Electric bicycle controller parameter adaptive system based on Bluetooth communication

Abstract

The utility model provides an electric bicycle controller parameter self-adaptive system based on Bluetooth communication, and aims to optimize the performance of daily commuting tools and the user experience through an intelligent means. According to the system, various sensors, such as acceleration, speed and power sensors, installed on the electric vehicle are used for monitoring the running state of the vehicle in real time, and data are transmitted to a smart phone of a user through Bluetooth. And an application program on the mobile equipment performs deep learning and pattern recognition on the data to generate personalized riding parameter optimization suggestions. And then, the optimized parameters are written into an electric vehicle controller through Bluetooth, and the original preset parameters are covered to adapt to the driving preference of the user. According to the scheme, the cruising ability of the electric bicycle can be remarkably improved, the service life of the electric bicycle can be remarkably prolonged, more comfortable and efficient riding experience can be provided according to user habits, and the method is particularly suitable for scenes where controller parameters need to be frequently adjusted to meet individual requirements.

Description

Technical Field

[0001] This utility model relates to the field of electric bicycle electronics and wireless communication technology, and in particular to an adaptive parameter system for an electric bicycle controller based on Bluetooth communication. Background Technology

[0002] With the development of technology, electric bicycles are gradually becoming more intelligent. As technology iterates, electric bicycles are constantly introducing new functions, and the vehicle controller, as the core component of the control system, also needs to be upgraded with firmware to meet the needs of modern users.

[0003] As the most common daily commuting mode of transportation, the effective configuration of the vehicle controller parameters has a significant impact on range and riding experience for electric bicycles. Typically, the parameters of the vehicle controller are set during manufacturing based on general expectations, such as maximum speed, acceleration, and power output. However, due to differences in user preferences and operating habits, these generic settings may not achieve optimal performance and efficiency. Furthermore, from a manpower, material, and feasibility standpoint, it is impractical to complete customized parameter adaptation for each user before shipment. Summary of the Invention

[0004] This invention overcomes the shortcomings of the prior art and provides an adaptive parameter system for electric bicycle controllers based on Bluetooth communication. It aims to reduce the pressure on the server to handle concurrent requests, while reducing traffic and time consumption during firmware upgrades, and improving the efficiency of firmware upgrades and user experience.

[0005] To achieve the above objectives, the technical solution adopted by this utility model is as follows: an electric bicycle controller parameter adaptive system based on Bluetooth communication, comprising an electric bicycle, characterized in that it further comprises several sensors installed on the electric bicycle, an electric bicycle controller with Bluetooth communication, and a mobile device;

[0006] The sensor is used to monitor the operating status of the electric vehicle and generate monitoring data;

[0007] The electric vehicle controller includes a data acquisition and preprocessing unit, a preset parameter unit, and an execution unit; the data acquisition and preprocessing unit is used to collect the monitoring data and generate actual driving parameters; the preset parameter unit stores preset driving parameters; the execution unit controls the electric vehicle's driving mode according to the preset driving parameters.

[0008] The mobile device includes a Bluetooth component, a processor, a memory, and a user interface; the Bluetooth component is used to establish a Bluetooth connection with the electric vehicle controller; the processor is used to read the actual driving parameters and generate corrected driving parameters, which are written into the memory and the preset parameter unit respectively; wherein, the corrected driving parameters written into the preset parameter unit overwrite the preset driving parameters; the user interface is used to provide a user operation interface.

[0009] In a preferred embodiment of this utility model, the sensor includes an acceleration sensor, which is used to monitor the acceleration changes of the electric vehicle and generate acceleration monitoring data.

[0010] In a preferred embodiment of this utility model, the sensor includes a speed sensor, which is used to monitor the driving speed of the electric vehicle and generate driving speed monitoring data.

[0011] In a preferred embodiment of this invention, the sensor includes a power sensor, which is used to monitor the power of the electric vehicle and generate power monitoring data.

[0012] In a preferred embodiment of the present invention, the data acquisition and preprocessing unit further includes a filter and a data compression component. The filter is used to remove noise from the sensor data, and the data compression component is used to reduce the amount of data transmitted to the mobile device and improve data transmission efficiency.

[0013] In a preferred embodiment of this invention, the electric vehicle controller further includes a power management unit to ensure stable power supply for the entire system.

[0014] In a preferred embodiment of this invention, the mobile device further includes a data analysis engine, which is used to perform deep learning and pattern recognition on the collected riding data to generate personalized riding parameter optimization suggestions.

