Method and system for dynamically adjusting temperature and power of vehicle-mounted wireless charger

Abstract

The invention provides a temperature and power dynamic adjusting method for a vehicle-mounted wireless charger, which comprises the following steps of: 1, presetting initial charging power and an initial temperature threshold value of the vehicle-mounted wireless charger, calibrating a temperature sensor, and checking working states of a power management module and a heat dissipation system; through real-time temperature monitoring and an intelligent power adjusting algorithm, on the premise of ensuring safety, the charging efficiency can be improved as much as possible, charging time prolonging caused by unnecessary power reduction is avoided, a user can fully charge a battery more quickly, and by optimizing the adjusting process between the temperature and the power, the user experience is improved. The charging process is more stable and reliable, and a user does not need to worry about charging interruption or power reduction caused by too high temperature, so that the user experience is improved.

Description

Technical Field

[0001] The present invention relates to a method and a system, specifically a method and a system for dynamically adjusting the temperature and power of an on-board wireless charger, belonging to the technical field of on-board wireless chargers. Background Art

[0002] An in-vehicle wireless charger utilizes wireless charging technology to charge devices within the vehicle. Eliminating the need for traditional charging cables and connectors, it establishes a contactless power transmission connection between the charger and the device through electromagnetic induction, enabling wireless power transmission and device charging.

[0003] Wireless charging technology uses electromagnetic induction, eliminating the potential safety hazards of traditional charging methods, such as sparks and short circuits, that can occur when plugging and unplugging charging cables. In-car wireless chargers also typically include protection against overcurrent, overvoltage, and overheating, ensuring a safe and reliable charging process.

[0004] The dynamic temperature and power regulation method of the on-board wireless charger mainly involves effectively managing the heat generated during the wireless charging process and dynamically adjusting the charging power according to the charging demand to ensure the safety and efficiency of the charging process; In existing technologies, the dynamic adjustment between temperature and power of in-vehicle wireless chargers is not linear enough, which reduces charging efficiency and affects user experience to a certain extent. The main reasons are as follows: (1) Currently, in-vehicle wireless chargers use a simple temperature threshold setting method, that is, when the charging temperature is detected to exceed the preset threshold, the charging power is automatically reduced to prevent overheating. This method ignores the continuity and dynamics between temperature and power, resulting in an insufficiently precise adjustment process. (2) In existing technologies, temperature and power regulation is often based on static threshold settings, lacking real-time temperature feedback and power regulation mechanisms. This makes it impossible for the system to dynamically adjust power output according to actual temperature changes, resulting in an inflexible regulation process. To this end, a method and system for dynamically adjusting the temperature and power of a vehicle-mounted wireless charger are proposed. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for dynamically adjusting the temperature and power of a vehicle-mounted wireless charger to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0006] The technical solution of the embodiment of the present invention is implemented as follows: A method for dynamically adjusting the temperature and power of a vehicle-mounted wireless charger includes the following steps: Step 1: Preset the initial charging power and initial temperature threshold of the car wireless charger, calibrate the temperature sensor, and check the working status of the power management module and cooling system; Step 2: Obtain temperature data during the charging process and send it to the processing module, which filters and removes noise from the data; Step 3: Use a prediction algorithm and temperature data to predict temperature trends, and use the temperature trends as the basis for power regulation; Step 4: Using an intelligent power regulation algorithm to calculate the optimal charging power based on input parameters, including current temperature, predicted temperature, charging time, battery status, and a preset safe temperature range; Step 5: The power management module receives the charging power instruction and adjusts its output power according to the received instruction; Step 6: Establish a feedback mechanism to monitor the actual effect of power regulation. If the temperature is too high or the power regulation is insufficient, re-execute the intelligent power regulation algorithm to make adjustments. Step 7: Establish an emergency protection mechanism, which is activated when the temperature rises abnormally, records abnormal information and generates a report; In step three, the prediction algorithm includes the following steps: S1: Acquire temperature data during the charging process and preprocess the temperature data, including noise removal, outlier processing, and data smoothing; S2: Using statistical methods or graphical displays to determine the temperature status, which includes rising, falling, and stable. The temperature status provides a basis for subsequent predictions; S3: Use vehicle interior temperature, vehicle speed, charging time, and battery status as input variables to build a prediction model; S4: Build a prediction model, train the model using historical data, adjust model parameters, and optimize model prediction performance; S5: Evaluate the trained prediction model and optimize the model based on the evaluation results, including adjusting model parameters, improving feature selection, and trying different model structures; S6: Input the current and recent temperature data and related features into the trained prediction model to calculate the predicted temperature; S7: Compare the actual temperature with the predicted temperature, analyze the cause of the prediction error, and iteratively improve the prediction algorithm accordingly.

