DESC:ELECTRONIC BRAKING SYSTEM AND METHOD OF OPERATION THEREOF
CROSS REFERENCE TO RELATED APPLICATIONS
The present application claims priority from Indian Provisional Patent Application No. 202421053197 filed on 12/07/2024, the entirety of which is incorporated herein by a reference.
TECHNICAL FIELD
The present disclosure generally relates to an electric motorcycle. Particularly, the present disclosure relates to an electronic braking system for an electric motorcycle. Furthermore, the present disclosure relates to a method of electronic braking in an electric motorcycle.
BACKGROUND
Vehicles have become indispensable for personal transportation, commuting, and logistics. The growing popularity of electric and hybrid vehicles, particularly two wheelers driven by environmental concerns and fuel efficiency, further underscores their importance.
Recently, with rapid advancement in vehicle technologies, safety systems particularly braking systems have seen significant evolution. The braking systems are critical for ensuring the safe operation of a vehicle under various driving conditions. Traditionally, vehicles employ mechanical braking systems wherein the operator applies braking force through a lever or pedal in response to road and traffic conditions. However, in conventional mechanical systems, operators often apply excessive braking force during panic situations, which may lead to wheel lock-up and subsequent skidding of the vehicle. Such events compromise vehicle stability and control, especially during emergency manoeuvres. To address these challenges, modern vehicles are equipped with advanced braking assistance technologies such as Anti-lock Braking Systems (ABS). These systems are designed to prevent wheel locking and improve directional stability. However, despite their advantages, these systems may not always optimize the available braking force, potentially resulting in extended braking distances. Moreover, many of these systems do not adapt to changes in road terrain, such as gradients, surface textures, or wet and slippery conditions, thereby limiting their effectiveness under diverse real-world driving scenarios.
Therefore, there exists a need for improved braking system for two-wheelers that overcomes the one or more problems associated as set forth above.
SUMMARY
An object of the present disclosure is to provide an electronic braking system for an electric motorcycle.
Another object of the present disclosure is to provide a method of electronic braking in an electric motorcycle.
In accordance with first aspect of the present disclosure, there is provided an electronic braking system for an electric motorcycle. The system comprises a plurality of sensors, a processing unit and a braking pressure regulator. The plurality of sensors comprising at least one inertial measurement unit (IMU) sensor, a gear position sensor, and a battery current sensor. The processing unit is configured to receive data from the plurality of sensors, classify a road surface condition based on the data from the at least one inertial measurement unit sensor, determine a current gear position based on the data from the gear position sensor, analyze a battery current draw based on the data from the battery current sensor, determine an optimal braking pressure based on the classified road surface condition, the current gear position, and the battery current draw and generate a control signal corresponding to the determined optimal braking pressure. The braking pressure regulator is configured to receive the control signal and adjust the braking pressure, in response to the received control signal.
The present disclosure provides the electronic braking system for the electric motorcycle. The system as disclosed by present disclosure is advantageous over conventional and existing braking systems. Beneficially, the system enables dynamic and real-time assessment of various riding conditions. Furthermore, the system allow to calculate the optimal braking pressure which leads to enhanced braking performance, improved vehicle stability, and reduced chances of wheel locking or skidding. Furthermore, the system allows for adaptive control, ensuring that braking is tailored to both rider behavior and environmental context. Advantageously, the system improves rider safety and also enhances braking efficiency, thereby reduces the braking distance and offers a smoother, more controlled deceleration experience particularly for electric motorcycles operating across varying terrains.
In accordance with second aspect of the present disclosure, there is provided a method of electronic braking in an electric motorcycle. The method comprising collecting data from a plurality of sensors comprising at least one inertial measurement unit (IMU) sensor, a gear position sensor, and a battery current sensor, classifying a road surface condition based on the data from the at least one inertial measurement unit sensor, determining a current gear position based on the data from the gear position sensor, analyzing a battery current draw based on the data from the battery current sensor, determining an optimal braking pressure based on the classified road surface condition, the current gear position, and the battery current draw, generating a control signal corresponding to the determined optimal braking pressure and adjusting the braking pressure using a braking pressure regulator based on the control signal.
Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.
It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.
BRIEF DESCRIPTION OF DRAWINGS
The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
FIG. 1 illustrates a block diagram of an electronic braking system for an electric motorcycle, in accordance with an aspect of the present disclosure.
FIG. 2 illustrates a flow chart of a method of electronic braking in an electric motorcycle, in accordance with another aspect of the present disclosure.
In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.
