A system to determine the drivable range for an electric vehicle

Abstract

A system for determining the driveable range for an electric vehicle. The system comprises at least one processor and at least one computer-readable non-transitory memory unit communicably coupled with the processor. The memory unit stores computer-readable instructions that, when executed by the processor, cause the processor to perform the following functions. System monitors the state of charge (SoC) in a battery pack. Additionally, the system collects various data including vehicle data, user data, coordinates of the destination, coordinates of a route to the destination, traffic load, weather conditions, or a combination thereof. Moreover, the system utilizes a machine learning module to determine the driveable range in real-time based on the monitored SoC in the battery pack, the current location, the destination, and the collected data. Lastly, the system presents the determined driveable range and route to the user on a display unit using a graphical user interface.

Description

[0001] A SYSTEM TO DETERMINE THE DRIVABLE RANGE FOR AN ELECTRIC

[0002] VEHICLE

[0003] RELATED APPLICATIONS

[0004] This application claims the benefit of priority under 35 USC § 119(e) of U.S. Provisional Patent Application No. 63 / 707,223 filed on October 15, 2024 and U.S. Provisional Patent Application No. 63 / 774,776 filed on March 20, 2025, the contents of which are incorporated herein by reference in their entirety.

[0005] FIELD OF THE INVENTION

[0006] The present disclosure in general relates to a system to accurately determine the range of an electric vehicle. In particular, the present disclosure relates to a system to accurately determine the drivable range of an electric vehicle in real time based on vehicle data, and user data.

[0007] BACKGROUND OF THE INVENTION

[0008] In recent years the development of electric vehicles (EVs) has been astonishing, and the popularity of such vehicles in the market is growing steadily.

[0009] However, the comparatively low range of electric vehicles in comparison to conventional internal combustion engine ICE powered vehicles is a crucial factor that is thought to be impeding their widespread adoption. Range-extending technologies, like hybrid cars, larger batteries, battery reserve facilities, and battery swapping stations, help to somewhat offset the limited range that comes with battery-powered vehicles.

[0010] To mitigate this problem various systems have been developed to predict drive range for a vehicle. One such system is described in US2011166810 which describes a vehicle including one or more controllers configured to determine a remaining energy of the vehicle's battery based on information derived during the current vehicle operating cycle. The system achieves this by determining a series of energy consumption rates of the vehicle, and selecting a set of energy consumption rates from the series. The one or more controllers may be further configured to determine an expected drive range for the vehicle based on the remaining energy and the selected set of energy consumption rates. US 2013 / 110331 Al discloses a method of determining a predicted range of an electric vehicle, the method comprising determining a range value during a current vehicle operating cycle using a first range model, wherein the first range model is dependent on an energy consumption rate value recorded during a previous vehicle operating cycle.

[0011] However, the range that is achievable by the vehicle when under electric power is still considered to be unpredictable and this has bred a fear that the vehicle has insufficient range to reach its destination, thus stranding the user at the roadside. Termed 'range anxiety', it is believed that this fear causes reluctance on the part of the user to accept electric vehicles as a serious proposition alongside conventional ICE vehicles.

[0012] Furthermore, range anxiety often deters potential buyers who are worried about being stranded or inconvenienced by the need to frequently charge. As a result, many consumers hesitate to transition from traditional internal combustion engine vehicles to EVs. To alleviate this anxiety and boost adoption, advancements in battery technology, increased availability of fast-charging infrastructure, and improvements in vehicle range are essential. Addressing these issues can enhance consumer confidence and accelerate the shift towards more sustainable transportation options.

[0013] Furthermore, range anxiety may be alleviated if the user is provided with an accurate indicator of the available range of the vehicle. It is against this context that the invention has been devised.

[0014] Therefore, there is a need for innovative solutions that can overcome these limitations and provide real-time drivable range predictions for electric vehicles.

[0015] BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Some embodiments of the invention are herein described, by way of example only, with reference to the accompanying drawings. With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of embodiments of the invention. In this regard, the description taken with the drawings makes apparent to those skilled in the art how embodiments of the invention may be practiced. In the drawings:

[0017] FIG. 1 : A block diagram illustrating a system, according to some embodiments of the current invention.

[0018] FIG. 2: A flow diagram illustrating a use of the system, according to some embodiments of the current invention.

[0019] FIG. 3: A block diagram illustrating a system, according to some embodiments of the current invention.

[0020] FIG. 4: A flow diagram illustrating a method of use of the system, according to some embodiments of the current invention.

[0021] SUMMARY OF THE INVENTION

[0022] A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.

[0023] Introduction

[0024] Embodiments of the present disclosure substantially eliminate and / or at least partially address the aforementioned problems in the prior art and improve the drivable range prediction for the electric vehicle by analyzing vehicle data and / or user data and / or using machine learning algorithms, e.g., neural network-based predictive model, regression, and / or another algorithm.

