DESC:A) CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the priority and the benefit of the Indian Provisional Patent Application (PPA) with serial number 202341006791 filed on 2nd February 2023, with the title, “A SYSTEM AND A METHOD FOR ELECTRIC VEHICLE TRAVEL OPTIMIZATION”, and the contents of which is incorporated in its entirety by reference herein.
B) TECHNICAL FIELD
[0001] The present invention is generally related to the field of electric vehicles. The present invention is particularly related to a system and method for electric vehicle travel optimization. The present invention is more particularly related to electric vehicle travel optimization using IoT-based hardware compatible with the majority of the existing EVs and a compute engine, that optimizes the EV performance based on vehicle data and travel route.
C) BACKGROUND OF THE INVENTION
[0002] With the development of society, electric automobiles become the development trend of the future automobile industry, bring important economic benefits for the sustainable development of world energy, and have a profound influence. However, the problem of charging electric vehicles at present is a difficult problem to solve urgently for charging vehicle users, charging station operators, and even government departments, conventional charging as required is an unordered charging strategy, and the charging strategy is unordered in processing and control so that the charging requirements of a large number of users are difficult to meet, the utilization rate of the charging pile is not high, and the problems of resource waste, long waiting time and the like are caused.
[0003] In addition, the use of EVs also contributes to limited travel distance and high charging time, which leads to a range of anxiety problems among people. The growth of EVs can increase even more rapidly if the range anxiety among people is reduced. There is a need for mitigation strategies to prevent agnosticism regarding the distance an EV can travel with its limited battery capacity.
[0004] Hence, in the view of this, there is a need for a full spectrum of smart EV performance optimization solutions, addressing the range anxiety problems.
[0005] The above-mentioned shortcomings, disadvantages and problems are addressed herein, and which will be understood by reading and studying the following specification.
D) OBJECT OF THE INVENTION
[0006] The primary object of the present invention is to provide a system and a method for electric vehicle travel optimization.
[0007] Another object of the present invention is to provide an electric vehicle travel optimization system, which optimizes the EV performance based on vehicle data and travel routes.
[0008] Yet another object of the present invention is to provide an electric vehicle travel optimization system, integrated with a plurality of Internet of Things (IoT) based on performance optimization solutions.
[0009] Yet another object of the present invention is to provide an electric vehicle travel optimization system, which can be modified in real-time.
[0010] Yet another object of the present invention is to provide an electric vehicle travel optimization system, that can control the over speed of the vehicle to optimize the performance and fuel economy of the vehicle.
[0011] Yet another object of the present invention is to provide an electric vehicle travel optimization system that can be integrated into existing EVs, without any major modification.
[0012] Yet another object of the present invention is to provide an electric vehicle travel optimization system that can control the modes of the vehicle.
[0013] Yet another object of the present invention is to provide an electric vehicle travel optimization system, which helps to generate regenerative current that recharges the EV battery.
[0014] Yet another object of the present invention is to provide an electric vehicle travel optimization system, which helps to prevent stranded travelers based on historic data and the drive score of the user.
[0015] These and other objects and advantages of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings.
E) SUMMARY OF THE INVENTION
[0016] The various embodiments of the present invention provide a system and a method for electric vehicle travel optimization. The present invention provides a system and method which optimizes the electric vehicle (EV) performance based on vehicle data and travel routes. The present invention comprises a plurality of Internet of Things (IoT) based hardware units integrated into an EV, to collect real-time data from the vehicles. The present invention also comprises a database in a cloud server with a compute engine for optimization. The database and the plurality of IoT-based hardware are connected through a communication protocol for sending and receiving the data.
[0017] According to one embodiment of the present invention, a system for electric vehicle travel optimization is provided. The system comprises a plurality of IoT-based hardware units integrated into multiple electric vehicles (EVs). The plurality of IoT-based hardware units is configured to capture real-time data and also comprises a telematic device or a smart speedometer, and a user mobile device. In addition, the system comprises a cloud server comprising a database and a compute engine. The compute engine includes an optimization algorithm configured to optimize real-time data received across an individual electric vehicle. Furthermore, the system comprises a communication protocol configured to connect the cloud server and the plurality of IoT-based hardware units. The IoT-based hardware units are configured to transmit the real-time data captured across the communication protocol to the database. Furthermore, the compute engine in the cloud server is also configured to transmit control signals through the communication protocol to enhance the performance of the vehicle.
[0018] According to one embodiment of the present invention, each of the plurality of IoT-based hardware units is further provided with a unique ID, configured to identify the vehicle and its associated hardware. The method for identifying the vehicle using a unique ID is provided. The method comprises activating the speedometer to register in the database by the user. The method further includes creating a vehicle identification number (VIN). In addition, the method involves uploading vehicle details or VINs in bulk or adding a particular vehicle’s VIN by the user. The VIN may be either added manually or generated automatically. The user can also download a sample file which will have the list of all the available VINs and MAC IDs. Furthermore, the method involves creating a vehicle list in sequential order by filing the fields and uploading the details of the vehicle. The vehicle list is based on the number of clusters or speedometers to be added.
[0019] According to one embodiment of the present invention, the real-time data comprises input from the user, input from the vehicle, and input from the database of the cloud server, which are historic data, the machine learning data models, configuration data, and other associated data.
[0020] According to one embodiment of the present invention, the input from the user includes the current location and destination, which ultimately provides the travel distance and the route, which is desired by the user to travel, and route options, such as avoiding highways, tolls, and ferries, and departure time, arrival time, and addition of stops. The travel distance acts as the constraint for the optimization algorithm, as the optimization task takes place between the provided travel distance by the user.
[0021] According to one embodiment of the present invention, the input from the vehicle includes the latitude/longitude and altitude of the vehicle, which provides the location of the vehicle. In addition, the input from the vehicle includes triaxial angles of vehicles, State of Charge (SOC), State of Health (SOH), battery voltage, battery current, wheel RPM, speed of the vehicle, regeneration current, and mode of operation depending upon the original equipment manufacturers (OEMs). For example, eco mode, ride mode, and sports mode etc. Furthermore, the road conditions and vehicle altitude changes also affect the regeneration current of EVs. SOC and SOH are the percentage format measures that represent the remaining battery capacity and health, respectively. Battery current represents the load on the vehicle and battery current, voltage, Ampere-hours (Ah), and current can be used to calculate the SOC of the battery. Wheel RPM is captured by a sensor to calculate the speed of the vehicle. Furthermore, mode of operation is used to know at which mode the vehicle is currently being driven. Hence, all these data help to understand real-time configuration of the vehicle.
[0022] According to one embodiment of the present invention, the input from the database of the cloud server includes information about the vehicle, and road conditions. The information about the vehicle includes wheel diameter, maximum speed, speed limit, brake regeneration limit, odometer, and drive score. Along with the wheel RPM, the wheel diameter of the vehicle model is used to calculate the speed of the vehicle. The speed of the vehicle is also obtained from the speedometer of the vehicle itself. Maximum Speed is the practical speed the vehicle model can achieve, and the speed limit is the maximum speed limit that the vehicle will operate at once set from the cloud. Furthermore, the information of the road conditions includes distance, road elevation, traffic, tolls, ferries, wildfires, and air quality. The road elevation and distance affect the regeneration and battery capacity of the vehicle. The information of the road condition is stored as historic data in the database and is further utilized to track if the same route is taken by another user.
