Method for path determination based on a vehicle and a drive system characterization

Abstract

A method (900) comprising: identifying (630), by a processor, a route between a destination and a current location; quantizing (640), by the processor, the route into two or more road segments; Characterizing (650), by the processor, the two or more road segments according to propulsion resources available to a vehicle, including Driver and traffic characterization, wherein the processor predicts a vehicle speed as a function of the distance of a road segment, determines a speed curve of the vehicle based on the predicted speed, and determines an acceleration curve of the vehicle based on the predicted speed; Vehicle characterization, where the processor determines vehicle characteristics and environmental conditions to calculate vehicle traction force time trajectories, Drive system characterization, wherein the processor converts the vehicle speed curve into a drive speed curve and converts the tractive force time curve into a drive system torque curve; performing a binary quantization of the road segments by the processor, whereby the total time of a route section is divided into a predetermined number of time intervals; Performing a micro-characterization of the road segments by the processor, including: ◯ Determining the average speed of the drive system during each time interval based on the drive speed history curve; ◯ Determining the average torque of the drive system during each time interval based on the torque curve; ◯ Characterise the drive power in each time interval based on the average speed and torque of the drive system in that interval; ◯ Characterizing the drive energy in each time interval based on the drive power in the respective time interval; ◯ Combining the characterized driving energy for all time intervals of a respective road segment and storing these combinations as the respective energy characterization of the road segment; Generating (660), by the processor, at least two energy paths over the route based on the characterization of the two or more road segments; and Providing (670), by the processor, an optimized energy path from the at least two energy paths., Making real-time decisions, by the processor, regarding the control of the drive system, including the control of the vehicle's available drive resources.

Description

INTRODUCTION

[0001] The disclosure of the subject matter relates to route determinations, in particular to route determinations taking into account the drive systems available to the vehicle.

[0002] Generally, navigation systems allow a user or operator of a vehicle to enter a starting point and a destination point. The navigation system can access a map database containing road information. The navigation system can use the starting point, the destination point, and the map database to calculate one or more routes from the starting point to the destination point. The navigation system can then display one or more of the calculated routes to a user. The displayed routes are typically based on a fastest or shortest route.

[0003] Multimodal vehicles are vehicles that use more than one type of propulsion and / or motive power source to propel the vehicle. Multimodal vehicles incorporate algorithms that control vehicle propulsion systems in everyday use. However, routes provided by navigation systems associated with each of the multimodal vehicles often fail to consider the best way to utilize the available propulsion types when calculating routes. Furthermore, even if existing propulsion control systems could anticipate terrain and other factors, the multimodal vehicle may inefficiently distribute utilization among the vehicles' modes for route calculation.

[0004] Accordingly, it is desirable to provide a system that can optimally determine the utilization of the available drive resources along the individual route sections when calculating the route.

[0005] DE 10 2013 016 569 A1 relates to an operating method for a hybrid drive of a motor vehicle, in particular a plug-in hybrid or range extender vehicle, which can be operated with multiple operating modes. The invention particularly relates to an operating method for selecting optimal operating modes of the hybrid drive along a travel route. The operating method comprises the steps of dividing a travel route into a sequence of route sections; determining at least one predicted target variable for each of the operating modes of the hybrid drive on each route section along the travel route; determining an optimal path for the travel route, wherein a path is a sequence of operating modes along the travel route such that each route section is assigned an operating mode and the optimal path is the path that has an optimal value for the at least one target variable.

[0006] US 2007 / 0 112 475 A1 describes devices, systems, and methods for managing the energy consumption of a motor vehicle and thus optimizing energy consumption. The devices and systems for controlling energy consumption typically include power management logic that calculates the power applied to the vehicle's engine. This logic considers information from the vehicle's external environment, the vehicle's operating status, driver commands, and vehicle operating parameters.