[0025] Some embodiments relate to a system to determine a drivable range for an electric vehicle comprising at least one processor and at least one computer-readable non-transitory memory unit communicably coupled with the processor storing computer-readable instructions which, when executed by the processor, causes the processor to monitor a state of charge (SoC) in a battery pack, determine a current location of the electric vehicle and at least one destination, collect at least one of vehicle data, user data, coordinates of the destination, coordinates of a route to the destination, traffic load, weather conditions, or a combination thereof.

[0026] The system then determines at least one drivable range in real-time by using a machine learning module based on the monitored SoC in the battery pack, the current location and the destination, and the collected data. It further determines at least one route from the determined current location to the destination by using the machine learning module based on the monitored SoC in the battery pack, the current location and the destination, and the collected data. Finally, it presents the determined drivable range and the determined route to a user on a display unit using a graphical user interface.

[0027] Some embodiments relate to a system for determining the drivable range of an electric vehicle comprising at least one processor and at least one non-transitory computer- readable memory unit communicably coupled with the processor wherein the memory unit stores computer-readable instructions that, when executed by the processor, causes the processor to monitor the state of charge (SoC) of the vehicle’s battery pack, identify the vehicle’s current location and at least one destination, collect data including at least one of the following: vehicle data, user data, destination coordinates, route coordinates, traffic load, weather conditions, road conditions, or a combination thereof.

[0028] In some embodiments, the system determines in real-time the drivable range based on a machine learning (ML) algorithm considering dynamic variables such as road inclines, road quality, traffic congestion, and driver behavior. It may suggest an optimal route considering the available driving range, battery status, and the collected data and displays the determined drivable range and the suggested route to the user through a graphical user interface (GUI).

[0029] System Components and Data Collection

[0030] Some embodiments of the present invention relate to a system that accurately determines the drivable range of an electric vehicle in real-time. Optionally, the drivable range may be determined for an electric vehicle by considering various factors such as the state of charge (SoC) in the battery pack, the current location of the vehicle, the destination, and collected data. The collected data may include vehicle data, user data, coordinates of the destination, coordinates of the route, traffic load, and weather conditions, etc.

[0031] Data may be collected from personal and / or proprietary data and / or publicly available data (for example, the system may find weather data on a network for use in predicting energy use by air conditioning, heating, wind, traffic, etc.). By using a machine learning module, the system may be able to determine the drivable range in real-time. By using a machine learning module, the system may be able to determine the most suitable route from the current location to the destination. This information may be presented to the user on a display unit, e.g., through a graphical user interface.

[0032] Optionally, the machine learning algorithm may include at least one of the following: a deep learning neural network model that processes historical data and real-time behavior, a dynamic regression-based predictive mechanism that adapts to driving habits and changing environmental conditions, and Al-based data processing to enhance predictive accuracy during the drive.

[0033] According to some embodiments, the system to determine the drivable range for an electric vehicle may include at least one processor. Optionally, the system may include at least one computer-readable non-transitory memory unit communicably coupled with the processor storing computer-readable instructions which, when executed by the processor, causes the processor to monitor a state of charge (SoC) in a battery pack. Optionally, the processor may create at least one individual identifier for the electric vehicle and / or at least one user of the electric vehicle. Optionally, individual identifiers may be created based on vehicle data and / or user data.

[0034] According to some embodiments, the vehicle data may include at least data regarding vehicle type, vehicle age, weight of the vehicle, repair history of the vehicle, maintenance history of the vehicle, or a combination thereof. Optionally, the system may be configured to receive real-time sensor data. Optionally, the sensor data may be from one or more vehicle sensors and / or one or more external sensors. Optionally, the system may include continuous network communication, e.g., wi-fi, Bluetooth, cellular network, satellite network, etc. System Synchronization and Integration

[0035] According to some embodiments, the system may be configured to synchronize with and / or connect to one or more onboard vehicle systems, e.g., engine sensors, navigation system, internal sensors, etc. Optionally, the system may be configured to connect to one or more additional sensors, e.g., dashboard camera, GPS, cellular phone sensor, etc. Optionally, the system may be configured for real-time data integration and / or dynamic modeling. Optionally, the system may be configured to incorporate vehicle data such as battery status, vehicle load (e.g., passenger and / or cargo weight), weather conditions, road quality, tire pressure, and traffic congestion, etc.

[0036] Optionally, the system may be configured to adjust route recommendations based on expected weather conditions, e.g., lower temperatures increase energy consumption, use of air conditioning increases energy consumption, etc. Alternatively or additionally, the system may predict changes in various parameters (e.g., road conditions, traffic, weather, vehicle conditions) and include these predictions in a recommendation of route and / or prediction of energy availability and / or energy use. For example, the system may account for expected increases in traffic at or near rush hour and / or adjust energy use predictions and / or route suggestions.

[0037] Optionally, the system may include probabilistic input and / or output. For example, the system may give the probability of arriving before or after a certain time and / or the probability of running out of power prematurely.

