DESC:FIELD OF INVENTION
The present invention relates to an artificial intelligence based smart switching system and method for multi drive bicycles more particularly the system uses a mid-drive and at least one hub drive to enable AI based auto switching with respect to terrain condition and rider’s biological data for increasing the range of pedal assist irrespective of battery percentage and reduce the size/weight of the battery.

BACKGROUND OF INVENTION AND PRIOR ART
Case study 1:
Devices for storing braking energy, and then drawing on the stored energy to accelerate, are known in the art. Many of the systems are depend on manual control to switch the drive from drive mode to generation mode. It is practically tough to instruct all the users to switch the mode manually. It varies based on terrain condition.

So, there is a requirement for smart switching system to switch the mode without manual intervention.

Case study 2:
Many of the drive assist bicycles are having single drive to drive the cycle upon user requirement. If the motor fails, user can not enjoy the benefit of motor assist.

So, there is a requirement for backup motor or dual motor to assist the rider for their critical condition or required condition.

Case study 3:
Generally, the Pedelec type bicycle supports the rider via electric power when the rider is fatigued or demanding aid/relief. Naturally, the Pedelec type bicycle does not completely rely on the battery. The proposed patent introduces the regeneration mode for reducing the dependency on the battery.

So, there is a requirement for on the go recharging option to extend the pedal assist throughout the riding cycle.

Case study 4:
The existing available regeneration unit for bicycle does not have any smart switching system and dual drive system. It increases the probability of failure during the long riding.

So, there is a requirement for AI based smart switching system for assisting the rider based geographical location and user’s biological data. The system can enable one or multiple drives based on terrain condition or user requirement. So, the probability of failure can be reduced.

Case study 5:
The proposed dual motor and smart switching configurations allows lower rating motors instead of one or more higher rating motors due to the split of the motors providing torque at the required locations. The lower rated motors at both locations is sufficient to meet the requirement on an overall basis. This potentially reduces the strain on the battery.

OBJECT OF THE INVENTION
An object of the invention is to provide a multi drive and AI based smart switching system for Pedelec type bicycles.

Another object of the invention is to provide middle drive and at least one hub motor for ensuring the failure free riding experience to the rider.

Yet another object of the invention is to extend the total riding distance by means of regeneration.

Yet another object of the invention is to increase the range of drive assist while riding the bicycle.

Yet another object of the invention is to reduce the size and weight of the battery by means of on the go charging option or regeneration.

Yet another object of the invention is to introduce smart switching option based on rider’s biological data and terrain conditions.

Yet another object of the invention is to provide option to enable one or more drives based on terrain condition, speed or torque requirement.

Further object of the invention is to enable auto switching based on historical machine learned data.

SUMMARY OF THE INVENTION
Generally, the inventive technology relates to a multi drive aided smart switching system and method for Pedelec type bicycles. More particularly, the inventive technology involves novel methods and systems for enabling AI based smart switching system to select the level of electric assist and human effort based on terrain condition, speed or torque requirement and user requirement. The inventive technology may be particularly suited to providing multi drive assist to the Pedelec type bicycle with respect to terrain and rider’s biological data.

The multi drive system is used in this embodiment to provide motorized support for the bicycle. The extent of electric motor assist will be priority to enable the best riding conditions. This will be controlled by an AI based smart switching circuit. The required power supply can be re-generated while riding the bicycle; the optimum re-generation can be obtained while riding the bicycle with the help of AI based smart switching system, which derives the terrain data to optimize the charging cycle.

BRIEF DESCRIPTION OF THE DRAWINGS
S.NO PART NAME PART NO
1. Middle drive 1
2. Hub drive 2
3. Battery 3
4. Switching unit 4
5. Control system 5
6. Sensors 6
7. Computing device/Mobile device 7
8. Physiological device/Smart watch 8
9. Remote server 9

Figure 1 illustrates the prior art system.
Figure 2 illustrates the embodiment of the invention which shows AI based switching pattern control in all possible terrain condition and speed/torque requirements.
Figure 3 illustrates the embodiment of the invention which shows the basic flow chart of the invention.
Figure 4 illustrates embodiment of the invention which shows the system configuration.
Figure 5 illustrates embodiment of the invention which shows the requirement of dual motor assist for both speed and torque requirements.