DETAILED DESCRIPTION
The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.
The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of an electronic braking system for an electric motorcycle and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.
While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.
The terms “comprise”, “comprises”, “comprising”, “include(s)”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by “comprises... a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.
In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.
The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.
As used herein, the terms “electric motorcycle” refers to a two-wheeled motor vehicle that is powered, either wholly or primarily, by an electric propulsion system. The electric propulsion system comprises one or more electric motors configured to drive the rear wheel or wheels of the motorcycle, and is powered by an on-board rechargeable energy storage system, such as a battery pack. The electric motorcycle may further include power electronics, a vehicle control unit, and other subsystems necessary for propulsion, energy management, and rider interface.
As used herein, the term “electronic braking system”, “system”, “electronic braking” and “braking system” are used interchangeably and refer to a vehicle braking system that employs electronic components and control logic, including sensors, processors, and actuators, to monitor, analyze, and regulate braking parameters in real time. The system utilizes sensor data to assess vehicle dynamics, operational conditions, and environmental factors, and electronically generates control signals to modulate braking force accordingly. Unlike conventional mechanical or purely hydraulic systems, the electronic braking system enables adaptive and intelligent braking control without relying solely on manual input, thereby enhancing braking performance, vehicle stability, and safety.
As used herein, the term “plurality of sensors” and “sensors” are used interchangeably and refer to two or more sensors configured to detect and measure different or same types of parameters associated with the operation and condition of a vehicle. In the context of the present invention, the plurality of sensors may include, but is not limited to, an inertial measurement unit (IMU) sensor configured to detect acceleration, angular velocity, and orientation of the vehicle; a gear position sensor configured to detect the current gear state of the transmission system; and a battery current sensor configured to measure the electrical current drawn from the battery during vehicle operation. The plurality of sensors are operatively connected to a processing unit for enabling real-time data acquisition and analysis to facilitate braking control decisions.
As used herein, the terms “processing unit” refers to a computational element that is operable to respond to and processes instructions that drive the system. Optionally, the processing unit includes, but is not limited to, a microprocessor, a micro-controller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or any other type of processing circuit. Furthermore, the term “processor” may refer to one or more individual processors, processing devices and various elements associated with a processing device that may be shared by other processing devices. Furthermore, the processing unit may comprise ARM Cortex-M series processors, such as the Cortex-M4 or Cortex-M7, or any similar processor designed to handle real-time tasks with high performance and low power consumption. Furthermore, the processing unit may comprise custom and/or proprietary processors.
As used herein, the term “inertial measurement unit sensor” and “IMU sensor” are used interchangeably and refer to an electronic device configured to measure and report a vehicle's specific force, angular rate, and sometimes magnetic field, using a combination of onboard accelerometers, gyroscopes, and optionally magnetometers. The IMU sensor generates data corresponding to linear acceleration, angular velocity, and orientation of the vehicle in three-dimensional space. The IMU sensor enables real-time monitoring of the dynamic behavior of the vehicle, such as lean angle, pitch, roll, yaw, and sudden movements, which are critical for evaluating riding conditions and road surface characteristics. The IMU sensor may be a single integrated module or a combination of discrete components, and may communicate with the processing unit via wired or wireless interfaces.
As used herein, the term “gear position sensor” refers to an electronic sensing device configured to detect and indicate the current gear engaged in a vehicle's transmission system. The sensor generates an electrical signal corresponding to the selected gear position, which may include neutral, forward gears, or reverse (if applicable). The gear position sensor may be implemented using various sensing technologies such as resistive, magnetic (e.g., Hall-effect), optical, or mechanical switches. The output of the gear position sensor is communicated to the processing unit, enabling real-time determination of the vehicle’s transmission state for dynamic control of braking or other vehicle functions.
As used herein, the term “battery current sensor” refers to a sensing device configured to measure the electrical current flowing into or out of a battery pack of an electric vehicle. The sensor may be implemented using one or more current sensing techniques, such as shunt-based sensing, Hall-effect sensing, or magneto-resistive sensing. The battery current sensor generates real-time current data, which can be utilized to estimate power demand, regenerative braking activity, or load conditions of the electric vehicle. The output from the battery current sensor is transmitted to a processing unit for further analysis and control decisions within the braking system.
