A method and system for dynamically adjusting temperature and power of a vehicle-mounted wireless charger

Abstract

The application provides a kind of vehicle-mounted wireless charger temperature and power dynamic adjustment method, comprising the following steps: step one: preset initial charging power and initial temperature threshold of vehicle-mounted wireless charger, calibrate temperature sensor, check the working state of power management module, heat dissipation system.The application can ensure to improve charging efficiency as much as possible under the premise of safety through real-time temperature monitoring and intelligent power regulation algorithm, avoid the extension of charging time caused by unnecessary power reduction, so that users can charge the battery faster, and through the optimization of the adjustment process between temperature and power, the charging process is more stable and reliable, users do not need to worry about the problem of charging interruption or power reduction caused by too high temperature, so as to improve user experience.

Description

Technical Field

[0001] This invention relates to a method and system, specifically a method and system for dynamic temperature and power adjustment of an in-vehicle wireless charger, belonging to the field of in-vehicle wireless charger technology. Background Technology

[0002] A car wireless charger is a device that uses wireless charging technology to provide charging services for devices that support wireless charging within a vehicle. It eliminates the need for traditional charging cables and interfaces, instead establishing a contactless power transfer connection between the charger and the charging device through electromagnetic induction, thereby enabling wireless power transmission and device charging.

[0003] Wireless charging technology uses the principle of electromagnetic induction, avoiding the safety hazards such as electrical sparks and short circuits that may occur when plugging and unplugging charging cables in traditional charging methods. At the same time, in-vehicle wireless chargers typically also have overcurrent, overvoltage, and overheat protection functions to ensure a safe and reliable charging process.

[0004] The method for dynamic temperature and power adjustment of in-vehicle wireless chargers mainly involves effectively managing the heat generated during wireless charging and dynamically adjusting the charging power according to charging demand to ensure the safety and efficiency of the charging process.

[0005] In existing technologies, the dynamic adjustment process of in-vehicle wireless chargers between temperature and power is not linear enough, which reduces charging efficiency and affects user experience to some extent. The main reasons are as follows:

[0006] (1) Currently, vehicle wireless chargers use a simple temperature threshold setting method, that is, when the charging temperature exceeds the preset threshold, the charging power is automatically reduced to prevent overheating. This method ignores the continuity and dynamism between temperature and power, resulting in an insufficiently precise adjustment process.

[0007] (2) In the prior art, temperature and power regulation is often based on static threshold setting, lacking real-time temperature feedback and power regulation mechanism, which makes the system unable to dynamically adjust power output according to actual temperature changes, resulting in an inflexible regulation process;

[0008] To address this, a method and system for dynamic temperature and power adjustment of an on-board wireless charger are proposed. Summary of the Invention

[0009] In view of this, the present invention provides a method and system for dynamic adjustment of temperature and power of a vehicle-mounted wireless charger, so as to solve or alleviate the technical problems existing in the prior art, and at least provide a beneficial option.

[0010] The technical solution of this invention is implemented as follows: A method for dynamically adjusting the temperature and power of an in-vehicle wireless charger, comprising the following steps:

[0011] 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 heat dissipation system;

[0012] Step 2: Acquire temperature data during the charging process and send it to the processing module, which then performs filtering and noise reduction on the data.

[0013] Step 3: Use a prediction algorithm and temperature data to predict the temperature trend, and use the temperature trend as the basis for power adjustment.

[0014] Step 4: Use the intelligent power adjustment algorithm and calculate the optimal charging power based on the input parameters, including the current temperature, predicted temperature, charging time, battery status, and preset safe temperature range;

[0015] Step 5: The power management module receives the optimal charging power command and adjusts its output power according to the received command;

[0016] Step 6: Establish a feedback mechanism to monitor the actual effect of power adjustment. When the temperature is too high or the power adjustment is insufficient, re-execute the intelligent power adjustment algorithm for adjustment.

