214 and the ranking subsystem 216 are parts of the cloud 112.

Computer memory elements may include any suitable memory device(s) for storing data and executable program, such as read only memory, random access memory, erasable programable read only memory, electrically erasable programmable read only memory, hard drive, removable media drive for handling memory cards and the like. Embodiments of the present subject matter may be implemented in conjunction with program modules, including functions, procedures, data structures, and application programs, for performing tasks, or defining abstract data types or low-level hardware contexts. Executable program stored on any of the above-mentioned storage media may be executable by the controller or the hardware processor(s) 220.

The plurality of subsystems 110 include the vehicle data receiving subsystem 204 that is communicatively connected to the hardware processor(s) 220. The vehicle data receiving subsystem 204 is configured to receive the captured plurality of primary vehicle parameters from the plurality of sensors 104 and the plurality of secondary vehicle parameters from the plurality of slave controllers 106 in real time. In an embodiment, the plurality of sensors 104 may include at least one of: the brake switch sensor 402, the tire pressure sensor 404, the throttle position sensor 406, an inertial measurement unit (IMU) sensor 408, a hall sensor 410, a worm wheel sensor 412, a current sensor 414, and the like.

The brake switch sensor 402 measures the number of brakes being applied during driving of the electric vehicle 102. The tire pressure sensor 404 measures the tire pressure of the electric vehicle 102. The throttle position sensor 406 measures the degree of throttle which the user rotates while operating the electric vehicle 102. For example, when the throttle is rotated to 30-degree, the throttle position sensor 406 measures that 2.5 volts signal is passed to the motor for propulsion of the electric vehicle 102. When the throttle is rotated to 60-degree, the throttle position sensor 406 measures that 5 volts signal is passed to the motor for propulsion of the electric vehicle 102. The inertial measurement unit (IMU) sensor 408 measures at least one of: force, angular rate, orientation, and the acceleration and deceleration at the particular distance of the electric vehicle 102. In an embodiment, the inertial measurement unit (IMU) is an electronic device that measures and reports an electric vehicle 102 body's specific force, angular rate, and also the orientation of the electric vehicle 102 body using a combination of accelerometers, gyroscopes, and sometimes magnetometers.

The hall sensor 410 measures the revolutions per minute (rpm) at which the motor of the electric vehicle 102 is running. The worm wheel sensor 412 measures the speed of the electric vehicle 102. The current sensor 414 measures electric current in a wire and generates a signal proportional to the electric current to estimate the state of charge of the electric vehicle 102. The plurality of sensors 104 transmits the plurality of primary vehicle parameters to at least one of: the plurality of slave controllers 106, and the electric vehicle rewarding system 108.

The plurality of slave controllers 106 include at least one of: a battery management system (BMS) 502 and a motor controller unit 504. The battery management system 502 provides the plurality of secondary vehicle parameters including at least one of: the state of health (SOH) of the battery including the state of charge-discharge current of the battery, the occurrence of charging of the battery, charging levels on which the charging of the battery is started and ended, the energy consumed to drive the electric vehicle 102 for one kilometer (km) distance to determine whether the electric vehicle 102 is overloaded, and the applied acceleration/brake is harsh. Further, the motor controller unit 504 provides the plurality of secondary vehicle parameters including applying of the brake based on the revolutions per minute data.

In an embodiment, the results from at least one of: the brake switch sensor 402, the throttle position sensor 406, and the hall sensor 410 are transmitted to the motor controller unit 504 of the plurality of slave controllers 106. In another embodiment, the results from the current sensor 414 are transmitted to the battery management system 502 of the plurality of slave controllers 106. In yet another embodiment, the plurality of slave controllers 106 may include a master controller that receives the results from at least one of: the tire pressure sensor 404, the inertial measurement unit (IMU) sensor 408, and the worm wheel sensor 412.

The plurality of subsystems 110 further include a battery charging pattern score estimation subsystem 206 that is communicatively connected to the hardware processor(s) 220. The battery charging pattern score estimation subsystem 206 extracts at least one of: a real time state of health (SOH) of a battery comprising a state of discharge current of the battery, and a distance driven by a user from the received plurality of secondary vehicle parameters. Further, the battery charging pattern score estimation subsystem 206 compares the extracted real time state of health of the battery with a prestored state of health of a battery including of a predefined number of charge-discharge cycles of the battery. Further, the battery charging pattern score estimation subsystem 206 calculates a first score point for a degradation of charge patterns of the battery based on the comparison between the extracted real time state of health of the battery with the prestored state of health of the battery. In an embodiment, the first score point refers to one single point that may be added to a final cumulative point.