As used herein, the term “road surface condition” refers to the physical and environmental characteristics of the surface on which the vehicle is operating. This includes, but is not limited to, parameters such as surface roughness, texture, gradient, moisture level (e.g., wet, dry, icy), surface material (e.g., asphalt, gravel, dirt), and traction capability. The road surface condition influences the frictional interaction between the vehicle’s tires and the ground, thereby affecting braking performance, vehicle stability, and manoeuvrability. The road surface condition is determined based on sensor data indicative of vehicle dynamics and environmental cues.
As used herein, the term “current gear position” refers to the presently engaged gear level of the transmission system of the electric motorcycle, as detected by the gear position sensor. The current gear position indicates the mechanical state of the transmission, which determines the torque and speed relationship between the motor and the drive wheel. The gear position may correspond to one of a plurality of discrete gear levels (e.g., first gear, second gear, etc.), neutral, or reverse, and is used by the system to assess the operational dynamics of the vehicle during braking events.
As used herein, the term “battery current draw” refers to the amount of electric current being withdrawn from the battery by the vehicle’s electrical components, particularly the traction motor, during operation. The battery current draw serves as an indicator of the vehicle's instantaneous power demand, acceleration or deceleration behavior, and load condition. The value of the battery current draw varies dynamically based on throttle input, motor load, gradient of the terrain, and regenerative braking activity. The monitoring and analyzing the battery current draw allows the system to infer the driving context and accordingly adjust braking behavior for improved safety and performance.
As used herein, the term “optimal braking pressure” refers to a dynamically determined braking force or hydraulic/electromechanical pressure applied to the braking components of a vehicle, which achieves effective deceleration of the vehicle while maintaining vehicle stability, minimizing wheel lock-up or skidding, and adapting to current operating conditions. The optimal braking pressure is calculated based on real-time inputs such as road surface condition, gear position, and battery current draw, and is intended to maximize braking efficiency without compromising safety or control of the vehicle.
As used herein, the term “control signal” refers to an electrical or electronic signal generated by a processing unit or controller that is configured to command, regulate, or influence the operation of an associated component or system. The control signal corresponds to a signal generated by the processing unit based on one or more input parameters, such as sensor data, and is configured to direct the braking pressure regulator to adjust the braking pressure accordingly. The control signal may include analog or digital signals, pulse-width modulated (PWM) signals, or any other form of communication suitable for controlling an actuator or electronic system.
As used herein, the term “braking pressure regulator” refers to a device or assembly configured to control and modulate the hydraulic or pneumatic pressure applied to the braking components of a vehicle, based on an input control signal. The braking pressure regulator may include one or more electronically controlled valves, actuators, or motorized mechanisms capable of precisely adjusting the braking force delivered to one or more wheels. The regulation of pressure may be dynamic and responsive to varying inputs such as road surface condition, vehicle speed, load, and rider input, enabling optimized braking performance and improved safety. The braking pressure regulator may operate independently or in conjunction with other vehicle systems such as electronic control units (ECUs), anti-lock braking systems (ABS), or combined braking systems (CBS).
As used herein, the term “machine learning algorithms” refers to a class of computer-implemented methods or models that enable a system to learn from data patterns and improve its performance in making predictions or decisions over time without being explicitly programmed for each specific outcome. The machine learning algorithms may include, but are not limited to, supervised learning, unsupervised learning, reinforcement learning, decision trees, support vector machines (SVM), neural networks, k-nearest neighbours (KNN), or deep learning models. These algorithms may be trained using historical sensor data to identify and classify road surface conditions, enabling the system to adapt braking pressure based on real-time input and previously learned patterns.
As used herein, the term “memory” refers to any suitable non-transitory computer-readable medium or storage component capable of storing data and/or executable instructions. The memory may include, but is not limited to, volatile memory such as random-access memory (RAM), and/or non-volatile memory such as read-only memory (ROM), flash memory, hard disk drives (HDD), solid-state drives (SSD), electrically erasable programmable read-only memory (EEPROM), or other forms of digital data storage. The memory may be used to store sensor data, control algorithms, machine learning models, braking pressure profiles, lookup tables, and/or any other software or data required for operation of the system.
As used herein, the term “plurality of pre-defined braking pressure profiles”, “braking pressure profile” and “pre-defined braking pressure profiles” are used interchangeably and refer to a set of distinct, digitally stored datasets that prescribe baseline braking-pressure parameters for the electronic braking system. Each profile corresponds to a specific combination of road-surface condition (e.g., dry asphalt, wet asphalt, loose gravel, compacted dirt, snow or ice), typical vehicle speed range, and expected tyre-road friction coefficient. A single profile contains a target hydraulic or regenerative braking pressure curve expressed as a sequence of pressure values versus deceleration demand or lever-stroke percentage, calibration factors for front- and rear-brake bias, permissible ramp-up and ramp-down rates (dP/dt) that limit how quickly pressure can change and safety bounds that cap maximum and minimum pressure values for the given condition.