[0038] According to some embodiments, the user data may include at least one of the user’s driving habits, driver behavior, driving style, address of origin, address of destination, preferred route signs from driving behavior, etc. Optionally, the system may be configured to incorporate driver-specific data for adaptation of the predicted drivable range. Optionally, the system may be configured to automatically fine-tune recommendations based on individual driving behavior.

[0039] Route Generation and Real-Time Updates According to some embodiments, the system may generate a route for the user of the electric vehicle from the origin to the destination. Optionally, the system may include determining the current location of the electric vehicle and at least one destination. Optionally, the system may mark road conditions that relate to energy use. For example, the system may mark where there are long unimpeded descents and / or where a descent may have impedances (e.g., traffic lights, traffic curves). Optionally, the system may mark relevant conditions with coloring and / or line types and / or symbols and / or colored symbols (e.g., colored dots), etc.

[0040] For example, the system may generate a route for the user of the electric vehicle from the origin to the destination in which the elevation in the generated route is marked in one color, descent in the generated route is marked in another color, and flat terrain in the generated route is marked in an additional color. Optionally, the system may accurately determine the drivable range of an electric vehicle in real-time and produce a route plan from the origin to the destination using machine learning. Additionally or alternatively, the system may give probabilities and / or confidence intervals for predictions (e.g., that there is enough energy to reach a destination, the time it will take to reach a destination) on one route and / or various routes.

[0041] In some embodiments, predictions could be imported (e.g., predicted traffic or weather conditions from Internet sources). Alternatively or additionally, predictions may use local resources (e.g., an Al routine may predict the effect of weather on traffic and / or project an existing traffic condition in one location to a future traffic condition in a location downstream and / or an expected occurrence (e.g., road work and / or closing down of a public transportation link and / or political activity and / or human activities [e.g., an event at a stadium and / or a demonstration]) to a future traffic condition).

[0042] Optionally, the system may provide range estimations factoring in external conditions in real-time such as road quality, weather, etc. In some cases, the system will integrate various data. For example, the system may update predictions of traffic based on predicted weather conditions and / or possible events. Real-Time Adjustments and Alerts

[0043] According to some embodiments, the system may determine at least one drivable range in real-time by using a machine learning module based on the monitored SoC of the battery pack, the current location, and the destination, and the collected data. Optionally, the system may include determining at least one route from the determined current location to the destination by using the machine learning module based on the monitored SoC in the battery pack, the current location, and the destination, and the collected data. Optionally, the system may include presenting the determined drivable range and the determined route to a user on a display unit using a graphical user interface.

[0044] According to some embodiments, when the monitored SoC in the battery pack is not sufficient to reach the desired destination, optionally, the processor of the system may identify nearby electric charging stations suitable for charging the electric vehicle. Optionally, the processor of the system may identify the suitable charging station by considering the available electrical power in the different charging stations. Optionally, the processor of the system may calculate the required charging time for the electric vehicle based on the monitored SoC, user’s route preference to the desired destination, location of the identified charging stations, etc. Optionally, the processor of the system may calculate an estimated number of stops for charging the electric vehicle along the determined route if the user has set a specific delay time for charging.

[0045] According to some embodiments, the processor of the system may be continually updated in real-time. Optionally, the processor may track the accuracy of the predicted drivable range. Optionally, the processor may continually update the predicted drivable range based on the collected information, e.g., information available over the internet (e.g., changing weather conditions, changing traffic conditions, etc.) and / or information collected via one or more vehicle systems (e.g., changing vehicle conditions (e.g., damage to the vehicle, addition or reduction of weight by addition or reduction of occupants and / or cargo, etc.), changing vehicle systems usage (e.g., use of air conditioner, lights, radio, navigation system, etc.), interaction with charging networks (e.g., optimizing routes based on available charging points and / or estimated waiting times).

[0046] According to some embodiments, the system may be configured to provide an alert and / or warning to the user when the accuracy of a predicted drivable range is found to be faulty and / or inaccurate beyond a predefined tolerance level. Optionally, the system may be configured to provide an alert to the user when one or more conditions affect the accuracy of a prediction, e.g., vehicle condition, driver condition, road conditions, traffic conditions, etc. Optionally, the system may be configured to account for the possibility of errors in the predicted drivable range, e.g., by issuing a warning suggesting alternative routes, etc.

[0047] Optionally, the system may be configured to suggest an alternative route and / or charging station to the user. Optionally, if a user drives aggressively and / or consumes more energy than expected, the system may provide an alert accordingly. Optionally, the system may provide an alert if the driver deviates from the optimal energy-efficient route.

[0048] Energy Consumption Forecasting and Route Adaptation

[0049] According to some embodiments, the system may be configured for energy consumption forecasting with route adaptation. Optionally, the system may be configured for geography and / or topography integration. Optionally, the system may employ a color- coded scheme to visually represent road conditions which may impact energy consumption, e.g., steep inclines, poor road conditions, frequent stoplights, sharp turns, etc.

