Navigation system architecture for electric vehicles, and method for providing one or more proposed driving routes for electric vehicles

Abstract

The present invention provides a navigation system architecture for electric vehicles and one or more proposed driving routes for electric vehicles. [Solution] The navigation system architecture comprises a navigation system configured to provide one or more proposed driving routes to a destination; a battery management system coupled to the navigation system, wherein the battery management system is configured to provide information relating to the state of the electric vehicle's battery; and a vehicle-to-anything communication system coupled to the navigation system, wherein the V2X communication system is configured to exchange information between the electric vehicle and one or more external entities and to provide the navigation system with information received from one or more external entities.

Description

Technical Field

[0001] This disclosure generally relates to a navigation system architecture for an electric vehicle (EV) and a method of providing one or more proposed driving routes to a destination for an electric vehicle.

Background Art

[0002] Especially with the acceleration of the transition to electric mobility, especially for two-wheel vehicles, several problems have emerged. One of the most common problems is the management of battery life and the availability of nearby EV charging stations.

[0003] Current navigation systems generally provide the fastest or shortest route without considering the state of the EV's battery, which may result in depletion during the journey. Moreover, riders may struggle to identify the location of charging stations in a timely manner due to inconsistent updates regarding the location of the stations, their real-time availability, and their compatibility with the rider's specific EV model.

[0004] Another major problem is route optimization for energy efficiency. Routes are usually calculated based on distance or estimated time, ignoring topographical aspects such as slopes or traffic congestion that can significantly affect battery consumption.

[0005] Furthermore, these systems rarely consider dynamic conditions such as weather that can affect the energy consumption rate or the availability of charging stations. For example, heavy rain may reduce the efficiency of solar-powered stations.

[0006] Therefore, there is a need for a navigation system architecture for electric vehicles and a method for providing one or more proposed driving routes for electric vehicles, aimed at addressing or mitigating at least one of the above problems. [Overview of the project]

[0007] According to a first aspect of the present disclosure, a navigation system architecture for an electric vehicle is provided, the navigation system architecture comprising: a navigation system configured to provide one or more proposed driving routes to a destination; a battery management system coupled to the navigation system, the battery management system configured to provide information relating to the state of the electric vehicle's battery; a Vehicle to Everything (V2X) communication system coupled to the navigation system, the V2X communication system configured to exchange information between the electric vehicle and one or more external entities and to provide the navigation system with information received from one or more external entities; and a charging station network system coupled to the navigation system via the V2X communication system, the charging station network system configured to provide information about one or more charging stations, wherein the one or more proposed driving routes provided by the navigation system are based on information provided by the battery management system, the V2X communication system, and the charging station network system.

[0008] In the navigation system architecture of this disclosure, the navigation system may be configured to fetch information regarding battery parameters and battery metrics from a battery management system.

[0009] In the navigation system architecture of this disclosure, the navigation system may be configured to fetch traffic and weather updates from a V2X communication system.

[0010] In the navigation system architecture of this disclosure, the navigation system may be configured to fetch from a charging station network system information regarding one or more locations of one or more charging stations, the availability of one or more charging stations, the compatibility of one or more charging stations with electric vehicles, and the estimated charging time of one or more charging stations.

[0011] In the navigation system architecture of this disclosure, the navigation system may be configured to continuously fetch real-time updates regarding information provided by the battery management system, the V2X communication system, and the charging station network system at defined time intervals.

[0012] In the navigation system architecture of this disclosure, the navigation system may be configured to recalculate one or more proposed driving routes to a destination based on real-time updated information provided by a battery management system, a V2X communication system, and a charging station network system.

[0013] In the navigation system architecture of this disclosure, the navigation system may be configured to generate one or more proposed driving routes using an informed search algorithm.

[0014] A second aspect of the present disclosure provides a method for providing one or more suggested driving routes to a destination for an electric vehicle, the method comprising: fetching information relating to the battery status of an electric vehicle from a battery management system; fetching information from one or more external entities via a vehicle-to-anything (V2X) communication system; and fetching information relating to one or more charging stations from a charging station network system via a V2X communication system, the method further comprising determining one or more suggested driving routes by a navigation system based on the information fetched from the battery management system, the V2X communication system, and the charging station network system.

[0015] This method may further include providing information on battery parameters and battery metrics through a battery management system.

[0016] This method may further include providing information on traffic and weather updates via a V2X communication system.

[0017] The method may further include providing information regarding the location of one or more charging stations, the availability of one or more charging stations, the compatibility of one or more charging stations with electric vehicles, and the estimated charging time of one or more charging stations through a charging station network system.

[0018] This method may further include continuously fetching real-time updates regarding information provided by the battery management system, V2X communication system, and charging station network system at defined time intervals.

[0019] This method may further include recalculating one or more proposed driving routes to the destination based on real-time updated information provided by the battery management system, V2X communication system, and charging station network system.

[0020] This method may further include generating one or more proposed driving routes using an informed search algorithm.

[0021] A third aspect of the present disclosure provides a non-temporary computer-readable storage medium storing instructions for instructing a processing unit of a navigation system architecture to perform a method for providing one or more proposed driving routes to a destination for an electric vehicle, wherein the method comprises fetching information relating to the battery status of an electric vehicle from a battery management system; fetching information from one or more external entities via a vehicle-to-everything (V2X) communication system; and fetching information of one or more charging stations from a charging station network system via a V2X communication system, and further comprising the method determining one or more proposed driving routes by a navigation system based on the information fetched from the battery management system, the V2X communication system, and the charging station network system.

[0022] Exemplary embodiments of the present invention will be well understood and readily apparent to those skilled in the art, simply by example, from the following description and in conjunction with the drawings. [Brief explanation of the drawing]

[0023] [Figure 1] This is a schematic block diagram of a navigation system architecture for an electric vehicle in an exemplary embodiment. [Figure 2]A schematic flowchart for showing a method of providing one or more proposed driving routes for an electric vehicle in an exemplary embodiment. [Figure 3] A schematic flowchart for showing a method of providing one or more proposed driving routes for an electric vehicle in an exemplary embodiment. [Figure 4] A schematic flowchart for showing a method of calculating an optimal driving route for an electric vehicle in an exemplary embodiment. [Figure 5] A sequence diagram for showing a method of calculating an optimal driving route for an electric vehicle in an exemplary embodiment. [Figure 6] A schematic flowchart for showing a method of monitoring the battery status of a battery in an electric vehicle in an exemplary embodiment. [Figure 7] A sequence diagram for showing a method of monitoring the battery status of a battery in an electric vehicle in an exemplary embodiment. [Figure 8] A schematic flowchart for showing a method of providing information of one or more charging stations in an exemplary embodiment. [Figure 9] A schematic block diagram of a charging station network system in an exemplary embodiment. [Figure 10] A schematic diagram of a computer system suitable for implementing an exemplary embodiment. [Figure 11] A schematic diagram of a communication device suitable for implementing an exemplary embodiment.

Embodiments for Carrying Out the Invention

[0024] Exemplary, non - limiting embodiments may provide a navigation system architecture for an electric vehicle and a method of providing one or more proposed driving routes to a destination for the electric vehicle.

[0025] Figure 1 is a schematic block diagram of a navigation system architecture 100 for an electric vehicle in an exemplary embodiment.

