current temperature, even if its temperature has not yet reached the absolute threshold, the system will increase its thermal stress index to trigger early warning or intervention measures, such as reducing charging power or enhancing heat dissipation, to prevent premature component failure.

[0073] In some embodiments, the step of correcting the temperature sensitivity curve and accelerated aging factor of critical components based on current workload information, historical operating data, and environmental exposure information includes: Obtain manufacturing batch information and material composition information of key components inside wireless charging devices; Based on manufacturing batch information and material composition information, retrieve the initial temperature sensitivity curve and initial accelerated aging factor that match the key components from the preset component characteristic database; Based on the current workload information, historical operating data, and environmental exposure information of key components, the parameters of the initial temperature sensitivity curve and the initial accelerated aging factor are adjusted to obtain the corrected temperature sensitivity curve and accelerated aging factor.

[0074] Specifically, manufacturing batch information refers to the specific batch identifier of a critical component during the production process. Its purpose is to distinguish components from those produced in different cycles, production lines, or process conditions, as these factors can lead to subtle differences in initial performance. Material composition information refers to the main materials constituting the critical component and their proportions. Its purpose is to reflect the component's physical and chemical properties, which directly affect its heat resistance, aging rate, etc. A component characteristic database can be understood as a collection storing a large number of initial performance parameters for components of different types, batches, and material compositions. This includes initial temperature sensitivity curves and initial accelerated aging factors, aiming to provide an accurate benchmark reference for a specific component. The initial temperature sensitivity curve describes the performance change trend of the component at different temperatures, while the initial accelerated aging factor reflects the benchmark value of the aging rate of the component under specific stress conditions. In practical applications, parameter adjustment refers to fine-tuning or optimizing these initial parameters after obtaining the initial temperature sensitivity curve and initial accelerated aging factor matched to the critical component, using the component's current workload information, historical operating data, and environmental exposure information, to more accurately reflect the dynamic characteristics of the component under actual operating conditions. For example, machine learning algorithms or adaptive filtering techniques can be used to iteratively correct curves and factors based on real-time data.

[0075] This application first obtains the manufacturing batch information and material composition information of key components, enabling the retrieval of the most suitable initial temperature sensitivity curve and initial accelerated aging factor from a pre-defined component characteristic database. Because components from different batches and with different materials may exhibit performance differences at the time of manufacture, this method of obtaining initial parameters based on the inherent properties of the components provides a more accurate benchmark for subsequent corrections. Based on this, and combined with the component's current workload information, historical operating data, and environmental exposure information, these initial parameters are finely adjusted, allowing the corrected temperature sensitivity curve and accelerated aging factor to more accurately reflect the dynamic thermal characteristics and aging trends of the component under actual operating conditions. This phased, coarse-to-fine correction strategy effectively compensates for potential initial deviations that may exist when correcting solely based on operating data, ensuring the accuracy and reliability of the thermal stress index calculation.

[0076] Suppose a power MOSFET inside a wireless charging device needs thermal stress evaluation. First, the system obtains the MOSFET's manufacturing batch number (e.g., 202301A) and material composition information (e.g., SiC-based). This information is then used to query a pre-defined component characteristic database. This database stores a large number of initial performance parameters for MOSFETs of different batches and materials, including initial temperature sensitivity curves and initial accelerated aging factors. Through matching, the system retrieves the initial curves and factors corresponding to the SiC-based MOSFET of batch 202301A. Next, the system continuously acquires the MOSFET's current operating load (e.g., instantaneous current, voltage), historical operating data (e.g., cumulative operating time, average temperature), and environmental exposure information (e.g., average humidity and vibration levels inside the device). Based on this real-time and historical data, the previously retrieved initial temperature sensitivity curves and initial accelerated aging factors are adjusted. For example, if historical data shows that the MOSFET exhibits a faster rate of temperature rise under specific high loads than predicted by the initial curve, the slope of the temperature sensitivity curve may be fine-tuned to reflect this reality; if ambient humidity is consistently high, the accelerated aging factor may be increased to reflect more stringent aging conditions. Ultimately, the corrected temperature sensitivity curve and accelerated aging factor are obtained, which are used to more accurately calculate the thermal stress index of the MOSFET.