[0007] DE 10 2015 223 588 A1 describes a control system with at least one electronic control unit for controlling an internal combustion engine in a hybrid vehicle, wherein the control unit is designed such that it evaluates input signals for acquiring data for detecting a current situation and for detecting at least one situation prevailing in the near future with regard to an expected speed and load profile and that it thus controls the starting and stopping of the internal combustion engine in a situation-adaptive manner. DESCRIPTION

[0008] The object of the invention is to better determine the utilization of available drive resources along individual route sections during route calculation. This object is achieved by the subject matter of claim 1. Further developments can be found in the subclaims.

[0009] According to one or more embodiments, a method is provided. The method identifies a route between a destination and a current location and quantizes the route into one or more road segments. The method includes characterizing the one or more road segments according to the propulsion resources available to a vehicle generating at least two energy paths over the route based on the characterization of the one or more road segments. The method also includes providing an optimized energy path from the at least two energy paths.

[0010] According to one or more embodiments or the method embodiment above, the characterization of each of the one or more road segments may be based on a fuel energy-battery energy curve.

[0011] According to one or more embodiments or any of the above method embodiments, characterizing each of the one or more road segments comprises a route characterization, wherein the processor identifies road markings and traffic information at specific intervals along each of the one or more road segments.

[0012] According to one or more embodiments or any of the above method embodiments, the characterization of each of the one or more road segments may include a driver and traffic characterization, wherein the processor predicts a vehicle speed as a function of road segment spacing.

[0013] According to one or more embodiments or any of the above method embodiments, the characterization of each of the one or more road segments may include a vehicle characterization, wherein the processor determines vehicle characteristics and environmental conditions to calculate time trajectories of the vehicle traction.

[0014] According to one or more embodiments or any of the method embodiments above, the characterization of each of the one or more road segments may include a propulsion system characterization that uses the vehicle's wheel and tire size to convert the vehicle speed and time trajectories of the tractive effort into the propulsion system speed and torque over time.

[0015] According to one or more embodiments or any of the above method embodiments, characterizing each of the one or more road segments may include calculating an energy characterization of the propulsion system given propulsion speed and torque trajectories of the propulsion system.

[0016] According to one or more embodiments or one of the above method embodiments, the characterization of the one or more road sections may be provided from a routing engine executed by the processor to a drive controller executed by a second processor.

[0017] According to one or more embodiments or one of the above method embodiments, the optimized energy path may be provided based on short-term energy properties and long-term energy properties.

[0018] According to one or more embodiments or any of the above method embodiments, the processor may determine whether the final goal is achieved.

[0019] According to one or more embodiments or one of the above method embodiments, since the optimized energy path is provided, a direction of the short-term trajectory can be used to control the energy use in a continuous loop, short-term aligned, to that direction.

[0020] According to one or more embodiments, a system is provided. The system includes a processor and a memory. The memory stores program instructions executable by the processor to cause the system to identify a route between a destination and a current location and quantize the route into one or more road segments. The program instructions are further executable by the processor to cause the system to characterize the one or more road segments according to the propulsion resources available to a vehicle generating at least two energy paths over the route based on the characterization of the one or more road segments. The program instructions are also executable by the processor to cause the system to provide an optimized energy path from the at least two energy paths.

[0021] According to one or more embodiments or the above system implementation, the characterization of each of the one or more road segments may be based on a fuel energy versus battery energy curve.

[0022] According to one or more embodiments or any of the above system embodiments, characterizing each of the one or more road segments comprises a route characterization, wherein the processor identifies road markings and traffic information at specific intervals along each of the one or more road segments.

[0023] According to one or more embodiments or any of the above system embodiments, the characterization of each of the one or more road segments may include a driver and traffic characterization, wherein the processor predicts a vehicle speed as a function of road segment spacing.

[0024] According to one or more embodiments or any of the above system embodiments, the characterization of each of the one or more road segments may include a vehicle characterization, wherein the processor determines vehicle characteristics and environmental conditions to calculate time trajectories of vehicle traction.