[0026] The navigation system architecture 100 comprises a navigation system 102 configured to provide one or more suggested driving routes to a destination. The navigation system architecture 100 further comprises a battery management system (BMS) 104 coupled to the navigation system 102, wherein the battery management system 104 is configured to provide information relating to the state of the electric vehicle's battery, such as real-time information. The navigation system architecture 100 further comprises a vehicle-to-anything (V2X) communication system 106 coupled to the navigation system 102, wherein the V2X communication system 106 is configured to exchange information, such as real-time information, between the electric vehicle and one or more external entities, and to provide information received from one or more external entities, such as real-time information, to the navigation system 102. The navigation system architecture 100 further comprises a charging station network system 108 coupled to the navigation system 102 via a V2X communication system 106, wherein the charging station network system 108 is configured to provide information about one or more charging stations, such as real-time information. The charging station network system 108 is an example of an external entity that provides information to the navigation system 102 via the V2X communication system 106. In an exemplary embodiment, one or more suggested driving routes provided by the navigation system 102 are determined based on information provided by the BMS 104, the V2X communication system 106, and the charging station network system 108, such as real-time information. In an exemplary embodiment, one or more suggested driving routes provided by the navigation system 102 are provided to the driver, such as the lidar 110 of an electric vehicle.

[0027] In an exemplary embodiment, the navigation system 102 is communicatively coupled to the BMS 104, the V2X communication system 106, and the charging station network system 108. As used herein, the term “communicatively coupled” means that components are coupled in such a way that they allow for the communication of information between them. That is, the components are able to transmit data signals to each other, for example, by transmitting electrical signals over a conductive medium, electromagnetic signals over air, or optical signals over an optical waveguide. As used herein, the term “communicatively coupled” also means either a direct or indirect communication connection.

[0028] In an exemplary embodiment, the navigation system 102 is configured to provide one or more proposed driving routes to a destination. In an exemplary embodiment, one or more proposed driving routes may be the optimal driving routes in terms of energy efficiency and / or time saving. In an exemplary embodiment, the navigation system 102 may be considered a central unit of the navigation system architecture 100, processing all data from other components of the navigation system architecture 100 and providing the most efficient route suggestion / proposed driving route. In an exemplary embodiment, the navigation system 102 may be configured to achieve dynamic route calculation by employing high-performance algorithms (for example, by generating one or more proposed driving routes using an informed search algorithm such as A* search with energy consumption heuristics) to dynamically calculate the most energy-efficient and time-saving route. In an exemplary embodiment, the navigation system 102 not only considers the shortest or fastest route, but also other factors in the elements, such as road gradient, traffic congestion, and current weather conditions, which can significantly affect the vehicle's battery consumption. For example, the navigation system 102 may receive the status of the electric vehicle's battery (i.e., battery status) from the battery management system 104, real-time traffic and weather updates from the V2X system 106, and charging station availability from the charging station network system 108. Based on these inputs, the navigation system 102 calculates the most energy-efficient and time-saving route, taking into account where and when charging stops may be necessary. In an exemplary embodiment, the navigation system 102 may be further configured to continuously fetch real-time updates regarding the information provided by the BMS 104, the V2X communication system 106, and the charging station network system 108 at defined time intervals.Based on the provided real-time update information, the navigation system 102 may be further configured to recalculate one or more suggested driving routes to the destination.

[0029] In an exemplary embodiment, the BMS 104 is configured to provide information relating to the state of the electric vehicle's battery. In an exemplary embodiment, the BMS 104 is configured to monitor the real-time battery status. In an exemplary embodiment, the navigation system 102 is deeply integrated with the electric vehicle's BMS 104. In an exemplary embodiment, the BMS 104 may be configured to report battery parameters and battery metrics relating to the state of the electric vehicle's battery to the navigation system 102. As used herein, battery parameters refer to the intrinsic characteristics of the battery. Battery parameters include, for example, voltage, capacity, internal resistance, and temperature. As used herein, battery metrics refer to derived values ​​or indicators used to assess the performance, condition, and use of a battery, either in real time or over the battery's lifespan. Battery metrics are generally calculated based on battery parameters and operating data (e.g., driving speed). Battery metrics include, for example, State of Charge (SoC), State of Health (SoH), State of Power (SoP), Depth of Discharge (DoD), charge / discharge efficiency, cell balance, remaining service life (RUL), average energy consumption of the electric vehicle, and expected battery depletion time based on the electric vehicle's current / ongoing usage patterns. The navigation system 102 may be configured to fetch information related to the battery status, such as battery parameters and battery metrics, from the BMS 104. The navigation system 102 may be configured to fetch the information from the BMS 104 at defined or scheduled time intervals, for example, every 10 minutes. This ensures that the rider 110 is always informed about the battery status of the rider 110's vehicle and that the system can calculate when and where the next charging stop should be.

[0030] In an exemplary embodiment, the V2X communication system 106 is configured to exchange information between the electric vehicle and one or more external entities and to provide the navigation system 102 with information received from one or more external entities. V2X is a communication technology that enables a vehicle to interact with other vehicles (V2V), infrastructure (V2I), pedestrians (V2P), and networks (V2N). In an exemplary embodiment, the navigation system 102 is integrated with and leverages the V2X communication technology to fetch real-time data. In an exemplary embodiment, the V2X communication system 106 uses wireless technology to provide real-time information exchange between the EV and other external entities such as vehicles, infrastructure, and network services. In an exemplary embodiment, one or more external entities may include other vehicles, infrastructure, and network services. In an exemplary embodiment, one or more external entities may be configured to provide information about traffic conditions, road conditions, and weather conditions. In an exemplary embodiment, the navigation system 102 may be configured to fetch traffic and weather updates from the V2X communication system 106. The navigation system 102 may be configured to fetch the information from the V2X communication system 106 at defined or scheduled time intervals, for example, every 10 minutes.

[0031] In a V2X environment, data flows continuously between components of the navigation system architecture 100. For example, the BMS 104 periodically sends battery status to the navigation system 102, the V2X communication system 106 provides real-time traffic and weather updates, and the charging station network system 108 notifies the navigation system 102 about available charging points / stations. The navigation system 102 uses all this data to dynamically suggest the most suitable route. Such real-time information, providing live updates on traffic congestion, road conditions, weather forecasts, and other relevant data, is crucial for dynamic traffic routing. This allows the navigation system 102 to receive and incorporate live updates on traffic conditions, road construction, accidents, and weather forecasts, all of which can affect both journey duration and battery usage.

[0032] In an exemplary embodiment, the charging station network system 108 is configured to provide information about one or more charging stations. In an exemplary embodiment, the charging station network system 108 is configured to maintain and / or fetch real-time data for all nearby charging stations. For example, the charging station network system 108 may maintain an up-to-date database of all nearby charging stations, accessed via a network connection. This data is consistently updated and communicated via the V2X communication system 106. In an exemplary embodiment, the data / information for one or more charging stations includes one or more locations of the one or more charging stations, the availability of the one or more charging stations, the compatibility of the one or more charging stations with electric vehicles (i.e., compatibility with a particular model of electric vehicle), and the estimated charging time of the one or more charging stations. The navigation system 102 may be configured to fetch the information about charging stations from the charging station network system 108. The navigation system 102 may be configured to fetch the information from the charging station network system 108 at defined or scheduled time intervals, for example, every 10 minutes. This allows the navigation system 102 not only to guide the rider 110 to the nearest available charging station when needed, but also to ensure that the charging station is suitable for use and available.

[0033] In an exemplary embodiment, the navigation system architecture 100 may further comprise a graphical user interface (GUI) coupled to the navigation system 102, wherein the GUI is configured to facilitate interaction between the LiDAR 110 and the navigation system 102, for example, enabling the LiDAR 110 to input information regarding a starting point and a destination, and to display navigation instructions for one or more suggested driving routes. In an exemplary embodiment, the GUI may further be configured to display information relating to the battery status, suggested stops at selected charging stations, estimated time to the destination, and live updates regarding traffic and road conditions. In an exemplary embodiment, the LiDAR 110 can interact with the navigation system 102 via the GUI. For example, the LiDAR 110 may provide input regarding the starting point and end point / destination of the journey. In an exemplary embodiment, the GUI may advantageously provide an intuitive, easy-to-use, user-friendly interface by displaying all relevant information, such as remaining battery life, suggested charging stops, estimated time to the destination, and live updates regarding route conditions. This helps Rider 110 make informed decisions and plan its journey more effectively.

