TW202415581A - Control system of adjustable shock absorber of electric bicycle - Google Patents

Abstract

A control system of an adjustable shock absorber of an electric bicycle includes an artificial intelligence processor, an environment-sensing module, an image-road-condition recognition module and a driving module. The artificial intelligence processor analyzes an image data and a road surface. The environment-sensing module is electrically connected to the artificial intelligence processor. The image-road-condition recognition module is electrically connected to the artificial intelligence processor. The driving module is electrically connected to the artificial intelligence processor. Moreover, the image-road-condition recognition module downloads a real-time road-condition image data. The artificial intelligence processor analyzes real-time road conditions ahead with the image data, or the environment-sensing module senses a road vibration in real time. The artificial intelligence processor outputs a signal to drive the driving module to rotate a damping adjuster to adjust a stroke and a speed of a piston rod, so as to adjust a hardness of the shock absorber.

Description

Control system of adjustable shock absorber for electric bicycle

The present invention relates to an electric bicycle, and more particularly to a control system that uses artificial intelligence (AI) big data and the Internet to detect road conditions and automatically adjusts the shock absorber to match the terrain or road conditions ahead.

Regarding E-bikes, many people think that since they are exercising, why do they need assistance? Of course, riding a bicycle is definitely better for health than riding an E-bike. If you compare E-bikes with walking, riding a bicycle has a better training effect.

According to a research report, electric bicycles can provide at least moderate intensity physical activity, which is lower than riding a regular bicycle, but higher than walking. Especially for those who have not ridden bicycles often in the past, electric bicycles are an easier start and can improve the physical health (such as cardiopulmonary function) of those who lack exercise.

At present, during the riding process of electric bicycles, the hardness of the shock absorber on the electric bicycle is set before leaving the factory and cannot be adjusted. Therefore, the hardness of the shock absorber of most electric bicycles is only suitable for riding on smooth roads or red brick roads. Once passing through gravel roads or bumpy roads, the shock absorption effect of the shock absorber of the electric bicycle will be too hard, which will not only make the electric bicycle difficult to ride, but also cause the electric bicycle to slip or roll over.

Therefore, some riders replace adjustable shock absorbers to cope with various road surfaces. However, this type of shock absorber is manually adjusted. After adjustment, if the shock absorber is suitable for riding on gravel or bumpy roads, once riding on smooth roads or red brick roads, the shock absorption effect of the shock absorber will appear too soft, causing the rider's frame to shake up and down easily.

Therefore, how to automatically adjust the softness and hardness of the shock absorber according to various road conditions to achieve a comfortable and safe ride is the main issue to be solved by the present invention.

Therefore, the main purpose of the present invention is to provide a method of connecting to the Internet through an image road condition recognition module to connect to the surveillance camera on the road and download real-time image data, and then use artificial intelligence to analyze the image data to determine the road condition to be passed ahead. The system will adjust the hardness of the shock absorber according to the road condition.

Another purpose of the present invention is to use an environmental sensing module to sense road vibration in real time, and then use artificial intelligence to analyze it to determine the real-time road condition. The system will adjust the hardness of the shock absorber according to the road condition.

Another object of the present invention is to utilize an acceleration sensor to sense the acceleration signal when the rider brakes suddenly and then analyze it with the artificial intelligence algorithm.

, to perform pre-judgment and automatically adjust the shock absorber to the predetermined hardness before the shock absorber hits the bottom to avoid slipping and rolling, thereby improving riding safety.

Another object of the present invention is to utilize an acceleration sensor to sense when the electric bicycle is in the air, and then the artificial intelligence makes a judgment to adjust the shock absorber so that the shock absorber is automatically adjusted to a predetermined softness, so that the electric bicycle can effectively reduce shock when it lands.

To achieve the above-mentioned purpose, the present invention provides a control system for an adjustable shock absorber of an electric bicycle, which is electrically connected to the damping adjuster of the piston rod of the shock absorber. The control system includes: an artificial intelligence processor, an environmental sensing module, an image road condition recognition module and a drive module. The artificial intelligence processor is internally loaded with an artificial intelligence algorithm to analyze image data, vehicle speed and road surface. The environmental sensing module is electrically connected to the artificial intelligence processor to sense road vibration. The image road condition recognition module is electrically connected to the artificial intelligence processor to connect to a monitor on the road via the Internet to download real-time road condition image data. The drive module is electrically connected to the artificial intelligence processor. The image road condition recognition module downloads real-time road condition image data, and the artificial intelligence processor analyzes the real-time road condition ahead from the image data, or the environmental sensing module senses road vibration in real time, and the artificial intelligence processor outputs a signal to drive the drive module to rotate the damping adjuster to adjust the piston rod movement stroke and speed, so as to adjust the softness and hardness of the shock absorber.