In one exemplary embodiment, the battery charging pattern score estimation subsystem 206 of the electric vehicle rewarding system 108 extracts a real time data of the state of health from the plurality of secondary vehicle parameters from the plurality of slave controllers 106 (i.e., specifically from the battery management system 502) as 90% state of health. Upon extracting, the battery charging pattern score estimation subsystem 206 compares with the prestored state of health of the battery which has nearby data as 95% state of health of battery at 300 cycles.

In one embodiment, the actual state of health of the battery is 90% and the prestored state of health of the battery is 95%. In such embodiment, a scaling output is actual/pre-stored value, i.e., 90/95=0.947. The first score point is 9.47. In an embodiment, this first score point remains the same until the user performs next charging cycle. In an embodiment, charge-discharge cycle represents one cycle with charging and discharging until the electric vehicle 102 battery is put for charge again.

In an embodiment, for such calculation of the first score point, the battery management system 502 submits the discharge current of battery to vehicle data receiving system 204 and the motor controller unit 504 submits the distance driven. The battery charging pattern score estimation system 206 computes the state of health of the battery based upon the inputs received and performs the scoring calculation as described above based upon scaling (i.e., extraction and comparison processes).

The plurality of subsystems 110 further include the braking pattern score estimation subsystem 214. In an embodiment, the plurality of subsystems 110 may include the braking pattern score estimation subsystem 214 that may be communicatively connected to the hardware processor(s) 220. The braking pattern score estimation subsystem 214 in the cloud 112 extracts an acceleration and deceleration data of the electric vehicle 102, and a number of brakes being applied during driving of the electric vehicle 102 from the plurality of primary vehicle parameters. Specifically, the braking pattern score estimation subsystem 214 extracts (a) the acceleration and deceleration data of the electric vehicle 102 from the IMU sensor 408 and (b) data associated with the number of brakes being applied (i.e., brake data) during driving of the electric vehicle 102 from the brake switch sensor 402. In an embodiment, the number of brakes being applied to drive a kilometer of the electric vehicle 102. In another embodiment, the limitation may be exceed more than one kilometer. In an embodiment, the brake data for a specific kilometre distance may include data from more than one electric vehicle 102. In an embodiment, the braking pattern score estimation subsystem 214 may extract the acceleration and deceleration data of the electric vehicle 102, and the number of brakes being applied during driving of the electric vehicle 102 from the plurality of primary vehicle parameters in the cloud 112.

The braking pattern score estimation subsystem 214 calculates a second score point for the braking applied during driving of the electric vehicle 102 based on the extracted acceleration and deceleration data of the electric vehicle 102 and the number of brakes being applied during driving of the electric vehicle 102. For example, the braking pattern score estimation subsystem 214 scores 10 for the electric vehicle 102 with least braking. The remaining electric vehicles may get corresponding scores from braking pattern score estimation subsystem 214 based on the scaling (i.e., calculation). In an embodiment, the braking pattern score estimation subsystem 214 calculates the second score point in the cloud 112.

The plurality of subsystems 110 further include the braking pattern score receiving subsystem 208 that is communicatively connected to the hardware processor(s) 220. The braking pattern score receiving subsystem 208 is configured to receive the calculated second score point from the braking pattern score estimation subsystem 214.

The plurality of subsystems 110 include the battery efficacy score estimation subsystem 210 that is communicatively connected to the hardware processor(s) 220. The battery efficacy score estimation subsystem 210 is configured to measure battery efficiency. In an embodiment, the battery efficiency is measured based on a plurality of factors including at least one of: sudden current drawn during overload, improper tire pressure, and the like. The battery efficacy score estimation subsystem 210 initially calculates power consumed during driving at a specified distance based on at least one of: the plurality of primary vehicle parameters and the plurality of secondary vehicle parameters received from the vehicle data receiving subsystem 204.