[0007] Further preferably, in step 4, the intelligent power regulation algorithm includes the following steps: Get input parameters and initialize them; Evaluate whether the current battery status and environmental conditions meet the requirements for safe charging; Apply decision logic to process input parameters and calculate the optimal charging power by adjusting and optimizing the calculation process. Factors affecting the calculation process include charging time, battery status, and environmental conditions. Automatically adjust the output power of the charging equipment based on the optimal charging power, continuously monitor changes in battery status and environmental conditions, and adjust the charging power in real time as needed; Feedback data is collected, including actual charging speed and battery temperature change data, and the feedback data is used to evaluate the effectiveness of the current charging strategy.

[0008] Further preferably, the decision logic includes a conditional judgment method, and the adjustment judgment method performs conditional judgment on the data parameters according to a preset threshold value.

[0009] Further preferably, the decision logic further includes a weight allocation method, which allocates different weights to the results of the adjustment judgment according to their importance and the degree of influence on the charging process.

[0010] Further preferably, the decision logic also includes a fuzzy control method. When the input parameters are uncertain or fuzzy, the fuzzy control method is used to convert the fuzzy information of the input parameters into a clear control output.

[0011] Further preferably, the decision logic also includes a neural network control method, which learns the relationship between input parameters and optimal charging power through training. During the real-time charging process, the neural network control method calculates the optimal charging power based on current and input parameters.

[0012] Further preferably, in step 1, when the vehicle-mounted wireless charger is enabled, the system sets the initial charging power and initial temperature threshold according to preset default values.

[0013] Further preferably, in step six, the temperature, power and battery status data during the charging process are continuously collected to form a historical database.

[0014] Further preferably, in step seven, when the emergency protection mechanism is triggered, a notification is sent to the user to inform him of the abnormal situation and to suggest corresponding measures.

[0015] A vehicle-mounted wireless charger temperature and power dynamic adjustment system includes a temperature sensor, a processing module, a prediction and adjustment module, a power management module, and a monitoring module. The signal end of the temperature sensor is connected to the signal end of the processing module, the signal end of the processing module is connected to the signal end of the prediction and adjustment module, the signal end of the prediction and adjustment module is connected to the signal end of the power management module, and the signal end of the monitoring module is connected to the signal end of the power management module. The temperature sensor is used to monitor the temperature of the vehicle wireless charger during the charging process in real time and then send the temperature to the processing module; The processing module is used to receive temperature data and perform filtering and denoising processing, and the filtering method includes low-pass filtering and median filtering; The prediction and regulation module is used to use a prediction algorithm to predict the temperature trend and use an intelligent power regulation algorithm to calculate the optimal charging power based on input parameters; The power management module is used to receive the calculated optimal charging power instruction and adjust its output power according to the received instruction; The monitoring module is used to monitor the actual effect of the power regulation of the vehicle-mounted wireless charger. When the temperature is too high or the power regulation is insufficient, the intelligent power regulation algorithm is re-executed for adjustment.

[0016] The embodiment of the present invention adopts the above technical solution, which has the following advantages: 1. Through real-time temperature monitoring and an intelligent power regulation algorithm, the present invention can maximize charging efficiency while ensuring safety, avoiding extended charging times due to unnecessary power reduction, thereby allowing users to fully charge their batteries more quickly. Furthermore, by optimizing the regulation between temperature and power, the charging process is made smoother and more reliable. Users do not need to worry about charging interruptions or power reductions due to excessive temperatures, thereby improving the user experience.

[0017] 2. The present invention adopts technologies such as intelligent power regulation algorithm and temperature prediction algorithm to make the car wireless charger have a higher level of intelligence. It can automatically adjust the power output according to real-time data without manual intervention, thereby reducing the usage threshold and maintenance costs.

[0018] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 This is a flowchart of the steps of the method for dynamically adjusting the temperature and power of a vehicle-mounted wireless charger according to the present invention; Figure 2 This is a structural diagram of the temperature and power dynamic adjustment system of the vehicle-mounted wireless charger of the present invention. DETAILED DESCRIPTION

[0021] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and description are to be considered as illustrative in nature and not restrictive.