In one embodiment of the present invention, the environment sensing module includes an environment sensor, a signal amplifying circuit and a signal converting circuit. The signal amplifying circuit is electrically connected to the environment sensor and the signal converting circuit, and the signal converting circuit is electrically connected to the artificial intelligence processor.

In one embodiment of the present invention, the environmental sensor is a strain gauge, and the change in resistance value can be measured using a Wheatstone bridge.

In one embodiment of the present invention, the signal conversion circuit converts an analog signal into a digital signal.

In one embodiment of the present invention, the driving module is a servo motor.

In one embodiment of the present invention, an acceleration sensor is further included. The acceleration sensor is electrically connected to the artificial intelligence processor.

In one embodiment of the present invention, during emergency braking, the acceleration sensor senses an acceleration signal which is analyzed by the algorithm of the artificial intelligence processor to perform a pre-judgment. Before the shock absorber hits the bottom, the drive module is driven to rotate the damping adjuster so that the shock absorber is adjusted to a predetermined hardness.

In one embodiment of the present invention, when the acceleration sensor senses that the electric bicycle is in the air, the artificial intelligence processor drives the driving module to rotate the damping adjuster, so that the shock absorber is automatically adjusted to a predetermined hardness, so that the electric bicycle can effectively absorb shock when it lands.

In one embodiment of the present invention, the acceleration sensor is a three-axis acceleration sensor.

In one embodiment of the present invention, a power supply unit is further included. The power supply unit is electrically connected to the artificial intelligence processor to provide the power required by the control system.

In one embodiment of the present invention, the power supply unit is a rechargeable battery.

In one embodiment of the present invention, the structure of the shock absorber at least includes: a barrel, a damping oil and the piston rod are arranged in the barrel, a piston is arranged on one end of the rod body extending inside the barrel, and the damping adjuster is arranged on one end of the rod body extending outside the barrel.

In one embodiment of the present invention, the piston rod has an adjustable valve hole.

In one embodiment of the present invention, a speed sensing unit is further included. The speed sensing unit is electrically connected to the artificial intelligence processor. The speed sensing unit senses the speed of the electric bicycle and transmits the sensing signal to the artificial intelligence processor for judgment and analysis. If it is judged that the speed of the electric bicycle reaches a high-speed setting value, the artificial intelligence processor outputs a signal to drive the drive module to adjust the hardness of the shock absorber to hard; if it is judged that the speed of the electric bicycle reaches a low-speed setting value, the artificial intelligence processor outputs a signal to drive the drive module to adjust the hardness of the shock absorber to soft.

The technical content and detailed description of the present invention are now described as follows with reference to the drawings:

Please refer to Figures 1 and 2, which are schematic diagrams of the appearance of the electric bicycle and the control system circuit block diagram of the variable shock absorber of the present invention. As shown in the figure: the control system of the adjustable shock absorber of the electric bicycle (e-bike) of the present invention is installed on the electric bicycle 200. The control system 100 includes: an artificial intelligence processor 10, an environment sensing module 20, an image road condition recognition module 30, an acceleration sensor 40, a drive module 50, a power supply unit 60 and a speed sensing unit 70. Among them, the vibration of the road surface is sensed to automatically adjust the softness and hardness of the shock absorber to achieve riding comfort and safety.

The artificial intelligence processor 10 has an artificial intelligence algorithm installed therein for analyzing images, vehicle speed and road surface.

The environment sensing module 20 is electrically connected to the artificial intelligence processor 10. The environment sensing module 20 includes an environment sensor 21, a signal amplifying circuit 22 and a signal converting circuit 23. The signal amplifying circuit 22 is electrically connected to the environment sensor 21 and the signal converting circuit 23. When the environment sensor 21 senses the vibration of the road surface, the sensed signal is transmitted to the signal amplifying circuit 22 for amplification. The amplified signal is converted into a digital signal by the signal converting circuit 23 and output to the artificial intelligence processor 10 to analyze the road surface condition by an artificial intelligence algorithm. In this figure, the environment sensor 21 is a strain gauge. It is worth mentioning that the strain gauge is made of metal wire, just like a resistor. The resistance value of the strain gauge will change as the object under test changes. The change in resistance value can be measured using a Wheatstone bridge, and the strain can be inferred from the change.