[0023] According to one embodiment of the present invention, the communication protocol employed to establish the connection between the plurality of IoT-based hardware units and the database includes a Global System for Mobile (GSM) communication or a combination of GSM and Bluetooth Low Energy (BLE) communication. The type of communication protocol used for sending the real-time data depends upon the hardware units used by the system. The real-time data received by the cloud server through the communication protocol is encrypted, and the real-time data is decrypted using defined logic to make the data comprehensible.
[0024] According to one embodiment of the present invention, the connection between the IoT-based hardware units and the cloud server is established after the hardware is physically connected to the vehicle. Moreover, the connection between the vehicle and the cloud server is persistent until the information about the hardware is deleted manually from the database.
[0025] According to one embodiment of the present invention, the optimization of the real-time data carried out by the optimization algorithm embedded in the compute engine includes finding the best operating performance of an EV concerning distance and time personalized for the user.
[0026] According to one embodiment of the present invention, the system is further configured to modify the driving operation of the multiple EVs in real-time by choosing different modes of driving or driving speed of the vehicle. For instance, if an EV can reach the desired distance in ECO mode and not in any other mode, then the vehicle is modified to run on ECO mode. However, ECO mode does not have a comfortable driving experience. Similarly, each mode has its own merits and demerits. This is possible only if the system has access to the controller of the EV. The control logic can be implemented by the firmware uploaded to the controller.
[0027] According to one embodiment of the present invention, the system is also configured to control the speed of the vehicle to optimize performance and fuel economy. The input from the vehicle, such as wheel RPM, the speed of the vehicle in real-time, and vehicle wheel diameter data are available in the database in the cloud server. Based on these two values, the real-time speed of the vehicle is calculated. The speed of the vehicle is also obtained from the vehicle itself. Using the speed of the vehicle, a speed limit or mode limit is set over a vehicle. Hence, the vehicle is set to operate at the set speed or mode depending upon the vehicle. Furthermore, the optimization of real-time data includes finding the best operating performance of EVs, which is personalized for the user, concerning distance and time.
[0028] According to one embodiment of the present invention, the system is also configured to find the route containing downhill paths for generating regenerative current, which recharges the EV battery during throttle-free movements of the vehicle during downhill movement. However, the regenerative current differs with different road conditions and traffic profiles. Contemporary route maps, such as Google Maps provide the road elevation levels, and the multiple EVs integrated with the plurality of IoT based hardware is configured to provide data of the vehicle, such as triaxial angles, altitude, longitude, and latitude. Further, the regenerative current can be forecasted across the route using the aforesaid data.
[0029] According to one embodiment of the present invention, the compute engine is configured to predict the battery energy consumption. Moreover, the compute engine is also configured to help the user decide the mode of the vehicle to complete the route and achieve optimized performance only if the optimization is enabled. EV manufacturers provide switches to change the mode of the vehicles. However, actual changes in the modes happen from the logic used in the controller of EV, which is the capability of the compute engine in the cloud server to control these parameters. If the compute engine notices that the desired destination is not reachable in a certain mode, then the user is prevented from accessing that mode. However, if the user does not enable the optimization mode provided by the compute engine in the cloud server, then the user is free to drive as the user desires.
[0030] According to one embodiment of the present invention, the system is also configured to assist in the reduction of cases of stranded travelers, including accidental, flood or traffic-prone areas based on the historic data and the drive score of the user. If the user wants to reach a desired location using the EV within a specific time, the battery capacity should be available. If the battery capacity is low, the user will get stranded on the road due to battery depletion. However, the compute engine in the cloud server is configured to provide optimization, and to help the vehicle reach the desired destination by maximizing the utilization of the battery capacity. The maximization of the utilization of battery capacity can also be performed by using historic data, provided by the database and the drive score of the user. The historic data includes the road conditions, and the driver’s score is associated with the user.
[0031] According to one embodiment of the present invention, a method for electric vehicle travel optimization is provided. The method involves obtaining input parameters and variables in real-time from a user, and vehicle through a plurality of IoT-based hardware units and obtaining historic data, machine learning data models, configuration data, or other data from a database embedded in a cloud server. The method further involves recognizing if an optimization is required for an EV or vehicle, based on the input parameter and variables received from the user, vehicle, and the database in the cloud server. If the optimization is required, then the vehicle optimization calculations are performed using a compute engine in the cloud server. The compute engine in the cloud server performs optimization calculations to optimize the vehicle across the specified constraints. In addition, the method involves performing vehicle optimization calculations continuously until the optimization is no longer required for the vehicle, using an optimization algorithm to optimize vehicles across the specified constraints. The method further involves updating vehicles with optimized configurations, and operating the EV as per the user instructions, if optimization is not required by the vehicle. The method also records the vehicle logs and vehicle configuration for feedback at each step of the operation.
[0032] According to one embodiment of the present invention, the input parameters, and variables in real-time comprise an input from the user, an input from the vehicle, and an input from the database of the cloud server including historic data, machine learning data models, configuration data, and other associated data.
[0033] According to one embodiment of the present invention, the input from the user includes the current location and destination, which ultimately provides the travel distance and the route, which is desired by the user to travel, and route options, departure time, arrival time, and addition of stops. Correspondingly, the input from the vehicle includes latitude/longitude and altitude of the vehicle, which provides the location of the vehicle, triaxial angles of vehicles, State of Charge (SOC), State of Health (SOH), battery voltage, battery current, wheel RPM, speed of the vehicle, regeneration current and mode of operation depending upon the original equipment manufacturers (OEMs). For example, eco mode, ride mode, and sports mode.
[0034] According to one embodiment of the present invention, the input from the database of the cloud server includes information about the vehicle including wheel diameter, maximum speed, speed limit, brake regeneration limit, odometer, and drive score, and road conditions including distance, road elevation, traffic, tolls, ferries, wildfires, and air quality. Moreover, the information of the road condition is stored as historic data in the database and is further utilized to track if the same route is taken by another user.
[0035] According to one embodiment of the present invention, the optimization of the real-time parameter and variables by the optimization algorithm includes finding the best operating performance of an EV with respect to the distance and time. Furthermore, the method is also configured to modify the driving operation of the multiple EVs in real-time by choosing different modes of driving or driving speed of the vehicle.
[0036] According to one embodiment of the present invention, the method for predicting the battery energy consumption is provided. The method comprises collecting metadata about the route and predicting battery energy consumption based on the past driving behavior of the user and default vehicle parameters. To predict the battery energy consumption, the method initially checks for the state of charge (SOC) of the battery to ensure that the ongoing trip can be completed. If the SOC is enough then no optimization is required for the user to complete the trip, else the method finds least energy consumption by adjusting control parameters. Furthermore, the method checks the SOC of the battery with updated parameters to complete the trip. If there is enough SOC constraining optimization to maximize energy consumption by adjusting control parameters to complete the trip, else prompting the user that SOC is not enough to complete the trip even after optimization.