The above figures and related written description are not intended to limit the scope of the inventive concepts in any manner. Rather, the figures and written description are provided to illustrate the inventive concepts to a person skilled in the art by reference to particular embodiments.

DETAILED DESCRIPTION OF THE INVENTION
One of the embodiment of the invention discloses about a method being performed in a multi drive bicycle having a an artificial intelligence based smart switching system, a middle-drive (1), a hub drive (2), battery (3), control system (5), plurality of sensors (6), a computing device (7), a physiological device (8), a remote server (9) and smart switching unit (4), the method comprises the steps of pairing the control system (5), sensors (6), computing device (7), remote server (9) and physiological device (8) then allowing the rider by the control system (5) to feed the weight, height, age and health data through the computing device (7). Acquiring the body temperature and heart rate data in real time by means of a physiological device (8) and acquiring GPS, terrain, gyro, weather and wind velocity data by means of a computing device (7) in this similar way acquiring the speed, battery percentage, voltage and current data by means of plurality of sensors (6) further deriving the health statistics data with respect to real time acquired physiological data and manually entered rider data. The main characterized part of the method is deriving the speed and torque statistics data with respect to real time acquired data and manually entered rider data, and mapping the real time data, daily riding pattern, calculated and derived data of the rider with drive assist requirement rate. Wherein each riding cycle being tracked and stored in the remote server (9) along with the above said real time data by the control system (5) to enable a machine learning and AI based switching pattern especially with speed, torque, battery requirements data thereby correspondingly enabling number of drive assist units (1 or 2 or both) to complete the regular riding cycle or extended riding cycle irrespective of available battery percentage, terrain condition and even any one drive (1 or 2) failure condition.

Another embodiment of the invention discloses about an artificial intelligence based smart switching system for multi drive bicycles consisting of a middle-drive (1), a hub drive (2), battery (3), control system (5), plurality of sensors (6), a computing device (7), a physiological device (8), a remote server (9) and smart switching unit (4). The hub drive configured in a wheel region of the bicycle and the middle drive configured in a pedal region of the bicycle. Wherein the control system (5), sensors (6), computing device (7), remote server (9) and physiological device (8) being paired to acquire weight, height, age and health data through the computing device (7). Acquire a body temperature and heart rate data in real time by means of a physiological device (8), acquire GPS, terrain, gyro, weather and wind velocity data by means of a computing device (7), acquire the speed, battery percentage, voltage and current data by means of plurality of sensors (6). Wherein the system derive the health statistics data with respect to real time acquired physiological data and manually entered rider data. The system mainly characterized for deriving the speed and torque statistics data with respect to real time acquired data and manually entered rider data. Wherein the system maps the real time data, daily riding pattern, calculated and derived data of the rider with drive assist requirement rate. Wherein system configured to track each riding cycle and store in the remote server (9) along with the above said real time data by the control system (5) to enable a machine learning and AI based switching pattern especially with speed, torque, battery requirements data thereby correspondingly enabling number of drive assist units (1 or 2 or both) to complete the regular riding cycle or extended riding cycle irrespective of available battery percentage, terrain condition and even any one drive (1 or 2) failure condition.

Yet another embodiment of the invention discloses about the switching pattern being changed through a relay unit.

Yet another embodiment of the invention discloses about the physiological device (8) is a smart watch or wearable device.

Yet another embodiment of the invention discloses about the rider data capable of being a weight and height data.

Yet another embodiment of the invention discloses about the control system (5) capable of being a micro controller.