[0017] Step 7: Establish an emergency protection mechanism, which is activated when the temperature rises abnormally, records abnormal information, and generates a report;

[0018] In step three, the prediction algorithm includes the following steps:

[0019] S1: Acquire temperature data during the charging process and preprocess the temperature data, including noise removal, outlier handling, and data smoothing;

[0020] S2: Use statistical methods or graphs to determine the temperature state, which includes three types: rising, falling, and stable. The temperature state provides a basis for subsequent predictions.

[0021] S3: Use in-vehicle temperature, vehicle speed, charging time, and battery status as input variables for building the prediction model;

[0022] S4: Build a prediction model, train the model using historical data, adjust the model parameters, and optimize the model's prediction performance;

[0023] 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;

[0024] S6: Input the current and recent temperature data and related features into the trained prediction model to calculate the predicted temperature;

[0025] S7: Compare the actual temperature with the predicted temperature, analyze the reasons for the prediction error, and iteratively improve the prediction algorithm accordingly.

[0026] More preferably, in step four, the intelligent power regulation algorithm includes the following steps:

[0027] Obtain the input parameters and initialize them.

[0028] Assess whether the current battery status and environmental conditions meet the requirements for safe charging;

[0029] The input parameters are processed using decision logic. By adjusting and optimizing the calculation process, the optimal charging power is calculated. The factors affecting the calculation process include charging time, battery status, and environmental conditions.

[0030] The system automatically adjusts the output power of the charging device based on the optimal charging power, continuously monitors changes in battery status and environmental conditions, and wirelessly adjusts the charging power in real time as needed.

[0031] Collect feedback data, including actual charging speed and battery temperature change data, which is used to evaluate the effectiveness of the current charging strategy.

[0032] More preferably, the decision logic includes a condition judgment method, wherein the condition adjustment judgment method performs condition judgment on the data parameters according to a preset threshold.

[0033] More preferably, the decision logic also includes a weight allocation method, which assigns different weights to the adjustment judgment result based on its importance and degree of influence on the charging process.

[0034] In a further preferred embodiment, the decision logic also includes a fuzzy control method. When the input parameters are uncertain or fuzzy, the fuzzy control method is used to transform the fuzzy information of the input parameters into a clear control output.

[0035] More preferably, the decision logic further 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 the current and input parameters.

[0036] More preferably, in step one, when the vehicle-mounted wireless charger is enabled, the system sets the initial charging power and initial temperature threshold according to preset default values.

[0037] In a further preferred embodiment, in step six, temperature, power, and battery status data during the charging process are continuously collected to form a historical database.

[0038] More preferably, in step seven, when the emergency protection mechanism is triggered, a notification is sent to the user to inform them of the abnormal situation and suggest appropriate measures.

[0039] A vehicle-mounted wireless charger temperature and power dynamic adjustment system includes a temperature sensor, a processing module, a predictive adjustment module, a power management module, and a monitoring module. The signal terminal of the temperature sensor is connected to the signal terminal of the processing module, the signal terminal of the processing module is connected to the signal terminal of the predictive adjustment module, the signal terminal of the predictive adjustment module is connected to the signal terminal of the power management module, and the signal terminal of the monitoring module is connected to the signal terminal of the power management module.

[0040] The temperature sensor is used to monitor the temperature of the vehicle wireless charger in real time during the charging process, and then send the data to the processing module.

[0041] The processing module is used to receive temperature data and perform filtering and noise reduction processing, including low-pass filtering and median filtering.

[0042] The prediction and adjustment module is used to predict the temperature trend using a prediction algorithm and to calculate the optimal charging power using an intelligent power adjustment algorithm based on the input parameters.

[0043] The power management module is used to receive the calculated optimal charging power command and adjust its output power according to the received command.

[0044] The monitoring module is used to monitor the actual effect of power adjustment of the vehicle wireless charger. When the temperature is too high or the power adjustment is insufficient, the intelligent power adjustment algorithm is re-executed for adjustment.

[0045] The embodiments of the present invention have the following advantages due to the adoption of the above technical solutions:

[0046] I. This invention, through real-time temperature monitoring and intelligent power adjustment algorithms, can ensure that charging efficiency is maximized while guaranteeing safety, avoiding prolonged charging time due to unnecessary power reduction. This allows users to fully charge their batteries faster. Furthermore, by optimizing the adjustment process between temperature and power, the charging process is made more stable and reliable. Users do not need to worry about charging interruptions or power reductions caused by excessively high temperatures, thereby improving the user experience.