[0022] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0023] like Figure 1-Figure 2 As shown, an embodiment of the present invention provides a method for dynamically adjusting the temperature and power of a vehicle-mounted wireless charger, comprising the following steps: Step 1: Preset the initial charging power and initial temperature threshold of the car wireless charger, calibrate the temperature sensor, and check the working status of the power management module and cooling system; When the car wireless charger is used for the first time or restarted, the system will set the initial charging power and initial temperature thresholds according to the preset default values. These values ​​can be adjusted according to different vehicle models, battery types and user needs; Step 2: Obtain temperature data during the charging process and send it to the processing module, which filters and removes noise from the data; The temperature data during the charging process is monitored in real time using a temperature sensor at a certain sampling frequency. This data will be continuously recorded and sent to the control unit for processing. The raw temperature data received by the control unit contains noise or abnormal values, and needs to be filtered and denoised to improve the accuracy and reliability of the data.

[0024] Step 3: Use a prediction algorithm and temperature data to predict temperature trends, and use the temperature trends as the basis for power regulation; Perform trend analysis on the preprocessed temperature data to determine whether the temperature is rising, falling, or stable, which helps predict temperature trends in the future.

[0025] Use an appropriate prediction model to predict the temperature, which should be able to take into account historical temperature data, current environmental conditions, and other relevant factors during the charging process; Calculate the predicted temperature for a period of time in the future based on the prediction model and use it as the basis for subsequent power adjustment; Step 4: Use the intelligent power regulation algorithm to calculate the optimal charging power based on input parameters, including current temperature, predicted temperature, charging time, battery status, and a preset safe temperature range; Improve charging efficiency while ensuring safety, avoid unnecessary power reduction, and calculate the optimal charging power that meets charging needs and ensures safety by comprehensively considering multiple factors; Step 5: The power management module receives the charging power instruction and adjusts its output power according to the instruction. At the same time, it monitors the temperature change after the power adjustment in real time to ensure that the adjustment effect meets the expectations. Step 6: Establish a feedback mechanism to monitor the actual effect of power regulation. If the temperature is too high or the power regulation is insufficient, re-execute the intelligent power regulation algorithm to make adjustments. At the same time, continuously collect temperature, power, and battery status data during the charging process to form a historical database. Continuously optimize the intelligent power regulation algorithm based on data analysis results to improve its accuracy and flexibility. At the same time, adjust the algorithm parameters and strategies based on user feedback and market demand; Step 7: Establish an emergency protection mechanism. The emergency protection mechanism will be activated when the temperature rises abnormally, record the abnormal information and generate a report; When the emergency protection mechanism is triggered, a notification is sent to the user to inform them of the abnormal situation and recommend appropriate measures; If power regulation fails to effectively reduce the temperature or other abnormal conditions occur, such as hardware failure or communication interruption, appropriate measures will be taken to ensure safety, such as reducing power to a safe level, sounding an alarm, or disabling charging functions; In step three, the prediction algorithm includes the following steps: S1: Acquire temperature data during the charging process and preprocess the temperature data, including noise removal, outlier processing, and data smoothing, to improve data accuracy and reliability. The preprocessed data will be used for subsequent analysis and prediction; S2: Use statistical methods or graphical displays to determine the temperature status. The temperature status includes rising, falling, and stable. The temperature status provides the basis for subsequent predictions. S3: Use vehicle interior temperature, vehicle speed, charging time, and battery status as input variables to build a prediction model; S4: Build a prediction model, train the model using historical data, adjust model parameters, and optimize the model's prediction performance. During the training process, pay attention to the model's accuracy, stability, and generalization ability. S5: Evaluate the trained prediction model and optimize the model based on the evaluation results, including adjusting model parameters, improving feature selection, and trying different model structures; S6: Input the current and recent temperature data and related features into the trained prediction model to calculate the predicted temperature. The prediction result is used as the basis for subsequent power adjustment to guide the power output of the on-board wireless charger; S7: Compare the actual temperature with the predicted temperature, analyze the cause of the prediction error, and iteratively improve the prediction algorithm accordingly. Continuously collect new data and update the model to adapt to various changes that may occur during the charging process.