The battery efficacy score estimation subsystem 210 further compares the calculated power consumed at the specified distance with a prestored energy consumption level at specified distance (i.e., a prestored dataset). In an embodiment, the prestored energy consumption level is stored in the database 224. The battery efficacy score estimation subsystem 210 further calculates a third score point for the power consumed at the specified distance in one or more test parameters. In an embodiment, the third score point corresponds to a battery efficacy score. In an embodiment, the prestored energy consumption level is updated using a deep machine learning model based on changes of the one or more test parameters.

In one embodiment, the one or more test parameters include at least one of: a constant (e.g., 5 meter) velocity stretch of the electric vehicle 102 for the user with a rider, the constant velocity stretch of the electric vehicle 102 for gross weight of the electric vehicle 102, and the constant velocity stretch of the electric vehicle 102 for overload (e.g., 20% overload). The above three scenario measurements are taken at different tire pressure and data is stored into the database 224. Such database 224 keeps on improving based upon different environmental conditions including at least one of: gradient, tire wear, and the like.

In an exemplary embodiment, “5 metre constant velocity distance with only rider upon the electric vehicle 102 and the electric vehicle 102 battery consumes ‘x’ Watt/km at specified tire pressure”, “5 metre constant velocity distance with rider and pillion upon the electric vehicle 102 and the electric vehicle 102 consumes ‘y’ Watt/km at specified tire pressure” and “5 metre constant velocity distance with 20% overload upon the electric vehicle 102 and the electric vehicle 102 consumes ‘z’ Watt/km at specified tire pressure.

In such embodiment, the third score point may be generated as “10 points for less than ‘x’ Watt/km at specified tire pressure comparison”, 9 to 5 points scaling between ‘y’ and ‘z’ Watt/km at specified tire pressure comparison”, and “0 points for consumption above ‘z’ Watt/km at specified tire pressure comparison”.

Now, since varying tire pressure is not considered for the above calculation, another secondary value will be derived to conclude on score of battery efficacy score estimation subsystem 210. For example, if recommended, tire pressure is 30 psi and if the rider is operating at 27 psi pressure (weightage average of front and rear tire pressure), secondary score is generated. The battery efficacy score estimation subsystem 210 multiplies the third score point and the secondary score to obtain a particular score point.

Furthermore, different scenarios may also generate over a period of time when the tire is having wear and tear, and the electric vehicle 102 battery has degradation. The pre-stored dataset may evolve based on such new different scenarios. This is deep learning of the secondary pre-stored dataset. Further, the battery efficacy score estimation subsystem 210 estimates the efficiency level of the battery associated with the electric vehicle 102. The battery efficacy subsystem 210 further calculates the third score point based on energy/power consumption of the battery to drive specified range of the electric vehicle 102.

The plurality of subsystems 110 further includes the reward score calculation subsystem 212 that is communicatively connected to the hardware processor(s) 220. The reward score calculation subsystem 212 is configured to compute average weightage points of the first score point, the second score point and the third score point from the battery charging pattern score estimation subsystem 206, the braking pattern score receiving subsystem 208, and the battery efficacy score estimation subsystem 210 respectively.

In one embodiment, the average weightage score points of all the electric vehicles 102 are sent the database 224 over the cloud 112 where the hardware processor(s) 220 processes all the real-time information at a specified distance covered by the electric vehicle 102 and provides percentile-based ranking.

The plurality of subsystems 110 further include the ranking subsystem 216. In an embodiment, the plurality of subsystems 110 may include the ranking subsystem 216 that may be communicatively connected to the hardware processor(s) 220. The ranking subsystem 216 is configured to determine rank for the electric vehicle 102 driven by the user in percentile based values based on average weightage points computed for the first score point, the second score point, and the third score point.

The plurality of subsystems 110 include a reward score generation subsystem (not depicted in FIG. 2) that is communicatively connected to the hardware processor(s) 220. The reward score generation subsystem is configured to generate score for the electric vehicle 102 corresponding to the rank determined for the electric vehicle 102 based on the percentile based values in order to reward the user of the electric vehicle 102 on the basis of drivetrain efficiency. In one exemplary embodiment, the user (i.e., driver or plurality of users) with most score points may be provided with incentive facilities. The incentive facilities may include economical insurance facility, low maintenance charge for the electric vehicle 102, and the like. The electric vehicle rewarding system 108 also includes the communication bus 218.