[0047] Second, this invention employs technologies such as intelligent power adjustment algorithms and temperature prediction algorithms, enabling the vehicle wireless charger to have a higher level of intelligence. It can automatically adjust the power output based on real-time data without manual intervention, thereby reducing the threshold for use and maintenance costs.

[0048] The above overview 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 invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart illustrating the steps of the method for dynamically adjusting the temperature and power of an in-vehicle wireless charger according to the present invention.

[0051] Figure 2 This is a structural diagram of the vehicle-mounted wireless charger temperature and power dynamic adjustment system of the present invention. Detailed Implementation

[0052] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0053] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0054] like Figures 1-2 As shown, this embodiment of the invention provides a method for dynamically adjusting the temperature and power of an in-vehicle wireless charger, including the following steps:

[0055] 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 heat dissipation system;

[0056] When the in-vehicle wireless charger is used for the first time or restarted, the system will set the initial charging power and initial temperature threshold according to the preset default values. These values ​​can be adjusted according to different vehicle models, battery types and user needs.

[0057] Step 2: Acquire temperature data during the charging process and send it to the processing module, which then performs filtering and noise reduction on the data.

[0058] Temperature data during the charging process is monitored in real time using a temperature sensor at a certain sampling frequency. This data is continuously recorded and sent to the control unit for processing. The raw temperature data received by the control unit contains noise or outliers, which need to be filtered and denoised to improve the accuracy and reliability of the data.

[0059] Step 3: Use a prediction algorithm and temperature data to predict the temperature trend, and use the temperature trend as the basis for power adjustment.

[0060] Trend analysis of the preprocessed temperature data can help determine whether the temperature is rising, falling, or stable, which can help predict the temperature trend in the future.

[0061] Use an appropriate prediction model to predict the temperature. The prediction model should be able to take into account historical temperature data, current environmental conditions, and other relevant factors during the charging process.

[0062] The predicted temperature for a future period is calculated based on the prediction model and used as the basis for subsequent power adjustment.

[0063] Step 4: Use the intelligent power adjustment algorithm and calculate the optimal charging power based on the input parameters, including the current temperature, predicted temperature, charging time, battery status, and preset safe temperature range.

[0064] To improve charging efficiency while ensuring safety and avoid unnecessary power reduction, the optimal charging power that can meet charging needs and ensure safety is calculated by comprehensively considering multiple factors.

[0065] Step 5: The power management module receives the optimal charging power command and adjusts its output power according to the received command. At the same time, it monitors the temperature change after the power adjustment in real time to ensure that the adjustment effect meets expectations.

[0066] Step Six: Establish a feedback mechanism to monitor the actual effect of power adjustment. When the temperature is too high or the power adjustment is insufficient, the intelligent power adjustment algorithm is re-executed for adjustment. At the same time, temperature, power and battery status data during the charging process are continuously collected to form a historical database.

[0067] The intelligent power regulation algorithm is continuously optimized based on data analysis results to improve its accuracy and flexibility. At the same time, the algorithm parameters and strategies are adjusted based on user feedback and market demand.

[0068] Step 7: Establish an emergency protection mechanism. The emergency protection mechanism will be activated when the temperature rises abnormally, record abnormal information and generate a report.

[0069] When the emergency protection mechanism is triggered, a notification is sent to the user to inform them of the abnormal situation and suggest appropriate measures;

[0070] If power regulation fails to effectively reduce the temperature or other abnormalities occur, such as hardware failure or communication interruption, take appropriate measures, such as reducing the power to a safe level, issuing an alarm, or turning off the charging function, to ensure safety.

[0071] In step three, the prediction algorithm includes the following steps:

[0072] S1: Acquire temperature data during the charging process, and preprocess the temperature data, including noise removal, outlier handling, and data smoothing, to improve the accuracy and reliability of the data. The preprocessed data will be used for subsequent analysis and prediction.