[0026] In one embodiment, in step 4, the intelligent power regulation algorithm includes the following steps: Obtain input parameters and initialize them to ensure data accuracy and consistency, providing a reliable basis for subsequent calculations; Evaluate whether the current battery status and environmental conditions meet the requirements for safe charging. This includes checking whether the battery temperature is within a safe range, whether the remaining power is sufficient to support the charging process, and predicting whether the temperature may cause safety issues such as overheating; Apply decision logic to process input parameters and calculate the optimal charging power by adjusting and optimizing the calculation process. Factors affecting the calculation process include charging time, battery status, and environmental conditions. Automatically adjust the output power of the charging device based on the optimal charging power, continuously monitor changes in battery status and environmental conditions, and adjust the charging power in real time as needed. By dynamically adjusting the power output, the algorithm can ensure that the charging process always remains in the optimal state; Collect feedback data, including actual charging speed and battery temperature change data. This feedback data is used to evaluate the effectiveness of the current charging strategy and provide optimization suggestions for future charging processes. By continuously learning and optimizing algorithm parameters, the intelligent power regulation algorithm can gradually improve its accuracy and reliability, providing users with a more efficient and safe charging experience.

[0027] In one embodiment, the decision logic includes a conditional judgment method, and the adjustment judgment method performs conditional judgment on the data parameters according to a preset threshold value; For example, whether the current temperature exceeds the upper or lower limit of the safe temperature range; whether the predicted temperature shows that the temperature will rise sharply in the future; whether the remaining battery power is lower than a certain critical value, and the charging speed needs to be accelerated; and whether there is a limit on the charging time, and charging needs to be completed within a specific time.

[0028] In one embodiment, the decision logic further includes a weight allocation method, which assigns different weights to the results of the adjustment judgment according to their importance and the degree of impact on the charging process; Safety factors have the highest weight because overheating can cause battery damage or even fire; charging efficiency and battery life have the second highest weight because they are directly related to user experience and long-term costs.

[0029] In one embodiment, the decision logic further includes a fuzzy control method, which is used to convert the fuzzy information of the input parameters into clear control outputs when the input parameters are uncertain or fuzzy. For example, when the battery temperature approaches but has not yet exceeded a safety threshold, the algorithm gradually reduces the charging power according to fuzzy rules to avoid a sharp temperature rise.

[0030] In one embodiment, the decision logic also includes a neural network control method, which learns the relationship between input parameters and optimal charging power through training. During the real-time charging process, the neural network control method calculates the optimal charging power based on current and input parameters.

[0031] A vehicle-mounted wireless charger temperature and power dynamic adjustment system includes a temperature sensor, a processing module, a prediction and adjustment module, a power management module, and a monitoring module, characterized in that: a signal end of the temperature sensor is connected to a signal end of the processing module, a signal end of the processing module is connected to a signal end of the prediction and adjustment module, a signal end of the prediction and adjustment module is connected to a signal end of the power management module, and a signal end of the monitoring module is connected to a signal end of the power management module; The temperature sensor is used to monitor the temperature of the vehicle wireless charger in real time during the charging process and then send it to the processing module; The processing module is used to receive temperature data and perform filtering and denoising processing, and the filtering methods include low-pass filtering and median filtering; The prediction and regulation module is used to predict the temperature trend using a prediction algorithm and calculate the optimal charging power based on the input parameters using an intelligent power regulation algorithm; The power management module is used to receive the calculated optimal charging power instruction and adjust its output power according to the received instruction; The monitoring module is used to monitor the actual effect of the power regulation of the vehicle wireless charger. When the temperature is too high or the power regulation is insufficient, the intelligent power regulation algorithm is re-executed for adjustment.

[0032] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various modifications and substitutions within the technical scope disclosed in the present invention, and such modifications and substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for dynamically adjusting temperature and power of a vehicle-mounted wireless charger, characterized in that: The following steps are involved: Step 1: Preset the initial charging power and initial temperature threshold of the vehicle wireless charger, calibrate the temperature sensor, and check the working status of the power management module and the cooling system; Step 2: Obtain temperature data during the charging process and send it to the processing module, which performs filtering and denoising on the data; Step 3: Use the prediction algorithm and temperature data to predict the temperature trend, and use the temperature trend as the basis for power regulation; Step 4: Using an intelligent power regulation algorithm to calculate the optimal charging power based on input parameters, the input parameters include current temperature, predicted temperature, charging time, battery status, and a preset safe temperature range; Step 5: The power management module receives the charging power instruction and adjusts its output power according to the received instruction; Step 6: Establish a feedback mechanism to monitor the actual effect of power regulation. When the temperature is too high or the power regulation is insufficient, re-execute the intelligent power regulation algorithm for adjustment; Step 7: Establish an emergency protection mechanism, which is activated when the temperature rises abnormally, records abnormal information and generates a report; In step three, the prediction algorithm includes the following steps: S1: Acquire temperature data during the charging process and pre-process the temperature data, including noise removal, outlier processing, and data smoothing; S2: using statistical methods or graphical display to determine the temperature status, the temperature status includes rising, falling, and stable, and the temperature status provides a basis for subsequent predictions; S3: Use the vehicle interior temperature, vehicle speed, charging time, and battery status as input variables for building a prediction model; S4: Build a prediction model, train the model using historical data, adjust model parameters and optimize model prediction performance; S5: Evaluate the trained prediction model and optimize the model based on the evaluation results, including adjusting model parameters, improving feature selection, and trying different model structures; S6: inputting current and recent temperature data and related features into the trained prediction model to calculate the predicted temperature; S7: Compare the actual temperature with the predicted temperature, analyze the cause of the prediction error, and iteratively improve the prediction algorithm accordingly.