The image road condition recognition module 30 is connected to the Internet through a base station (not shown in the figure) to a monitor (not shown in the figure) installed on the road. The monitor (camera) is used to obtain image data of the road surface condition in front. The artificial intelligence processor 10 performs road image data analysis to obtain data on the road surface condition of the road to be passed, so as to adjust the hardness of the shock absorber 201 according to the image data.

The acceleration sensor 40 is electrically connected to the artificial intelligence processor 10. When the rider brakes suddenly, the acceleration sensor 40 can sense the acceleration signal, and after the algorithm analysis of the artificial intelligence processor 10, it can perform a pre-judgment, and drive the driving module 50 to rotate the damping adjuster 2015 before the shock absorber 201 hits the bottom, so that the shock absorber 201 is adjusted to a predetermined softness and hardness to avoid slipping and rolling, thereby improving the safety of riding. At the same time, the acceleration sensor 40 can sense that the electric bicycle 200 is in the air, and the artificial intelligence processor 10 also drives the driving module 50 to rotate the damping adjuster 2015, so that the shock absorber 201 will automatically adjust to a predetermined softness and hardness, so that the electric bicycle 200 can effectively absorb shock when landing. In this figure, the acceleration sensor 40 is a three-axis acceleration sensor.

The driving module 50 is electrically connected to the artificial intelligence processor 10. The driving module 50 receives the signal output by the artificial intelligence processor 10 to drive the damping adjuster 2015 installed on the shock absorber 201 to rotate, so as to adjust the valve hole size on the piston rod (not shown) of the shock absorber 201 to control the stroke and speed of the piston rod. In this figure, the driving module 50 is a servo motor.

The power supply unit 60 is electrically connected to the artificial intelligence processor 10 to provide the power required by the control system 100. The power supply unit 60 is a rechargeable battery.

The speed sensing unit 70 is electrically connected to the artificial intelligence processor 10. The speed sensing unit 70 senses the speed of the electric bicycle 200 and transmits the sensing signal to the artificial intelligence processor 10 for judgment and analysis. If it is determined that the speed of the electric bicycle 200 reaches a high speed setting value (for example, 50 kilometers to 60 kilometers), the artificial intelligence processor 10 outputs a signal to drive the driving module 50, and the driving module 50 rotates the damping adjuster 2015 to adjust the hardness of the shock absorber 201 to hard (small piston rod stroke). If it is determined that the speed of the electric bicycle 200 reaches a low speed setting value (below 25 kilometers), the artificial intelligence processor 10 outputs a signal to drive the driving module 50, and the driving module 50 rotates the damping adjuster 2015 to adjust the hardness of the shock absorber 201 to soft (large piston rod stroke) to ensure driving safety. The image road condition recognition module 30 obtains image data from a monitor (not shown) connected to the road via the Internet, and the artificial intelligence processor 10 performs image road recognition to obtain the road condition of the road to be passed in front, and then the environmental sensing module 20 senses the actual road surface condition to automatically adjust the shock absorption hardness of the shock absorber 201 to provide a comfortable riding feeling.

Please refer to Figures 3-7, which are cross-sectional views of the shock absorber of the present invention and schematic diagrams of the electric bicycle running on smooth roads, red brick roads, gravel roads, bumpy roads and the shock absorber's action comparison; at the same time, please refer to Figures 1-2. As shown in the figure: The shock absorber 201 used in the present invention is shown in Figure 3, but is not limited to the shock absorber 201 structure. The shock absorber 201 is mounted on the front fork structure 202 or the frame 203 of the electric bicycle 200. The shock absorber 201 structure at least includes: a barrel 2011, the barrel 2011 is provided with a damping oil 2012 and a piston rod 2013, the piston rod 2013 extends inside the barrel 2011 and is provided with a piston 2014 on one end of the rod body, and the piston rod 2013 extends outside the barrel 2011 and is provided with a damping adjuster 2015 on one end of the rod body. The piston rod is provided with an adjustable valve hole 2016.

When the rider rides the electric bicycle 200, the image road condition recognition module 30 can be connected to the Internet to a road monitor (not shown) to download the real-time image data. The artificial intelligence processor 10 analyzes the road image data to find out whether the road ahead is smooth, red brick, gravel or bumpy. The intelligent processor 10 will output a signal to drive the driving module 50, and the driving module 50 will rotate the damping adjuster 2015 of the adjustable shock absorber 201 to adjust the size of the valve hole 2016 on the piston rod 2013 to control the stroke and speed of the piston rod 2013 to achieve the hardness adjustment of the shock absorber 201 to provide a comfortable and safe riding mode.