[0077] In some embodiments, the step of adjusting the parameters of the initial temperature sensitivity curve and the initial accelerated aging factor based on the current workload information, historical operating data, and environmental exposure information of the key components to obtain the corrected temperature sensitivity curve and accelerated aging factor further includes: Perform integrity checks and timeliness verifications on the received current workload information, historical operating data, and environmental exposure information; When the results of integrity checks and timeliness verifications show that information is missing or delayed, the missing or delayed information is supplemented or predicted based on the recent operating trends of key components and real-time data of adjacent components. Based on supplementary or predicted information, the parameters of the initial temperature sensitivity curve and the initial accelerated aging factor are adjusted.

[0078] Specifically, integrity checks refer to verifying the received current workload information, historical operational data, and environmental exposure information in terms of data format, data type, and data range to ensure data integrity and validity. For example, it can check for null values, outliers, or data that does not conform to preset specifications. Timeliness verification refers to checking the timestamps of this information to determine whether they are within a preset valid time window to ensure the real-time nature and freshness of the data. For example, a threshold can be set; if the data collection time exceeds this threshold, the data is considered to be delayed.

[0079] When integrity checks and timeliness verifications reveal missing or delayed information, to avoid impacting the accuracy of parameter adjustments due to insufficient data, the missing or delayed information can be supplemented or predicted based on the recent operating trends of key components and real-time data from adjacent components. Specifically, recent operating trends can be obtained through statistical analysis of historical operating data of key components over a period of time (e.g., calculating moving averages, performing trend fitting, or employing time series forecasting models). Real-time data from adjacent components refers to the real-time monitoring data of other components physically close to or functionally related to the target key component. This data typically exhibits some correlation and can be used to help infer missing data for the target component. For example, if temperature data for a power amplifier component is missing, it can be predicted by referring to temperature data from nearby driver chips or heat sinks.

[0080] The supplemented or predicted information will be used to replace the original missing or delayed information, thus forming a complete and timely dataset. Based on this supplemented or predicted information, the parameters of the initial temperature sensitivity curve and the initial accelerated aging factor can be adjusted according to predetermined algorithms and models to obtain a more accurate and reliable corrected temperature sensitivity curve and accelerated aging factor.

[0081] This application ensures the quality of data used to correct temperature sensitivity curves and accelerated aging factors from the source by introducing mechanisms for integrity checks and timeliness verification of key information. When missing or delayed data is detected, adjustments are no longer made simply based on incomplete data. Instead, the data is supplemented or predicted intelligently by utilizing recent operating trends of key components and real-time data from adjacent components. This method effectively compensates for deficiencies in the data acquisition process, enabling the generation of a relatively complete and highly reliable dataset even when some data is unavailable. Consequently, the corrected temperature sensitivity curves and accelerated aging factors can more accurately reflect the actual thermal characteristics and aging state of key components, thus providing a more solid data foundation for subsequent thermal stress index calculations and overall thermal state assessments.

[0082] The following is a specific example to illustrate this.

[0083] Suppose a core power conversion module in a wireless charging device requires dynamic adjustments to the temperature sensitivity curves and accelerated aging factors of its critical components (e.g., MOSFETs) based on their current workload, historical operating data, and environmental exposure information. During a data acquisition cycle, due to an intermittent sensor malfunction, the current workload information of the MOSFET fails to be uploaded to the control system in a timely manner, resulting in data loss.

[0084] At this point, the temperature control method of this application will first perform an integrity check on the received information and immediately identify any missing information regarding the current operating load of the MOSFET. Subsequently, the system will not directly use the incomplete data for parameter adjustment, but will instead initiate a data supplementation or prediction mechanism. Specifically, the system will retrieve the MOSFET's operating data from the past few hours, analyzing its recent operating trends such as power output and current changes, for example, finding that its operating load has stabilized within a specific range over the past hour. Simultaneously, the system will also acquire real-time operating load data from adjacent components (e.g., driver chips or inductors) within the same heat dissipation area as the MOSFET; the data from these adjacent components shows that their operating loads are also in a normal and stable state.

[0085] Based on these recent operating trends and real-time data from adjacent components, the system intelligently predicts the reasonable current workload of the MOSFET. For example, by linearly extrapolating the recent trends and correcting them with real-time data from adjacent components, a reliable predicted value is obtained. Ultimately, this supplementary or predicted current workload information, along with other complete and valid historical operating data and environmental exposure information, is used to adjust the parameters of the MOSFET's initial temperature sensitivity curve and initial accelerated aging factor. In this way, even in the event of temporary missing critical data, the corrected parameters accurately reflect the actual state of the component, thereby maintaining the accuracy of the thermal stress index calculation and avoiding thermal management failures due to incomplete data.