[0073] S2: Use statistical methods or graphical representations to determine the temperature state, which includes three types: rising, falling, and stable. The temperature state provides a basis for subsequent predictions.

[0074] S3: Use in-vehicle temperature, vehicle speed, charging time, and battery status as input variables for building the prediction model;

[0075] S4: Build a prediction model, train the model using historical data, adjust the model parameters and optimize the model's prediction performance. During the training process, it is necessary to pay attention to the model's accuracy, stability and generalization ability.

[0076] 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;

[0077] S6: Input the current and recent temperature data and related features into the trained prediction model to calculate the predicted temperature. The prediction result serves as the basis for subsequent power adjustment and guides the power output of the vehicle wireless charger.

[0078] S7: Compare the actual temperature with the predicted temperature, analyze the reasons for 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.

[0079] In one embodiment, step four of the intelligent power regulation algorithm includes the following steps:

[0080] Obtain the input parameters, initialize them to ensure data accuracy and consistency, and provide a reliable foundation for subsequent calculations;

[0081] Assess 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 charge is sufficient to support the charging process, and predicting whether the temperature may cause overheating or other safety issues.

[0082] The input parameters are processed using decision logic. By adjusting and optimizing the calculation process, the optimal charging power is calculated. The factors affecting the calculation process include charging time, battery status, and environmental conditions.

[0083] The algorithm automatically adjusts the output power of the charging device based on the optimal charging power, continuously monitors changes in battery status and environmental conditions, and adjusts the wireless charging power in real time as needed. By dynamically adjusting the power output, the algorithm can ensure that the charging process is always kept in the optimal state.

[0084] Collect feedback data, including actual charging speed and battery temperature change data. The feedback data is used to evaluate the effectiveness of the current charging strategy and provide optimization suggestions for future charging processes.

[0085] 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 safer charging experience.

[0086] In one embodiment, the decision logic includes a condition judgment method, which performs condition judgment on data parameters based on a preset threshold.

[0087] For example, whether the current temperature exceeds the upper or lower limit of the safe temperature range; whether the predicted temperature indicates that the temperature will rise sharply in the near future; whether the remaining battery power is below a certain critical value, requiring faster charging; and whether there are time limits for charging, requiring charging to be completed within a specific time.

[0088] In one embodiment, the decision logic also includes a weighting method, which assigns different weights to the results of the adjustment judgment based on their importance and degree of influence on the charging process.

[0089] Safety factors have the highest weight because overheating can damage the battery or even cause a fire; while charging efficiency and battery life have the second highest weight because they are directly related to user experience and long-term costs.

[0090] In one embodiment, the decision logic also includes a fuzzy control method. When the input parameters are uncertain or fuzzy, the fuzzy control method is used to transform the fuzzy information of the input parameters into a clear control output.

[0091] For example, when the battery temperature is close to but has not yet exceeded the safety threshold, the algorithm gradually reduces the charging power according to fuzzy rules to avoid a sharp rise in temperature.

[0092] In one embodiment, the decision logic further 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 the current and input parameters.

[0093] A vehicle-mounted wireless charger temperature and power dynamic adjustment system includes a temperature sensor, a processing module, a predictive adjustment module, a power management module, and a monitoring module. The system is characterized in that: the signal terminal of the temperature sensor is connected to the signal terminal of the processing module; the signal terminal of the processing module is connected to the signal terminal of the predictive adjustment module; the signal terminal of the predictive adjustment module is connected to the signal terminal of the power management module; and the signal terminal of the monitoring module is connected to the signal terminal of the power management module.

[0094] The temperature sensor is used to monitor the temperature of the onboard wireless charger in real time during the charging process, and then send the data to the processing module.

[0095] The processing module is used to receive temperature data and perform filtering and noise reduction processing, including low-pass filtering and median filtering.

[0096] The predictive adjustment module is used to predict temperature trends using a predictive algorithm and to calculate the optimal charging power using an intelligent power adjustment algorithm based on input parameters.

[0097] The power management module is used to receive the calculated optimal charging power command and adjust its output power according to the received command.