[0050] Optionally, the system may be integrated with an existing navigation system. Optionally, the system may include an App for installation on a vehicle navigation system, personal computing device, or vehicle system. Optionally, the color-coded route map may be displayed to the user indicating varying energy consumption levels along the route, e.g., color X for road inclines that require higher energy consumption, color Y for downhill sections where regenerative charging occurs, color Z for flat sections with stable energy consumption, etc.

[0051] According to some embodiments, the system may alert the user to road conditions which may impact energy consumption, e.g., steep inclines, poor road conditions, frequent stoplights, sharp turns, etc. Optionally, the elevation changes along the determined route. Optionally, elevation changes along the determined route may aid in determining the energy efficiency of a journey. Optionally, the color scheme may simplify understanding of users by highlighting whether a section of the route involves an ascent, descent, or a flat segment and / or how these factors affect electricity consumption and / or battery regeneration. For example, any portion of the route where the vehicle is ascending is marked in red. Ascending requires more energy leading to higher electricity consumption. Optionally, visual cues may assist users to anticipate parts of the trip that may demand more power and / or may reduce battery life more quickly.

[0052] Optionally, a color may signify that the vehicle is traveling downhill allowing the battery to regenerate due to the energy-saving properties of gravity and reduced power demand. For example, descending sections are represented in green. Descents may be more economical in terms of power usage. Optionally, users may expect to recharge a portion of the battery during these segments.

[0053] Optionally, when the route is flat or involves no significant ascent or descent, a color may indicate steady energy usage with minimal variation offering neither significant consumption nor regeneration of battery power. Optionally, the color may indicate stable predictable driving conditions. For example, when the route is flat or involves no significant ascent or descent, the section is marked in light blue.

[0054] Fleet Applications and Smart Grid Integration

[0055] In some embodiments, the system for determining the drivable range of an electric vehicle may be adapted for use in various types of vehicle fleets including, for example, public passenger vehicles (buses and cabs), share cars, transport vehicles (e.g., trucks), and mixed company fleets.

[0056] In some embodiments, a system for fleets of public passenger vehicles like buses and cabs may facilitate efficient route planning and reduce downtime due to charging. In some embodiments, the machine learning algorithm may include a deep learning neural network model that processes historical data and real-time behavior, a dynamic regression-based predictive mechanism that adapts to driving habits and changing environmental conditions, and Al-based data processing to enhance predictive accuracy during the drive. For example, the system may determine dynamically which car and / or driver is suited and / or prepared for a given trip ordered by a customer. For example, certain vehicles or drivers may perform more efficiently in urban and / or open roads. For example, certain drivers and / or vehicles may be approaching a time for rest and / or charging and / or some other fixed task. Optionally, such a driver and / or vehicle may be sent to a location where the expected further business is likely to bring them closer to their upcoming destination.

[0057] Alternatively or additionally, the system may send drivers who seek longer-term business to destinations where they are likely to be kept busy longer. When dispatching a vehicle, the system may consider predicted road and / or business conditions when the driver and / or vehicle is to return to its base and / or scheduled activities.

[0058] In some embodiments, the system for determining the drivable range of an electric vehicle can be modified to cater to a fleet of public passenger vehicles such as buses and cabs. Optionally, the system can incorporate additional data points relevant to fleet operations such as the number of passengers, average passenger weight, and frequent stop locations.

[0059] For example, the system can be configured to account for the frequent stopping and starting patterns typical of public transport vehicles which can significantly impact energy consumption. Additionally, the system may integrate with public transportation schedules to optimize route planning and energy usage based on peak and off-peak hours. Alternatively or additionally, the system may provide real-time updates to drivers and fleet managers through a centralized dashboard ensuring efficient management of multiple vehicles simultaneously.

[0060] When applied to a fleet of share cars, the system can enhance the user experience by providing accurate range estimations and route suggestions tailored to individual driving habits and preferences. The system may incorporate user data such as acceleration habits, braking patterns, average speed, and past trips to fine-tune recommendations based on individual driving behavior.

[0061] In some embodiments, the system can adjust route recommendations based on user preferences such as shorter charging times or energy-efficient routes. The system may direct users to cars that are more efficient for their intended trip and / or better suited and / or have enough energy. Optionally, the system can be adapted to include user-specific data from multiple drivers who may use the same vehicle.

[0062] In some embodiments, the system may create individual identifiers for each driver tracking their driving habits, preferred routes, and charging preferences. Optionally, the system can synchronize with a mobile application used by share car users providing personalized route recommendations and energy consumption forecasts based on historical driving data.

[0063] For example, the system may suggest optimal charging stations based on user preferences for cost, wait time, and charger type availability. Additionally, the system can provide real-time notifications to users about the current state of charge and estimated range and / or an estimate of the charge that will be available at a future time after another user finishes using the vehicle, enhancing the user experience and reducing range anxiety.