[0034] In exemplary embodiments, the navigation system architecture 100 can advantageously provide a comprehensive, dynamic, and user-friendly navigation system that helps EV users, such as riders, optimize their journeys while maximizing the battery life of their vehicles. The navigation system architecture 100 can advantageously provide a comprehensive solution tailored specifically for motorcycles, focusing on addressing common challenges related to battery management, charging station accessibility, and route optimization. The navigation system architecture 100 leverages state-of-the-art technologies to seamlessly integrate multiple aspects of electric vehicle journeys, from real-time battery monitoring to dynamic route suggestions. By integrating several technologies, the navigation system architecture 100 ensures seamless communication, real-time updates, and efficient route suggestions, making it a robust solution to the evolving needs of EVs, including electric motorcycle riders.

[0035] Figure 2 is a schematic flowchart 200 illustrating a method for providing one or more proposed driving routes to a destination for an electric vehicle in an exemplary embodiment. In step 202, information relating to the battery status of the electric vehicle is fetched from a battery management system (see 104 in Figure 1). In step 204, information from one or more external entities is fetched via a vehicle-to-anything (V2X) communication system (see 106 in Figure 1). In step 206, information relating to one or more charging stations is fetched from a charging station network system (see 108 in Figure 1) via the V2X communication system. In step 208, one or more proposed driving routes are determined by a navigation system (see 102 in Figure 1) based on the information fetched from the battery management system, the V2X communication system, and the charging station network system.

[0036] In exemplary embodiments, the method may further include providing information on battery parameters and battery metrics by a battery management system. In exemplary embodiments, the method may further include providing information on traffic and weather updates by a V2X communication system. In exemplary embodiments, the method may further include providing information on one or more locations of one or more charging stations, the availability of one or more charging stations, the compatibility of one or more charging stations with electric vehicles, and the estimated charging time of one or more charging stations by a charging station network system. In exemplary embodiments, the method may further include continuously fetching real-time updates on the information provided by the battery management system, the V2X communication system, and the charging station network system at defined time intervals. In exemplary embodiments, the method may further include recalculating one or more proposed driving routes to a destination based on the real-time updated information provided by the battery management system, the V2X communication system, and the charging station network system. In exemplary embodiments, the method may further include generating one or more proposed driving routes using an informed search algorithm.

[0037] In one example, a method for providing one or more suggested driving routes for an electric vehicle may begin after route information (e.g., destination address, map coordinates, zip code, start point, end point) is provided to the navigation system. Such route information may be provided by the electric vehicle's LiDAR (see Figure 110) via a graphical user interface. The navigation system then receives information such as battery status from a battery management system, available charging points / stations between the start and end points of the route from a charging station network system, and real-time traffic and weather updates from a V2X communication system. Based on the provided information, the navigation system determines one or more suitable route suggestions after considering energy efficiency and time-saving considerations. Along the way, the battery management system periodically updates the navigation system with battery status, the charging station network system periodically updates the navigation system with available charging points / stations, and the V2X communication system periodically provides real-time traffic and weather updates. Based on the updated information provided, the navigation system may dynamically update route suggestions to achieve energy efficiency and time savings.

[0038] Figure 3 is a schematic flowchart 300 illustrating a method for providing one or more proposed driving routes for an electric vehicle in an exemplary embodiment.

[0039] In step 302, the LiDAR (see 110 in Figure 1) interacts with the navigation system (see 102 in Figure 1) to provide one or more suggested driving routes for the electric vehicle. In step 304, the LiDAR selects a destination. For example, the LiDAR may input information about the destination via a graphical user interface (GUI) coupled to the navigation system. In step 306, the LiDAR begins navigation to the destination. For example, the LiDAR may indicate that it has confirmed the destination information via the GUI, for example, by pressing a "Confirm" button.

[0040] In step 308, the navigation system is initialized to generate one or more suggested driving routes. To facilitate the generation of one or more suggested driving routes, the navigation system fetches information from multiple systems coupled to the navigation system. In step 310, the navigation system fetches the battery status of the electric vehicle from a battery management system (BMS) coupled to the navigation system (see 104 in Figure 1). In step 312, the navigation system fetches traffic and weather updates from a vehicle-to-everything (V2X) communication system coupled to the navigation system (see 106 in Figure 1). In step 314, the navigation system fetches charging station information from a charging station network coupled to the navigation system (see 108 in Figure 1) via the V2X communication system. In step 316, the navigation system calculates the route to the destination, taking into account the information fetched from multiple systems. In step 318, the navigation system calculates the optimal route to the destination. It should be understood that the fetching of information in steps 310-314 may be performed simultaneously.

[0041] In step 320, the navigation system determines whether the route to the destination should be recalculated. If it is determined that the route should be recalculated, the navigation system then, in step 322, fetches real-time updates from the BMS, V2X, and charging station network, and in step 324, recalculates the route to the destination. If it is determined that the route does not need to be recalculated, the navigation system skips steps 322 and 324 and proceeds to step 326. Route recalculation can be initiated by the rider.

[0042] In step 326, the navigation system performs navigation to the destination based on the calculated optimal route. In step 328, navigation instructions are provided to the rider. In step 330, the navigation system monitors for real-time updates. For example, real-time updates may come from the BMS, V2X, and charging station network.

[0043] Figure 4 is a schematic flowchart 400 illustrating a method for calculating the optimal driving route for an electric vehicle in an exemplary embodiment.

[0044] In step 402, the LiDAR (see 110 in Figure 1) interacts with the navigation system (see 102 in Figure 1) to provide one or more suggested driving routes for the electric vehicle. In step 404, the LiDAR selects a destination. For example, the LiDAR may input information about the destination via a graphical user interface (GUI) coupled to the navigation system. In step 406, the LiDAR begins navigation to the destination. For example, the LiDAR may indicate that it has confirmed the destination information via the GUI, for example, by pressing a "Confirm" button.

[0045] In step 408, the navigation system fetches data to facilitate the calculation of the optimal driving route. The navigation system fetches information from multiple systems coupled to it. In step 410, the navigation system fetches the real-time battery status of the electric vehicle from a battery management system (BMS) coupled to it (see 104 in Figure 1). In step 412, the navigation system fetches real-time traffic updates from a vehicle-to-everything (V2X) communication system coupled to it (see 106 in Figure 1). In step 414, the navigation system fetches real-time weather updates from the V2X communication system. In step 416, the navigation system fetches charging station information from a charging station network coupled to it (see 108 in Figure 1).

[0046] In step 418, the navigation system performs data processing on data fetched from multiple systems coupled to the navigation system. For example, the navigation system may analyze, assess, and / or evaluate the data to facilitate the calculation of the optimal driving route to the destination.

[0047] In step 420, the navigation system analyzes the road gradient. Generally, road gradient, or the slope of the road, can affect the energy consumption, motor load, battery performance, and driving dynamics of an electric vehicle. Positive road gradients (i.e., uphill or inclines) require higher energy consumption, which can lead to faster battery depletion and a reduced range of the electric vehicle. Negative road gradients (i.e., downhill or inclines) may require lower energy consumption because gravity assists the movement of the electric vehicle when traveling downhill. Also, regenerative braking in electric vehicles converts a portion of kinetic energy into stored energy in the battery, which can effectively extend the range of the electric vehicle.