[0037] According to one embodiment of the present invention, the method is also configured to control the speed of the vehicle to optimize the performance and fuel economy, and also configured to find the route containing downhill paths for generating regenerative current, which recharges the EV battery during throttle free movements of the vehicle during downhill movement. In addition, the method is further configured to help the user decide the mode of the vehicle to complete the route and to achieve optimized performance. Furthermore, the method is configured to assist in the reduction of cases of stranded travelers, including accidental, flood or traffic prone areas based on the historic data and the drive score of the user.
[0038] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating the preferred embodiments and numerous specific details thereof, are given by way of an illustration and not of a limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.
F) BRIEF DESCRIPTION OF THE DRAWINGS
[0029] The other objects, features, and advantages will occur to those skilled in the art from the following description of the preferred embodiment and the accompanying drawings in which:
[0030] FIG. 1 illustrates a block diagram of an exemplary system for electric vehicle travel optimization, according to an embodiment of the present invention.
[0031] FIG. 2 illustrates a flowchart on the method for electric vehicle travel optimization, according to an embodiment of the present invention.
[0032] FIG. 3 illustrates a detailed flowchart on the method for electric vehicle travel optimization, according to an embodiment of the present invention.
[0033] FIG. 4 illustrates an example of a Hardware architecture for a cluster for speedometer activation, according to an embodiment of the present invention.
[0034] FIG. 5A illustrates an exemplary diagram of an Electric two-wheeler vehicle, according to an embodiment of the present invention.
[0035] FIG. 5B illustrates the circuit diagram of the electric two-wheeler vehicle for travel optimization, according to an embodiment of the present invention.
[0036] FIG. 6 illustrates a detailed flowchart on the method for identifying the vehicle using a unique ID, according to an embodiment of the present invention.
[0037] FIG. 7 illustrates a block diagram of an exemplary system of speedometer workflow, according to an embodiment of the present invention.
[0038] FIG. 8 illustrates an exemplary block diagram using machine learning models to predict the energy consumption of a trip, according to an embodiment of the present invention.
[0039] FIG. 9 illustrates a flowchart of the method for finding the best operating performance of the EV using an optimization algorithm, according to an embodiment of the present invention.
[0040] Although the specific features of the present invention are shown in some drawings and not in others. This is done for convenience only as each feature may be combined with any or all of the other features in accordance with the present invention.
F) DETAILED DESCRIPTION OF THE INVENTION
[0041] In the following detailed description, a reference is made to the accompanying drawings that form a part hereof, and in which the specific embodiments that may be practiced is shown by way of illustration. These embodiments are described in sufficient detail to enable those skilled in the art to practice the embodiments and it is to be understood that the logical, mechanical, and other changes may be made without departing from the scope of the embodiments. The following detailed description is therefore not to be taken in a limiting sense.
[0042] The various embodiments of the present invention provide a system and a method for electric vehicle travel optimization. The present invention provides a system and method which optimizes the electric vehicle (EV) performance based on vehicle data and travel routes. The present invention comprises a plurality of Internet of Things (IoT) based hardware units integrated into an EV, to collect real-time data from the vehicles. The present invention also comprises a database in a cloud server with a compute engine for optimization. The database and the plurality of IoT-based hardware are connected through a communication protocol for sending and receiving the data.
[0043] According to one embodiment of the present invention, a system for electric vehicle travel optimization is provided. The system comprises a plurality of IoT-based hardware units integrated into multiple electric vehicles (EVs). The plurality of IoT-based hardware units is configured to capture real-time data and also comprises a telematic device or a smart speedometer, and a user mobile device. In addition, the system comprises a cloud server comprising a database and a compute engine. The compute engine includes an optimization algorithm configured to optimize real-time data received across an individual electric vehicle. Furthermore, the system comprises a communication protocol configured to connect the cloud server and the plurality of IoT-based hardware units. The IoT-based hardware units are configured to transmit the real-time data captured across the communication protocol to the database. Furthermore, the compute engine in the cloud server is also configured to transmit control signals through the communication protocol to enhance the performance of the vehicle.
[0044] According to one embodiment of the present invention, each of the plurality of IoT-based hardware units is further provided with a unique ID, configured to identify the vehicle and its associated hardware. The method for identifying the vehicle using a unique ID is provided. The method comprises activating the speedometer to register in the database by the user. The method further includes creating a vehicle identification number (VIN). In addition, the method involves uploading vehicle details or VINs in bulk or adding a particular vehicle’s VIN by the user. The VIN may be either added manually or generated automatically. The user can also download a sample file which will have the list of all the available VINs and MAC IDs. Furthermore, the method involves creating a vehicle list in sequential order by filing the fields and uploading the details of the vehicle. The vehicle list is based on the number of clusters or speedometers to be added.
[0045] Alternatively, the plurality of IoT-based hardware units can also function as a tracker, apart from a speedometer. The speedometer tailored for EVs to comprehend the network functionality is essential that a suitable cluster is integrated into the EV, enabling real-time data collection. The data from control or various sources is contained in various packets based on its source and sent to the cloud server. The speedometer is also referred to as a cluster. The connection, established through the communication protocol in a mobile device, modifies the driving operation of the EV in real-time.
[0046] According to one embodiment of the present invention, the real-time data comprises an input from the user, an input from the vehicle, and an input from the database of the cloud server, which are historic data, machine learning data models, configuration data, and other associated data.
[0047] According to one embodiment of the present invention, the input from the user includes the current location and destination, which ultimately provides the travel distance and the route, which is desired by the user to travel, and route options, such as avoiding highways, tolls, and ferries, and departure time, arrival time, and addition of stops. The travel distance acts as the constraint for the optimization algorithm, as the optimization task takes place between the provided travel distance by the user.
[0048] According to one embodiment of the present invention, the input from the vehicle includes the latitude/longitude and altitude of the vehicle, which provides the location of the vehicle. In addition, the input from the vehicle includes triaxial angles of vehicles, State of Charge (SOC), State of Health (SOH), battery voltage, battery current, wheel RPM, speed of the vehicle, regeneration current, and mode of operation depending upon the original equipment manufacturers (OEMs). For example, eco mode, ride mode, and sports mode. Furthermore, the road conditions and vehicle altitude changes also affect the regeneration current of EVs. SOC and SOH are the percentage format measures that represent the remaining battery capacity and health, respectively. Battery current represents the load on the vehicle and battery current, voltage, Ampere-hours, and current can be used to calculate the SOC of the battery. Wheel RPM is captured by a sensor to calculate the speed of the vehicle. Furthermore, mode of operation is used to know at which mode the vehicle is currently being driven. Hence, all these data help to understand real-time configuration of the vehicle.