[0064] In some embodiments, for transport vehicles like trucks, the system can optimize logistics by providing accurate drivable range predictions and route planning that considers cargo weight and road conditions. In some embodiments, the system may collect data regarding vehicle type, vehicle age, weight of the vehicle, repair history, and maintenance history.

[0065] The system may also receive real-time sensor data from onboard vehicle systems such as engine sensors, navigation systems, and internal sensors as well as additional sensors like dashboard cameras and GPS. The system can integrate this data to provide real-time updates and dynamic modeling ensuring that route recommendations are adjusted based on expected weather conditions, traffic congestion, available energy, charging time, and other variables.

[0066] Optionally, the system can be modified to consider additional factors specific to heavy-duty vehicles. In some embodiments, the system may incorporate data on cargo weight, loading and unloading patterns, and the impact of varying road conditions on large vehicles. Optionally, the system can predict energy consumption based on the type of cargo being transported and the route’s topography. For example, the system may provide route recommendations that minimize energy consumption by avoiding steep inclines and congested areas. Additionally, the system can integrate with logistics management software to optimize delivery schedules and charging stops ensuring timely deliveries while maintaining energy efficiency.

[0067] Alternatively or additionally, the system may provide fleet managers with a centralized interface to monitor and manage the energy consumption and route planning of multiple transport vehicles, enhancing operational efficiency and reducing costs.

[0068] In some embodiments, the system described in the claims and specification could be modified to integrate with smart grids to optimize electric vehicle charging based on realtime electricity pricing and availability. For example, the system may incorporate additional data inputs from smart grid networks enabling it to dynamically adjust charging schedules and locations based on fluctuating electricity rates and grid demand.

[0069] The processor may be configured to receive real-time pricing data and availability information from the smart grid allowing the machine learning algorithm to suggest optimal charging times and locations that minimize cost and maximize efficiency. Additionally or alternatively, the system may include communication modules to interface directly with smart grid infrastructure

[0070] SPECIFIC EMBODIMENTS

[0071] Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not necessarily limited in its application to the details of construction and the arrangement of the components and / or methods set forth in the following description and / or illustrated in the drawings and / or the Examples. The invention is capable of other embodiments or of being practiced or carried out in various ways.

[0072] Referring to FIG. 1, illustrated is a block diagram that describes system 100, according to some embodiments of the current invention. For example, system 100 may include at least one computer- readable non-transitory memory unit 110 communi cab ly coupled with the processor 120 storing computer-readable instructions, which when executed by the processor 120, causes the processor to determine at least one drivable range in real-time by using a machine learning module 130, based on the monitored SoC in the battery pack, the current location and the destination, and the collected data. The system 100 may determine at least one route from the determined current location to the destination by using the machine learning module 130, based on the monitored SoC in the battery pack, the current location and the destination, and the collected data.

[0073] FIG. 2 is a flow diagram illustrating a method of use of the system, according to some embodiments of the current invention. For example, in method 200 for determining a drivable range for an electric vehicle using a system comprising at least one processor and at least one computer-readable non-transitory memory unit communicably coupled with the processor storing computer-readable instructions. The processor is configured to include an Al module. Method 200 includes monitoring 202 a state of charge (SoC) in a battery pack. Determining 204 a current location of the electric vehicle and at least one destination. Collecting 206 at least one of vehicle data, a user data, coordinates of the destination, coordinates of a route to the destination, traffic load, weather conditions, road conditions, or a combination thereof. Determining 208 at least one drivable range in real-time by using a machine learning module, based on the monitored SoC in the battery pack, the current location and the destination, and the collected data. Determining 210 at least one route from the determined current location to the destination by using the machine learning module, based on the monitored SoC in the battery pack, the current location and the destination, and the collected data. Presenting 212 the determined drivable range and the determined route to a user on a display unit using a graphical user interface.

[0074] FIG. 3 is a block diagram illustrating a system, according to some embodiments of the current invention. For example, system 300 may include at least one computer-readable non-transitory memory unit 310 communicably coupled with processor 320 storing computer-readable instructions, which when executed by processor 320, causes the processor to monitor a state of charge (SoC) of a vehicle’s battery pack, identify the vehicle’s current location and at least one destination, collect data including at least one of the following: vehicle data (e.g., vehicle type, vehicle age, weight of the vehicle, repair history of the vehicle, maintenance history of the vehicle, or a combination thereof), user data (e.g., user’s driving habits including acceleration habits, braking patterns, average speed, past trips, etc.), destination coordinates, route coordinates, traffic load, weather conditions, road conditions, determine in real-time a drivable range based on a machine learning (ML) algorithm 330 of processor 320, wherein ML algorithm 330 considers dynamic variables including road inclines, road quality, traffic congestion, and driver behavior, suggest an optimal route considering the available driving range, battery status, and the collected data, and display the determined drivable range and the suggested route to the user through an interactive graphical user interface (GUI) 340.