[0025] According to one or more embodiments or any of the above system embodiments, the characterization of each of the one or more road segments may include a propulsion system characterization that uses the vehicle's wheel and tire size to convert the vehicle speed and time trajectories of the tractive effort into the propulsion system speed and torque over time.

[0026] According to one or more embodiments or any of the above system embodiments, characterizing each of the one or more road segments may include calculating an energy characterization of the propulsion system given propulsion system input speed and torque trajectories.

[0027] According to one or more embodiments or any of the above system embodiments, the characterization of the one or more road segments may be provided from a routing engine executed by the processor to a drive controller executed by a second processor.

[0028] According to one or more embodiments, a computer program product is provided. The computer program product includes a computer-readable storage medium having processor-executable program instructions. The program instructions are processor-executable to cause the processor to identify a route between a destination and a current location and to quantize the route into one or more road segments. The program instructions are further processor-executable to cause the processor to characterize the one or more road segments according to the propulsion resources available to a vehicle generating at least two energy paths over the route based on the characterization of the one or more road segments.The program instructions are also executable by the processor to cause the processor to provide an optimized power path from the at least two power paths.

[0029] The above features and advantages as well as further features and advantages of the disclosure will become apparent from the following detailed description in conjunction with the accompanying figures. BRIEF DESCRIPTION OF THE CHARACTERS

[0030] Further features, advantages and details appear only as examples in the following detailed description, which refers to the figures: Fig. 1 is a computing environment according to one or more embodiments; Fig. 2 is a block diagram illustrating an example of a processing system according to one or more embodiments; Fig. 3 illustrates a road segment characterization based on available propulsion types for a vehicle traveling a calculated route according to one or more embodiments; Fig. 4 illustrates a road section characterization according to one or more embodiments; Fig. 5 illustrates a flowchart of a method for routing a vehicle based on one or more road segment characterizations, according to one or more embodiments; Fig. 6 illustrates a process flow according to one or more embodiments; Fig. 7 illustrates a process flow according to one or more embodiments; Fig. 8 illustrates a process flow according to one or more embodiments; and Fig. 9A-9B illustrates a process flow according to one or more embodiments. DETAILED DESCRIPTION

[0031] The present disclosure is merely exemplary in nature and is not intended to limit its application or uses. It should be understood that throughout the figures, corresponding reference numerals indicate like or corresponding parts and features. As used herein, the term module refers to processing circuitry, which may include an application-specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group), and memory executing one or more software or firmware programs, combinational logic circuitry, and / or other suitable components that provide the described functionality.

[0032] Embodiments herein relate to a system that, during route calculation, can determine the utilization of available propulsion resources in an optimized manner along segments of the routes to enable energy path optimization. In this context, a system is provided herein that identifies a route between a destination and a current location and quantizes the route into one or more road segments. Each road segment is then characterized by the system according to the propulsion resources available to a vehicle. Based on the characterization of the one or more road segments, at least two energy paths over the route are generated, and an optimized energy path is provided from the at least two energy paths. A technical effect and benefit of the system is thus to provide insight into the future by having, processing, and acting on an upcoming path.

[0033] According to one or more embodiments, Fig. 1, a computing environment 50. As illustrated, the computing environment 50 includes one or more computing devices, such as a server / cloud 54B and / or an on-board vehicle computer system 54N integrated with each of the plurality of autonomous or non-autonomous vehicles 55 connected via the network 56. The on-board vehicle computer system 54N may include a navigation processing system 57, which may further include a routing engine 58, and a propulsion control system 59 that controls a propulsion system.

[0034] One or more computing devices may communicate with each other over network 56. Server / cloud 54B illustrates cloud-based systems that provide processing resources to the vehicle's processing system 55, represented by automotive onboard computer system 54N. Processing resources may include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), field-programmable gate arrays (FPGAs), etc.