[0098] The monitoring module is used to monitor the actual effect of the vehicle wireless charger's power adjustment. When the temperature is too high or the power adjustment is insufficient, the intelligent power adjustment algorithm is re-executed for adjustment.

[0099] 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 variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for dynamically adjusting the temperature and power of an in-vehicle wireless charger, characterized in that, Includes the following steps: 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 heat dissipation system; Step 2: Acquire temperature data during the charging process and send it to the processing module, which then performs filtering and noise reduction on the data. Step 3: Use a prediction algorithm and temperature data to predict the temperature trend, and use the temperature trend as the basis for power adjustment. Step 4: Use the intelligent power adjustment algorithm and calculate the optimal charging power based on the input parameters, including the current temperature, predicted temperature, charging time, battery status, and preset safe temperature range; Step 5: The power management module receives the optimal charging power command and adjusts its output power according to the received command; Step 6: Establish a feedback mechanism to monitor the actual effect of power adjustment. When the temperature is too high or the power adjustment is insufficient, re-execute the intelligent power adjustment 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 preprocess the temperature data, including noise removal, outlier handling, and data smoothing; S2: Use statistical methods or graphs to determine the temperature state, which includes three types: rising, falling, and stable. The temperature state provides a basis for subsequent predictions. S3: Use in-vehicle temperature, vehicle speed, charging time, and battery status as input variables for building the prediction model; S4: Build a prediction model, train the model using historical data, adjust the model parameters, and optimize the model's 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 reasons for the prediction error, and iteratively improve the prediction algorithm accordingly.

2. The method for dynamic temperature and power adjustment of an on-board wireless charger according to claim 1, characterized in that: In step four, the intelligent power regulation algorithm includes the following steps: Obtain the input parameters and initialize them. Assess whether the current battery status and environmental conditions meet the requirements for safe charging; The input parameters are processed using decision logic. By adjusting and optimizing the calculation process, the optimal charging power is calculated. The factors affecting the calculation process include charging time, battery status, and environmental conditions. The system automatically adjusts the output power of the charging device based on the optimal charging power, continuously monitors changes in battery status and environmental conditions, and adjusts the wireless charging power in real time as needed. Collect feedback data, including actual charging speed and battery temperature change data, which is used to evaluate the effectiveness of the current charging strategy.

3. The method for dynamic temperature and power adjustment of an in-vehicle wireless charger according to claim 2, characterized in that: The decision logic includes a condition judgment method, which performs condition judgments on data parameters based on preset thresholds.

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

5. The method for dynamic temperature and power adjustment of an in-vehicle 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 transform the fuzzy information of the input parameters into a clear control output.

6. The method for dynamic temperature and power adjustment of an in-vehicle wireless charger according to claim 5, characterized in that: The decision-making 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 the current and input parameters.

7. The method for dynamic temperature and power adjustment of an on-board wireless charger according to claim 1, characterized in that: In step one, when the vehicle wireless charger is enabled, the system sets the initial charging power and initial temperature threshold according to preset default values.

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

9. The method for dynamic temperature and power adjustment of an in-vehicle 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 them of the abnormal situation and suggest appropriate 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 as described in any one of claims 1-9, comprising a temperature sensor, a processing module, a predictive adjustment module, a power management module, and a monitoring module, characterized in that: The signal terminal of the temperature sensor is connected to the signal terminal of the processing module, the signal terminal of the processing module is connected to the signal terminal of the predictive adjustment module, the signal terminal of the predictive adjustment module is connected to the signal terminal of the power management module, and the signal terminal of the monitoring module is connected to the signal terminal 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 the data to the processing module. The processing module is used to receive temperature data and perform filtering and noise reduction processing, including low-pass filtering and median filtering. The prediction and adjustment module is used to predict the temperature trend using a prediction algorithm and to calculate the optimal charging power using an intelligent power adjustment algorithm based on the input parameters. The power management module is used to receive the calculated optimal charging power command and adjust its output power according to the received command. The monitoring module is used to monitor the actual effect of power adjustment of the vehicle wireless charger. 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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