[0048] In step 422, the navigation system analyzes traffic congestion. Generally, traffic conditions, especially congestion, can lead to higher energy consumption and reduced range (e.g., due to frequent acceleration and deceleration). In step 424, the navigation system evaluates current weather conditions. Generally, weather conditions, especially rain or extreme weather conditions, can lead to higher energy consumption and reduced range. For example, in cold weather conditions, low temperatures can reduce battery efficiency, leading to reduced range and slower charging times. In hot weather conditions, the battery management system of an electric vehicle may need to keep the battery cool, which consumes energy and reduces range. Wet roads can reduce traction, which may lead to more frequent use of traction control and anti-lock braking systems, both of which can increase energy consumption.

[0049] In step 426, the navigation system analyzes the battery status. Generally, the battery status affects range estimation, route planning, and charging strategy. For example, the battery's State of Charge (SoC) indicates how much energy remains in the battery, thereby affecting the estimated range of the electric vehicle. A lower SoC may require route adjustments to reach a charging station before the battery is depleted. A lower SoC may require a shorter route or a route with available charging stations along the way.

[0050] In step 428, the navigation system checks the availability of one or more charging stations. Generally, the availability of charging stations affects the calculation of the driving route. The driving route may be planned so that charging stations are strategically located along or near the driving route. If charging stations are sparsely located, the navigation system may have to plan the driving route around the available charging stations. The availability of charging stations also depends on whether the charging station is occupied when the rider arrives. Long waiting times at charging stations can lead to potential delays. The availability of charging stations also depends on the type of charging station, e.g., fast charging or slow charging, and the suitability of the charging station for the model of the electric vehicle. Fast charging stations or slow charging stations can affect the estimated charging time. Therefore, real-time information on the availability of charging stations can help the navigation system decide whether to stop at a particular charging station.

[0051] In step 430, the navigation system calculates the optimal route to the destination. When calculating the optimal route, the navigation system considers multiple pieces of information fetched from multiple systems. In step 432, an algorithm, such as A* search with an energy consumption heuristic, is used to calculate the optimal route to the destination. For example, A* search is a graph traversal and pathfinding algorithm. Given a weighted graph, source nodes (i.e., the rider's starting point), and goal nodes (i.e., the rider's destination), the algorithm can be configured to find the shortest path from the source to the goal with respect to given weights (e.g., weights assigned to information such as battery status, traffic and weather conditions, and charging station information). Those skilled in the art will understand that it is possible to implement informed search algorithms such as A* search.

[0052] In step 434, the necessary charging stops along the optimal route are determined. Charging stops are necessary, for example, if the distance of the journey is longer than the available range of the electric vehicle based on the battery status. In step 436, the navigation system provides navigation instructions. In step 438, the calculated optimal route is output to the driver.

[0053] In step 440, the navigation system monitors for real-time updates. In step 442, the navigation system continuously monitors for updates from the BMS, V2X system, and charging station network. In step 444, the navigation system dynamically recalculates the route if necessary. For example, traffic congestion may occur along the original optimal route to the destination. Upon receiving real-time traffic updates, the navigation system may recalculate the route to generate a new optimal route to the destination.

[0054] Figure 5 is a sequence diagram 500 illustrating a method for calculating the optimal driving route for an electric vehicle in an exemplary embodiment. The method for calculating the optimal driving route for an electric vehicle is substantially the same as the method described with reference to Figure 4.

[0055] As shown in Figure 5, the lidar of the electric vehicle interacts with the navigation system (see 102 in Figure 1) by selecting a destination and initiating navigation based on navigation instructions provided by the navigation system.

[0056] When inputting information related to a destination (for example, address information such as building names, street names, and postal codes), the navigation system fetches multiple pieces of information to facilitate the generation of the optimal driving route. These multiple pieces of information may be fetched from different systems integrated into the navigation system. Fetching multiple pieces of information can be done simultaneously.

[0057] The navigation system fetches information about the battery status of the electric vehicle's battery from a battery management system (BMS) (see 104 in Figure 1) coupled to the navigation system. The BMS provides the navigation system with the battery's real-time battery status. The battery status may be provided at defined time intervals and / or when queried by the navigation system.

[0058] The navigation system also fetches traffic and weather updates from a vehicle-to-anything (V2X) communication system coupled to the navigation system (see 106 in Figure 1). The V2X communication system provides traffic and weather updates to the navigation system. The provision of traffic and weather updates may occur at defined time intervals and / or when queried by the navigation system.

[0059] The navigation system also fetches charging station information from a charging station network coupled to the navigation system (see 108 in Figure 1). The charging station network provides information about charging stations, including charging station availability, charging station suitability, charging station location, and estimated charging time. The provision of charging station information may be performed at defined time intervals and / or when queried by the navigation system.

[0060] The navigation system processes data provided by the BMS, V2X communication system, and charging station network (e.g., data on road gradient, traffic, and weather). Based on the provided data, the navigation system calculates the optimal route to the destination. Based on the calculated optimal route, the navigation system provides navigation instructions to the rider.

[0061] In exemplary embodiments, the navigation system may monitor real-time updates for battery status, traffic, weather, and charging station information. Monitoring of real-time updates may be performed continuously or at defined time intervals (for example, every 5 minutes). While monitoring real-time updates, the navigation system fetches information about the battery status of the electric vehicle's battery from the battery management system (BMS), traffic and weather updates from the V2X communication system, and charging station information from the charging station network. In response to the provided real-time update information, the navigation system may dynamically recalculate the driving route to the destination and update navigation instructions for the rider.

[0062] Figure 6 is a schematic flowchart 600 illustrating a method for monitoring the battery status of a battery in an electric vehicle in an exemplary embodiment.

[0063] In step 602, a battery management system (BMS) (see 104 in Figure 1) is used to monitor the battery parameters of the battery. As used herein, battery parameters refer to the intrinsic characteristics of the battery. Battery parameters include, for example, voltage, capacity, internal resistance, and temperature.

[0064] In step 604, the battery's State of Charge (SoC) is measured. SoC refers to the current charge level of the battery relative to its capacity. SoC is generally expressed as a percentage, where 0% means the battery is completely discharged and 100% means the battery is fully charged. SoC is useful as an indicator of how much energy is available for use and helps determine the remaining driving range of an electric vehicle.

[0065] In step 606, the state of health (SoH) of the battery is measured. SoH indicates the overall condition and performance of the battery compared to when it was new. SoH takes into account factors such as capacity degradation, internal resistance, and the battery's ability to deliver power. SoH is generally expressed as a percentage. SoH is useful as an indicator for assessing the aging and wear of the battery over time, showing how much longer the battery can function efficiently.

[0066] In step 608, the battery voltage is measured. In step 610, the battery current is measured. In step 612, the battery temperature is measured. The operating temperature of the battery can affect the performance and safety of the battery.

[0067] In step 614, the power state (SoP) of the battery is measured. SoP represents the maximum power that the battery can currently deliver under specified conditions. It is a measure of the battery's ability to supply energy at a given moment. SoP is useful for understanding how well the battery can meet the power demands of an electric vehicle, especially during acceleration or under heavy loads.

[0068] In step 616, the internal resistance of the battery is measured. The internal resistance of the battery affects the battery's efficiency and heat generation.

[0069] In step 618, the battery's depth of discharge (DoD) is measured. DoD refers to the amount of charge used relative to the battery's total capacity. If a battery has a capacity of 100kWh and 50kWh has been used, the DoD would be 50%. DoD is useful for battery management because deep discharge (high DoD) can accelerate battery wear and reduce the battery's lifespan. Managing DoD helps extend battery life.

[0070] In step 620, the battery's charge / discharge efficiency is measured. Charge / discharge efficiency refers to the ratio of energy output during discharge to energy input during charge. This reflects how efficiently the battery can store and deliver energy. This efficiency is usually expressed as a percentage. Charging efficiency measures how effectively the battery can store energy during the charging process and is calculated as the ratio of the energy stored in the battery to the energy supplied by the charger. Discharge efficiency measures how effectively the battery can deliver the stored energy during the discharge process and is calculated as the ratio of the energy delivered to the load (e.g., an electric motor) to the energy initially stored in the battery. Charge / discharge efficiency measures how effectively the battery stores and delivers energy, and higher efficiency leads to better energy utilization and a longer driving range.