2. A method for dynamically adjusting temperature and power of a vehicle-mounted wireless charger according to claim 1, characterized in that: In step 4, the intelligent power regulation algorithm includes the following steps: Get input parameters and initialize them; Evaluate whether the current battery status and environmental conditions meet the requirements for safe charging; Use decision logic to process input parameters and calculate the optimal charging power by adjusting and optimizing the calculation process. The factors affecting the calculation process include charging time, battery status and environmental conditions. Automatically adjust the output power of the charging equipment according to the optimal charging power, continuously monitor changes in battery status and environmental conditions, and adjust the charging power in real time as needed; Feedback data is collected, including actual charging speed and battery temperature change data, and the feedback data is used to evaluate the effectiveness of the current charging strategy.

3. A method for dynamically adjusting temperature and power of a vehicle-mounted wireless charger according to claim 2, characterized in that: The decision logic includes a conditional judgment method, and the adjustment judgment method performs conditional judgment on data parameters according to a preset threshold value.

4. A method for dynamically adjusting temperature and power of a vehicle-mounted wireless charger according to claim 3, characterized in that: The decision logic also includes a weight allocation method, which allocates different weights to the results of the adjustment judgment according to their importance and the degree of influence on the charging process.

5. A method for dynamically adjusting temperature and power of a vehicle-mounted wireless charger according to claim 4, characterized in that: The decision logic also includes a fuzzy control method. When the input parameters are uncertain or fuzzy, the fuzzy control method is used to convert the fuzzy information of the input parameters into a clear control output.

6. A method for dynamically adjusting temperature and power of a vehicle-mounted wireless charger according to claim 5, characterized in that: The decision logic also includes a neural network control method, which learns the relationship between input parameters and optimal charging power through training. During real-time charging, the neural network control method calculates the optimal charging power based on current and input parameters.

7. The method for dynamically adjusting temperature and power of a vehicle-mounted wireless charger according to claim 1, characterized in that: In step 1, when the vehicle wireless charger is enabled, the system sets the initial charging power and the initial temperature threshold according to preset default values.

8. The method for dynamically adjusting temperature and power of a vehicle-mounted wireless charger according to claim 1, characterized in that: In step six, the temperature, power, and battery status data during the charging process are continuously collected to form a historical database.

9. The method for dynamically adjusting temperature and power of a vehicle-mounted wireless charger according to claim 1, characterized in that: In step seven, when the emergency protection mechanism is triggered, a notification is sent to the user to inform him of the abnormal situation and suggest corresponding measures.

10. A vehicle-mounted wireless charger temperature and power dynamic adjustment system, applied to the vehicle-mounted wireless charger temperature and power dynamic adjustment method according to any one of claims 1 to 9, comprising a temperature sensor, a processing module, a prediction and adjustment module, a power management module and a monitoring module, characterized in that: The signal end of the temperature sensor is connected to the signal end of the processing module, the signal end of the processing module is connected to the signal end of the prediction and regulation module, the signal end of the prediction and regulation module is connected to the signal end of the power management module, and the signal end of the monitoring module is connected to the signal end of the power management module; The temperature sensor is used to monitor the temperature of the vehicle-mounted wireless charger during the charging process in real time, and then send it to the processing module; The processing module is used to receive temperature data and perform filtering and denoising processing, and the filtering method includes low-pass filtering and median filtering; The prediction and regulation module is used to use a prediction algorithm to predict the trend of temperature and use an intelligent power regulation algorithm to calculate the optimal charging power based on input parameters; The power management module is used to receive the calculated optimal charging power instruction and adjust its output power according to the received instruction; The monitoring module is used to monitor the actual effect of the vehicle-mounted wireless charger after power adjustment. When the temperature is too high or the power adjustment is insufficient, the intelligent power adjustment algorithm is re-executed for adjustment.

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