Emission-optimized vehicle route and charging

Abstract

The present disclosure relates to emissions optimized vehicle routing and charging. Systems and methods for vehicle route planning are disclosed. The system is configured to identify one or more charging stations based on one or more geographic locations selected for route planning. The system is also configured to analyze carbon emission data for the one or more charging stations based on utility grid location associated with the one or more charging stations. The system is further configured to determine a route associated with the one or more geographic locations and select at least one charging station on the route for charging a vehicle based on the analysis of the carbon emission data associated with the one or more charging stations. The system is still further configured to provide the route and the at least one charging station to a user for navigating the vehicle.

Description

Background Technology

[0001] This disclosure relates generally to the field of automobiles and vehicle routing planning. More specifically, this disclosure relates to vehicle routing and charging planning systems and methods for optimizing carbon emissions.

[0002] Typical vehicle routing plans usually consider the current location or origin of the trip, the destination, the distance traveled, and other information. For example, when a user at the current location enters their desired destination into their infotainment or navigation system or mobile device, the vehicle routing system can display several available detours that the user can select. These detour options can be configured by the user based on distance, road type, and / or other considerations.

[0003] This introduction is provided only as an illustrative context and should not be construed as limiting in any way. It will be apparent to those skilled in the art that the concepts and principles of this disclosure are equally applicable to other contexts. Summary of the Invention

[0004] This disclosure provides a carbon emission optimized charging vehicle route planning system and method that improves conventional vehicle route planning by considering and illustrating the carbon emissions in the presentation and selection of available routes and charger locations. The optimized route and charging recommendations are used based on the location of charging stations, and particularly for each location's public power grid, time of day, emission forecasts for a specific public power grid, battery SOC, etc., to minimize carbon emissions associated with charging.

[0005] In one exemplary embodiment, this disclosure provides a vehicle routing system. The system includes one or more processors and a memory storing computer-executable instructions that, when executed, cause the one or more processors to: identify one or more charging stations based on one or more geographic locations selected for route planning; analyze carbon emission data for the one or more charging stations based on the location of a public power grid associated with the one or more charging stations; determine a route associated with the one or more geographic locations, and select at least one charging station on the route for charging the vehicle based on the analysis of the carbon emission data associated with the one or more charging stations; and provide a user with the route and at least one charging station for navigating the vehicle.

[0006] In another exemplary embodiment, this disclosure provides a method. The method includes analyzing carbon emissions associated with one or more charging stations at one or more public power grid locations. The method also includes identifying at least one charging station from the one or more charging stations based at least on the analyzed carbon emissions. The method further includes determining a route based on the identified at least one charging station. The method also includes providing the route and at least one charging station for display on a vehicle.

[0007] In another exemplary embodiment, this disclosure provides a method for vehicle route planning. The method includes determining a path between points of interest that optimizes carbon emissions associated with charging a vehicle traveling along the path by analyzing carbon emission data associated with charging stations along the path for locations on a public power grid to identify which charging station minimizes carbon emissions. The method also includes providing a user with the path and the identified charging stations for navigating vehicles along it.

[0008] In yet another exemplary embodiment, this disclosure provides a non-transitory computer-readable storage medium having computer-readable code stored thereon for programming one or more processors to perform steps. The steps include analyzing carbon emission data for one or more charging stations based on the location of a public power grid associated with those charging stations. The steps also include identifying at least one charging station for charging a vehicle based on the analysis of the carbon emission data for the one or more charging stations. The steps further include determining a path associated with one or more geographic locations that includes at least one charging station thereon. The steps also include providing a user with the path and at least one charging station for navigating the vehicle. Attached Figure Description

[0009] This disclosure is illustrated and described with reference to various accompanying drawings, wherein similar reference numerals are used as appropriate to denote similar system components / method steps, and wherein:

[0010] Figure 1 A schematic diagram of an exemplary implementation of the carbon emission optimized charging vehicle route planning system of this disclosure;

[0011] Figure 2 A map showing a snapshot of emission intensity in an exemplary area;

[0012] Figure 3 A schematic diagram of an exemplary implementation of a user interface (UI) that highlights a path map for carbon optimization used in this disclosure;