[0071] In step 622, the battery cell balance is measured. Cell balancing refers to the process of ensuring that all individual cells within the battery pack maintain the same voltage level and charge state. Proper cell balancing is crucial for battery health and safety. Imbalances in cell voltage can lead to overcharging or deep discharging of individual cells, which can damage the battery pack, reduce its overall efficiency, and even pose a safety risk. In electric vehicles, balanced cells ensure consistent performance, optimize battery capacity, and extend the battery pack's lifespan.

[0072] In step 624, the remaining lifespan (RUL) of the battery is estimated. RUL estimates the number of hours or cycles remaining before the battery reaches the end of its lifespan. RUL takes into account factors such as State of Home (SOH), usage patterns, and operating conditions. RUL is useful for predicting when the battery may need replacement or significant maintenance.

[0073] In step 626, the battery metrics of the battery are measured. These battery metrics include, for example, SoC, SoH, SoP, DoD, charge / discharge efficiency, cell balance, and RUL. In step 628, the average energy consumption of the electric vehicle is calculated. In step 630, the battery depletion time is estimated based on the usage pattern.

[0074] In step 632, the battery status is updated. In step 634, the measured parameters are updated in the battery management system. In step 636, the battery status is reported to the navigation system (see 102 in Figure 1). In step 638, the battery status is sent to the navigation system.

[0075] Figure 7 is a sequence diagram 700 illustrating a method for monitoring the battery status of a battery in an electric vehicle in an exemplary embodiment. The method for monitoring the battery status of a battery in an electric vehicle is substantially the same as the method described with reference to Figure 6.

[0076] In an exemplary embodiment, a battery management system (BMS) 702 (see 104 in Figure 1) is provided for monitoring the battery status of a battery in an electric vehicle. The BMS 702 comprises a battery monitoring module 704, a battery metric calculation module 706, a battery status update module 708, and a battery status communication module 710 coupled to the BMS 702.

[0077] In an exemplary embodiment, the navigation system (see 102 in Figure 1) sends a request for battery status to the BMS 702. Upon receiving the request, the BMS 702 instructs the battery monitoring module 704 to measure the battery parameters of the battery. The battery monitoring module 704 is configured to measure raw battery data and provide it to the BMS 702. Examples of battery parameters include, but are not limited to, voltage, current, temperature, and internal resistance.

[0078] Based on the provided raw battery data, the BMS 702 instructs the battery metric calculation module 706 to calculate the battery metrics for the battery. The battery metric calculation module 706 is configured to calculate the battery metrics and provide the calculated battery metrics to the BMS 702. Examples of battery metrics include, but are not limited to, State of Charge (SoC), State of Health (SoH), Power State (SoP), Depth of Discharge (DoD), Charge / Discharge Efficiency, Cell Balance, and Estimated Remaining Life (RUL).

[0079] Based on the calculated metrics provided, the BMS 702 instructs the battery status update module 708 to update the battery status. The battery status update module 708 is configured to provide the updated battery status to the BMS 702. The updated battery status is reported to the navigation system via the battery status communication module 710.

[0080] Figure 8 is a schematic flowchart 800 illustrating a method for providing information on one or more charging stations in an exemplary embodiment.

[0081] In step 802, a charging station network system (see 108 in Figure 1) is provided and configured to schedule data fetches at defined time intervals / frequency (e.g., every 10 minutes). In step 804, real-time data fetching is performed. In step 806, data is retrieved from the charging stations using industry standard protocols. In step 808, the database of the charging station network system is updated. In step 810, the database is updated with the latest information about the charging stations. In step 812, the updated data is communicated via a vehicle-to-anything (V2X) system (see 106 in Figure 1). In step 814, the updated data is sent to the V2X system in a readable format. In step 816, the updated data is provided to the navigation system (see 102 in Figure 1). In step 818, the navigation system queries the database for information. In step 820, the charging station network system waits for the next time interval (e.g., 10 minutes). In step 822, the charging station network system determines whether there is more data to fetch. If the charging station network system determines that there is more data to fetch, steps 804-820 are repeated.

[0082] Figure 9 is a schematic block diagram of a charging station network system 900 in an exemplary embodiment. The charging station network system 900 is a central system connected to a plurality of charging stations. For illustrative purposes, the charging station network system 900 is connected to a first charging station 902 and a second charging station 904. The charging station network system 900 is configured to monitor the status of each charging station and to collect data from the plurality of charging stations. The charging station network system 900 is further configured to transmit signals at a defined frequency to a vehicle-to-anything (V2X) communication system 906 (see 106 in Figure 1). The V2X communication system 906 is configured to broadcast information about the plurality of charging stations to a navigation system 908 (see 102 in Figure 1).

[0083] Multiple charging stations may be spread across different locations. Each charging station may be configured to provide the charging station network system 900 with information such as the availability of charging points, estimated charging time and slot availability, current waiting time, charging speed, suitability for a particular vehicle model, charging station information about other nearby stations, and operational issues at a particular station.

[0084] In the exemplary embodiment described, the algorithm is used to calculate the optimal route to a destination, for example, a driving route.

[0085] In some exemplary embodiments, route-finding and graph-scanning algorithms, such as A* search, may be used to find the optimal route from a starting point to a goal / destination. Route-finding and graph-scanning algorithms may use heuristics to estimate the cost of reaching the goal, which guides the search. The heuristics may be designed to estimate the total energy consumption required to reach the goal, taking into account factors such as road conditions, altitude, and traffic.

[0086] In the context of search algorithms, such as the A* pathfinding algorithm or other pathfinding algorithms, states and cost functions are fundamental concepts used to describe the current state of the system being analyzed and how that system will evolve toward its goal. Each state may represent the location of an EV on a road network, along with its corresponding battery level. The cost function may combine energy usage and travel time.

[0087] In some exemplary embodiments, an algorithm for finding the optimal route may follow a method that includes creating a graphical representation of the road network, integrating parameters of a battery management system (BMS), and creating an energy consumption heuristic.

[0088] In some exemplary embodiments, creating a graph representation of a road network may involve representing the road network as a graph G(V,E), where V represents nodes (e.g., intersections, road termination points) and E represents edges (e.g., road segments between intersections) with associated weights (e.g., distance, elevation change, average speed). The edge weights are important when calculating both travel time and energy consumption.

[0089] In some exemplary embodiments, integrating BMS parameters may include utilizing the charge state (SoC), health state (SoH), voltage, current, and temperature from the BMS to track the vehicle's energy capacity and consumption rate. In some exemplary embodiments, integrating BMS parameters may further include tracking the current battery capacity and the relationship between the current battery capacity and distance (for example, how far the vehicle can travel before needing to be recharged).

[0090] In some exemplary embodiments, creating an energy consumption heuristic may involve creating a heuristic function (h(n)) that estimates the energy cost from the current node n to the goal node G. The heuristic should take into account factors such as altitude changes, traffic conditions, speed limits, and road type. Regarding altitude changes, uphill roads increase energy use, while downhill roads may regenerate some energy. Regarding traffic conditions, real-time traffic data can be used to estimate stop-and-go situations that affect energy consumption. Regarding speed limits and road type, different road types (e.g., highways, urban streets) have different energy profiles based on speed and braking.

[0091] In some exemplary embodiments, the potential heuristic can be expressed using the following formula: h(n) = α * distance(n, G) + β * altitude cost(n) + γ * transportation cost(n) Here, α, β, and γ are weights that balance the relative influences of distance, altitude, and traffic on energy consumption, distance (n, G) is the straight-line distance to the goal, altitude cost (n) is an estimate of the energy change based on the altitude difference, and traffic cost (n) adjusts the energy estimate for current road conditions using real-time traffic data.