[0035] The network 56 may be, for example, a cellular network, a local area network (LAN), a wide area network (WAN), such as the Internet and Wi-Fi, a dedicated short-range communications network (e.g., V2V (vehicle-to-vehicle) communication, V2X (vehicle-to-everything) communication, V2I (vehicle-to-infrastructure) communication, and V2P (vehicle-to-pedestrian) communication), or any combination thereof, and may include wired, wireless, fiber optic, or any other connection. The network 56 may be any combination of connections and protocols that supports communication between the server / cloud 54B and / or the plurality of vehicle onboard computer systems 54N. Coordination between the server / cloud 54B and the vehicle onboard computer system 54N may be via cellular or Wi-Fi communication to transfer image data, key images, position data, and driving data between the vehicle 55 and the remote resource.The coordination between the server / cloud 54B and the vehicle on-board computer system 54N allows the vehicle on-board computer system 54N to perform SLAM operations with latency, while the server / cloud 54B can perform the operations that have no latency.

[0036] The onboard vehicle computer systems 54N for each of the plurality of vehicles 55 may include a Global Positioning System (GPS) transceiver (not shown) operable to receive position signals from a plurality of GPS satellites (not shown) that provide signals representative of a location for each of the mobile resources. In addition to the GPS transceiver, each vehicle 55 associated with one of the plurality of onboard vehicle computer systems 54N may include the navigation processing system 57, which may be arranged to communicate with a server / cloud 54B via the network 56. Accordingly, each vehicle 55 associated with one of the plurality of onboard vehicle computer systems 54N is capable of determining location information and transmitting that location information to the server / cloud 54B or another onboard vehicle computer system 54N.

[0037] A vehicle onboard computer system 54N may also include one or more active and passive sensors (e.g., radar, light detection and ranging (LIDAR), internal and external cameras, weather, longitudinal acceleration, speech recognition, or the like). The vehicle onboard computer system 54N may also include one or more microphones and a speech processing application.

[0038] Additional transmitted and received signals may include data (e.g., image data from cameras associated with the vehicle's onboard computer system 54N), communications, and / or other transmitted signals (e.g., signals associated with LIDAR and / or radar). Furthermore, it should be noted that the functions of the transmitter and receiver can be combined to form a signal transceiver.

[0039] The vehicle onboard computer system 54N and the server / cloud 54B may both include storage components that store high-resolution feature set data, as well as processing components that process the high-resolution map data. For example, each vehicle 55 may store high-resolution feature set data in non-volatile memory. The vehicle onboard computer system 54N and the server / cloud 54B may both store the same or similar information regarding the feature set data and routing information.

[0040] The server / cloud 54B can serve as a remote compute resource (e.g., a server and / or a cloud system, including an edge cloud). The server / cloud 54B can be implemented as a service delivery model to enable convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal administrative effort or interaction with a service provider.

[0041] According to an exemplary embodiment, Fig. 2 illustrates a processing system 200 for implementing the teachings contained herein. Processing system 200 may form at least a portion of one or more computing devices, such as server / cloud 54B and / or vehicle onboard computer system 54N. Processing system 200 may include one or more CPUs (processors) 201a, 201b, 201c, etc. (collectively or generically referred to as processor 201). Processor 201 is coupled to system memory 202 and various other components via a system bus 203. System memory 202 may include read-only memory (ROM) 204, may include random access memory (RAM) 205, and may include a basic input / output system (BIOS) that controls certain basic functions of processing system 200.

[0042] Fig. 2 further illustrates a network adapter 206 and an input / output (I / O) adapter 207 coupled to the system bus 203. The I / O adapter 207 may be a small computer system interface (SCSI) adapter that communicates with a mass storage device 208, which may include a hard disk 209 and / or other storage drive or other similar component. An operating system 210 for execution on the processing system 200 may be stored in the mass storage device 208. The operating system 210 may form at least a portion of the software for determining the optimal utilization of available propulsion resources along the route segments during route calculation. Note that this determination could also occur in the processor 201 (e.g., if the processing system 200 determines that a best travel path may be different, then the processing system 200 characterizes and determines an optimal energy path for that best travel path).The network adapter 206 connects the system bus 203 to an external network 211 so that the processing system 200 can communicate with other such systems.