[0013] Figure 4A schematic diagram of an exemplary implementation of the UI, which highlights a path map with thresholded deviation paths including paths used for carbon optimization in this disclosure;

[0014] Figure 5 A flowchart of an exemplary implementation of a method for vehicle routing planning, which optimizes carbon emissions by providing power via a public power grid for charging electric vehicles of this disclosure;

[0015] Figure 6 A network diagram of cloud-based systems used to implement the various systems and methods of this disclosure;

[0016] Figure 7 For can Figure 6 A block diagram of a server / processing system used in a cloud-based system or used independently; and

[0017] Figure 8 For can Figure 6 A block diagram of a remote device used in or independently in a cloud-based system. Detailed Implementation

[0018] Similarly, in various embodiments, this disclosure relates to a carbon-emission optimized charging vehicle route planning system and method that improves conventional vehicle route planning by considering and illustrating the presentation and selection of carbon emissions in available routes and charging station locations. Specifically, charging stations (such as fast-charging locations) are each mapped to associated public grid locations, and emissions data for those grid locations (such as real-time emissions data, historical emissions data, and projected emissions data) are used to determine / predict the carbon emissions of each charging station when a vehicle is expected to pass through and utilize the corresponding charging station. As will be discussed in further detail below, this information is presented to a user (such as a vehicle operator), such as a determined route between at least two points of interest, which of those routes will minimize carbon emissions, and which charging station along each route will minimize carbon emissions, to allow the user to choose which route to follow and which charging station to utilize.

[0019] Figure 1 This is a schematic diagram of an exemplary embodiment of the carbon emission optimized charging vehicle routing system 10 of this disclosure. In various embodiments, the vehicle routing system 10 includes at least a vehicle 140 and one or more data sources 30. The data source 30 is carbon emission data for the associated public grid location of the charging station 50. The charging station 50 is adapted to charge the battery 142 of the vehicle 140, such as an arrangement of battery cells. In some embodiments, the charging station 50 is equipped with one or more renewable energy sources 55, such as solar panels, adapted to provide power for charging the vehicle 140.

[0020] In some implementations, one of the cloud system 100, the user device, or a combination thereof utilizes carbon emission data to determine the carbon emissions of each charging station in the charging station 50 as the vehicle 140 is expected to pass through and utilize the charging station 50, and is configured to optimize the vehicle path between at least two points of interest while minimizing carbon emissions, generating the power consumed to charge the vehicle 140 while traveling along the vehicle path. In some implementations, determining the carbon emissions of each charging station in the charging station 50 is further based on one or more renewable energy sources 55 at the charging station 50, such as the percentage of power provided by one or more renewable energy sources 55 for charging the vehicle 140. In some implementations, the user device is one of the controller 145 of the vehicle 140 and the mobile device 150. In some implementations, the controller 145 is any control system, infotainment system, etc., or part thereof of the vehicle 140; and the mobile device 150 is a cellular phone, tablet, laptop, etc., or part thereof. In various implementations, cloud system 100, user device, or combination thereof utilizes data including carbon emission data associated with each charging station 50, vehicle SOC, expected power consumption / range of vehicle 140, etc., to optimize carbon emissions for charging vehicle 140 by providing both the path that minimizes carbon emissions generated by the power consumed by vehicle 140 and the charging station 50.

[0021] In some implementations, a data aggregation system 40 is utilized. This data aggregation system 40 is configured to acquire carbon emission data associated with a public grid location and provide carbon emission data including one or more of real-time carbon emission data, historical carbon emission data, and predicted carbon emission data. In these implementations, a cloud system 100 or a user device obtains the carbon emission data from the data aggregation system 40. In other implementations, the cloud system 100 is configured to acquire carbon emission data associated with a public grid location from a data source 30 and determine emission data for each charging station 50, which includes one or more of real-time emission data, historical emission data, and predicted emission data for each charging station 50. In implementations, the emission data is any one of carbon emission amounts, proportion fractions such as the ratio of clean emissions to dirty emissions, etc. In some implementations, the data source 30 is a public grid location. In some implementations, the cloud system 100 is also configured to acquire data from each charging station 50 for one or more renewable energy sources 55, such as the power generated therefrom, the percentage of power supplied to the charging station 50 therefrom, etc. In some of these implementations, cloud system 100 is configured to combine carbon emission data for public grid locations with renewable energy data to determine emission data for each charging station 50.