[0049] According to one embodiment of the present invention, the input from the database of the cloud server includes information about the vehicle and road conditions. The information about the vehicle includes wheel diameter, maximum speed, speed limit, brake regeneration limit, odometer, and drive score. Along with the wheel RPM, the wheel diameter of the vehicle model is used to calculate the speed of the vehicle. The speed of the vehicle is also obtained from the speedometer of the vehicle itself. Maximum Speed is the practical speed the vehicle model can achieve, and the speed limit is the maximum speed limit that the vehicle will operate at once set from the cloud. Furthermore, the information of the road conditions includes distance, road elevation, traffic, tolls, ferries, wildfires, and air quality. The road elevation and distance affect the regeneration and battery capacity of the vehicle. The information of the road condition is stored as historic data in the database and is further utilized to track if the same route is taken by another user. For example, if any road has too many potholes, then the regeneration current generated is not significant and the user or driver is not recommended to drive in the high-speed mode.
[0050] According to one embodiment of the present invention, the communication protocol employed to establish the connection between the plurality of IoT-based hardware units and the database includes a Global System for Mobile (GSM) communication or a combination of GSM and Bluetooth Low Energy (BLE) communication. The type of communication protocol used for sending real-time data depends upon the hardware units used by the system. The real-time data received by the cloud server through the communication protocol is encrypted, and the real-time data is decrypted using defined logic to make the data comprehensible.
[0051] According to one embodiment of the present invention, the connection between the IoT-based hardware units and the cloud server is established after the hardware is physically connected to the vehicle. Moreover, the connection between the vehicle and the cloud server is persistent until the information about the hardware is deleted manually from the database.
[0052] According to one embodiment of the present invention, the optimization of the real-time data carried out by the optimization algorithm embedded in the compute engine includes finding the best operating performance of an EV, personalized for the user concerning distance and time.
[0053] Particle Swarm Optimization (PSO) is a computational method inspired by the collective behavior of organisms like a school of fish or a flock of birds that move together to achieve a common goal. In PSO, a group of particles or possible solutions navigates through a problem’s solution space to find the best possible solution. Each particle adjusts its position not only based on its best-known local solution but also based on the best solution discovered by the entire group. Hence, this collaborative movement helps the particles converge towards the optimal solution over iterations. PSO is best used to find the maximum or minimum of a function defined on a multidimensional vector space. Discrete PSO is a variant of the PSO that supports discrete-valued solutions.
[0054] According to one embodiment of the present invention, the system is further configured to modify the driving operation of the multiple EVs in real-time by choosing different modes of driving or driving speed of the vehicle. For instance, if an EV can reach the desired distance in ECO mode and not in any other mode, then the vehicle is modified to run on ECO mode. However, ECO mode does not have a comfortable driving experience. Similarly, each mode has its own merits and demerits. This is possible only if the system has access to the controller of the EV. The control logic can be implemented by the firmware uploaded to the controller.
[0055] The various modes of operation of the vehicle depend upon the original equipment manufacturers (OEMs). For example, eco mode, ride mode, and sports mode. The eco mode is tailored to enhance the fuel economy, maintaining a lower speed compared to other modes. In eco mode the vehicle operates with reduced stress, thereby promoting energy conservation. Correspondingly, the ride mode offers a balance between fuel efficiency and a comfortable driving experience, and the ride mode sacrifices minimal fuel economy. The speed limit is moderate, ensuring an equilibrium between energy and power utilization from the electric vehicle. The sports mode is geared for the thrill-seekers. The sports mode unleashes the full speed potential of the vehicle by prioritizing high power, although at the expense of fuel economy. In sports mode, the vehicle can tap into maximum power via the throttle, and the energy depletion occurs at an accelerated rate relative to time.
[0056] According to one embodiment of the present invention, the system is also configured to control the speed of the vehicle to optimize performance and fuel economy. The input from the vehicle, such as wheel RPM, the speed of the vehicle in real-time, and vehicle wheel diameter data are available in the database in the cloud server. Based on these two values, the real-time speed of the vehicle is calculated. The speed of the vehicle is also obtained from the vehicle itself. Using the speed of the vehicle, a speed limit or mode limit is set over a vehicle. Hence, the vehicle is set to operate at the set speed or mode depending upon the vehicle. Furthermore, the optimization of real-time data includes finding the best operating performance of EVs personalized for the user, concerning distance and time. For instance, if the distance to be covered is 50 km, but the battery capacity only supports the driving for 45 km without optimization. Then, optimization of the EV will provide an additional 5 km and make the vehicle reach the destination. The 5 km can be added by regeneration current or by saving the fuel using ECO mode. But in some instances, reaching the desired destination within time is mandatory. Hence, finding the optimal solution between distance and time is required. In addition, the system is further configured to be integrated to the existing EVs.
[0057] According to one embodiment of the present invention, the cloud server is also configured to find the route containing downhill paths for generating regenerative current, which recharges the EV battery during throttle-free movements of the vehicle during downhill movement. However, the regenerative current differs with different road conditions and traffic profiles. Contemporary route maps, such as Google Maps provide the road elevation levels, and the multiple EVs integrated with the plurality of IoT based hardware is configured to provide data of the vehicle, such as triaxial angles, altitude, longitude, and latitude. Further, the regenerative current can be forecasted across the route using the aforesaid data.
[0058] According to one embodiment of the present invention, the compute engine is configured to predict the battery energy consumption. Moreover, the compute engine is also configured to help the user decide the mode of the vehicle to complete the route and achieve optimized performance only if the optimization is enabled. EV manufacturers provide switches to change the mode of the vehicles. However, actual changes in the modes happen from the logic used in the controller of EV, which is the capability of the compute engine in the cloud server to control these parameters. If the compute engine notices that the desired destination is not reachable in a certain mode, then the user is prevented from accessing that mode. However, if the user does not enable the optimization mode provided by the compute engine in the cloud server, then the user is free to drive as the user desires. For instance, If the desired travel distance is large, then fuel economy is important for the travel, hence eco mode will be utilized to cover the distance without fear of stranding. Furthermore, the system is configured to change the mode of the vehicle, only if the optimization is enabled, which is provided by the compute engine. The user is not completely restricted from accessing the modes. If the user has no intention of following the notification and mode recommendation provided by the compute engine in the cloud server, the user is free to drive as per the user's requirements.
[0059] According to one embodiment of the present invention, the system is also configured to assist in the reduction of cases of stranded travelers, including accidental, flood or traffic prone areas based on the historic data and the drive score of the user. If the user wants to reach a desired location using the EV within a specific time, the battery capacity should be available. If the battery capacity is low, the user will get stranded on the road due to battery depletion. However, the compute engine in the cloud server is configured to provide optimization, and to help the vehicle reach the desired destination by maximizing the utilization of the battery capacity. The maximization of the utilization of battery capacity can also be performed by using historic data, provided by the database and the drive score of the user. The historic data includes the road conditions, and the driver’s score is associated with the user. For instance, if we already know an accident-prone route and the drive score is low then the compute engine in the cloud server recommends safe mode to the user. Furthermore, if the historic data of the route shows too many potholes on the downhill road, then regeneration current will be less. Therefore, the compute engine in the cloud server recommends safe mode and alternate route to the user.