[0043] A display adapter 212 may connect a display 215 (e.g., a display monitor) to the system bus 203 and may include a graphics card to enhance the performance of graphics-intensive applications and a video controller. In one embodiment, the adapters 207, 206, and 212 may be connected to one or more I / O buses connected to the system bus 203 via an intermediate bus bridge. Suitable I / O buses for connecting peripheral devices such as disk controllers, network adapters, and graphics adapters typically include common protocols such as the Peripheral Component Interconnect (PCI). Additional input / output devices are shown as connected to the system bus 203 via a user interface adapter 220.A camera 222 and a speaker 223 may all be connected to the system bus 203 via the user interface adapter 220, which may include, for example, a super I / O chip that integrates multiple device adapters into a single integrated circuit.

[0044] Processing system 200 may additionally include a graphics processing unit 230. Graphics processing unit 230 is a special electronic circuit designed to manipulate and alter memory to accelerate the generation of images in a frame buffer intended for output to a display and / or for detecting and analyzing a feature set. In general, graphics processing unit 230 is very efficient at manipulating computer graphics and image processing and has a highly parallel structure, making it more effective than general-purpose CPUs for algorithms that require parallel processing of large blocks of data.

[0045] Thus, the processing system 200 includes, as shown in Fig. 2, the processing capability in the form of processors 201, the storage capability including the system memory 202 and the mass storage 208, input means such as the camera 222 and the output capability including the speaker 223 and the screen 215. In one embodiment, a portion of the system memory 202 and the mass storage 208 jointly store the operating system 210 to coordinate the operations of the various components.

[0046] Fig. 3 represents a road section characterization 300 (e.g. by the computing environment 50 of Fig. 1 and / or the processing system 200 of Fig. 2) based on available propulsion types for a vehicle traveling a calculated route according to one or more embodiments. The navigation processing system 57 associated with each of the plurality of vehicle onboard computer systems 54N may determine a drivable route from a requested starting location (e.g., and while traveling from a current location) to a destination. The navigation processing system 57 may include the routing engine 58, which may include computer-readable instructions that, in response to execution by the processor(s) 201, cause operations to be performed, including creating a drivable route from a requested starting point or current location 305 to a destination 315.

[0047] The routing engine 58 may use stored map data connected to the vehicle on-board computer systems 54N and / or received map data (e.g., from the server / cloud 54B) for a plurality of road / route segments. Note that the drivable route may be determined first, and then the drivable route is quantized into smaller road segments. If the road segments are known, the routing engine 58 may also query. The map data associated with each segment may include: a set of travel lanes, road and infrastructure information (e.g., road closures, traffic, accidents, locations of stop signs, traffic interchanges, on-ramps, off-ramps, etc.), elevation information, gradient information (rise / decline), weather data, curvatures associated with each portion of the road segment (e.g.,B, segment topography), fuel consumption according to the speed at which the segment and / or segment topography is traversed, battery energy consumption / gain (utilization) according to the speed at which the segment and / or segment topography is traversed (which could, for example, be a by-product of embodiments herein), and / or any combination thereof.

[0048] The routing engine 58 may also utilize driver-specific data. For example, the driver-specific data may be related to previous crossings of road segments that the routing engine 58 may use as part of the traversable route, including data regarding how a driver traversed the road segment (e.g., fast, slow, normal, aggressive, leisurely, etc.).

[0049] Routing engine 58 may also utilize vehicle information about the vehicle traveling the traversable route. For example, routing engine 58 may receive vehicle data (e.g., make, model, size, weight, tire size, propulsion resources (e.g., fuel, electric, hydrogen, hybrid, etc.), or the like) from vehicle onboard computer systems 54N and / or server / cloud 54B. Note that a hybrid propulsion system may include gas and battery systems, diesel and battery systems, hydrogen / fuel cells, and battery systems.