[0022] In some implementations, cloud system 100 is configured to map each charging station 50 to a public grid location to identify which public grid location supplies power to it. In other implementations, data aggregation system 40 performs this function. Figure 2 Map 200 is a snapshot of exemplary regional emission intensity. (Reference) Figure 2 The regional public power grid locations 210, 220, 230, 240, and 250 have emission intensities based on how power is generated within the region. Figure 2 In the map 200 shown, emission intensity is highest at regional utility grid location 210, followed by regional utility locations 220 and 230. Therefore, charging vehicle 140 at one of the charging stations 50 within regional utility grid location 210 will most likely result in a higher net emission effect compared to charging vehicle 140 at one of the charging stations 50 located at regional utility grid locations 220 and 230. In some embodiments, other factors, such as the travel deviation to each charging station 50 and the expected power consumption of the vehicle traveling along those deviations, are also considered when determining that charging vehicle 140 at each location will have a net effect. For example, different distances traveled, different changes in altitude, etc., can affect the power consumed by vehicle 140 while traveling to / from a charging station 50.

[0023] Figure 3 This is a schematic diagram of an exemplary embodiment of a user interface (UI) 300, which highlights a path map 302 for carbon optimization purposes according to this disclosure. The UI 300 is presented to a user on a display of a user device, such as the display of a controller 145 in vehicle 140 or a display of a mobile device 150. In some embodiments, information for the UI can be shared between the controller 145 and the mobile device 150, such as through push notifications from one to the other.

[0024] UI 300 is configured to display a path map 302 showing a path 310 between at least two points of interest, the path including a starting point indicated by a starting point icon 315, a destination indicated by a destination icon 317, and one or more charging stations along the path 310 indicated by charging station icons 350. In embodiments, UI 300 is configured to identify charging stations 350 that optimize carbon emissions, such as by minimizing carbon emissions to generate power used by the vehicle 140 while traveling along the path 310. In various embodiments, this identification is performed by distinguishing charging stations 350 in UI 300 with some type of boundary line 320 or by removing other charging stations 350 from the path map 302. In embodiments, the boundary line 320 is any one of the following: displaying charging station icons 350 in a different color, a symbol positioned on or near a charging station icon 350, a boundary placed around a charging station icon 350, etc.

[0025] In some implementations, UI 300 is configured to display charging station information 330 for each charging station, such as near the associated charging station icon 350. In some of these implementations, the charging station information 330 includes emissions data, such as any one of current emissions data, historical emissions data, and projected emissions data for the expected arrival time of vehicle 140 traveling on path 310. Other information, such as charging station availability, waiting time, etc., may also be displayed. In some implementations, charging station information 330 is always displayed. In other implementations, charging station information 330 is displayed when the corresponding charging station icon 350 is selected or by activating an option for its display.

[0026] Figure 4 This is a schematic diagram of an exemplary embodiment of UI 300, highlighting a path map 302 that includes a path 311 for carbon emission optimization purposes according to this disclosure, with thresholded deviation paths 311, 312, and 313. In this embodiment, one of the cloud system 100, a user device (such as a controller 145 of vehicle 140 or a mobile device 150), or a combination thereof, determines multiple deviations for travel between a origin and a destination. Once determined, UI 300 is configured to display those deviation paths 311, 312, and 313. In this embodiment, each of these deviations is thresholded to provide the user with travel options. For example, in... Figure 4In the illustrated implementation, path 311 is thresholded to minimize carbon emissions, path 312 is thresholded to minimize travel distance, and path 313 is thresholded to minimize travel time. In some implementations, other deviations are also presented in UI 300, such as paths that include one or more other points of interest, paths that consider expected waiting times at charging stations, or paths that include a mixture of thresholds (such as travel time, charging time / waiting time, distance, and carbon optimization).