[0092] In some exemplary embodiments, the cost function f(n) may be expressed as the sum of g(n) + h(n), where g(n) is the actual cost from the starting node to the current node n, which takes into account the energy consumed along the path taken and the time spent traveling, and h(n) is the heuristic estimate from the current node n to the goal node G. In some exemplary embodiments, the cost function is f(n) = g(n) + h(n), where g(n) is calculated as a function of the distance traveled, velocity, elevation change, traffic, and BMS parameters, and h(n) is a heuristic as described above.

[0093] In some exemplary embodiments, the search algorithm may be configured to consider charging stops (if necessary). In some exemplary embodiments, if the battery level SoC falls below a defined threshold, the search should identify potential charging stations along the route. In some exemplary embodiments, charging station network data (e.g., availability, suitability, charging time) may be used to incorporate potential charging stops into the route. In some exemplary embodiments, the g(n) cost may be updated to include estimated charging time and energy recovery at each potential stop.

[0094] In some exemplary embodiments, the execution of the algorithm may include an initialization step that defines the start node of the electric vehicle (EV) with the current SoC and other BMS parameters of the EV.

[0095] In some exemplary embodiments, the execution of the algorithm may further include initializing data structures such as a priority queue (open list) and a closed list. In some exemplary embodiments, the priority queue (open list) uses the priority queue to select nodes based on a minimum cost function f(n). In some exemplary embodiments, the closed list maintains a set of nodes that have already been visited and processed.

[0096] In some exemplary embodiments, the execution of the algorithm may further include a node expansion step. In some exemplary embodiments, in each iteration of the search, a node is selected and expanded from a prioritized queue (open list). In some exemplary embodiments, for each node, g(n) (cost from the start to the node) and h(n) (energy consumption heuristic) are calculated. In some exemplary embodiments, for each neighboring node, the cost of that neighboring node is updated if a better path is found.

[0097] In some exemplary embodiments, the execution of the algorithm may further include a goal test step to determine whether the goal node (destination) has been reached. In some exemplary embodiments, if the goal node (destination) has been reached, the path is extracted and the total energy consumed and travel time are calculated.

[0098] In some exemplary embodiments, the execution of the algorithm may further include a route optimization step. In some exemplary embodiments, the final route should be the route with the minimum total cost (f(n)) that is optimized for both energy consumption and travel time. Furthermore, the algorithm should take into account charging stops as necessary to ensure that the EV can reach its destination without running out of power.

[0099] As used herein, the terms “joined” or “connected” shall, unless otherwise specified, cover both direct connection and connection through one or more intermediate means.

[0100] In some parts, the descriptions herein may be explicitly or implicitly expressed as algorithms and / or functional operations that operate with respect to data in computer memory or electronic circuits. These algorithmic descriptions and / or functional operations are typically used by those skilled in the field of information / data processing technology for efficient explanation. An algorithm generally relates to a consistent sequence of steps that produce a desired result. Algorithmic steps may include physical manipulation of physical quantities, such as electrical signals, magnetic signals or optical signals, which can be stored, transmitted, transferred, combined, compared, and possibly manipulated.

[0101] Furthermore, unless otherwise specified, and as will be generally apparent from the following, throughout this specification, descriptions using terms such as “scanning,” “calculating,” “determining,” “replacing,” “generating,” “initializing,” and “outputting” refer to the actions and processes of a processor / computer system or similar electronic circuit / device / component that instructs to manipulate / process data expressed as physical quantities within the system described, and to convert that data into other data similarly expressed as physical quantities within the system or other information storage, transmission, or display devices.

[0102] This specification also discloses relevant devices / apparatus for carrying out the steps of the method described herein. Such apparatus may be specifically constructed for the purposes of this method, or may comprise a general-purpose computer / processor or other device selectively activated or reconfigured by a computer program stored in a memory component. The algorithms and representations described herein are, in essence, independent of any particular computer or other device. It should be understood that general-purpose devices / machines may be used in accordance with the teachings herein. Alternatively, the construction of a specialized device / apparatus for carrying out the steps of this method may be desirable.

[0103] Furthermore, it is also stated that this specification implicitly covers computer programs, in that it will be evident that the steps of the methods described herein can be implemented by computer code. It will be understood that a wide variety of programming languages ​​and coding can be used to implement the teachings described herein. Moreover, computer programs, where applicable, are not limited to a particular control flow and may use different control flows without departing from the scope of the invention.

[0104] Furthermore, one or more steps of a computer program may be executed in parallel and / or sequentially, where applicable. Such a computer program may be stored on any computer-readable medium, where applicable. Computer-readable mediums may include storage devices such as magnetic or optical disks, memory chips, or other storage devices suitable for interfacing with a suitable reader / general-purpose computer. In such cases, the computer-readable storage medium is non-temporary. Such storage mediums also cover all computer-readable mediums, such as register memory, processor cache, and random access memory (RAM), which store data only for short periods of time and / or only in the presence of power. Computer-readable mediums may also include wired mediums, as exemplified in internet systems, or wireless mediums, as exemplified in Bluetooth technology. When a computer program is loaded and executed on a suitable reader, it effectively produces a device capable of implementing the steps of the method described.

[0105] The exemplary embodiments may also be implemented as hardware modules. A module is a functional hardware unit designed for use in conjunction with other components or modules. For example, a module may be implemented using digital or discrete electronic components, or it may form part of an entire electronic circuit, such as an application-specific integrated circuit (ASIC). Those skilled in the art will understand that the exemplary embodiments may also be implemented as a combination of hardware modules and software modules.

[0106] Furthermore, when describing certain embodiments, this disclosure may disclose methods and / or processes as a specific sequence of steps. However, unless otherwise required, it should be understood that the methods or processes should not be limited to a specific sequence of steps disclosed. Other sequences of steps may be possible. The specific order of steps disclosed herein should not be construed as an undue limitation. Unless otherwise required, the methods and / or processes disclosed herein should not be limited to the steps being performed in the order in which they are written. The sequence of steps may vary and remain within the scope of this disclosure.

[0107] Furthermore, in the context of this specification, the word “substantially” is understood, whenever used, to include, but not limited to, “entirely” or “completely.” Additionally, terms such as “comprising” and “comprise” are considered, whenever used, non-restrictive descriptive expressions in which they broadly include elements / components expressed after such terms, in addition to other components not explicitly stated. For example, when “comprising” is used, a reference to “one” feature is also a reference to “at least one” of those features. Terms such as “consisting” and “consist” may, in appropriate contexts, be considered a subset of terms such as “comprising” and “comprise.” Accordingly, in embodiments disclosed herein that use terms such as “comprising” or “comprise,” it will be understood that these embodiments provide teachings for corresponding embodiments that use terms such as “consisting” or “consist.” Furthermore, whenever terms such as “about” or “approximately” are used, they generally mean a reasonable variation, for example, a variation of + / - 5% of the disclosed value, or a 4% variation of the disclosed value, or a 3% variation of the disclosed value, a 2% variation of the disclosed value, or a 1% variation of the disclosed value.

[0108] Furthermore, several values ​​may be disclosed within a range in this description. Values ​​indicating the end point of a range shall indicate a preferred range. Whenever a range is described, that range shall cover and teach all possible subranges and individual numerical values ​​within that range. That is, the end point of a range should not be interpreted as an inflexible limitation. For example, a description of the range 1% to 5% shall disclose in detail subranges such as 1% to 2%, 1% to 3%, 1% to 4%, 2% to 3%, etc., as well as values ​​within that range individually such as 1%, 2%, 3%, 4%, and 5%. The intent of the particular disclosures described above is applicable to any depth / width of a range.