When riding on a smooth road, the environment sensing module 20 senses the vibration of the road surface and transmits the sensing signal to the artificial intelligence processor 10. The artificial intelligence processor 10 will output a signal to drive the driving module 50. The driving module 50 will rotate the damping adjuster 2015 of the adjustable shock absorber 201 to adjust the size of the valve hole 2016 on the piston rod 2013 so that when the piston rod 2013 actuates in the cylinder 2011, the actuation stroke accounts for approximately 10% of the distance of the cylinder 2011.

When riding on a red brick road surface, the environment sensing module 20 senses the vibration of the road surface and transmits the sensing signal to the artificial intelligence processor 10. The artificial intelligence processor 10 will output a signal to drive the driving module 50. The driving module 50 will rotate the damping adjuster 2015 of the adjustable shock absorber 201 to adjust the size of the valve hole 2016 on the piston rod 2013, so that when the piston rod 2013 actuates in the cylinder 2011, the actuation stroke accounts for approximately 30% of the distance of the cylinder 2011.

When riding on a gravel road in real time, the environment sensing module 20 senses the vibration of the road surface and transmits the sensing signal to the artificial intelligence processor 10. The artificial intelligence processor 10 will output a signal to drive the driving module 50. The driving module 50 will rotate the damping adjuster 2015 of the adjustable shock absorber 201 to adjust the size of the valve hole 2016 on the piston rod 2013 so that when the piston rod 2013 actuates in the cylinder 2011, the actuation stroke accounts for approximately 50% of the distance of the cylinder 2011.

When riding on a bumpy road in real time, the environment sensing module 20 senses the vibration of the road surface and transmits the sensing signal to the artificial intelligence processor 10. The artificial intelligence processor 10 will output a signal to drive the driving module 50. The driving module 50 will rotate the damping adjuster 2015 of the adjustable shock absorber 201 to adjust the size of the valve hole 2016 on the piston rod 2013, so that when the piston rod 2013 actuates in the cylinder 2011, the actuation stroke accounts for approximately 80% of the distance of the cylinder 2011.

In addition, the acceleration sensor 40 can sense the acceleration signal when the rider brakes suddenly. After being analyzed by the algorithm of the artificial intelligence processor 10, it can perform pre-judgment and drive the drive module 50 to rotate the damping adjuster 2015 before the shock absorber 201 hits the bottom to adjust the size of the valve hole 2016 on the piston rod 2013, so that the shock absorber 201 is adjusted to a predetermined hardness to avoid slipping and rolling over, thereby improving riding safety. At the same time, when the acceleration sensor 40 senses that the electric bicycle 200 is airborne, the artificial intelligence processor 10 also drives the driving module 50 to rotate the damping adjuster 2015, so that the shock absorber 201 will automatically adjust to a predetermined hardness, so that the electric bicycle 200 can effectively absorb shock when landing.

When the speed sensing unit 70 senses the speed of the electric bicycle 200, the speed sensing unit 70 transmits the sensing signal to the artificial intelligence processor 10 for judgment and analysis. If it is determined that the speed of the electric bicycle 200 reaches the high-speed setting value, the artificial intelligence processor 10 outputs a signal to drive the driving module 50, and the driving module 50 rotates the damping adjuster 2015 to adjust the hardness of the shock absorber 201 to hard. If it is determined that the speed of the electric bicycle 200 reaches the low speed setting value, the artificial intelligence processor 10 outputs a signal to drive the driving module 50, and the driving module 50 rotates the damping adjuster 2015 to adjust the hardness of the shock absorber 201 to soft to ensure driving safety.

However, the above is only a preferred embodiment of the present invention and is not intended to limit the patent protection scope of the present invention. Therefore, all equivalent changes made by using the contents of the description or drawings of the present invention are also included in the scope of protection of the present invention and are hereby stated.

100: Control system 10: Artificial intelligence processor 20: Environmental sensing module 21: Environmental sensor 22: Signal amplification circuit 23: Signal conversion circuit 30: Image road condition recognition module 40: Acceleration sensor 50: Drive module 60: Power supply unit 200: Electric bicycle 201: Shock absorber 2011: Cylinder 2012: Damping oil 2013: Piston rod 2014: Piston 2015: Damping adjuster 2016: Valve hole

FIG1 is a schematic diagram of the appearance of the electric bicycle of the present invention;

FIG. 2 is a schematic diagram of a control system circuit block diagram of a variable shock absorber of an electric bicycle of the present invention.