[0027] In the implementation scheme, upon receiving a selection of one of paths 311, 312, and 313, UI 300 is configured to display only the selected path, such as... Figure 3 The path 310 is shown. In some of these embodiments, the UI 300 is configured to identify charging stations that will optimize carbon emissions by displaying a corresponding charging station icon 350 with a dividing line 320. In some embodiments, the UI 300 is configured to receive a selection of the charging station icon 350 to identify which charging station the user intends to use to charge the vehicle 140. In some of these embodiments, upon receiving a selection, the vehicle routing system 10, such as via any combination of user device, cloud system 100, and charging station 50, reserves a charger at charging station 50 for the vehicle 140 at the expected arrival time, such as an arrival window.

[0028] In some implementations, paths 311, 312, and 313 are optimized using multiple points of interest over a multi-day trip. In these implementations, vehicle charging is optimized over multiple days rather than a single day.

[0029] In some implementations, charging is re-optimized during the trip to account for any changes in carbon emissions at any charging station in charging station 50, changes in the SOC of the battery 142 of vehicle 140, etc. Re-optimization can be performed in real time, at intervals, etc.

[0030] Figure 5 This is a flowchart of an exemplary embodiment of a method 500 for vehicle routing, used to optimize carbon emissions by providing power via a public power grid for charging electric vehicles of this disclosure. The method includes determining a path between points of interest at step 502, which optimizes carbon emissions associated with charging vehicles traveling along the path by analyzing carbon emission data associated with charging stations along the path for locations on the public power grid to identify which charging station minimizes carbon emissions. The method also includes providing a user with the path and the identified charging stations at step 504 for navigating vehicles on that path.

[0031] In an embodiment of the method, providing the user with the route and identified charging stations includes presenting a route map to the user on a user interface. This route map shows the route and marks charging station icons to identify charging stations optimized for carbon emissions. In another embodiment of the method, the carbon emission data includes at least one of real-time carbon emission data, historical carbon emission data, and projected carbon emission data.

[0032] In an implementation of this method, identifying which charging station on the route minimizes carbon emission data is based on the carbon emissions associated with the vehicle's estimated arrival time at each charging station.

[0033] In some implementations, the vehicle's battery SOC is used to determine which charging stations are within the vehicle's range, and only those charging stations are considered for that route, at least those charging stations used for the first charge of the vehicle traveling along the route that will require multiple charges. In some implementations of multiple charges required for travel between two points of interest, the method includes determining how much to charge the battery at each location to minimize carbon emissions associated with battery charging, while ensuring sufficient charge is available for travel to the next charging station. For example, if the journey requires stopping at a first and a second charging station, and the first charging station has a higher emissions score than the second, carbon emissions are optimized by limiting battery charging to the amount required for the vehicle to reach the second charging station and then performing a full charge of the battery at the second charging station. In some implementations where the route requires multiple charging sessions, the method includes: identifying a first charging station within the vehicle's range based on the battery's State of Charge (SOC), the first charging station optimizing carbon emissions; identifying a second charging station within the vehicle's range, different from the first charging station, and optimizing carbon emissions; comparing the carbon emissions generated by the first charging station and the second charging station; and, in response to the second charging station being associated with fewer carbon emissions than the first charging station, determining how much to charge the vehicle's battery at the first charging station in order to reach the second charging station and recommending to the user how much to charge the battery at the first charging station.

[0034] In some implementations, the method further includes determining additional paths between points of interest based on other thresholds, including paths that minimize travel time and distance. In some of these implementations, the method also includes identifying which charging stations on the path that minimizes travel time and distance are used to minimize carbon emissions for charging vehicles traveling on that path.

[0035] In some embodiments, the method further includes mapping each charging station in the charging station network to a corresponding public grid location and obtaining carbon emission data for the public grid location. In some embodiments, the method further includes obtaining renewable energy data from each charging station that includes renewable energy sources, and using the carbon emission data and the renewable energy data to determine emission data for each charging station.

[0036] In the implementation scheme, the method and any of the implementation schemes outlined above are performed by a vehicle routing system comprising a system selected from cloud system 100, user devices, and combinations of cloud system 100 and user devices. In some of these implementation schemes, the user device is one of controller 145 of vehicle 140 and mobility device 150.