[0109] Different exemplary embodiments may be implemented in the context of data structures, program modules, programs, and computer instructions executed in a computer implementation environment. A general-purpose computing environment is briefly disclosed herein. One or more exemplary embodiments may be implemented in one or more computer systems, as schematically shown in Figure 10.

[0110] One or more exemplary embodiments may be implemented as software, such as a computer program, which runs within a computer system 1000 and instructs the computer system 1000 to perform the method of one exemplary embodiment.

[0111] The computer system 1000 comprises a computer unit 1002, input modules such as a keyboard 1004 and a pointing device 1006, and multiple output devices such as a display 1008 and a printer 1010. A user can interact with the computer unit 1002 using the above devices. The pointing device may be implemented as a mouse, trackball, pen device, or any similar device. One or more other input devices (not shown), such as a joystick, gamepad, satellite dish, scanner, or touch-sensitive screen, may also be connected to the computer unit 1002. The display 1008 may include a cathode ray tube (CRT), liquid crystal display (LCD), field emission display (FED), plasma display, or any other device that produces an image viewable by the user.

[0112] Computer unit 1002 may be connected to computer network 1012 via a suitable transceiver device 1014 to enable access to, for example, the Internet, or other network systems such as a local area network (LAN), wide area network (WAN), or personal network. Network 1012 may include servers, routers, network personal computers, peer devices or other common network nodes, wireless telephones, or wireless personal information terminals. Networking environments may be found in offices, enterprise-scale computer networks, and home computer systems. The transceiver device 1014 may be a modem / router unit located inside or outside computer unit 1002, and may be any type of modem / router, such as a cable modem or satellite modem.

[0113] The network connection shown is illustrative, and it should be understood that other methods may be used to establish communication links between computers. The presence of any of the various protocols, such as TCP / IP, Frame Relay, Ethernet, FTP, or HTTP, is assumed, and computer unit 1002 may operate in a client-server configuration to allow a user to retrieve web pages from a web-based server. Furthermore, any of the various web browsers may be used to view and manipulate the data on the web pages.

[0114] In this example, the computer unit 1002 comprises a processor 1018, random access memory (RAM) 1020, and read-only memory (ROM) 1022. The ROM 1022 may be system memory that stores basic input / output system (BIOS) information. The RAM 1020 can store one or more program modules, such as an operating system, application programs, and program data.

[0115] The computer unit 1002 further comprises several input / output (I / O) interface units, for example, an I / O interface unit 1024 to the display 1008 and an I / O interface unit 1026 to the keyboard 1004. The components of the computer unit 1002 generally communicate and interface / couple via an interconnected system bus 1028 and in a manner known to those skilled in the art. The bus 1028 may be one of several types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of the various bus architectures.

[0116] It will be understood that other devices may also be connected to system bus 1028. For example, a Universal Serial Bus (USB) interface may be used to connect a video or digital camera to system bus 1028. An IEEE 1394 interface may be used to connect additional devices to computer unit 1002. Other manufacturer interfaces are also possible, such as FireWire developed by Apple Computer and i.Link developed by Sony. Connecting devices to system bus 1028 may also be via parallel ports, game ports, PCI boards, or any other interfaces used to connect input devices to the computer. It will be understood that sound / audio may be recorded and played back by a microphone and speakers, although the components are not shown in the diagram. A sound card may be used to connect a microphone and speakers to system bus 1028. It will be understood that several peripheral devices may be connected to system bus 1028 simultaneously via alternative interfaces.

[0117] Application programs encoded / stored on a data storage medium, such as a CD-ROM or flash memory carrier, may be supplied to a user of computer system 1000. The application programs may be read using the corresponding data storage medium drive of data storage device 1030. The data storage medium is not limited to being portable and may include instances embedded within computer unit 1002. Data storage device 1030 may comprise a hard disk interface unit and / or a removable memory interface unit (neither of which are shown in detail) that connect a hard disk drive and / or a removable memory drive to system bus 1028, respectively. This may enable reading / writing of data. Examples of removable memory drives include magnetic disk drives and optical disk drives. These drives, and their associated computer-readable media, such as floppy disks, provide non-volatile storage for computer-readable instructions, data structures, program modules, and other data for computer unit 1002. It will be understood that computer unit 1002 may include some of such drives. Furthermore, the computer unit 1002 may include a drive for interfacing with other types of computer-readable media.

[0118] The application program is read and controlled during the execution of the application program by processor 1018. Intermediate storage of program data may be achieved using RAM 1020. One or more methods of the exemplary embodiments may be implemented as computer-readable instructions, computer-executable components, or software modules. One or more software modules may be used as alternatives. These may include executable programs, data link libraries, configuration files, databases, graphical images, binary data files, text data files, object files, source code files, etc. When one or more computer processors execute one or more of the software modules, the software modules interact with one or more computer systems to cause them to implement according to the teachings herein.

[0119] The operation of the computer unit 1002 can be controlled by various different program modules. Examples of program modules include routines, programs, objects, components, data structures, and libraries that perform specific tasks or implement specific abstract data types. Exemplary embodiments can also be practiced in other computer system configurations, including handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, personal digital assistants (PDAs), and mobile phones. Furthermore, exemplary embodiments can also be practiced in a distributed computing environment where tasks are performed by remote processing devices linked through wireless or wired communication networks. In a distributed computing environment, program modules can reside in both local and remote memory storage devices.

[0120] Different exemplary embodiments may be implemented in the context of data structures, program modules, programs, and computer instructions executed in a communication device. Exemplary communication devices are briefly disclosed herein. One or more exemplary embodiments may be implemented in one or more communication devices, for example, 1100, as schematically shown in Figure 11.

[0121] One or more exemplary embodiments may be implemented as software, such as a computer program, which runs within the communication device 1100 and instructs the communication device 1100 to perform the method of one exemplary embodiment.

[0122] The communication device 1100 comprises a processor module 1102, an input module such as a touchscreen interface or keypad 1104, and an output module such as a display 1106 on a touchscreen.

[0123] The processor module 1102 is coupled to a first communication unit 1108 for communication with the cellular network 1110. The first communication unit 1108 may include, but is not limited to, a subscriber identification module (SIM) card loading bay. The cellular network 1110 may be, for example, a 3G, 4G, 5G, or later generation network.

[0124] The processor module 1102 is further coupled to a second communication unit 1112 for connection to the network 1114. For example, the second communication unit 1112 can enable access to the Internet or other network systems such as a local area network (LAN), wide area network (WAN), or personal network. The network 1114 may include servers, routers, network personal computers, peer devices or other common network nodes, wireless telephones, or wireless personal digital assistants. Networking environments can be found in offices, enterprise-scale computer networks, and home computer systems, etc. The second communication unit 1112 may include, but is not limited to, a wireless network card or an Ethernet network cable port. The second communication unit 1112 may also be a modem / router unit and may be any type of modem / router, such as a cable-type modem or a satellite-type modem.

[0125] The network connection shown is illustrative, and it should be understood that other methods may be used to establish a communication link between computers. The presence of any of the various protocols, such as TCP / IP, Frame Relay, Ethernet, FTP, or HTTP, is assumed, and the communication device 1100 may operate in a client-server configuration to allow a user to retrieve web pages from a web-based server. Furthermore, any of the various web browsers may be used to view and manipulate the data on the web pages.

[0126] In this example, the processor module 1102 includes a processor 1116, random access memory (RAM) 1118, and read-only memory (ROM) 1120. The ROM 1120 may be system memory that stores basic input / output system (BIOS) information. The RAM 1118 can store one or more program modules, such as an operating system, application programs, and program data.

[0127] The processor module 1102 also includes several input / output (I / O) interfaces, such as an I / O interface 1122 to the display 1106 and an I / O interface 1124 to the keypad 1104.

