A system for estimating smart vehicle range

Abstract

The present invention relates to a system (1) for estimating smart vehicle (2) range by means of a machine learning model which takes into account user experience, road and vehicle (2) conditions.

Description

[0001] A SYSTEM FOR ESTIMATING SMART VEHICLE RANGE

[0002] Technical Field

[0003] The present invention relates to a system for estimating smart vehicle range by means of a machine learning model which takes into account user experience, road and vehicle conditions.

[0004] Background of the Invention

[0005] Today, there is no way to estimate vehicle range by incorporating the behavior of the specific driver and also the behavior of other vehicle drivers into the machine learning model and using much more extensive sensor data.

[0006] Therefore, considering the studies and shortcomings included in the current technique, it is understood that there is need for a system for estimating smart vehicle range by means of a machine learning model which takes into account user experience, road and vehicle conditions.

[0007] The United States patent document no. US2022410750A1, an application included in the state of the art, discloses a system configured to run a machine learning model and to predict the range of an electric vehicle by taking into account driver behavior and route conditions. The current invention provides a system based on artificial intelligence for management of electric vehicles fleet. The system receives live data and historical data from charging stations, fleet telematics, meteorological services, traffic management, mobile application, fleet dashboard, renewable source of energy, battery energy storage system, and the electric utility grid. The system utilizes machine learning algorithms to predict energy usage and optimize the charging schedule of electric vehicle. The system uses real-time data to generate electric vehicle trip condition training feature for predicting the remaining driving range. The system is configured to predict the vehicle’s arrival time to the charging station based on telematics data of each vehicle collected from the fleet management system. The system takes into account the traffic and weather conditions of the route to be traveled. Real-time sensor data received from the vehicle also plays an important role in range prediction. In addition, the past behavior of the driver also contributes to the prediction.

[0008] Summary of the Invention

[0009] An objective of the present invention is to realize a system developed for estimating smart vehicle range by means of a machine learning model which takes into account user experience, road and vehicle conditions.

[0010] Detailed Description of the Invention

[0011] “A System for Estimating Smart Vehicle Range” realized to fulfil the objective of the present invention is shown in the figure attached, in which:

[0012] Figure l is a schematic view of the inventive system.

[0013] The components illustrated in the figure are individually numbered, where the numbers refer to the following:

[0014] 1. System

[0015] 2. Vehicle

[0016] 3. Sensor

[0017] 4. Server The inventive system (1) for estimating smart vehicle range by means of a machine learning model comprises at least one vehicle (2) which is a motorized and wheeled land transportation vehicle and configured to be designed to carry passengers and / or load; at least one sensor (3) which is positioned on the vehicle (2) and configured to detect all physical and / or chemical changes occurring in the vehicle (2) and the environment; at least one server (4) which is configured to establish communication with the vehicle (2) by using any communication protocol and to access the data on the sensor (3) through this communication established; to store the information received from the sensors (3) positioned on the vehicle (2); to score how much range all users have traveled over how long a distance by accessing the data on sensor (3); to make a range estimation based on the geographical information of the route to be traveled when the new driving experience is started in the trained machine learning model, weather data, sensor information in the vehicle, the user’s average driver score for the last one month and the improvement rate in the driver score.

[0018] The vehicle (2) included in the inventive system (1) is an automobile such as a motorized and wheeled land transportation vehicle. The vehicle (2) is configured to consist of a combination of advanced communication technologies and advanced driving control units included in the state of the art. The vehicle (2) is configured to establish connection with the server (4) by using any remote communication protocol included in the state of the art.

[0019] The sensor (3) included in the inventive system (1) is configured to detect all physical and / or chemical changes occurring in the vehicle (2) and in the environment and then to transmit them to the related electronic control units in order to operate the equipment such as engine management, driving safety and handling, active and passive safety units and comfort units correctly in vehicles (2) included in the known state of the art. In the said invention, the sensor (3) is configured to be positioned on the vehicle (2). In the said invention, the sensor (3) is configured to be a sensor such as a temperature sensor used to measure the temperature of the battery, a voltage sensor used to monitor the voltage of the battery, a current sensor used to measure the current received from or reaching the battery, a state-of-charge sensor used to measure exactly how much charge the battery has, a health status sensor used to monitor the health of the battery, an overheating sensor used to detect battery overheating and to issue warnings or protection measures in the event of overheating, a speed sensor used to measure the speed of the vehicle (2), a road sensor used to provide information on the condition and surface of the road, a steering rotation sensor used to measure how much the steering wheel turns, an acceleration sensor used to measure the increase or decrease of vehicle (2) speed, a tire pressure sensor used to measure the air pressure of each tire, a vehicle (2) load sensor used to measure the load on the vehicle (2) or the weight of passengers, and a vehicle (2) position sensor used to determine the vehicle (2) position.

[0020] The server (4) included in the inventive system (1) is configured to establish communication with the vehicle (2) by using any communication protocol included in the state of the art and to access the data on the sensor (3) through this communication established. The server (4) is configured to determine information such as battery capacity, energy efficiency, driving speed, air temperature, driving style, traffic conditions, road gradient, wind resistance, battery age and health, regenerative braking, road surface and topography of the vehicle (2) by accessing the data on the sensor (3). The server (4) is configured to score how far each user has traveled on a road by accessing the data on the sensor (3). The server (4) is configured to calculate each user’s driving average over the last month. The server (4) is configured to calculate the percentage improvement in the user’s driving over the last three months. The server (4) is configured to train a supervised machine learning model based on the data from the sensors, the driving average data of each user over the last month and the percentage improvement in the user’s driving over the last three months. The server (4) is configured to make a range estimation based on the geographical information of the route to be traveled when the new driving experience is started in the trained machine learning model, weather data, sensor information in the vehicle, the user’s average driving score for the last month and the improvement rate of the user’s driving score. The server (4) is configured to detect the last available charging station for the current battery based on the distance of the smart charging stations.