[0037] Figure 6 This is a network diagram of a cloud system 100 used to implement various cloud-based services applicable to this disclosure. The cloud system 100 includes one or more cloud nodes (CNs) 102 communicatively coupled to the Internet 104, etc. In embodiments, the cloud node 102 may be implemented as a server or other processing system 110 (such as...). Figure 7 (As shown), and geographically distinct from one another, such as various data centers located in the United States or around the world. Furthermore, in some embodiments, cloud system 100 includes one or more central authority (CA) nodes 106, which are similarly implemented as servers 110 and connected to CN 102. For illustrative purposes, cloud system 100 is connected to data source 30, data aggregation system 40, charging station 50, individual homes 130, vehicles 140, and mobile devices 150, each communicatively coupled to one of the CNs in CN 102. For illustrative purposes, these locations 30, 40, and 130, and devices 140 and 150, are shown, and those skilled in the art will recognize that various access scenarios to cloud system 100 exist, all of which are contemplated herein. Cloud system 100 can be a private cloud, a public cloud, a combination of private and public clouds (hybrid cloud), etc.

[0038] Similarly, cloud system 100 provides any functionality to charging station 50, device, individual home 130, vehicle 140 and mobile device 150 through services such as Software as a Service (SaaS), Platform as a Service, Infrastructure as a Service, Security as a Service, Virtual Network Functions (VNF) in Network Functions Virtual (NFV) Infrastructure (NFVI) etc.

[0039] Cloud computing systems and methods abstract physical servers, storage devices, networks, etc., instead providing them as on-demand and elastic resources. The National Institute of Standards and Technology (NIST) provides a concise and specific definition, stating that cloud computing is used to enable convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage devices, applications, and services) that can be rapidly configured and released with minimal management effort or service provider interaction. Cloud computing differs from the classic client-server model in that applications are served from servers executed and managed by clients' web browsers, etc., without requiring pre-installed client versions of the applications. Centralization provides cloud service providers with complete control over the browser-based versions and other applications provided to clients, eliminating the need for version upgrades or license management on individual client computing devices. The phrase "Software as a Service" is sometimes used to describe applications delivered via cloud computing. The common abbreviation for the cloud computing services provided (or even the aggregation of all existing cloud services) is "the cloud." Cloud-based system 100 is shown herein as an exemplary implementation of a cloud-based system, and those skilled in the art will recognize that the systems and methods described herein are not necessarily limited thereto.

[0040] Figure 7 For cloud-based systems 100 ( Figure 6 A block diagram of a server or other processing system 110 used in, in other systems, or independently, such as a server or other processing system used in the vehicle itself. For example, CN 102 ( Figure 6 ) and Central Institution Node 106 ( Figure 6 This can be configured as one or more servers in server 110. In an embodiment, server 110 is a digital computer, which, in terms of hardware architecture, typically includes a processor 112, an input / output (I / O) interface 114, a network interface 116, a data storage repository 118, and memory 120. Those skilled in the art will understand that... Figure 7 The server or other processing system 110 is depicted in an oversimplified manner, and actual implementations may include additional components and appropriately configured processing logic to support known or common operating characteristics not described in detail herein. Components (112, 114, 116, 118, and 120) are communicatively coupled via a local interface 122. The local interface 122 may be, for example, but not limited to, one or more buses or other wired or wireless connections known in the art. The local interface 122 may have additional elements omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communication. Furthermore, the local interface 122 may include address, control, and / or data connections to enable appropriate communication between the aforementioned components.

[0041] Processor 112 is a hardware device for executing software instructions. Processor 112 can be any custom or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with server 110, a semiconductor-based microprocessor (in the form of a microchip or chipset), or any device typically used for executing software instructions. When server 110 is in operation, processor 112 is configured to execute software stored in memory 120, transfer data to and from memory 120, and typically control the operation of server 110 according to software instructions. I / O interface 114 can be used to receive user input from one or more devices or components and / or provide system output to them.