[0015] In a preferred embodiment of the present invention, the mobile device further includes a security verification unit, which is used to verify the modification permission of the electric vehicle controller parameters.

[0016] In a preferred embodiment of this utility model, the data acquisition and preprocessing unit, preset parameter unit and execution unit of the electric vehicle controller are connected by circuit connection and integrated on the same main control board or connected to each other as independent modules.

[0017] In a preferred embodiment of the present invention, the system further includes a cloud server, which includes computing resources and a database, and the cloud server is connected to the mobile device via a network.

[0018] This utility model solves the defects existing in the background technology, and has the following beneficial effects:

[0019] This invention provides an adaptive parameter system for electric bicycle controllers based on Bluetooth communication. By monitoring and analyzing the user's riding data in real time, the system dynamically adjusts the parameters of the electric bicycle controller, thereby optimizing the performance of the electric bicycle and the user experience.

[0020] This invention includes sensors such as an acceleration sensor, a speed sensor, and a power sensor installed on an electric vehicle, an electric vehicle controller with Bluetooth communication capability, and a mobile device. A data acquisition and preprocessing unit collects sensor data, generates actual driving parameters, and transmits them to the mobile device via Bluetooth. The mobile device's data analysis engine performs deep learning and pattern recognition on the data, generates personalized riding parameter optimization suggestions, and writes the corrected parameters into the preset parameter unit of the electric vehicle controller, overwriting the original preset driving parameters. By monitoring and analyzing the user's riding data in real time, the system can automatically adjust the controller parameters to adapt to the user's specific needs.

[0021] This invention provides a data analysis engine for mobile devices that generates personalized riding parameter optimization suggestions through deep learning and pattern recognition of stored actual riding data. The processor generates corrected riding parameters based on these suggestions and writes them into the preset parameter unit of the electric vehicle controller. Users can view the current vehicle status, adjust settings, and confirm parameter updates through the mobile device's user interface. The system can provide customized parameter settings based on the user's riding habits and preferences, thereby better meeting the user's needs.

[0022] Existing electric vehicle controller parameters are typically set during manufacturing based on general expectations, failing to dynamically adapt to each user's specific needs. This invention, however, utilizes real-time data monitoring and adaptive adjustment to enable controller parameters to more accurately match the user's riding habits, providing a personalized parameter optimization solution that improves the overall performance and efficiency of the electric vehicle. Furthermore, automated adjustment reduces the complexity and time cost of manual parameter adjustments, lowering maintenance costs. Attached Figure Description

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments;

[0024] Figure 1 This is a schematic diagram of the structure of the Bluetooth-based electric bicycle controller parameter adaptive system provided by this utility model;

[0025] In the diagram: 1. Sensor; 2. Electric vehicle controller; 3. Mobile device; 4. Cloud server; 11. Speed ​​sensor; 12. Accelerometer; 13. Power sensor; 21. Data acquisition and preprocessing unit; 22. Preset parameter unit; 23. Execution unit; 24. Power management unit; 31. Bluetooth component; 32. Processor; 33. Memory; 34. User interface; 35. Data analysis engine; 36. Security verification unit; 41. Computing resources; 42. Database. Detailed Implementation

[0026] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. These drawings are simplified schematic diagrams, which are only used to illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0027] Application Overview:

[0028] This invention is applicable to electric vehicles used in environments with low bandwidth or no stable network connection. It utilizes Bluetooth technology to achieve efficient firmware difference file transfer and updates, meeting the personalized riding settings of each user. With the widespread use of smartphones and their robust ecosystem, real-time data interaction with the vehicle controller is achieved via Bluetooth, recording the controller's internal parameters during riding. This data is then systematically and quantitatively analyzed to accurately adapt the vehicle controller parameters for each user. This not only helps optimize battery efficiency, increase range, and extend vehicle lifespan, but also significantly improves the riding experience.

[0029] Exemplary system:

[0030] like Figure 1 As shown, an electric bicycle controller parameter adaptive system based on Bluetooth communication includes an electric bicycle, characterized in that it further includes several sensors 1 installed on the electric bicycle, an electric bicycle controller 2 with Bluetooth communication, and a mobile device 3.

[0031] Sensor 1 is used to monitor the operating status of the electric vehicle and generate monitoring data;

[0032] The electric vehicle controller 2 includes a data acquisition and preprocessing unit 21, a preset parameter input 22, and an execution unit 23. The data acquisition and preprocessing unit 21 is used to collect monitoring data and generate actual driving parameters. The preset parameter input 22 stores preset driving parameters. The execution unit 23 controls the driving mode of the electric vehicle according to the preset driving parameters.