[0075] Machine learning algorithm 330 may include at least one of a deep learning neural network model that processes historical data and real-time behavior, and / or a dynamic regression-based predictive mechanism that adapts to driving habits and changing environmental conditions, and / or Al-based data processing configured to enhance predictive accuracy during the drive.

[0076] Machine learning algorithm 330 may be automatically updated based on real-time sensor data. The sensor data may include data from the Battery Management System (BMS), and / or data from road load sensors and tire pressure sensors, and / or weather data retrieved from external internet sources, and / or communication with charging networks to predict charging station availability and electricity pricing.

[0077] GUI 340 may be configured to display to the user a color-coded route map indicating varying energy consumption levels along the route, e.g., color X for road inclines that require higher energy consumption, color Y for downhill sections where regenerative charging occurs, color Z for flat sections with stable energy consumption, etc.

[0078] FIG. 4 is a flow diagram illustrating a method of use of the system, according to some embodiments of the current invention. For example, method 400 for determining the drivable range of an electric vehicle includes monitoring 402 a state of charge (SoC) of a vehicle’s battery pack, identifying 404 the vehicle’s current location and at least one destination; collecting 406 data including at least one of the following: vehicle data (e.g., vehicle type, vehicle age, weight of the vehicle, repair history of the vehicle, maintenance history of the vehicle, or a combination thereof), user data (user’s driving habits including acceleration habits, braking patterns, average speed, past trips, etc.), destination coordinates, route coordinates, traffic load, weather conditions, road conditions; processing the collected data and determining 408 in real-time a drivable range based on a machine learning (ML) algorithm, considering dynamic variables including road inclines, road quality, traffic congestion, and driver behavior; suggesting 410 an optimal route considering the available driving range, battery status, and the collected data; and displaying 412 the determined drivable range and the suggested route to the user through an interactive graphical user interface (GUI).

[0079] Additionally, the method may include automatically updating the ML algorithm based on real-time sensor data. The sensor data may include data from the Battery Management System (BMS), and / or data from road load sensors and tire pressure sensors, and / or weather data retrieved from external internet sources, and / or communication with charging networks to predict charging station availability and electricity pricing. The machine learning algorithm may include at least one of a deep learning neural network model that processes historical data and real-time behavior, and / or a dynamic regression-based predictive mechanism that adapts to driving habits and changing environmental conditions, and / or AL based data processing configured to enhance predictive accuracy during the drive.

[0080] The method may include suggesting alternative routes incorporating optimal charging stations based on wait time, cost, and charger type availability when the system determines that an available battery charge is insufficient to reach the selected destination, and adjusting route recommendations based on user preferences, wherein the user preferences include shorter charging times or energy-efficient routes.

[0081] The method may include incorporating advanced security layers, e.g., end-to-end encryption to prevent algorithm replication, individual identifiers (IDs) assigned to each vehicle and user to prevent unauthorized access to driving and energy consumption data, a user-controlled data privacy mechanism allowing users to delete historical data, limit third- party sharing, and anonymize driving records.

[0082] The GUI may display a color-coded route map indicating varying energy consumption levels along the route, e.g., color X for road inclines that require higher energy consumption, color Y for downhill sections where regenerative charging occurs, color Z for flat sections with stable energy consumption, etc.

[0083] These embodiments are provided by way of example and are in no means intended to limit the scope of the invention.

[0084] While the invention has been described in its preferred form or embodiment with some degree of particularity, it is understood that this description has been given only by way of example and that numerous changes in the details of construction, fabrication, and use, including the combination and arrangement of parts, may be made without departing from the spirit and scope of the invention.

[0085] GENERAL

[0086] It is expected that during the life of a patent maturing from this application many relevant building technologies, artificial intelligence methodologies, computer user interfaces, image capture devices will be developed and the scope of the terms for design elements, analysis routines, user devices is intended to include all such new technologies a priori.

[0087] Unless otherwise defined, all technical and / or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. Although methods and materials similar or equivalent to those described herein may be used in the practice or testing of embodiments of the invention, exemplary methods and / or materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting.

[0088] As will be appreciated by one skilled in the art, some embodiments of the present invention may be embodied as a system, method or computer program product. Accordingly, some embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit," "module" or "system." Some embodiments of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon. Implementation of the method and / or system of some embodiments of the invention may involve performing and / or completing selected tasks manually, automatically, or a combination thereof. Moreover, according to the actual instrumentation and equipment of some embodiments of the method and / or system of the invention, several selected tasks could be implemented by hardware, by software or by firmware and / or by a combination thereof, e.g., using an operating system.

[0089] For example, hardware for performing selected tasks according to some embodiments of the invention could be implemented as a chip or a circuit. As software, selected tasks according to some embodiments of the invention could be implemented as a plurality of software instructions being executed by a computer using any suitable operating system. In an exemplary embodiment of the invention, one or more tasks according to some exemplary embodiments of method and / or system as described herein are performed by a data processor, such as a computing platform for executing a plurality of instructions. Optionally, the data processor includes a volatile memory for storing instructions and / or data and / or a non-volatile storage, for example, a magnetic hard-disk and / or removable media, for storing instructions and / or data. Optionally, a network connection is provided as well. A display and / or a user input device such as a keyboard or mouse are optionally provided as well.