[0060] According to one embodiment of the present invention, a method for electric vehicle travel optimization is provided. The method involves obtaining input parameters and variables in real-time from a user, and vehicle through a plurality of IoT-based hardware units and obtaining historic data, machine learning data models, configuration data, or other data from a database embedded in a cloud server. The method further involves recognizing if an optimization is required for an EV or vehicle, based on the input parameter and variables received from the user, vehicle, and the database in the cloud server. If the optimization is required, then the vehicle optimization calculations are performed using a compute engine in the cloud server. The compute engine in the cloud server performs optimization calculations to optimize the vehicle across the specified constraints. In addition, the method involves performing vehicle optimization calculations continuously until the optimization is no longer required for the vehicle, using an optimization algorithm to optimize vehicles across the specified constraints. The method further involves updating vehicles with optimized configurations, and operating the EV as per the user instructions, if optimization is not required by the vehicle. The method also records the vehicle logs and vehicle configuration for feedback at each step of the operation.
[0061] According to one embodiment of the present invention, the input parameters, and variables in real-time comprise an input from the user, an input from the vehicle, and an input from the database of the cloud server including historic data, machine learning data models, configuration data, and other associated data.
[0062] According to one embodiment of the present invention, the input from the user includes the current location and destination, which ultimately provides the travel distance and the route, which is desired by the user to travel, and route options, departure time, arrival time, and addition of stops. Correspondingly, the input from the vehicle includes latitude/longitude and altitude of the vehicle, which provides the location of the vehicle, triaxial angles of vehicles, State of Charge (SOC), State of Health (SOH), battery voltage, battery current, wheel RPM, speed of the vehicle, regeneration current and mode of operation depending upon the original equipment manufacturers (OEMs). For example, eco mode, ride mode, and sports mode.
[0063] According to one embodiment of the present invention, the input from the database of the cloud server includes information about the vehicle including wheel diameter, maximum speed, speed limit, brake regeneration limit, odometer, and drive score, and road conditions including distance, road elevation, traffic, tolls, ferries, wildfires, and air quality. Moreover, the information of the road condition is stored as historic data in the database and is further utilized to track if the same route is taken by another user.
[0064] According to one embodiment of the present invention, the optimization of the real-time parameters and variables by the optimization algorithm includes finding the best operating performance of an EV with respect to distance and time. Furthermore, the method is also configured to modify the driving operation of the multiple EVs in real-time by choosing different modes of driving or driving speed of the vehicle.
[0065] According to one embodiment of the present invention, the method for predicting the battery energy consumption is provided. The method comprises collecting metadata about the route and predicting battery energy consumption based on the past driving behavior of the user and default vehicle parameters. To predict the battery energy consumption, the method initially checks for the state of charge (SOC) of the battery to ensure that the ongoing trip can be completed. If the SOC is enough then no optimization is required for the user to complete the trip, else the method finds least energy consumption by adjusting control parameters. Furthermore, the method checks the SOC of the battery with updated parameters to complete the trip. If there is enough SOC constraining optimization to maximize energy consumption by adjusting control parameters to complete the trip, else prompting the user that SOC is not enough to complete the trip even after optimization.
[0066] Furthermore, the method employs a combination of an ANN model to predict speed profiles and a regression model to predict the battery energy consumption before the start of the trip. The method utilizes various real-time data, such as vehicle model details, historic trip details, road characteristics, and weather data. The method also utilizes calculated features and vehicle control parameters to predict the battery energy consumption. The vehicle model details include VIN, maximum torque, and maximum regenerative braking power; the historic trip details include trip ID, initial SOC, SOH, speed, latitude-longitude coordinates, accelerometer data, headlight/horn/indicator values, distance, trip time, and driving mode. Furthermore, the road characteristics include road type, elevation, and crossings obtained through maps, such as Google Maps, APIs, and OSM data. The weather data include wind speed and ambient temperature. Moreover, the calculated features include traffic data, such as time of the day, day of the week, number of stops, such as traffic signals or high traffic. The number of stops may be determined when the speed becomes zero for a few seconds. In addition, the vehicle control parameters include top speed limit, that can be changed step wise, such as 5km/hour from 30 – 100 km/hour, and default 100 km/hour. Furthermore, the other vehicle control parameters include the percentage of maximum torque, percentage of maximum braking regeneration, and driving mode. The percentage of maximum torque can be changed in steps of 10% from 0 – 100%, and the default is 70%. The percentage of maximum braking regeneration can be changed in steps of 10% from 0 – 100%, and the default is 70%. Furthermore, the driving mode includes eco, sports, and city with the city as default.
[0067] According to one embodiment of the present invention, the method is also configured to control the speed of the vehicle to optimize the performance and fuel economy, and also configured to find the route containing downhill paths for generating regenerative current, which recharges the EV battery during throttle free movements of the vehicle during downhill movement. In addition, the method is further configured to help the user decide the mode of the vehicle to complete the route and to achieve optimized performance. Furthermore, the method is configured to assist in the reduction of cases of stranded travelers, including accidental, flood or traffic-prone areas based on the historic data and the drive score of the user.
[0068] FIG. 1 illustrates a block diagram of an exemplary system for electric vehicle travel optimization, according to an embodiment of the present invention. The system 100 comprises a plurality of IoT-based hardware units 101 integrated into multiple electric vehicles (EVs) 102. The plurality of IoT-based hardware units 101 are configured to capture real-time data and also comprise a telematic device or a smart speedometer 101a, and a user mobile device 101b. In addition, the system 100 comprises a cloud server 104 comprising a database 103 and a compute engine 105. The compute engine 105 includes an optimization algorithm configured to optimize real-time data received across an individual electric vehicle. Furthermore, the system 100 comprises a communication protocol 106 configured to connect the cloud server 104 and the plurality of IoT-based hardware units 101. The IoT-based hardware units 101 are configured to transmit the real-time data captured across the communication protocol 106 to the database 103. Furthermore, the compute engine 105 in the cloud server 104 is also configured to transmit control signals through the communication protocol 106 to enhance the performance of the vehicle.
[0069] FIG. 2 illustrates a flowchart on the method for electric vehicle travel optimization, according to an embodiment of the present invention. The method 200 comprises obtaining input parameters and variables in real-time from a user, vehicle through a plurality of IoT-based hardware units and obtaining historic data, machine learning data models, configuration data, or other data from a database in a cloud server at step 202. The method 200 further includes recognizing if an optimization is required for an EV, based on the input parameter and variables received from the user, vehicle, and the cloud database at step 204. If the optimization is required, then the method 200 performs vehicle optimization calculations using a compute engine in the cloud server at step 206. The compute engine performs optimization calculations to optimize the vehicle across the specified constraints. The method 200 further involves updating the vehicle with an optimized configuration at step 208. The optimization calculations are performed continuously until the optimization is no longer required for the vehicle. Furthermore, in case the optimization is not required by the vehicle on obtaining the inputs parameters and variables, EV operates as per the user instructions at step 210. The method 200 further records the vehicle logs and vehicle configuration for feedback at each step of the operation from step 202 to step 210.