[0033] The mobile device 3 includes a Bluetooth component 31, a processor 32, a memory 33, and a user interface 34. The Bluetooth component 31 is used to establish a Bluetooth connection with the electric vehicle controller 2. The processor 32 is used to read actual driving parameters and generate corrected driving parameters, which are written into the memory 33 and the preset parameter input 22, respectively. The corrected driving parameters written into the preset parameter input 22 overwrite the preset driving parameters. The user interface 34 is used to provide a user operation interface.

[0034] In this embodiment, the mobile device 3 can be a mobile phone, tablet, computer, or other device that supports Bluetooth communication and can execute applications.

[0035] The data acquisition and preprocessing unit 21 also includes a filter and a data compression component. The filter is used to remove noise from the data of the sensor 1, and the data compression component is used to reduce the amount of data transmitted to the mobile device 3 and improve data transmission efficiency.

[0036] The electric vehicle controller 2 also includes a power management unit to ensure a stable power supply for the entire system.

[0037] The mobile device 3 also includes a data analytics engine 35, which performs deep learning and pattern recognition on the collected riding data to generate personalized riding parameter optimization suggestions.

[0038] The mobile device 3 also includes a security verification unit 36, which is used to verify the modification permission of the electric vehicle controller 2 parameters, ensuring that only authorized users can modify the parameters of the electric vehicle controller 2, thereby protecting user privacy and electric vehicle safety.

[0039] The data acquisition and preprocessing unit 21, preset parameter input 22 and execution unit 23 of the electric vehicle controller 2 are connected to the same main control board via circuit connection.

[0040] The system also includes a cloud server 4, which includes computing resources 41 and a database 42. The cloud server 4 is connected to the mobile device 3 via a network to support complex data analysis tasks and provide personalized parameter optimization suggestions.

[0041] Sensor 1 includes an acceleration sensor 12, which monitors changes in the acceleration of the electric vehicle and generates acceleration monitoring data. The data acquisition and preprocessing unit 21 collects the acceleration monitoring data and generates actual acceleration parameters. The processor 32 generates corrected acceleration parameters based on the actual acceleration parameters and writes them into the preset parameter input 22 to adapt to the user's starting speed habits. In this embodiment, the acceleration sensor 12 is model ADXL345.

[0042] Sensor 1 includes a speed sensor 11, which monitors the vehicle's speed and generates speed monitoring data. The data acquisition and preprocessing unit 21 collects the monitoring data of the maximum speed and average speed, and generates actual speed parameters. The processor 32 generates corrected speed parameters based on the actual maximum speed parameters and writes them into the preset parameter input 22 to adapt to the user's driving speed preferences. In this embodiment, the speed sensor 11 is a Hall effect sensor.

[0043] Sensor 1 includes a power sensor 13, which monitors the power of the electric vehicle and generates power monitoring data. The data acquisition and preprocessing unit 21 collects the power monitoring data and generates actual power parameters; the processor 32 generates corrected power parameters based on the actual power parameters and writes them into the preset parameter input 22 to adapt to the user's power needs. In this embodiment, the power sensor 13 includes a current sensor 1 and a voltage sensor 1.

[0044] Based on the user's starting speed habits, maximum speed preferences, and power requirements, the system can generate and apply more suitable parameter settings, significantly improving the performance and efficiency of the electric vehicle. For example, for users who like rapid acceleration, the system can generate higher acceleration parameters; for users who frequently need to climb hills, the system can generate higher output power parameters.

[0045] When the user starts the electric vehicle, the electric vehicle controller 2 begins to operate. The acceleration sensor 12, speed sensor 11, and output power sensor 13 are activated and monitor the operating status of the electric vehicle in real time, generating monitoring data.

[0046] Sensor 1 continuously monitors the electric vehicle's acceleration, speed, and output power, generating corresponding monitoring data. Data acquisition and preprocessing unit 21 collects this monitoring data, removes noise using a filter, reduces the data volume using a data compression component, and generates actual driving parameters. It should be noted that this process is continuous and does not depend on the connection of mobile device 3. The generated actual driving parameters are temporarily stored in data acquisition and preprocessing unit 21, awaiting subsequent transmission to mobile device 3 or cloud server 4.

[0047] Based on the user having at least three riding histories, i.e., when the data acquisition and preprocessing unit 21 stores three sets of monitoring data, the mobile device 3 is connected to the vehicle controller:

[0048] The user opens the application on mobile device 3 and establishes a connection with electric vehicle controller 2 via Bluetooth component 31. Electric vehicle controller 2 transmits the stored actual driving parameters to mobile device 3 via Bluetooth.