[0086] In some embodiments, the steps described above for supplementing or predicting the missing or delayed information based on the recent operating trends of key components and real-time data of adjacent components when the integrity check and timeliness verification results show that information is missing or delayed include: Based on the recent operating data of key components, identify whether there are abnormal fluctuations or insufficient data in the recent operating data; When abnormal fluctuations or insufficient data are identified, supplementation or prediction based on recent operating trends is suspended, and real-time data from adjacent components is used instead for supplementation or prediction. When there is local electromagnetic interference or sensor failure in the real-time data of adjacent components, the system retrieves the initial temperature sensitivity curve and initial accelerated aging factor that match the key component from the preset component characteristic database based on the manufacturing batch information and material composition information of the key component. The system also supplements or predicts the missing or delayed information by combining the current workload information of the key component.

[0087] Specifically, before supplementing or predicting missing or delayed information, it is necessary to first analyze the recent operating data of key components to identify any abnormal fluctuations or insufficient data. For example, statistical analysis methods, such as moving averages, standard deviation analysis, or outlier detection algorithms, can be used to determine whether the data deviates from the normal range or whether the data points are sparse.

[0088] When the system identifies abnormal fluctuations or insufficient data in recent operational data, it will suspend the use of recent operational trends for supplementation or prediction to avoid errors caused by predictions based on inaccurate or insufficient data. Instead, the system will prioritize using real-time data from adjacent components for supplementation or prediction. This is because adjacent components are usually in similar operating environments and thermally coupled relationships, and their data can reflect the status of the target critical component to a certain extent. Furthermore, real-time data typically has higher timeliness and reliability.

[0089] If local electromagnetic interference or sensor malfunctions are detected in the real-time data of adjacent components, rendering their data unreliable, this application provides a more robust supplementation or prediction mechanism. Specifically, the system retrieves the initial temperature sensitivity curve and initial accelerated aging factor matching the critical component from a pre-set component characteristic database based on the manufacturing batch information and material composition information of the critical component. These initial parameters represent the inherent characteristics of the component under ideal or standard conditions. Subsequently, combined with the current workload information of the critical component, missing or delayed information is supplemented or predicted. This method utilizes the inherent characteristics of the component and its current actual operating state, providing relatively accurate supplementation or prediction results even when external data sources are unreliable.

[0090] This application effectively addresses the problem of inaccurate predictions due to unreliable data sources when information is missing or delayed by introducing multi-level data verification and an intelligent rollback mechanism. First, by detecting anomalies in recent operational data of key components, it avoids blindly relying on trends for prediction when data quality is poor. Second, when recent operational data is unreliable, it prioritizes real-time data from adjacent components, using their correlations to supplement the prediction, thus improving accuracy and real-time performance. Finally, when all external real-time data sources are problematic, this application can roll back to using the inherent characteristics of the components (manufacturing batch, material composition, initial curves, and factors) combined with the current workload for prediction, ensuring that reasonable supplementary or predictive results are still provided even in extreme cases, thereby maintaining the continuity and reliability of thermal state assessment.

[0091] Suppose that temperature sensor data for a power MOSFET within a wireless charging device suddenly becomes intermittently missing. First, the system analyzes the MOSFET's recent temperature operating data. If drastic fluctuations are detected, such as a sudden drop in temperature from normal to extremely low values ​​within a short period, or if the data points are sparse enough to form a valid trend, the system pauses predictions based on the MOSFET's recent trend. At this point, the system checks the real-time temperature data of adjacent inductors that are thermally coupled to the MOSFET. If the adjacent inductors' temperature data is stable and normal, the system prioritizes using their temperature trend to predict the missing temperature data for the MOSFET. However, if further detection reveals that the temperature sensors of adjacent inductors also report abnormal data, such as periodic spikes due to external electromagnetic interference, the system initiates a final fallback mechanism. In this case, the system retrieves the initial temperature sensitivity curve and initial accelerated aging factor of the MOSFET batch from a pre-defined component characteristic database, based on the MOSFET's manufacturing batch and material composition information. Combining this with the MOSFET's current actual operating load (e.g., estimated through drive current and voltage), the system uses these inherent characteristics to estimate and supplement the missing temperature information. This multi-level intelligent backoff mechanism ensures accurate supplementation and prediction of temperature information for critical components, even in the event of multiple faults in sensor data, thereby maintaining the accuracy of thermal state assessment.