[0070] FIG. 3 illustrates a detailed flowchart on method for electric vehicle travel optimization, according to an embodiment of the present invention. The method 300 illustrates a detailed flowchart on method for electric vehicle travel optimization. The method 300 comprises obtaining input parameters and variables in real-time from the vehicle, user, and the cloud server at step 301. Based on the input, recognize if optimization is required for the EV at step 302. If the optimization is not required, the EV will operate as per the user at step 303. However, if the method 300 detects the need for optimization, then a compute engine in the cloud server performs calculations to optimize the vehicle across the specified constraints at step 304. Further, the method 300 involves updating the vehicle with optimization configurations at step 305. The calculation will be performed continuously until the optimization is no longer required by the vehicle. All logs and vehicle configurations will be recorded for feedback at each step of the operation from 301 to step 305.
[0071] FIG. 4 illustrates an example of a speedometer, according to an embodiment of the present invention. The speedometer is tailored for electric vehicles (EVs), to comprehend the network's functionality. The speedometer is also referred to as a cluster. The speedometer is integrated into the EV, enabling real-time data collection. The data from the controller or various sources are contained in various packets based on its source and sent to the cloud server. The connection is established through direct GSM or GSM via mobile phone, modifying the driving operation of the EV in real-time.
[0072] FIG. 5A illustrates an exemplary diagram of an Electric two-wheeler vehicle, according to an embodiment of the present invention. FIG. 5B illustrates the circuit diagram of the electric two-wheeler vehicle for travel optimization, according to an embodiment of the present invention. The two-wheeler EV as illustrated in FIG. 5A includes various components, such as motor 501, controller 502, battery 503, DC-to-DC converter 504, MCB 505, smart speedometer 506, and junction box 507 which are interconnected to ensure optimal performance. Furthermore, communication protocols such as Controller Area Network (CAN), UART, or One Wire play a pivotal role in facilitating this interconnectedness. The motor 501 serves as the powerhouse, providing torque and propulsion, while the controller 502 acts as the system's brain, transmitting instructions to the motor 501. In addition, the battery 503, communicating its state of charge via the CAN protocol, serves as the primary energy source. The DC-to-DC converter 504 ensures precise voltage distribution, dynamically adjusting based on the vehicle's operating conditions communicated through CAN. Furthermore, the MCB 505, integrated into the CAN network, safeguards the electrical system by interrupting current flow in case of faults. The speedometer 506, connected to the CAN network, offers real-time information on speed, battery status, and motor performance, enhancing the overall monitoring of the vehicle's performance and providing a comprehensive riding experience.
[0073] FIG. 6 illustrates a detailed flowchart on method for identifying the vehicle using a unique ID, according to an embodiment of the present invention. The method 600 comprises activating the speedometer to register in the database by a user and creating a vehicle identification number at step 601. In addition, the method 600 involves uploading vehicle details or VINs in bulk or adding a particular vehicle’s VIN by the user at step 602. The VIN is either added manually or generated automatically at step 603. Furthermore, the method 600 involves creating a vehicle list in a sequential order by filing the fields and uploading the details of the vehicle at step 604. The vehicle list generated is based on the number of clusters or speedometers to be added.
[0074] FIG. 7 illustrates a block diagram of an exemplary system of speedometer workflow, according to an embodiment of the present invention. The system 700 comprises a speedometer 701, a plurality of IoT-based hardware units, such as 4G module 702a, mobile app 702b, or a telematic tracker 702c, a cloud server 703 comprising a database 704, and an analytics module 705. The speedometer 701 communicates with the cloud server 703 over multiple channels, such as 4G module 702a, mobile app 702b, or a telematic tracker 702c. The multiple channels work as an interface between the cloud server 703 and the speedometer or cluster 701. Furthermore, the cloud server 703 obtains the data from the speedometer 701 through multiple channels, processes it, and stores the data in the database 704 and also draws analytics from the data obtained using the analytics module 705, such as maintaining the sanity of the data, generating trips, distance travelled, driver score, and health of the vehicle. In addition, the data is further used to generate alerts, such as ignition, anti-theft, crash detection, geo-fence breach, etc, and notify the user.
[0075] Furthermore, the mobile app 702b suitable for both Android and iOS devices establishes connections with the speedometer 701. In addition, the users can log in, retrieve cluster details, activate uninitialized clusters, and perform firmware Over-The-Air (OTA) updates, etc. The cluster identification involving a crucial identifier for the clusters is the VIN, wherein the cloud server hosts services related to authorization, retail, leasing, file management, and subscription services. Furthermore, OTA updates are served via the cloud server for each VIN, and the VIN is marked for OTA through the cloud server. In addition, the mobile app connects with the device and can update the cluster with new firmware through OTA. Cloud connectivity enables seamless integration with services, providing users with comprehensive tools. The cluster and the mobile app connection are established over BLE. As soon as the connection is established, the API server starts fetching all the data of the cluster or speedometer to the cloud server. Moreover, when a cluster is initially flashed, at the commencement of its lifecycle, its memory undergoes a complete erasure, it lacks information about the key, ID, or model configuration. This ensures a fresh and secure start for the cluster's operations.
[0076] Moreover, an Advanced Encryption Standard (AES) key is generated during cluster activation. The AES key is used for authentication during the connection between the mobile app and the cluster or speedometer. AES serves as a distinctive code to authenticate users with the cluster. The connection between the App and the cluster is established through a handshaking process that necessitates an AES key. To connect with a specific cluster, the mobile app must possess the corresponding AES key which is different for each cluster. The mobile app initiates a request to the cloud server to acquire vital vehicle details such as the model, vehicle ID, and the key required for the handshaking. Each cluster is associated with a unique AES key. The purpose of storing the key is to prevent unauthorized users from connecting to the device. Since BLE allows any random device to connect to the cluster. To prevent unauthorized connections via random Mac IDs where, other BLE apps lead to data spoofing and cluster corruption, thus ensuring strong security is imperative.
[0077] Moreover, the mobile app initiates the initial handshake authentication process, providing the cluster with the necessary key. Once received, the cluster stores this key along with other relevant information, including configuration details. Subsequently, the cluster validates the AES key used during activation against the key transmitted from the mobile app. If the key matches, then the cluster establishes the connection. In case of mismatched keys, the cluster denies the connection. Furthermore, distinct characteristics of the cluster are exposed, enabling the Android device to identify, request, transmit, and read data. This communication between the connected user and the cluster is facilitated through these unique characteristics. This comprehensive functionality empowers users to perform a range of actions in collaboration with the cluster.
[0078] FIG. 8 illustrates an exemplary block diagram using machine learning models to predict energy consumption of a trip, according to an embodiment of the present invention. FIG. 8 illustrates a combination of an Artificial Neural Network (ANN) model to predict speed profiles and a regression model to predict energy consumption. The method utilizes road segmentation data 802, such as traffic data, weather, road characteristics, distance, and auxiliary data to predict the energy consumption of the trip. In road segmentation, the road is divided into segments based on road type, such as highway, primary, secondary, tertiary roads, etc. Furthermore, the road is also divided based on altitude/elevation, traffic data, and crossings. Each road segment has a distinct speed profile.
[0079] Furthermore, the total traction force may be:

Where, is the total traction force acting on vehicle;
is the aerodynamic drag force, the resistance encountered as the vehicle moves through the air.