[0050] Accordingly, rather than simply attempting to maintain a current state of charge (SoC) along a drivable route, the present routing engine 58 may utilize map data, driver-specific data, and vehicle information associated with each road segment to determine an optimized drivable route that optimizes when to use an available propulsion resource on a road segment-by-road segment basis to minimize fuel consumption during battery charging or discharging to maintain a desired SoC. The propulsion control system 59 (e.g., implemented in one of the processors) may utilize the drivable route provided by the routing engine 58 to make real-time fuel / battery consumption decisions for a vehicle traversing the drivable route by discriminating between short-term fuel / battery energy characteristics and long-term characteristics.

[0051] For example, if a traversable route from 305 to 315 is determined, the routing engine 58 may calculate an energy path 340 and an energy path 350. Note that each segment of the energy paths 340 and 350 represents fuel consumption and includes a value indicating an estimated amount of fuel consumption for that segment. The (non-optimal) energy path 340 simply attempts to maintain a 0% SoC (i.e., zero net charge or discharge) without consideration of segment topography, vehicle information, or driver-specific data. Accordingly, the energy path 340 merely considers SoC opportunities when traversing a road segment. As such, a propulsion control system 59 of a vehicle traversing the road segment 320 of the energy path 340 attempts to charge a battery for the vehicle even though the road segment does not promote battery charging or optimal battery charging.Accordingly, when attempting to charge the battery while traveling along road segment 320, fuel must be used to propel the vehicle along the travelable path, increasing fuel consumption and decreasing the vehicle's fuel economy.

[0052] The (optimal) energy path 350 considers segment topography, vehicle information, and / or driver-specific data to maintain a desired SoC, here 0%, while simultaneously optimizing fuel consumption by considering battery charging and discharging opportunities for a specific road segment while considering the entire drivable route. The propulsion control system 59 of the vehicle traversing the road segment 320 of the energy path 350 may detect that the road segment 320 does not contribute to battery charging (e.g., the road segment 320 is flat or slightly inclined in the direction of travel) and may also detect that other road sections of the drivable route offer better opportunities for charging the battery, since the propulsion control system 59 considers charging opportunities along the entire drivable route.Accordingly, the propulsion control system 59, detecting that the road segment 320 is flat or slightly sloped, will not attempt to charge the vehicle's battery because the road segment 320 does not optimally support battery charging and other road segments exist along the travelable route that are better suited to charging the battery. Accordingly, the propulsion control system 59 may maintain or decrease the vehicle's fuel consumption, thereby increasing fuel consumption.

[0053] When crossing road segment 330 in energy path 340, propulsion control system 59 may use the battery to cross road segment 330 or assist in crossing road segment 330 while the SoC remains above 0% because the battery is in a state +4% above the SoC target. The propulsion control system 59 of the vehicle traversing energy path 350 may also use the battery to cross road segment 330 or assist in traversing road segment 330. However, the propulsion control system 59 of the vehicle traversing energy path 350 may recognize that more battery power may be consumed when traversing road segment 330 compared to energy path 340 because the vehicle's onboard computer system 54N uses segment topography, vehicle information, and / or driver-specific data (i.e.,Path traversal factors) for the entire energy path 350 to better determine which propulsion resource or combination thereof should be used to traverse the road segment 330, as well as when and how the propulsion resource or combination thereof should be used to traverse the road segment 330. Because the vehicle onboard computer system 54N has predicted a potential energy expenditure for each road segment of the energy path 350 after determining the energy path 350, the vehicle onboard computer system 54N may instruct the propulsion control system 59 to use additional battery power to traverse the road segment 330, which may result in the SoC possibly falling below (e.g., below 0%) the desired SoC because it is recognized that opportunities to charge the battery to the desired SoC exist in one or more of the remaining road segments of the energy path 350.As the battery SoC for the vehicle-crossing road segment 330 falls below the desired SoC, fuel consumption for the road segment 330 is optimized by considering the charging opportunities remaining on the travelable route (i.e., long-term charging opportunities).