[0092] like Figure 2 As shown in the figure, this application embodiment proposes a temperature control system for the operation of a wireless charging device, including: Information acquisition module 1 is used to acquire internal temperature information, external environment information and energy conversion efficiency information during the operation of the wireless charging device. The external environment information includes the temperature of the wireless charging device shell and the ambient air temperature. The quantity conversion module 2 is used to perform unified quantity conversion on the acquired internal temperature information, external environmental information, and energy conversion efficiency information. The thermal state assessment module 3 is used to assess the thermal state of the wireless charging device based on the internal temperature information, external environmental information, and energy conversion efficiency information after unified value conversion, combined with the deviation information between the actual output power and the set output power of the wireless charging device and the high-power continuous operation time information. Specifically, it obtains a comprehensive thermal state index by weighted summing of the internal temperature information, external environmental information, energy conversion efficiency information, power deviation information, and operation time information after unified value conversion. The comprehensive thermal state index is used to quantitatively characterize the thermal state of the wireless charging device. The assessment may also include judging the degree of deviation between the energy conversion efficiency and the preset efficiency. The intervention execution module 4 is used to execute graded intervention measures based on the assessed thermal state. The graded intervention measures include: when the thermal state reaches a preset warning level, improving the heat dissipation capacity of the wireless charging device; and when the thermal state reaches a preset intervention level, reducing the energy generation of the wireless charging device.

[0093] The system proposed in this application can achieve accurate sensing, intelligent assessment and dynamic management of the thermal state of wireless charging devices.

[0094] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for temperature control during the operation of a wireless charging device, characterized in that, include: Acquire internal temperature information, external environmental information, and energy conversion efficiency information during the operation of wireless charging devices; The acquired internal temperature information, external environment information, and energy conversion efficiency information are uniformly converted into numerical values. Based on the internal temperature information, external environmental information, and energy conversion efficiency information after unified value conversion, and combined with the deviation information between the actual output power and the set output power of the wireless charging device and the high-power continuous operation time information, the thermal state of the wireless charging device is evaluated. Based on the assessed thermal state, tiered intervention measures are implemented.

2. The method for temperature control during the operation of a wireless charging device according to claim 1, characterized in that, The thermal state of the wireless charging device is evaluated based on the internal temperature information, external environmental information, and energy conversion efficiency information after unified value conversion, combined with the deviation information between the actual output power and the set output power of the wireless charging device and the high-power continuous operation time information. Specifically: By weighted summing of the internal temperature information, external environment information, energy conversion efficiency information, deviation information between the actual output power and the set output power of the wireless charging device, and high-power continuous operation time information after unified value conversion, a comprehensive thermal state index is obtained. The comprehensive thermal state index is used to quantitatively characterize the thermal state of the wireless charging device.

3. The method for temperature control during the operation of a wireless charging device according to claim 2, characterized in that, Also includes: Obtain real-time relative humidity information of the operating environment of the wireless charging device; The humidity influence coefficient is calculated based on the difference between real-time relative humidity information and the preset normal humidity baseline. Based on the humidity influence coefficient, the first weight corresponding to the internal temperature information, the second weight corresponding to the external environment information, and the third weight corresponding to the energy conversion efficiency information are adjusted respectively. The adjusted first, second, and third weights are then normalized. The comprehensive thermal state index is recalculated based on the adjusted and normalized first, second, and third weights. Based on real-time relative humidity information, humidity risk items are identified; The humidity risk factor is added to the recalculated comprehensive thermal state index to obtain the updated comprehensive thermal state index, which is used to quantitatively characterize the thermal state of wireless charging devices under the influence of real-time relative humidity.

4. The method for temperature control during the operation of a wireless charging device according to claim 1, characterized in that, The steps for acquiring internal temperature information, external environmental information, and energy conversion efficiency information during the operation of the wireless charging device include: Obtain temperature information of key components inside wireless charging devices; Estimate the temperature of unmonitored areas inside the wireless charging device based on temperature information from key components; Based on the temperature information of key components and the estimated temperature of unmonitored areas inside the wireless charging device, determine the rate of temperature rise or the temperature difference between the local peak temperature and the adjacent areas inside the wireless charging device. Based on the assessment results, implement tiered intervention measures to improve the heat dissipation capabilities of wireless charging devices.