Additionally, the plurality of subsystems 110 includes an output subsystem (not depicted in FIG. 2). The output subsystem that is configured to generate recommendations comprising insights driven from at least one of: the first score point, the second score point and the third score point using an artificial intelligence. The output subsystem further outputs the generated recommendations to the user of the electric vehicle 102. In an embodiment, generated recommendations are outputted to the user of the electric vehicle 102 through a display of at least one of: the electric vehicle 102 and a user device of the user.

The communication bus 218 helps to transfer the computed average score points to the cloud 112 where the hardware processor(s) 220 processes real time information and provides ranking.

Furthermore, when the battery state of charge reaches below “x” value, the electric vehicle rewarding system 108 may restrict the top speed of the electric vehicle 102 to “y” value in order to get maximum efficiency out of the battery. The restriction as stated may hamper acceleration performance but provides good, reserved range during low state of charge condition.

FIG. 3A and FIG. 3B are graphical representations depicting calculation of the first score point related to the state of health of the battery and calculation of the second score point related to the braking applied during driving of the electric vehicle 102, in accordance with an embodiment of the present disclosure. The first score point associated with the state of health of the battery is calculated by dividing the actual state of health of the battery by the prestored state of health of the battery including the charge-discharge cycle, as shown in FIG. 3A. The charge-discharge cycle represents the one cycle with charging and discharging until the electric vehicle 102 battery is put for charge again.

Further, the second score point associated with the braking applied during driving of the electric vehicle 102 is calculated by calculating the number of brakes being applied during driving of the electric vehicle (102) at a specified kilometer (i.e., 1 km) distance and calculation of the acceleration and deceleration data of the electric vehicle (102), as shown in FIG. 3B.

FIG. 4 is an exploded view of the plurality of sensors 104 arranged at the plurality of positions of the electric vehicle 102, such as those shown in FIG. 1, in accordance with an embodiment of the present disclosure. The plurality of sensors 104 include the brake switch sensor 402 detects the brake application during electric vehicle 102 operation. The tire pressure sensor 404 measures the tire pressure of the electric vehicle 102 in real time.

The throttle position sensor 406 measures the degree of throttle the electric vehicle 102 driver is rotating while operating the electric vehicle 102. In such embodiment, if the throttle is rotated to 30-degree, 2.5 volts signal is passed as measurement. If the throttle is rotated to 60-degree, 5 volts signal is passed as measurement. The hall sensor 410 measures the revolutions per minute at which the motor of the electric vehicle 102 is running. In one specific embodiment, the brake switch sensor 402 measurement, the throttle position sensor 406 measurement and the hall sensor 410 measurements are passed on to the motor controller unit 504 of the electric vehicle 102 for operation.

The worm wheel sensor 412 measures the speed of the electric vehicle 102. An inertial measurement unit (IMU) sensor 408 is the electronic device that measures and reports an electric vehicle body's 102 specific force, angular rate, and sometimes the orientation of the electric vehicle 102 body, using a combination of accelerometers, gyroscopes, and sometimes magnetometers. The current sensor 414 is the device that detects electric current in a wire and generates a signal proportional to that current and helps to estimate the state of charge of the electric vehicle 102.

In operation, the plurality of sensors 104 capture the plurality of primary vehicle parameters like brake on/off, throttle position, tire pressure, instantaneous acceleration & deceleration, revolutions per minute of motor or wheel and discharge current of the battery from the electric vehicle 102 and transfers it to the plurality of slave controllers 106 or directly to the electric vehicle rewarding system 108. In such embodiment, the real time captured plurality of primary vehicle parameters are transferred to a computing system 106.

FIG. 5 is an exploded view of a plurality of slave controllers 106, as such those shown in FIG. 1, in accordance with an embodiment of the present disclosure. The plurality of slave controllers 106 includes the battery management system 502 and the motor controller unit 504. The battery management system 502 provide information about how many charging cycles has occurred and those charging started and ended at what charge levels. The battery management system 502 also provides energy consumed to drive one km which enables us to know whether the electric vehicle 102 is overloaded, or acceleration/braking is harsh. The motor controller unit 504 provides revolutions per minute data and additionally data whether brake is applied or not.

FIG. 6 is a process flowchart illustrating an exemplary method