[0090] Any combination of one or more computer readable medium(s) may be utilized for some embodiments of the invention. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that may contain, or store a program for use by or m connection with an instruction execution system, apparatus, or device. A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that may communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0091] Throughout the present disclosure, the term "computer- readable" may refer to data or information that may be accessed, processed, or stored by a computer system. The term "non-transitory memory unit" may refer to a storage device that retains data even when power is removed, such as a hard disk drive or solid-state drive. Particularly, “non-transitory memory unit” may refer to a component and / or device capable of storing and / or retrieving data in a computer system, typically in the form of binary digits (bits) or bytes. The term "communi cab ly coupled" may refer to the connection or linkage between two or more devices that may enable them to exchange data or information. The term "computer-readable instructions" may refer to a set of commands or statements that may be executed by a computer system to perform a specific task or function. The term "executed" may refer to the act of carrying out or performing a set of computer-readable instructions by a computer system.

[0092] Program code embodied on a computer readable medium and / or data used thereby may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0093] Computer program code for carrying out operations for some embodiments of the present invention may be written in any combination of one or more programming languages, including an object-oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0094] Some embodiments of the present invention may be described below with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0095] These computer program instructions may be stored in a computer readable medium that may direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks.

[0096] The computer program instructions may be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0097] Data and / or program code may be accessed and / or shared over a network, for example the Internet. For example, data may be shared and / or accessed using a social network. A processor may include remote processing capabilities for example available over a network (e.g., the Internet). For example, resources may be accessed via cloud computing. The term "cloud computing" refers to the use of computational resources that are available remotely over a public network, such as the internet, and that may be provided for example at a low cost and / or on an hourly basis. Any virtual or physical computer that is in electronic communication with such a public network could potentially be available as a computational resource. To provide computational resources via the cloud network on a secure basis, computers that access the cloud network may employ standard security encryption protocols such as SSL and PGP, which are well known in the industry.

[0098] Some of the methods described herein are generally designed only for use by a computer, and may not be feasible or practical for performing purely manually, by a human expert. A human expert who wanted to manually perform similar tasks might be expected to use completely different methods, e.g., making use of expert knowledge and / or the pattern recognition capabilities of the human brain, which would be vastly more efficient than manually going through the steps of the methods described herein.

[0099] As used herein the term "about" refers to ± 10%

[0100] The terms "multiple" and "plurality" are used interchangeably, and as used herein refers to 1, 2, 3, 4, 5, 10, 12, 15, 20, or more.

[0101] Throughout the description and claims of this specification, the words "comprise", "include", "have", and "contain" and variations of these words, for example "comprising" and "comprises", mean "including but not limited to", and do not exclude other components, items, integers, or steps not explicitly disclosed to be present. Moreover, the singular encompasses the plural unless the context otherwise requires. In particular, where the indefinite article is used, the specification is to be understood as contemplating plurality as well as singularity, unless the context requires otherwise.

[0102] The term "consisting essentially of means that the composition, method or structure may include additional ingredients, steps and / or parts, but only if the additional ingredients, steps and / or parts do not materially alter the basic and novel characteristics of the claimed composition, method or structure.

[0103] As used herein, the singular form "a", "an" and "the" include plural references unless the context clearly dictates otherwise. Throughout this application, various embodiments of this invention may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as user numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as user numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range. Whenever a numerical range is indicated herein, it is meant to include any cited numeral (fractional or integral) within the indicated range. The phrases "ranging / ranges between" a first indicate number and a second indicate number and "ranging / ranges from" a first indicate number "to" a second indicate number are used herein interchangeably and are meant to include the first and second indicated numbers and all the fractional and integral numerals therebetween.

[0104] It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may be provided separately or in any suitable sub-combination or as suitable in any other described embodiment of the invention. Certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment is inoperative without those elements.

[0105] Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims.

[0106] All publications, patents and patent applications mentioned in this specification are herein incorporated in their entirety by reference into the specification, to the same extent as if each user publication, patent or patent application was specifically and individually indicated to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention. To the extent that section headings are used, they should not be construed as necessarily limiting. Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims. All publications, patents and patent applications mentioned in this specification are herein incorporated in their entirety by reference into the specification, to the same extent as if each user publication, patent or patent application was specifically and individually indicated to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention. To the extent that section headings are used, they should not be construed as necessarily limiting.

Claims

CLAIMSWhat is claimed is:

1. A system for determining a drivable range of an electric vehicle, comprising: at least one processor; at least one non-transitory computer-readable memory unit communicably coupled with the processor, wherein the memory unit stores computer-readable instructions which, when executed by the processor, causes the processor to: monitor a state of charge (SoC) of a vehicle’s battery pack; identify the vehicle’s current location and a destination; collect driving data including at least one of the following: vehicle data, user data, destination coordinates, route coordinates, traffic load, weather conditions, road conditions, road inclines, road quality, traffic congestion, and driver behavior; and determine in real-time a drivable range based on a machine learning (ML) algorithm, considering driving data.