[0042] Network interface 116 can be used to enable server 110 to operate on networks (such as the Internet 114). Figure 6 The network interface 116 may include, for example, an Ethernet card or adapter (e.g., 10BaseT, Fast Ethernet, Gigabit Ethernet, or 10GbE) or a wireless LAN (WLAN) card or adapter (e.g., 802.11a / b / g / n / ac). The network interface 116 may include address, control, and / or data connections to enable appropriate communication on the network. The data repository 118 may be used to store data. The data repository 118 may include any volatile memory element (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), non-volatile memory elements (e.g., ROM, hard disk drive, magnetic tape, CD-ROM, etc.), and combinations thereof. Furthermore, the data repository 118 may include electronic, magnetic, optical, and / or other types of storage media. In one example, the data repository 118 may be located inside server 110, such as an internal hard disk drive connected, for example, to local interface 122 in server 110. Alternatively, in another embodiment, the data repository 118 may be located outside of server 110, such as via an external hard drive (e.g., SCSI or USB connection) connected to I / O interface 114. In another embodiment, the data repository 118 may be connected to server 110 via a network (e.g., a network-attached file server).

[0043] In implementations, memory 120 may include any volatile memory element (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), non-volatile memory elements (e.g., ROM, hard disk drive, magnetic tape, CD-ROM, etc.), and combinations thereof. Furthermore, memory 120 may include electronic, magnetic, optical, and / or other types of storage media. It should be noted that memory 120 may have a distributed architecture, where various components are located remotely to each other but are accessible by processor 112. The software in memory 120 may include one or more software programs, each including an ordered list of executable instructions for implementing logical functions. The software in memory 120 includes a suitable operating system (O / S) 124 and one or more programs 126. Operating system 124 substantially controls the execution of other computer programs (such as one or more programs 126) and provides scheduling, input / output control, file and data management, memory management, and communication control and related services. One or more programs 126 may be configured to implement the various processes, algorithms, methods, techniques, etc., described herein.

[0044] It should be understood that some embodiments described herein may include: one or more general-purpose or special-purpose processors (“one or more processors”) (such as microprocessors); central processing units (CPUs); digital signal processors (DSPs); custom processors, such as network processors (NPs) or network processing units (NPUs), graphics processing units (GPUs), etc.; field-programmable gate arrays (FPGAs); etc., and a uniquely stored set of program instructions (including both software and firmware) for controlling them, to implement some, most, or all of the functions of the methods and / or systems described herein in combination with certain non-processor circuitry. Alternatively, some or all of the functions may be implemented by a state machine without stored program instructions or in one or more application-specific integrated circuits (ASICs), wherein each function or some combination of certain functions is implemented as custom logic or circuitry. Of course, combinations of the methods described above may be used. For some of the embodiments described herein, the corresponding means in hardware, and optionally having software, firmware, and combinations thereof, may be referred to as “circuit configured or adapted to perform a set of operations, steps, methods, processes, algorithms, functions, techniques, etc. on digital and / or analog signals as described herein for various embodiments”, “logic configured or adapted to perform a set of operations, steps, methods, processes, algorithms, functions, techniques, etc. on digital and / or analog signals as described herein for various embodiments”, etc.

[0045] Furthermore, some embodiments may include a non-transitory computer-readable medium having computer-readable code stored thereon for programming a computer, server, appliance, device, processor, circuit, etc., each of which may include a processor to perform the functions described and claimed herein. Examples of such computer-readable media include, but are not limited to, hard disks, optical storage devices, magnetic storage devices, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, etc. When stored in a non-transitory computer-readable medium, software may include instructions executable by a processor or device (e.g., any type of programmable circuitry or logic) that, in response to such execution, cause the processor or device to perform a set of operations, steps, methods, processes, algorithms, functions, techniques, etc., as described herein with respect to various embodiments.

[0046] Figure 8 For cloud systems 100 ( Figure 6 This is a block diagram of a user device 160 used in, as part of, or independently of a network. In embodiments, user device 160 is one of a controller 145 in a vehicle or a mobile device 150, such as a smartphone, tablet, smartwatch, laptop, etc. User device 160 can be a digital device, which, in terms of hardware architecture, typically includes a processor 162, an I / O interface 164, a radio device 166, a data storage device 168, and a memory 170. Those skilled in the art will understand that... Figure 8 User device 160 is depicted in an oversimplified manner, and actual implementations may include additional components and appropriately configured processing logic to support known or conventional operating features not described in detail herein. Components (162, 164, 166, 168, and 170) are communicatively coupled via local interface 172. Local interface 172 may be, for example, but not limited to, one or more buses or other wired or wireless connections known in the art. Local interface 172 may have additional elements omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communication. Furthermore, local interface 172 may include address, control, and / or data connections to enable appropriate communication between the aforementioned components.