[0049] The processor 32 of the mobile device 3 reads the actual driving parameters transmitted from the electric vehicle controller 2 and stores them in the memory 33 of the mobile device 3. The data analysis engine 35 of the mobile device 3 performs deep learning and pattern recognition on the stored actual driving parameters to generate personalized riding parameter optimization suggestions. The processor 32 generates corrected driving parameters based on the optimization suggestions.

[0050] The processor 32 writes the generated corrected driving parameters into the memory 33 of the mobile device 3. The generated corrected driving parameters are then written into the preset parameter input 22 of the electric vehicle controller 2 via Bluetooth component 31, overwriting the original preset driving parameters. The execution unit 23 controls the driving mode of the electric vehicle according to the updated preset driving parameters.

[0051] Users can view the current vehicle status, adjust settings parameters, and confirm parameter updates through the user interface 34 of the mobile device 3. The security verification unit 36 ​​ensures that only authorized users can modify the parameters of the electric vehicle controller 2.

[0052] Mobile device 3 can upload driving data to cloud server 4. Cloud server 4 performs more complex data analysis and provides personalized parameter optimization suggestions. Cloud server 4 can also store users' historical data for long-term tracking and optimization.

[0053] Based on the preferred embodiments of this utility model described above, those skilled in the art can make various changes and modifications without departing from the technical concept of this utility model. The technical scope of this utility model is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A Bluetooth-based electric bicycle controller parameter adaptive system, comprising an electric bicycle, characterized in that, It also includes several sensors installed on the electric vehicle, an electric vehicle controller with Bluetooth communication, and a mobile device; The sensor is used to monitor the operating status of the electric vehicle and generate monitoring data; The electric vehicle controller includes a data acquisition and preprocessing unit, a preset parameter unit, and an execution unit; the data acquisition and preprocessing unit is used to collect the monitoring data and generate actual driving parameters; the preset parameter unit stores preset driving parameters; the execution unit controls the electric vehicle's driving mode according to the preset driving parameters. The mobile device includes a Bluetooth component, a processor, a memory, and a user interface; the Bluetooth component is used to establish a Bluetooth connection with the electric vehicle controller; the processor is used to read the actual driving parameters and generate corrected driving parameters, which are written into the memory and the preset parameter unit respectively; wherein, the corrected driving parameters written into the preset parameter unit overwrite the preset driving parameters; the user interface is used to provide a user operation interface.

2. The electric bicycle controller parameter adaptive system based on Bluetooth communication according to claim 1, characterized in that, The sensor includes an acceleration sensor, which is used to monitor changes in the acceleration of the electric vehicle and generate acceleration monitoring data.

3. The electric bicycle controller parameter adaptive system based on Bluetooth communication according to claim 1, characterized in that, The sensor includes a speed sensor, which is used to monitor the vehicle speed and generate vehicle speed monitoring data.

4. The electric bicycle controller parameter adaptive system based on Bluetooth communication according to claim 1, characterized in that, The sensor includes a power sensor, which is used to monitor the power of the electric vehicle and generate power monitoring data.

5. The electric bicycle controller parameter adaptive system based on Bluetooth communication according to claim 1, characterized in that, The data acquisition and preprocessing unit also includes a filter and a data compression component. The filter is used to remove noise from the sensor data, and the data compression component is used to reduce the amount of data transmitted to the mobile device and improve data transmission efficiency.

6. The electric bicycle controller parameter adaptive system based on Bluetooth communication according to claim 1, characterized in that, The electric vehicle controller also includes a power management unit to ensure a stable power supply for the entire system.

7. The electric bicycle controller parameter adaptive system based on Bluetooth communication according to claim 1, characterized in that, The mobile device also includes a data analysis engine, which performs deep learning and pattern recognition on the collected riding data to generate personalized riding parameter optimization suggestions.

8. The electric bicycle controller parameter adaptive system based on Bluetooth communication according to claim 1, characterized in that, The mobile device also includes a security verification unit, which is used to verify the permission to modify the parameters of the electric vehicle controller.

9. The electric bicycle controller parameter adaptive system based on Bluetooth communication according to claim 1, characterized in that, The data acquisition and preprocessing unit, preset parameter unit, and execution unit of the electric vehicle controller are connected by circuitry and integrated onto the same main control board or connected as independent modules.

10. The electric bicycle controller parameter adaptive system based on Bluetooth communication according to claim 1, characterized in that, The system also includes a cloud server, which includes computing resources and a database, and the cloud server is connected to the mobile device via a network.

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