Cx = coefficient of aerodynamic drag,
S= front Surface of vehicle, and is air density.
Furthermore,
Where, is the rolling resistance, it occurs due to the friction between the tires and the road surface. It's influenced by factors such as tire characteristics, road conditions, and vehicle speed.
Similarly,
Where, M= Mass of EV, g=gravity, Cs rolling coefficient, and Angle of the slope.
In addition,
Where, is the force induced by gravity when driving on a non-horizontal road, which is conservative and considerably influences the vehicle behavior, and Propulsion force from Motor.
Furthermore, energy consumption can then be modelled by the equation:

Where, t is time, s is distance, and is the fraction of time the auxiliaries are switched on.
Hence, this can be expressed as the summation of energy consumed over each road segment:




Where, : Regression coefficients
: Energy
: Vehicle speed at time ??i
: Wind speed value projected on the driving direction at time ????
: Distance
: Distance driven between ????-1and ????
: Fraction of time auxiliaries are switched on
: Time
: Positive elevation changes
: Negative elevation changes
: Error term
: Number of data points in segment ??
Moreover, the speed profiles SPAF & SPCMF are predicted for each road segment based on the road type, elevation, crossings, weather data, such as temperature, and wind speed, traffic data, and different vehicle control parameter values. These input parameters are fed into an ANN model 804 which then outputs the speed profiles. The auxiliary data can be approximated by considering the proportion of time the auxiliary components, such as headlights, indicators, and horns were in use during the trip. Furthermore, once the speed profiles are obtained, they are fed into a linear regression model 806 to predict the energy consumption for each road segment. The total energy consumed would be the sum of predicted energy consumption across all road segments. Root mean square error (RMSE) is used to ascertain the model’s performance.
[0080] In addition, driving behavior can be optimized to be energy efficient based on the road characteristics and the control parameters, and is carried out for each road segment with constraints of the maximum time that it can take the user to traverse the road segment. The method comprises using Discrete Particle Swarm optimization (PSO) to find the optimal vehicle control parameters in each road segment to minimize energy consumption on each road segment. Apart from PSO, other constrained non-linear optimization algorithms, such as Dynamic Programming (DP), Genetic Algorithms(GA) are also considered. Hence, it is important to minimize the energy consumption (???seg) along each road segment.
[0081] Furthermore, the time taken to traverse a road segment is less than the maximum time allowed for each road segment. Once the vehicle control parameters are calculated for each road segment, the total energy consumption after optimization is summed across all road segments. Further, if the value of the total energy consumption is less than the energy available in the battery, the controller alters the vehicle control parameters and the user completes the trip. Moreover, even after optimization, if the value of the total energy consumption is more than the energy available in the battery, a prompt is sent to the user that the SOC is not enough to complete the trip. Therefore, the effectiveness of the algorithm is examined by comparing it to different algorithms based on the solution space and the computation complexity or cost.
[0082] FIG. 9 illustrates a flowchart of the method for finding the best operating performance of the EV using an optimization algorithm, according to an embodiment of the present invention. The method includes initializing the position and velocity of the particles at step 902 considering the objective function. The method further includes evaluating the objective function of the initial population at step 904. In addition, the method includes updating the velocity and position of the particles at step 906 and evaluating the objective function for each particle at step 908. Furthermore, the method includes computing pbest and gbest at step 910, until a stopping criterion is satisfied at step 912. If the stopping criterion is not satisfied the method is iterated. Finally, the method includes obtaining the best operating performance or best solution at step 914.
[0083] Mathematically, the particle velocities are updated by the equation:

Where r1, r2 are random numbers between 0 and 1, constants w, c1, and c2 are parameters to the algorithm, pbest is the position that gives the best solution ever explored by particle i and gbest is the best solution explored by all the particles in the swarm.
Furthermore, the position of each particle would be updated by the equation:

Where Xi(t) is the position of the particle i at time t.
For the discrete PSO algorithm, each particle’s position and velocity are represented as probability distributions.
In addition, A particle p’s position is represented by :
Xp = [Dp,1, Dp,2, . . . , Dp,n]
where each Dp,i denotes probability distribution for variable Xi. Each entry in the particle’s position vector is itself comprised of a set of distributions:
Dp,i = [dap,i, dbp,i, . . . , dkp,i]
djp,I represent the probability that variable Xi takes on value j for particle p.
Therefore, a particle’s velocity is a vector of n vectors f, one for each variable in the solution, that adjust the particle’s probability distributions. Formally, this is represented as:
Vp = [fp,1, fp,2, . . . , fp,n]
fp,i = [?ap,i, ?bp,i, . . . , ?kp,i]
where ?jp,i is particle p’s velocity for variable i in state j.
Since these probabilities are continuous, the velocity update equations can be used identically to the traditional PSO.
[0084] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments.
[0085] It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modifications.
G) ADVANTAGES OF THE INVENTION
[0086] The various embodiments of the present invention provide a system and a method for electric vehicle travel optimization. The present invention provides a system and a method for optimizing the utilization of the existing capacity of the battery, to solve the range anxiety problems. The present invention also provides various other advantages, such as controlling the vehicle mode of operation to complete the route, thereby providing optimized performance. Current technologies find the best route based on the shortest distance, less time and traffic. However, additionally, the present invention helps to solve the problem of distance reachability. Distance reachability is the ability of the EV to reach the desired destination without depleting the battery midway. For instance, if the distance of route is 50 km but the battery capacity only supports the driving for 45 km without optimization. Then the present invention helps in optimization of the EV by providing an additional 5 km and making the vehicle reach the destination. Additionally, the present invention helps in finding the route containing downhill paths for getting regenerative current. Furthermore, the present invention also provides EV travel optimization by allowing reduction in the cases of stranded travelers.
[0087] Moreover, the present invention also integrates the system into the existing EVs without any changes, with complete flexibility in the usage. The flexibility includes offering a compatible system with most of the vehicle communication protocols. Flexibility to choose any hardware for integration as long as the hardware can send the required data in real-time. Furthermore, the present invention provides an option to toggle any feature and also provides modification in the UI of application and speedometer. Moreover, the present invention finds application in fleet operators of EVs, EV manufactures, EV rent service providers, delivery and pick up service and bike taxi service providers.
[0088] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such as specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments.
[0089] It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modifications. However, all such modifications are deemed to be within the scope of the claims.
,CLAIMS:We claim:
1. A system (100) for electric vehicle travel optimization, the system (100) comprising:
a. a plurality of IoT-based hardware units (101) integrated into a plurality of electric vehicles (EVs) (102) configured to capture real-time data, and comprising a telematic device or a smart speedometer (101a) and a user mobile device (101b);
b. a cloud server (104) comprising a database (103) and a compute engine (105); and wherein the compute engine (105) comprises an optimization algorithm configured to optimize real-time data received across an individual electric vehicle; and
c. a communication protocol (106) configured to connect the cloud server (104) and the plurality of IoT-based hardware units (101); and wherein the IoT-based hardware units (101) are configured to transmit the real-time data captured across the communication protocol (106) to the database (103); and wherein the compute engine (105) in the cloud server (104) is also configured to transmit control signals through the communication protocol to enhance the performance of the vehicle.