FIG3 is a schematic cross-sectional view of a shock absorber used in the present invention;

FIG4 is a schematic diagram showing the electric bicycle of the present invention running on a smooth road and the operation of the shock absorber;

FIG5 is a schematic diagram showing the electric bicycle of the present invention running on a red brick road and the operation of the shock absorber;

FIG6 is a schematic diagram showing the electric bicycle of the present invention running on a gravel road and the operation of the shock absorber;

FIG. 7 is a schematic diagram showing the electric bicycle of the present invention running on a bumpy road and the operation of the shock absorber.

100: Control system

10: Artificial Intelligence Processor

20: Environmental sensing module

21: Environmental sensor

22: Signal amplifier circuit

23:Signal conversion circuit

30: Image road condition recognition module

40: Acceleration sensor

50:Drive module

60: Power supply unit

201:Shock absorber

2015: Damping adjuster

Claims (14)

A control system for an adjustable shock absorber of an electric bicycle is electrically connected to a damping adjuster of a piston rod of the shock absorber. The control system includes: An artificial intelligence processor, which has an artificial intelligence algorithm installed therein, for analyzing image data, vehicle speed and road surface; An environmental sensing module, which is electrically connected to the artificial intelligence processor, for sensing road vibration; An image road condition recognition module, which is electrically connected to the artificial intelligence processor, for connecting to a surveillance camera on the road via the Internet to download image data of real-time road conditions; A driving module, which is electrically connected to the artificial intelligence processor; The image road condition recognition module downloads real-time road condition image data, and the artificial intelligence processor analyzes the real-time road condition ahead from the image data, or the environmental sensing module senses the road vibration in real time, and the artificial intelligence processor outputs a signal to drive the drive module to rotate the damping adjuster to adjust the piston rod movement stroke and speed, so as to adjust the softness and hardness of the shock absorber. A control system for an adjustable shock absorber as described in claim 1, wherein the environment sensing module includes an environment sensor, a signal amplifying circuit and a signal converting circuit, the signal amplifying circuit is electrically connected to the environment sensor and the signal converting circuit, and the signal converting circuit is electrically connected to the artificial intelligence processor. A control system for an adjustable shock absorber as described in claim 2, wherein the environmental sensor is a strain gauge and the change in resistance value can be measured using a Wheatstone bridge. A control system for an adjustable shock absorber as described in claim 2, wherein the signal conversion circuit converts an analog signal into a digital signal. A control system for an adjustable shock absorber as described in claim 1, wherein the drive module is a servo motor. The control system of the adjustable shock absorber as described in claim 1 further includes an acceleration sensor, which is electrically connected to the artificial intelligence processor. A control system for an adjustable shock absorber as described in claim 6, wherein during emergency braking, the acceleration sensor senses an acceleration signal which is analyzed by the algorithm of the artificial intelligence processor to perform a pre-judgment, and drives the drive module to rotate the damping adjuster before the shock absorber hits the bottom, so that the shock absorber is adjusted to a predetermined hardness. A control system for an adjustable shock absorber as described in claim 6, wherein when the acceleration sensor senses that the electric bicycle is airborne, the artificial intelligence processor drives the drive module to rotate the damping adjuster, so that the shock absorber is automatically adjusted to a predetermined hardness, so that the electric bicycle can effectively absorb shock when it lands. A control system for an adjustable shock absorber as described in claim 6, wherein the acceleration sensor is a three-axis acceleration sensor. The control system of the adjustable shock absorber as described in claim 1 further includes a power supply unit, which is electrically connected to the artificial intelligence processor to provide the power required by the control system. A control system for an adjustable shock absorber as described in claim 10, wherein the power supply unit is a rechargeable battery. A control system for an adjustable shock absorber as described in claim 1, wherein the structure of the shock absorber comprises at least: a cylinder body, wherein the cylinder body is provided with a damping oil and the piston rod, wherein the piston rod has a piston disposed on one end of the rod body extending inside the cylinder body, and the piston rod has a damping adjuster on one end of the rod body extending outside the cylinder body. A control system for an adjustable shock absorber as described in claim 12, wherein the piston rod has an adjustable valve hole. The control system of the adjustable shock absorber as described in claim 1 further includes a speed sensing unit, which is electrically connected to the artificial intelligence processor. The speed sensing unit senses the speed of the electric bicycle and transmits the sensing signal to the artificial intelligence processor for judgment and analysis. If it is judged that the speed of the electric bicycle reaches a high-speed setting value, the artificial intelligence processor outputs a signal to drive the drive module to adjust the hardness of the shock absorber to hard; if it is judged that the speed of the electric bicycle reaches a low-speed setting value, the artificial intelligence processor outputs a signal to drive the drive module to adjust the hardness of the shock absorber to soft.

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