Yet another embodiment of the invention discloses about a method being performed in a multi drive bicycle having a an artificial intelligence based smart switching system, at least one middle-drive (1), at least one hub drive (2), battery (3), control system (5), plurality of sensors (6), a computing device (7) having a Gyro sensor, GPS and GSM, and a physiological device (8), a remote server (9) and smart switching unit (4) the method comprises the steps of first pairing the control system, sensors, computing device, remote server and physiological device, second allowing the rider by the control system to feed the user data through the computing device, the novel part of the invention is to acquire real time GPS, terrain, environmental and user data through the plurality of sensors (6) or computing device (7) or physiological device (8) or combination thereof. Further acquiring the daily riding pattern and biological data of the rider, and mapping the same with drive assist requirement rate, each riding cycle being tracked and stored in the remote server (9) along with acquired inputs by the control system (5) to enable machine learning and AI based switching thereby correspondingly enabling number of drive assist units to complete the regular riding cycle or extended riding cycle irrespective of available battery percentage, terrain condition and even any one drive failure condition.

Yet another embodiment of the invention discloses about the biological data capable of being a heart rate or BMI/weight data or concluded rider capability or aerobic and anaerobic respiratory range or combination thereof.

Yet another embodiment of the invention discloses about the physiological device (8) is a smart watch/device which acquires the heartbeat, body temperature, ambient temperature and health statistics.

Yet another embodiment of the invention discloses about wind velocity parameter to calculate the impacts of drag force while riding.

Yet another embodiment of the invention discloses about the position of drive units in the bicycle. One of the drive units (1) is placed in the pedal region of the bicycle and another drive (2) is placed in any one of the hub portion of the bicycle wheel.

Yet another embodiment of the invention discloses about the other possible application of the proposed system. The system can be adapted with all kinds of multi motor bicycles.

Yet another embodiment of the invention discloses about the Pedelec type bicycle with dual motor configuration. This type of bicycle does not require huge battery size and weight. The maximum output can be achieved in very short time due to its requirement based assisting nature.

Yet another embodiment of the invention discloses about the multi drive bicycles enabled, and an artificial intelligence based smart switching system comprises of at least one middle drive (1) , at least one hub drive (2), battery (3), switching unit (4) control system (5), plurality of sensors (6), a computing device (7) having a Gyro sensor, GPS and GSM, and a physiological device (8), and a remote server (9). The system acquires the daily riding pattern and biological data of the rider, and mapping the same with drive assist requirement rate, each riding cycle being tracked and stored in the remote server along with acquired inputs by the control system to enable machine learning and AI based switching pattern thereby correspondingly enabling number of drive assist units to complete the regular riding cycle or extended riding cycle or terrain condition or rider’s bio-logical data irrespective of available battery percentage, terrain condition and even any one drive failure condition.

Further embodiment of the invention discloses about in-built motor drive and switching pattern control unit of proposed system. The proposed invention can control the both motors based on acquired inputs from sensors, machine learned data, real time acquired data via mobile device (7) and physiological device (8). The switching unit capable of being a relay unit which enables the middle drive (1) and hub drive (2) based on sensed values.

ADVANTAGES
1. The system will increase the total range of power assist during the riding.
2. The system can eliminate the intermediate charge requirement for the unplanned riding trip.
3. The system reduces the size and weight of the battery.
4. The system reduces the need of manual intervention.
5. The system can provide automatic high speed control.
6. The system can provide can full fill the automatic torque adjustment while riding in the slope.
7. The system can ensure the safe riding of the user.
8. The system can assist the rider in all terrain condition.
9. The system can assist the rider any one of the drive failure condition.
10. The system supports the auto drive management.
11. The method can collect all possible data and assist for machine learning to provide AI based switching pattern for assisting the rider in all possible way.
12. The method can enable hassle free riding experience to the user.
13. The method can enable analyzed driving control which is better than manual control.
14. The method improves the riding pattern of the rider.
15. The method eliminates the requirement of skilled rider to enable the number of drive unit with respect to terrain condition.
16. The method creates the better riding pattern also for the co-riders upon request.
17. The derived switching pattern can be mapped and analyzed, and stored in the centralized platform for providing learning support to beginners.