2. The system (100) as claimed in Claim 1, wherein each of the plurality of IoT-based hardware units (101) is further provided with a unique ID, and wherein the cloud server is configured to identify the vehicle and its associated hardware using the assigned ID.
3. The system (100) as claimed in Claim 2, wherein cloud server is configured for identifying the vehicle by:
a. activating the speedometer to register in the database by a user;
b. creating a vehicle identification number (VIN);
c. uploading vehicle details or VINs in bulk or adding a particular vehicle’s VIN by the user; and wherein the VIN is either added manually or generated automatically;
d. creating a vehicle list in a sequential order by filing the fields and uploading the details of the vehicle; and wherein the vehicle list is based on the number of clusters or speedometers to be added.
4. The system (100) as claimed in Claim 1, wherein the real-time data comprises an input from the user, an input from the vehicle, and an input from the database of the cloud server, which are historic data, machine learning data models, configuration data, and other associated data.
5. The system (100) as claimed in Claim 4, wherein the input from the user includes current location and destination, which ultimately provides the travel distance and the route, which is desired by the user to travel, and route options, departure time, arrival time, and addition of stops.
6. The system (100) as claimed in Claim 4, wherein the input from the vehicle includes latitude/longitude and altitude of the vehicle, which provides the location of the vehicle, and triaxial angles of vehicles, State of Charge (SOC), State of Health (SOH), battery voltage, battery current, wheel RPM, speed of the vehicle, regeneration current and mode of operation depending upon the original equipment manufacturers (OEMs).
7. The system (100) as claimed in Claim 4, wherein the input from the database of the cloud server includes information about the vehicle including wheel diameter, maximum speed, speed limit, brake regeneration limit, odometer, and drive score, and road conditions including distance, road elevation, traffic, tolls, ferries, wildfires, and air quality; and wherein the information of the road condition is stored as historic data in the database and is further utilized to track if the same route is taken by another user.
8. The system (100) as claimed in Claim 1, wherein the communication protocol employed to establish the connection between the plurality of IoT-based hardware units and the database includes Global System for Mobile (GSM) communication or a combination of GSM and Bluetooth Low Energy (BLE) communication.
9. The system (100) as claimed in Claim 1, wherein the IoT-based hardware units is communicatively coupled with the cloud server after the hardware is physically connected to the vehicle, and the communicative connection between the vehicle and cloud server is maintained until the information about the hardware is deleted manually from the database.
10. The system (100) as claimed in Claim 1, wherein the compute engine is configured for optimization of the real-time data by the optimization algorithm by finding the best operating performance of an EV personalized for the user with respect to the distance and time.
11. The system (100) as claimed in Claim 1, wherein the cloud server is configured to control the IoT-based hardware units to modify the driving operation of the multiple EVs in real-time by choosing different modes of driving or driving speed of the vehicle, and also configured to be integrated into the existing EVs.
12. The system (100) as claimed in Claim 1, wherein the cloud server is configured to control the IoT-based hardware units to control the speed of the vehicle to optimize the performance and fuel economy, and also configured to find the route containing downhill paths for generating regenerative current, which recharges the EV battery during throttle free movements of the vehicle during downhill movement.
13. The system (100) as claimed in Claim 1, wherein the compute engine is also configured to predict the battery energy consumption.
14. The system (100) as claimed in Claim 1, wherein the compute engine is also configured to help the user to decide the mode of the vehicle to complete the route, and to achieve optimized performance only if the optimization is enabled; and wherein the system is further configured to assist in the reduction of cases of stranded travelers, including accidental, flood or traffic prone areas based on the historic data and the drive score of the user.
15. A method (200) for electric vehicle travel optimization, the method (200) comprising the steps of:
a. obtaining input parameters and variables in real-time from a user, vehicle through a plurality of IoT-based hardware units and obtaining historic data, machine learning data models, configuration data or other data from a database embedded in a cloud server (202);
b. recognizing if an optimization is required for an EV or vehicle, based on the input parameter and variables received from the user, vehicle, and the database in the cloud server (204);
c. performing vehicle optimization calculations continuously until the optimization is no longer required for the vehicle, using an optimization algorithm to optimize vehicles across the specified constraints (206);
d. updating vehicles with optimized configurations (208); and
e. operating the EV as per the user instructions if optimization is not required by the vehicle (210).
16. The method (200) as claimed in Claim 15, wherein the input parameters and variables in real-time comprise an input from the user, an input from the vehicle, and an input from the database of the cloud server including historic data, machine learning data models, configuration data, and other associated data.
17. The method (200) as claimed in Claim 16, wherein the input from the user includes current location and destination, which ultimately provides the travel distance and the route, which is desired by the user to travel, and route options, departure time, arrival time, and addition of stops.
18. The method (200) as claimed in Claim 16, wherein the input from the vehicle includes latitude/longitude and altitude of the vehicle, which provides the location of the vehicle, and triaxial angles of vehicles, State of Charge (SOC), State of Health (SOH), battery voltage, battery current, wheel RPM, speed of the vehicle, regeneration current and mode of operation depending upon the original equipment manufacturers (OEMs).
19. The method (200) as claimed in Claim 16, wherein the input from the database of the cloud server includes information about the vehicle including wheel diameter, maximum speed, speed limit, brake regeneration limit, odometer, and drive score, and road conditions including distance, road elevation, traffic, tolls, ferries, wildfires, and air quality; and wherein the information of the road condition is stored as historic data in the database and is further utilized to track if the same route is taken by another user.
20. The method (200) as claimed in Claim 15, wherein the optimization of the real-time parameter and variables by the optimization algorithm includes finding the best operating performance of an EV personalized for the user with respect to the distance and time.
21. The method (200) as claimed in Claim 15, further comprises modifying the driving operation of the multiple EVs in real-time by choosing different modes of driving or driving speed of the vehicle.
22. The method (200) as claimed in Claim 15, wherein the method for predicting the battery energy consumption comprises the steps of:
a. collecting metadata about the route;
b. predicting battery energy consumption based on past driving behaviour of the user and default vehicle parameters;
c. checking the state of charge (SOC) of the battery to ensure that the ongoing trip can be completed; and wherein on determining enough SOC, no optimization is required for the user to complete the trip or finding the least energy consumption by adjusting the vehicle control parameters if there is no enough SOC; and
d. checking the SOC of the battery to complete the trip with updated parameters; and wherein on determining enough SOC, constraining optimization to maximize energy consumption by adjusting the vehicle control parameters to complete the trip or prompting the user that SOC is not enough to complete the trip even after optimization.
23. The method (200) as claimed in Claim 15, further comprises controlling the speed of the vehicle to optimize the performance and fuel economy, and also configured to find the route containing downhill paths for generating regenerative current, which recharges the EV battery during throttle free movements of the vehicle during downhill movement.
24. The method (200) as claimed in Claim 15, comprises helping the user to decide the mode of the vehicle to complete the route, and to achieve optimized performance; and assisting in reduction of cases of stranded travelers, including accidental, flood or traffic prone areas based on the historic data and the drive score of the user.