[0047] Processor 162 is a hardware device for executing software instructions. In embodiments, processor 162 is any custom or commercially available processor, CPU, auxiliary processor among several processors associated with user device 160, semiconductor-based microprocessor (in the form of a microchip or chipset), or any device generally used for executing software instructions. When user device 160 is in operation, processor 162 is configured to execute software stored in memory 170 to transfer data to and from memory 170, and generally control the operation of user device 160 according to software instructions. In embodiments, processor 162 may include a mobile processor optimized (e.g., optimized for power consumption and mobile applications). In embodiments, I / O interface 164 is used to receive user input from system outputs and / or to provide system outputs and includes a touchscreen display. User input may be provided via, for example, a user interface on a touchscreen display (such as UI 300), a keypad, a scroll ball, scroll bars, buttons, etc. System outputs may be provided via a display device (such as a liquid crystal display (LCD), touchscreen, etc.).

[0048] Radio device 166 enables wireless communication with external access devices or networks. Radio device 166 may support any number of suitable wireless data communication protocols, technologies, or methods, including any protocols used for wireless communication. Data repository 168 can be used to store data. Data repository 168 may include any volatile memory element (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), non-volatile memory element (e.g., ROM, hard disk drive, magnetic tape, CDROM, etc.), and combinations thereof. Furthermore, data repository 168 may include electronic, magnetic, optical, and / or other types of storage media.

[0049] Similarly, in the implementation, memory 170 includes any volatile memory element (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), non-volatile memory elements (e.g., ROM, hard disk drive, etc.), and combinations thereof. Furthermore, memory 170 may include electronic, magnetic, optical, and / or other types of storage media. It should be noted that memory 170 may have a distributed architecture, where various components are located remotely to each other but are accessible by processor 162. The software in memory 170 may include one or more software programs, each including an ordered list of executable instructions for implementing logical functions. Figure 8In this example, the software in memory 170 includes a suitable operating system 174 and program 176. Operating system 174 essentially controls the execution of other computer programs and provides scheduling, input / output control, file and data management, memory management, and communication control and related services. Program 176 may include various applications, additions, etc., configured to provide end-user functionality to user device 160. For example, example program 176 may include, but is not limited to, web browsers, social networking applications, streaming media applications, games, map and location applications, email applications, financial applications, etc. In a typical example, the end user typically uses one or more of program 176 in conjunction with a network (such as cloud system 100). Figure 6 )).

[0050] While this disclosure has been illustrated and described with reference to exemplary embodiments and specific examples thereof, it will be apparent to those skilled in the art that other embodiments and examples may perform similar functions and / or achieve similar results. All such equivalent embodiments and examples are contemplated and intended to be covered by the following non-limiting claims for all purposes within the spirit and scope of this disclosure.

Claims

1. A vehicle routing system, comprising: The system includes one or more processors and a memory storing computer-executable instructions that, when executed, cause the one or more processors to: Identify one or more charging stations based on one or more geographical locations selected for route planning; Carbon emission data for each of the one or more charging stations is analyzed based on the public grid location associated with each of the charging stations. Determine a path associated with the one or more geographic locations, and select at least one charging station on the path for charging the vehicle based on analysis of the carbon emission data associated with each of the one or more charging stations. as well as The user is provided with the route and the at least one charging station for navigating the vehicle.

2. The route planning system of claim 1, wherein the system is selected from a cloud system, a user device, and a combination of the cloud system and the user device, and wherein the user device is selected from a controller and a mobility device of the vehicle.

3. The route planning system of claim 1, wherein providing the user with the route and the at least one charging station includes presenting a route map on a user interface, the route map showing the route and marking charging station icons to identify the selected at least one charging station.

4. The route planning system according to claim 1, wherein the carbon emission data includes at least one type of data selected from real-time carbon emission data, historical carbon emission data, and projected carbon emission data.

5. The route planning system of claim 1, wherein the at least one charging station is selected based on one or more predicted times when the vehicle will arrive at each of the one or more charging stations, wherein the carbon emissions associated with each of the one or more charging stations are estimated based on the predicted times, and wherein the one or more charging stations are selected based on the carbon emissions determined at the predicted times.