Figure 1, in accordance with an embodiment describes an electronic braking system 100 for an electric motorcycle. The system 100 comprises a plurality of sensors 102, a processing unit 104 and a braking pressure regulator 106. The plurality of sensors 102 comprising at least one inertial measurement unit (IMU) sensor 102a, a gear position sensor 102b, and a battery current sensor 102c. The processing unit 104 configured to receive data from the plurality of sensors 102, classify a road surface condition based on the data from the at least one inertial measurement unit sensor 102a, determine a current gear position based on the data from the gear position sensor 102b, analyze a battery current draw based on the data from the battery current sensor 102c, determine an optimal braking pressure based on the classified road surface condition, the current gear position, and the battery current draw and generate a control signal corresponding to the determined optimal braking pressure. The braking pressure regulator 106 is configured to receive the control signal and adjust the braking pressure, in response to the received control signal.
In an embodiment, the at least one IMU sensor 102a includes an accelerometer, a gyroscope and a magnetometer. The accelerometer may be configured to measure linear acceleration along multiple axes, the gyroscope may be configured to detect angular velocity or rotational motion of the motorcycle, and the magnetometer may be configured to sense the earth’s magnetic field for determining heading or orientation. The three components together enable the IMU sensor 102a to provide comprehensive motion and orientation data of the vehicle in real time. Beneficially, by incorporating the IMU sensor 102a with the accelerometer, gyroscope, and magnetometer, the system 100 achieves high-accuracy measurement of vehicle dynamics, such as lean angle, tilt, yaw rate, and directional heading. Advantageously, the detailed motion data enhances the ability of the processing unit 104 to accurately classify the road surface condition under varying ride conditions.
In an embodiment, the processing unit 104 employs machine learning algorithms to classify the road surface condition. The IMU sensor 102a collectively provide real-time data related to the vehicle’s motion, orientation, and acceleration behavior. The machine learning algorithms may be trained using historical datasets comprising various sensor input patterns corresponding to different road surface types such as dry asphalt, wet pavement, loose gravel, or inclined surfaces. The processing unit 104 employing machine learning includes enhanced accuracy and adaptability in identifying road surface conditions under dynamic and unpredictable riding environments. Unlike rule-based classification methods, the use of machine learning allows the system 100 to detect subtle patterns and correlations in sensor data, even in mixed or transitional terrains. Moreover, the system 100 with machine learning algorithms may lead to more reliable classification results which enables the processing unit 104 to determine optimal braking pressure with higher precision. Additionally, the machine learning-based system 100 may improve over time by incorporating new sensor data, thereby enhancing braking safety, minimizing the likelihood of wheel skidding or lock-up, and maintaining vehicle stability across diverse and evolving road scenarios.
In an embodiment, the system 100 comprises a memory 108 configured to store a plurality of pre-defined braking pressure profiles corresponding to different surface conditions. Each profile of the plurality of pre-defined braking pressure profiles corresponds to the specific road surface condition, such as dry asphalt, wet pavement, gravel, or slippery terrain. The braking pressure profiles may include baseline parameters such as braking force distribution, front-rear brake bias, pressure ramp rates, and maximum allowable braking pressure for each condition. Furthermore, the processing unit 104 may be configured to retrieve the suitable braking pressure profile from the memory 108 based on the classification of the current road surface condition, as determined using data from the inertial measurement unit sensor 102a. By using the pre-defined braking pressure profiles, the system 100 may be able to apply a calibrated and context-sensitive braking response even before performing additional real-time computations, thereby improving the response time of system 100. Advantageously, the pre-defined braking pressure profile provides the advantage of fast and efficient decision-making by eliminating the need to compute braking parameters from scratch for each condition. Subsequently, the barking pressure profile helps the system 100 to ensures a consistently safe and optimized braking response by referencing validated pressure maps tailored to known surface types. Furthermore, the ability to store and utilize multiple braking pressure profiles allows the system 100 to adapt to a wide range of road conditions, enhancing overall braking performance, reducing stopping distance, preventing wheel skidding, and improving rider safety and comfort.