5. The method for temperature control during the operation of a wireless charging device according to claim 2, characterized in that, Also includes: Obtain temperature information of key components inside wireless charging devices; Based on the temperature information of key components, combined with the preset temperature sensitivity curve and accelerated aging factor of key components, the thermal stress index of key components is calculated. The thermal stress index is used as an independent risk factor and superimposed on the comprehensive thermal state index to obtain the updated comprehensive thermal state index. The updated comprehensive thermal state index is used to quantitatively characterize the thermal state of wireless charging devices considering the impact of thermal accumulation damage to key components.

6. The method for temperature control during the operation of a wireless charging device according to claim 5, characterized in that, The step of calculating the thermal stress index of the key component based on the temperature information of the key component, combined with the preset temperature sensitivity curve and accelerated aging factor of the key component, includes: Obtain current workload information, historical operating data, and environmental exposure information of key components inside wireless charging devices; Based on current workload information, historical operating data, and environmental exposure information, the temperature sensitivity curves and accelerated aging factors of key components are corrected. Based on the temperature information of key components, combined with the corrected temperature sensitivity curve of key components and the accelerated aging factor, the thermal stress index of key components is calculated.

7. The method for temperature control during the operation of a wireless charging device according to claim 6, characterized in that, The step of correcting the temperature sensitivity curves and accelerated aging factors of key components based on current workload information, historical operating data, and environmental exposure information includes: Obtain manufacturing batch information and material composition information of key components inside wireless charging devices; Based on manufacturing batch information and material composition information, retrieve the initial temperature sensitivity curve and initial accelerated aging factor that match the key components from the preset component characteristic database; Based on the current workload information, historical operating data, and environmental exposure information of key components, the parameters of the initial temperature sensitivity curve and the initial accelerated aging factor are adjusted to obtain the corrected temperature sensitivity curve and accelerated aging factor.

8. The method for temperature control during the operation of a wireless charging device according to claim 7, characterized in that, The step of adjusting the parameters of the initial temperature sensitivity curve and the initial accelerated aging factor based on the current workload information, historical operating data, and environmental exposure information of the key components to obtain the corrected temperature sensitivity curve and accelerated aging factor further includes: Perform integrity checks and timeliness verifications on the received current workload information, historical operating data, and environmental exposure information; When the results of integrity checks and timeliness verifications show that information is missing or delayed, the missing or delayed information is supplemented or predicted based on the recent operating trends of key components and real-time data of adjacent components. Based on supplementary or predicted information, the parameters of the initial temperature sensitivity curve and the initial accelerated aging factor are adjusted.

9. The method for temperature control during the operation of a wireless charging device according to claim 8, characterized in that, When the integrity check and timeliness verification results show that information is missing or delayed, the step of supplementing or predicting the missing or delayed information based on the recent operating trends of key components and real-time data of adjacent components includes: Based on the recent operating data of key components, identify whether there are abnormal fluctuations or insufficient data in the recent operating data; When abnormal fluctuations or insufficient data are identified, supplementation or prediction based on recent operating trends is suspended, and real-time data from adjacent components is used instead for supplementation or prediction. When there is local electromagnetic interference or sensor failure in the real-time data of adjacent components, the system retrieves the initial temperature sensitivity curve and initial accelerated aging factor that match the key component from the preset component characteristic database based on the manufacturing batch information and material composition information of the key component. The system also supplements or predicts the missing or delayed information by combining the current workload information of the key component.

10. A temperature control system for the operation of a wireless charging device, characterized in that, include: The information acquisition module is used to acquire internal temperature information, external environmental information, and energy conversion efficiency information during the operation of the wireless charging device. The quantity conversion module is used to perform unified quantity conversion on the acquired internal temperature information, external environmental information, and energy conversion efficiency information. The thermal state assessment module is used to assess the thermal state of a wireless charging device based on internal temperature information, external environmental information, and energy conversion efficiency information after unified value conversion, combined with the deviation information between the actual output power and the set output power of the wireless charging device and the high-power continuous operation time information. The intervention implementation module is used to implement tiered interventions based on the assessed thermal state.

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