6. The path planning system of claim 1, wherein the instructions, when executed, cause the one or more processors to: Other paths associated with the one or more geographic locations are determined based on other thresholds, including paths that minimize travel time and distance; and The analysis of the carbon emission data associated with the one or more charging stations is used to select charging stations on each of the paths that minimize travel time and distance.

7. The path planning system of claim 1, wherein the instructions, when executed, cause the one or more processors to: Renewable energy data is obtained from the one or more charging stations that include renewable energy sources, wherein the analysis of carbon emission data for the one or more charging stations is further based on the renewable energy data.

8. A method for vehicle routing planning, comprising: Identify one or more charging stations based on one or more geographical locations selected for route planning; Carbon emission data for each of the one or more charging stations is analyzed based on the public grid location associated with each of the charging stations. Determine a path associated with the one or more geographic locations, and select at least one charging station on the path for charging the vehicle based on analysis of the carbon emission data associated with each of the one or more charging stations. as well as The user is provided with the route and the at least one charging station for navigating the vehicle.

9. The method of claim 8, wherein providing the user with the path and the at least one charging station includes presenting a path map on a user interface, the path map showing the path and marking charging station icons to identify the selected at least one charging station.

10. The method of claim 8, wherein the carbon emission data includes at least one type of data selected from real-time carbon emission data, historical carbon emission data, and projected carbon emission data.

11. The method of claim 8, wherein the at least one charging station is selected based on one or more predicted times at which the vehicle will arrive at each of the one or more charging stations, wherein the carbon emissions associated with each of the one or more charging stations are estimated based on the predicted times, and wherein the one or more charging stations are selected based on the carbon emissions determined at the predicted times.

12. The method according to claim 8, further comprising: Other paths associated with the one or more geographic locations are determined based on other thresholds, including paths that minimize travel time and distance; as well as The analysis of the carbon emission data associated with the one or more charging stations is used to select charging stations on each of the paths that minimize travel time and distance.

13. The method of claim 8, further comprising mapping each of the one or more charging stations to a corresponding public grid location and obtaining the carbon emission data for the corresponding public grid location.

14. The method of claim 8, further comprising obtaining renewable energy data from the one or more charging stations including renewable energy sources, wherein the analysis of carbon emission data for the one or more charging stations is further based on the renewable energy data.

15. A non-transitory computer-readable storage medium having computer-readable code stored thereon for programming one or more processors to perform the following steps: Carbon emission data for each of the one or more charging stations is analyzed based on the public grid location associated with each of the charging stations. At least one charging station for charging the vehicle is identified based on the analysis of the carbon emission data for each of the one or more charging stations. Determine a path associated with the one or more geographic locations and including the at least one charging station thereon; as well as The user is provided with the route and the at least one charging station for navigating the vehicle.

16. The non-transitory computer-readable storage medium of claim 15, wherein providing the user with the path and the at least one charging station includes presenting a path map on a user interface, the path map showing the path and marking the locations of the at least one charging station thereon.

17. The non-transitory computer-readable storage medium of claim 15, wherein the carbon emission data includes at least one type of data selected from real-time carbon emission data, historical carbon emission data, and projected carbon emission data.

18. The non-transitory computer-readable storage medium of claim 15, wherein the at least one charging station is identified based on one or more times when the vehicle is predicted to arrive at each of the one or more charging stations, wherein the carbon emissions associated with each of the one or more charging stations are estimated based on the predicted one or more times, and wherein the one or more charging stations are identified based on the carbon emissions determined at the predicted one or more times.

19. The non-transitory computer-readable storage medium of claim 15, wherein the step comprises: Alternative routes associated with the one or more geographic locations are determined based on other thresholds, including routes that minimize travel time and distance. as well as The charging station on each of the alternative routes is selected based on the analysis of the carbon emission data associated with the one or more charging stations.

20. The non-transitory computer-readable storage medium of claim 15, wherein the step includes obtaining renewable energy data for each of the one or more charging stations including renewable energy sources, wherein the analysis of carbon emission data for the one or more charging stations is further based on the renewable energy data.

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