[0128] The components of the processor module 1102 are generally connected and interfaced / coupled via an interconnected bus 1126 in a manner known to those skilled in the art. The bus 1126 may be one of several types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of the various bus architectures.

[0129] It should be understood that other devices may also be connected to system bus 1126. For example, a Universal Serial Bus (USB) interface may be used to connect communication device accessories, such as card readers, to system bus 1126.

[0130] The application program is generally supplied to the user of the communication device 1100, encoded on a data storage medium such as a flash memory module or memory card / stick, and read using the corresponding memory reader / writer of the data storage device 1128. The data storage medium is not limited to being portable and may include instances embedded within the communication device 1100.

[0131] The application program is read and controlled during the execution of the application program by the processor 1116. Intermediate storage of program data can be achieved using RAM 1118. One or more methods of the exemplary embodiments may be implemented as computer-readable instructions, computer-executable components, or software modules. One or more software modules may be used as alternatives. These may include executable programs, data link libraries, configuration files, databases, graphical images, binary data files, text data files, object files, source code files, etc. When one or more processor modules execute one or more of the software modules, the software modules interact with the one or more processor modules to cause them to perform according to the teachings herein.

[0132] The operation of the communication device 1100 can be controlled by various different program modules. Examples of program modules include routines, programs, objects, components, data structures, and libraries that perform specific tasks or implement specific abstract data types.

[0133] The exemplary embodiments may also be practiced in other computer system configurations, including handheld devices, multiprocessor systems / servers, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, personal digital assistants (PDAs), and mobile phones. Furthermore, the exemplary embodiments may also be practiced in a distributed computing environment where tasks are performed by remote processing devices linked through wireless or wired communication networks. In a distributed computing environment, program modules may reside in both local and remote memory storage devices.

[0134] In exemplary embodiments, the electric vehicle may be a two-wheeled electric vehicle. In exemplary embodiments, the electric vehicle may be a four-wheeled electric vehicle. In exemplary embodiments, the electric vehicle may have five or more wheels.

[0135] As will be broadly described, it will be understood by those skilled in the art that other variations and / or modifications can be made to specific embodiments without departing from the scope of the invention. For example, in this description, features of different exemplary embodiments may be mixed, combined, interchanged, incorporated, adopted, modified, and included across different exemplary embodiments. Thus, these embodiments should be considered in all respects as illustrative rather than restrictive. [Explanation of symbols]

[0136] 100 Navigation System Architectures 102 Navigation System 104 Battery Management System 106 Vehicle-to-all-things communication system 108 Charging Station Network System 110 Driver 702 Battery Management System 704 Battery Monitoring Module 706 Battery Metric Calculation Module 708 Battery Status Update Module 710 Battery Status Communication Module 900 Charging Station Network System 902 1st charging station 904 Second charging station 906 Vehicle-to-all-things communication system 908 Navigation System 1000 Computer Systems 1002 Computer Unit 1004 Keyboard 1006 Pointing device 1008 displays 1010 Printer 1012 Computer Networks 1014 Transceiver Device 1018 Processor 1020 random access memory 1022 Read-only memory 1024 I / O Interface Unit 1026 I / O Interface Unit 1028 System Bus 1030 Data Storage Devices 1100 Communication devices 1102 Processor Module 1104 Keypad 1106 Display 1108 First communication unit 1110 Cellular Network 1112 Second communication unit 1114 Network 1116 processors 1118 Random Access Memory 1120 Read-only memory 1122 I / O Interfaces 1124 I / O interfaces 1126 System Bus 1128 Data Storage Devices

Claims

1. A navigation system architecture (100) for an electric vehicle, wherein the navigation system architecture (100) is A navigation system (102) configured to provide one or more suggested driving routes to a destination, A battery management system (104) coupled to the navigation system (102), wherein the battery management system (104) is configured to provide information relating to the state of the electric vehicle's battery, A vehicle-to-anything (V2X) communication system (106) coupled to the navigation system (102), wherein the V2X communication system (106) is configured to exchange information between the electric vehicle and one or more external entities and to provide the information received from the one or more external entities to the navigation system (102), A charging station network system (108) is connected to the navigation system (102) via the V2X communication system (106), wherein the charging station network system (108) is configured to provide information on one or more charging stations. Equipped with, Navigation system architecture (100) characterized in that the one or more proposed driving routes provided by the navigation system (102) are based on the information provided by the battery management system (104), the V2X communication system (106), and the charging station network system (108).

2. The navigation system architecture (100) according to claim 1, wherein the navigation system (102) is configured to fetch information regarding battery parameters and battery metrics from the battery management system (104).

3. The navigation system architecture (100) according to claim 1 or 2, wherein the navigation system (102) is configured to fetch traffic and weather updates from the V2X communication system (106).

4. Navigation system architecture (100) according to any one of claims 1 to 3, wherein the navigation system (102) is configured to fetch from the charging station network system (108) information relating to one or more locations of the one or more charging stations, the availability of the one or more charging stations, the compatibility of the one or more charging stations with the electric vehicle, and the estimated charging time of the one or more charging stations.

5. Navigation system architecture (100) according to any one of claims 1 to 4, wherein the navigation system (102) is configured to continuously fetch real-time updates regarding the information provided by the battery management system (104), the V2X communication system (106), and the charging station network system (108) at defined time intervals.

6. The navigation system architecture (100) according to claim 5, wherein the navigation system (102) is configured to recalculate the one or more proposed driving routes to the destination based on the real-time update information provided by the battery management system (104), the V2X communication system (106), and the charging station network system (108).

7. The navigation system architecture (100) according to any one of claims 1 to 6, wherein the navigation system (102) is configured to generate the one or more proposed driving routes using an informed search algorithm.

8. A method for providing one or more proposed driving routes to a destination for an electric vehicle, wherein the method is Fetching information related to the battery status of the electric vehicle from the battery management system (104), Fetching information from one or more external entities via a vehicle-to-anything (V2X) communication system (106), The V2X communication system (106) fetches information about one or more charging stations from the charging station network system (108) and Includes, The method described above is A method further comprising determining the one or more proposed driving routes by a navigation system (102) based on the information fetched from the battery management system (104), the V2X communication system (106), and the charging station network system (108).

9. The method according to claim 8, further comprising providing information regarding battery parameters and battery metrics by the battery management system (104).

10. The method according to claim 8 or 9, further comprising providing information regarding traffic and weather updates via the V2X communication system (106).

11. The method according to any one of claims 8 to 10, further comprising providing information by the charging station network system (108) regarding one or more locations of the one or more charging stations, the availability of the one or more charging stations, the compatibility of the one or more charging stations with the electric vehicles, and the estimated charging time of the one or more charging stations.

12. The method according to any one of claims 8 to 11, further comprising continuously fetching real-time updates regarding the information provided by the battery management system (104), the V2X communication system (106), and the charging station network system (108) at defined time intervals.

13. The method according to claim 12, further comprising recalculating the one or more proposed driving routes to the destination based on the real-time update information provided by the battery management system (104), the V2X communication system (106), and the charging station network system (108).

14. The method according to any one of claims 8 to 13, further comprising generating the one or more proposed driving routes using an informed search algorithm.

15. A non-temporary computer-readable storage medium storing instructions for instructing a processing unit of a navigation system architecture to perform a method for providing one or more proposed driving routes to a destination for an electric vehicle, wherein the method is Fetching information related to the battery status of the electric vehicle from the battery management system (104), Fetching information from one or more external entities via a vehicle-to-anything (V2X) communication system (106), The V2X communication system (106) fetches information on one or more charging stations from the charging station network system (108). Includes, The method described above is A non-temporary computer-readable storage medium, further comprising a navigation system (102) determining one or more proposed driving routes based on the information fetched from the battery management system (104), the V2X communication system (106), and the charging station network system (108).

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