So that the manner in which the features, advantages and objects of the invention, as well as others which will become apparent, may be understood in more detail, more particular description of the invention briefly summarized above may be had by reference to the embodiment thereof which is illustrated in the appended drawings, which form a part of this specification. It is to be noted, however, that the
drawing illustrate only a preferred embodiment of the invention and is therefore not to be considered limiting of the invention’s scope as it may admit to other equally effective embodiments.
,CLAIMS:WE CLAIM
1. A method being performed in a multi drive bicycle having a an artificial intelligence based smart switching system, a middle-drive (1), a hub drive (2), battery (3), control system (5), plurality of sensors (6), a computing device (7), a physiological device (8), a remote server (9) and smart switching unit (4), the method comprises the steps of ;
a. pairing the control system (5), sensors (6), computing device (7), remote server (9) and physiological device (8) ,
b. allowing the rider by the control system (5) to feed the weight, height, age and health data through the computing device (7),
c. acquiring the body temperature and heart rate data in real time by means of a physiological device (8),
d. acquiring GPS, terrain, gyro, weather and wind velocity data by means of a computing device (7),
e. acquiring the speed, battery percentage, voltage and current data by means of plurality of sensors (6),
f. deriving the health statistics data with respect to real time acquired physiological data and manually entered rider data,
characterized in that
g. deriving the speed and torque statistics data with respect to real time acquired data and manually entered rider data,
h. mapping the real time data, daily riding pattern, calculated and derived data of the rider with drive assist requirement rate,
i. wherein each riding cycle being tracked and stored in the remote server (9) along with the above said real time data by the control system (5) to enable a machine learning and AI based switching pattern especially with speed, torque, battery requirements data thereby correspondingly enabling number of drive assist units (1 or 2 or both) to complete the regular riding cycle or extended riding cycle irrespective of available battery percentage, terrain condition and even any one drive (1 or 2) failure condition.

2. An artificial intelligence based smart switching system for multi drive bicycles consisting of a middle-drive (1), a hub drive (2), battery (3), control system (5), plurality of sensors (6), a computing device (7), a physiological device (8), a remote server (9) and smart switching unit (4),
• the hub drive configured in a wheel region of the bicycle,
• the middle drive configured in a pedal region of the bicycle,
• wherein the control system (5), sensors (6), computing device (7), remote server (9) and physiological device (8) being paired to acquire
o weight, height, age and health data through the computing device (7),
o acquire a body temperature and heart rate data in real time by means of a physiological device (8),
o acquire GPS, terrain, gyro, weather and wind velocity data by means of a computing device (7),
o acquire the speed, battery percentage, voltage and current data by means of plurality of sensors (6),
o wherein the system derive the health statistics data with respect to real time acquired physiological data and manually entered rider data,

characterized in that
• wherein the system configured to derive the speed and torque statistics data with respect to real time acquired data and manually entered rider data,
• wherein the system maps the real time data, daily riding pattern, calculated and derived data of the rider with drive assist requirement rate,
• wherein system configured to track each riding cycle and store in the remote server (9) along with the above said real time data by the control system (5) to enable a machine learning and AI based switching pattern especially with speed, torque, battery requirements data thereby correspondingly enabling number of drive assist units (1 or 2 or both) to complete the regular riding cycle or extended riding cycle irrespective of available battery percentage, terrain condition and even any one drive (1 or 2) failure condition.

2. The system as claimed in claim 2, wherein the switching pattern being changed through a relay unit.

3. The system as claimed in claim 2, wherein the physiological device (8) is a smart watch or wearable device.

4. The system as claimed in claim 2, wherein the rider data capable of being a weight and height data.

5. The system as claimed in claim 2, wherein the control system (5) capable of being a micro controller.

6. The system as claimed in claim 2, wherein the multi drive with dynamic charging capacity capable to reduce the battery size.