[0021] Industrial Applicability of the Invention

[0022] In the inventive system (1), the server (4) determines information such as the battery capacity, energy efficiency, driving speed of the vehicle (2), air temperature, driving style, traffic conditions, road gradient, wind resistance, battery age and health, regenerative braking, road surface and topography by accessing the data on the sensor (3). The server (4) scores how far each user has traveled on a road by accessing the data on sensor (3). The server (4) calculates each user’s driving average over the last month and the percentage improvement in the user’s driving over the last three months. The server (4) trains a supervised machine learning model based on the data received from the sensors, each user’s driving average over the last month, and the percentage improvement in the user’ s driving over the last three months. The server (4) estimates the range when the new driving experience is started in the trained machine learning model based on the geographical information of the route to be traveled, weather data, sensor information in the vehicle, the user’s average driver score for the last month and the improvement rate in the driver score. The server (4) detects the last available charging station for the available battery according to the distances of the smart charging stations. Thus, it becomes possible to estimate the range of the smart vehicle by using a machine learning model that takes into account the user experience, road and vehicle conditions. Within these basic concepts; it is possible to develop various embodiments of the inventive “System (1) for Estimating Smart Vehicle Range”; the invention cannot be limited to examples disclosed herein and it is essentially according to claims.

Claims

CLAIMS1. A system (1) for estimating smart vehicle range by means of a machine learning model; comprising at least one vehicle (2) which is a motorized and wheeled land transportation vehicle and configured to be designed to carry passengers and / or load; at least one sensor (3) which is positioned on the vehicle (2) and configured to detect all physical and / or chemical changes occurring in the vehicle (2) and the environment; and characterized by at least one server (4) which is configured to establish communication with the vehicle (2) by using any communication protocol and to access the data on the sensor (3) through this communication established; to store the information received from the sensors (3) positioned on the vehicle (2); to score how much range all users have traveled over how long a distance by accessing the data on sensor (3); to make a range estimation based on the geographical information of the route to be traveled when the new driving experience is started in the trained machine learning model, weather data, sensor information in the vehicle, the user’s average driver score for the last one month and the improvement rate in the driver score.

2. A system (1) according to Claim 1; characterized by the vehicle (2) which is an automobile such as a motorized and wheeled land transportation vehicle.

3. A system (1) according to Claim 1 or 2; characterized by the vehicle (2) which is configured to consist of a combination of advanced communication technologies and advanced driving control units.

4. A system (1) according to Claim 3; characterized by the vehicle (2) which is configured to establish connection with the server (4) by using any remote communication protocol.

5. A system (1) according to any of the preceding claims; characterized by the sensor (3) which is configured to detect all physical and / or chemical changes occurring in the vehicle (2) and in the environment and then to transmit them to the related electronic control units in order to operate the equipment such as engine management, driving safety and handling, active and passive safety units and comfort units correctly in vehicles (2).

6. A system (1) according to any of the preceding claims; characterized by the sensor (3) which is configured to be positioned on the vehicle (2).

7. A system (1) according to any of the preceding claims; characterized by the sensor (3) which is configured to be a sensor such as a temperature sensor used to measure the temperature of the battery, a voltage sensor used to monitor the voltage of the battery, a current sensor used to measure the current received from or reaching the battery, a state-of-charge sensor used to measure exactly how much charge the battery has, a health status sensor used to monitor the health of the battery, an overheating sensor used to detect battery overheating and to issue warnings or protection measures in the event of overheating, a speed sensor used to measure the speed of the vehicle (2), a road sensor used to provide information on the condition and surface of the road, a steering rotation sensor used to measure how much the steering wheel turns, an acceleration sensor used to measure the increase or decrease of vehicle (2) speed, a tire pressure sensor used to measure the air pressure of each tire, a vehicle (2) load sensor used to measure the load on the vehicle (2) or the weight ofpassengers, and a vehicle (2) position sensor used to determine the vehicle (2) position.

8. A system (1) according to any of the preceding claims; characterized by the server (4) which is configured to establish communication with the vehicle (2) by using any communication protocol and to access the data on the sensor (3) through this communication established.

9. A system (1) according to any of the preceding claims; characterized by the server (4) which is configured to determine information such as battery capacity, energy efficiency, driving speed, air temperature, driving style, traffic conditions, road gradient, wind resistance, battery age and health, regenerative braking, road surface and topography of the vehicle (2) by accessing the data on the sensor (3).

10. A system (1) according to any of the preceding claims; characterized by the server (4) which is configured to score how far each user has traveled on a road by accessing the data on the sensor (3).

11. A system (1) according to any of the preceding claims; characterized by the server (4) which is configured to calculate each user’s driving average over the last month.

12. A system (1) according to any of the preceding claims; characterized by the server (4) which is configured to calculate the percentage improvement in the user’s driving over the last three months.

13. A system (1) according to any of the preceding claims; characterized by the server (4) which is configured to train a supervised machine learning model based on the data from the sensors, the driving average data of eachuser over the last month and the percentage improvement in the user’s driving over the last three months.

14. A system (1) according to any of the preceding claims; characterized by the server (4) which is configured to make a range estimation based on the geographical information of the route to be traveled when the new driving experience is started in the trained machine learning model, weather data, sensor information in the vehicle, the user’s average driving score for the last month and the improvement rate of the user’s driving score.

15. A system (1) according to any of the preceding claims; characterized by the server (4) which is configured to detect the last available charging station for the current battery based on the distance of the smart charging stations.

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