2. The system of claim 1, wherein, when the computer readable instructions are executed by the processor, the processor is further caused to suggest an optimal route considering an available driving range, battery status, and the driving data.

3. The system of claim 2, further comprising an interactive graphical user interface (GUI) configured to display the determined drivable range and the suggested route to the user.

4. The system according to claim 1, wherein the machine learning algorithm includes at least one of the following: a deep learning neural network model that processes historical data and real-time behavior; a dynamic regression-based predictive mechanism that adapts to driving habits and changing environmental conditions; andAl-based data processing configured to enhance predictive accuracy during the drive.

5. The system according claim 1, wherein the machine learning algorithm is automatically updated based on real-time sensor data, including: data from a Battery Management System (BMS); data from road load sensors and tire pressure sensors; weather data retrieved from external internet sources; and communication with charging networks to predict charging station availability and electricity pricing.

6. The system according claim 1, wherein: when the system determines that an available battery charge is insufficient to reach the destination, it suggests alternative routes incorporating optimal charging stations based on wait time, cost, and charger type availability; and the system is configured to adjust route recommendations based on user preferences, wherein the user preferences include shorter charging times or energyefficient routes.

7. The system according to claim 1, wherein the vehicle data comprises at least data regarding vehicle type, vehicle age, weight of the vehicle, repair history of the vehicle, maintenance history of the vehicle, or a combination thereof.

8. The system according to claim 1, wherein the user data comprises at least one of user’s driving habits including acceleration habits, braking patterns, average speed, and past trips.

9. The system of claim 1, wherein the system is configured for use in a fleet of public passenger vehicles, including buses and cabs, and is further configured to: incorporate additional data points relevant to fleet operations, including a number of passengers, average passenger weight, and frequent stop locations; account for frequent stopping and starting patterns typical of public transport vehicles to optimize energy consumption; and integrate with public transportation schedules to optimize route planning and energy usagebased on peak and off-peak hours.

10. The system of claim 1, wherein the system is configured for use in a fleet of share cars and is further configured to: incorporate user-specific data from multiple drivers who may use the same vehicle, including individual driving habits, preferred routes, and charging preferences; synchronize with a mobile application used by share car users to provide personalized route recommendations and energy consumption forecasts based on historical driving data; and provide real-time notifications to users about a current state of charge, estimated range, and an estimate of the charge available at a future time after another user finishes using the vehicle.

11. The system of claim 1, wherein the machine learning algorithm includes probabilistic assessments to provide confidence intervals for the drivable range predictions, factoring in variables such as traffic conditions, weather conditions, and road quality.

12. The system of claim 1, wherein the machine learning algorithm utilizes a dynamic regression-based predictive mechanism that continuously updates the drivable range predictions based on changing environmental conditions and driving habits, providing realtime adjustments to the predicted range.

13. A method for determining a drivable range of an electric vehicle, the method comprising: monitoring a state of charge (SoC) of a vehicle’s battery pack; identifying the vehicle’s current location and a destination; collecting driving data including at least one of the following: vehicle data, user data, destination coordinates, route coordinates, traffic load, weather conditions, road conditions; processing the driving data and determining in real-time a drivable range based on a machine learning (ML) algorithm, considering dynamic variables including at least one of road inclines, road quality, traffic congestion, and driver behavior.

14. The method of claim 13, further comprising: suggesting a suggested route considering an available driving range, battery status, and the driving data.

15. The method of claim 14, further comprising displaying the drivable range and the suggested route to the user through an interactive graphical user interface (GUI).

16. The method according to claim 13, further comprising automatically updating the algorithm based on real-time sensor data, including: data from a Battery Management System (BMS); data from road load sensors and tire pressure sensors; weather data retrieved from external internet sources; and communication with charging networks to predict charging station availability and electricity pricing.

17. The method according to claim 13, further comprising automatically updating the algorithm based on real-time sensor data, including: data from a Battery Management System (BMS); data from road load sensors and tire pressure sensors; weather data retrieved from external internet sources; and communication with charging networks to predict charging station availability and electricity pricing.

18. The method according claim 13, further comprising: suggesting alternative routes incorporating optimal charging stations based on wait time, cost, and charger type availability when an available battery charge is insufficient to reach the destination; and adjusting route recommendations based on user preferences, wherein the user preferences include shorter charging times or energy-efficient routes.

19. The method according to claim 13, further comprising displaying to the user via a GUI a coded route map indicating varying energy consumption factors along the route.

20. The method according to claim 16, further comprising integrating with logistics management software to optimize delivery schedules and charging stops, facilitating timely deliveries while maintaining energy efficiency.

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