In an embodiment, the processing unit 104 is configured to select one of the pre-defined braking pressure profiles based on the classified road surface condition and modify the selected profile based on the current gear position and the battery current draw. The classification of the road surface condition may be performed using sensor data, particularly from the inertial measurement unit (IMU) sensor 102a. Once the suitable profile corresponding to the identified surface condition (e.g., wet road, gravel, or asphalt) may be selected, the processing unit 104 further modifies the selected profile by considering the current gear position obtained from the gear position sensor 102b and the battery current draw determined from the battery current sensor 102c. The modification includes adjusting braking pressure limits, ramp-up rates, and front-rear brake distribution based on load and torque demand, as inferred from the gear and battery data. Beneficially, the modification enables dynamic customization of braking response, ensuring that the braking force is optimally tuned to the surface condition and also to real-time vehicle dynamics. Moreover, by considering the gear position and battery current draw, the system 100 accounts for factors such as motor resistance, vehicle speed, and load distribution, thereby enhancing the braking accuracy. Additionally, the classification of the road surface condition and the modified selected profile results in improved stability, reduced braking distance, and better utilization of both regenerative and mechanical braking components and also helps in reducing brake fade and improving energy efficiency in electric motorcycles, especially in varying terrains and riding conditions.
The present disclosure provides the electronic braking system 100 for the electric motorcycle. The electronic braking system as disclosed by present disclosure is advantageous for enhanced braking efficiency, rider safety, and vehicle control across varying road conditions. Beneficially, by utilizing the plurality of sensors 102 including the inertial measurement unit (IMU) sensor 102a, the gear position sensor 102b and the battery current sensor 102c, the system 100 enables real-time acquisition of multiple dynamic parameters essential for accurate braking control. Advantageously, the IMU sensor 102a comprising the accelerometer, gyroscope, and magnetometer allows the processing unit 104 to precisely classify the road surface condition such as dry, wet, or uneven terrain through analysis of the motorcycle’s motion characteristics. The classification is further enhanced through the application of machine learning algorithms, which enable adaptive learning and improved accuracy over time. Moreover, the incorporation of the gear position sensor 102b and battery current sensor 102c provides contextual inputs related to the powertrain status and vehicle load which enables the system 100 to determine the optimal braking pressure that is neither excessive nor insufficient under given operating conditions. Advantageously, the tailored braking response of the system 100 reduces the risk of wheel lock-up and improves traction management, especially on slippery or unpredictable surfaces. Furthermore, the use of pre-defined braking pressure profiles stored in the memory 108 allows the system 100 to implement terrain- and context-specific pressure curves, which may be further customized in real-time based on current gear position and battery current draw. The real-time customization of the braking pressure profile ensures that braking performance remains both responsive and stable, regardless of environmental conditions or vehicle dynamics. Additionally, by modulating the braking pressure through the electronically controlled braking pressure regulator 106 based on the generated control signal, the system 100 enables smooth and progressive braking, minimizing rider discomfort and enhances the overall vehicle control. Furthermore, the system 100 optimizes the utilization of regenerative and hydraulic braking capabilities and also reduces braking distance and contributes to the durability of braking components, thereby offers a comprehensive and intelligent braking solution for modern electric motorcycles.
In an embodiment, the electronic braking system 100 comprises the plurality of sensors 102, the processing unit 104 and the braking pressure regulator 106. The plurality of sensors 102 comprising the at least one inertial measurement unit (IMU) sensor 102a, the gear position sensor 102b, and the battery current sensor 102c. The processing unit 104 configured to receive data from the plurality of sensors 102, classify the road surface condition based on the data from the at least one inertial measurement unit sensor 102a, determine the current gear position based on the data from the gear position sensor 102b, analyze the battery current draw based on the data from the battery current sensor 102c, determine the optimal braking pressure based on the classified road surface condition, the current gear position, and the battery current draw and generate the control signal corresponding to the determined optimal braking pressure. The braking pressure regulator 106 is configured to receive the control signal and adjust the braking pressure, in response to the received control signal. Furthermore, the at least one IMU sensor 102a includes the accelerometer, the gyroscope and the magnetometer. Furthermore, the processing unit 104 employs machine learning algorithms to classify the road surface condition. Furthermore, the system 100 comprises the memory 108 configured to store the plurality of pre-defined braking pressure profiles corresponding to different surface conditions. Furthermore, the processing unit 104 is configured to select one of the pre-defined braking pressure profiles based on the classified road surface condition and modify the selected profile based on the current gear position and the battery current draw.
Figure 2, describes a method 200 of electronic braking in an electric motorcycle. The method 200 starts at step 202 and completes at step 214. At step 202, the method 200 comprises collecting data from a plurality of sensors 102 comprising at least one inertial measurement unit (IMU) sensor 102a, a gear position sensor 102b, and a battery current sensor 102c. At step 204, the method 200 comprises classifying a road surface condition based on the data from the at least one inertial measurement unit sensor 102a. At step 206, the method 200 comprises determining a current gear position based on the data from the gear position sensor 102b. At step 208, the method 200 comprises analyzing a battery current draw based on the data from the battery current sensor 102c. At step 210, the method 200 comprises determining an optimal braking pressure based on the classified road surface condition, the current gear position, and the battery current draw. At step 212, the method 200 comprises generating a control signal corresponding to the determined optimal braking pressure. At step 214, the method 200 comprises adjusting the braking pressure using a braking pressure regulator 106 based on the control signal
In an embodiment, classifying the road surface condition comprises applying machine learning algorithms to the data from the at least one inertial measurement unit sensor 102a.
In an embodiment, the method 200 comprising accessing a database of pre-defined braking pressure profiles for different surface conditions.
In an embodiment, the method 200 determining the optimal braking pressure comprises selecting a pre-defined braking pressure profile based on the classified road surface condition and adjusting the selected profile based on the current gear position and the battery current draw.
In an embodiment, the at least one inertial measurement unit sensor 102a includes an accelerometer, a gyroscope and a magnetometer.
It would be appreciated that all the explanations and embodiments of the portable device 100 also applies mutatis-mutandis to the method 200.
In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms “disposed,” “mounted,” and “connected” are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Modifications to embodiments and combination of different embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as “including”, “comprising”, “incorporating”, “have”, “is” used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.
Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
,CLAIMS:WE CLAIM:
1. An electronic braking system (100) for an electric motorcycle, wherein the system (100) comprises:
- a plurality of sensors (102) comprising at least one: inertial measurement unit (IMU) sensor (102a), a gear position sensor (102b), and a battery current sensor (102c);
- a processing unit (104) configured to:
- receive data from the plurality of sensors (102);
- classify a road surface condition based on the data from the at least one inertial measurement unit sensor (102a);
- determine a current gear position based on the data from the gear position sensor (102b);
- analyze a battery current draw based on the data from the battery current sensor (102c);
- determine an optimal braking pressure based on the classified road surface condition, the current gear position, and the battery current draw; and
- generate a control signal corresponding to the determined optimal braking pressure; and
- a braking pressure regulator (106) configured to receive the control signal and adjust the braking pressure, in response to the received control signal.
2. The system (100) as claimed in claim 1, wherein the processing unit (104) employs machine learning algorithms to classify the road surface condition.
3. The system (100) as claimed in claim 1, wherein the system (100) comprises a memory (108) configured to store a plurality of pre-defined braking pressure profiles corresponding to different surface conditions.
4. The system (100) as claimed in claim 3, wherein the processing unit (104) is configured to select one of the pre-defined braking pressure profiles based on the classified road surface condition and modify the selected profile based on the current gear position and the battery current draw.
5. The system (100) as claimed in claim 1, wherein the at least one IMU sensor (102a) includes an accelerometer, a gyroscope and a magnetometer.
6. A method (200) of electronic braking in an electric motorcycle, comprising:
- collecting data from a plurality of sensors (102) comprising at least one: inertial measurement unit (IMU) sensor (102a), a gear position sensor (102b), and a battery current sensor (102c);
- classifying a road surface condition based on the data from the at least one inertial measurement unit sensor (102a);
- determining a current gear position based on the data from the gear position sensor (102b);
- analyzing a battery current draw based on the data from the battery current sensor (102c);
- determining an optimal braking pressure based on the classified road surface condition, the current gear position, and the battery current draw;
- generating a control signal corresponding to the determined optimal braking pressure; and
- adjusting the braking pressure using a braking pressure regulator (106) based on the control signal.
7. The method (200) as claimed in claim 6, wherein classifying the road surface condition comprises applying machine learning algorithms to the data from the at least one inertial measurement unit sensor (102a).
8. The method (200) as claimed in claim 6, comprising accessing a database of pre-defined braking pressure profiles for different surface conditions.
9. The method (200) as claimed in claim 8, wherein determining the optimal braking pressure comprises selecting a pre-defined braking pressure profile based on the classified road surface condition and adjusting the selected profile based on the current gear position and the battery current draw.
10. The method (200) as claimed in claim 6, wherein the at least one inertial measurement unit sensor (102a) includes an accelerometer, a gyroscope and a magnetometer.