[0054] According to an exemplary embodiment, Fig. 4 illustrates road segment characterization diagrams 405, 410, and 415 according to one or more embodiments. The road segment characterization diagrams 405, 410, and 415 for each road segment in a drivable route may be used to determine short-term energy characteristics (i.e., current available propulsion resources and their levels, as well as when and how the available propulsion resources should be utilized) that may relate to a currently traversed road segment of a drivable route, and long-term energy characteristics that may relate to a road segment of the drivable route to be traversed in the future.

[0055] The characterization of the road segment may represent the fuel energy 420 versus battery energy 425 curves 405, 410, and 415. An amount of the fuel energy 420 may be represented in fuel mass, fuel volume, fuel energy, and / or a ratio of the amount to a particular fuel sum (e.g., a total tankful). The amount of battery energy 425 may be represented in a change in the energy state of the battery and / or a change in the SoC of the battery. At least a portion of the fuel energy 420 versus battery energy 425 curves 405, 410, and 415 may be associated with a respective segment topography. Curve 405 may represent a fuel energy 420 versus battery energy 425 curve for a first road segment associated with a drivable route.Curve 405 may range from full battery consumption and no fuel consumed to no battery consumption and fuel used only to power the vehicle. Curve 410 may represent a fuel energy 420 versus battery energy 425 curve for a second road segment associated with the drivable route. Curve 415 may represent a fuel energy 420 versus battery energy 425 curve that represents a combination of curves for the first road segment and the second road segment.

[0056] Additional road segments of the drivable route may be combined by the routing engine 58 for use by the propulsion control system 59 of a vehicle (e.g., the one or more processors 201). Each road segment may be assigned a weight based on an associated curve, which may be used by the routing engine 58 to determine an optimized consumption of propulsion resources for each road segment of the drivable route. The optimization may be performed by considering long-term fuel / battery expenditures and battery charge over an entire drivable route. Other propulsion resources may be used to characterize a road segment.

[0057] Fig. 5 illustrates a flowchart of a method 500 for routing a vehicle based on one or more road segment characterizations, according to one or more embodiments. At block 505, the automotive onboard computer systems 54N or a server, e.g., server / cloud 54B, may receive a destination for use in calculating / determining a drivable route for a vehicle. At block 510, the automotive onboard computer systems 54N or server / cloud 54B may determine a current location of the vehicle or receive a starting location for calculating or determining the drivable route for the vehicle.

[0058] At block 515, the automotive onboard computer systems 54N or server / cloud 54B may calculate and identify one or more drivable routes in response to the received destination and current location or the received starting location. At block 520, the automotive onboard computer systems 54N or server / cloud 54B may subdivide each of the one or more drivable routes into two or more road segments. Furthermore, portions of a road network may be pre-divided into road segments and stored in a memory, e.g., system memory 214.

[0059] At block 525, the automotive onboard computer systems 54N or server / cloud 54B may characterize each of the two or more road segments (e.g., as illustrated in curves 405 and 410). Furthermore, the road segment characterizations may be associated with a respective road segment and stored in a memory, e.g., system memory 214. The characterization may be based on an associated fuel energy-to-battery energy curve associated with each road segment. At block 530, the automotive onboard computer systems 54N or server / cloud 54B may select / determine an optimized drivable route from the determined one or more drivable routes, which may be provided to a driver (visually via display and / or audibly via speakers) based on the characterizations associated with each road segment.

[0060] At decision block 535, the automotive onboard computer system 54N or server / cloud 54B may determine whether an adjustment to one or more road segment characterizations is necessary due to changes in map data and / or driver-specific data. For example, a driving style for a driver of a vehicle may change from normal to aggressive. Accordingly, a change in driving style may impact both battery costs and fuel consumption. If the vehicle onboard computer system 54N or server / cloud 54B determines that an adjustment to one or more road segment characterizations is not necessary, the method 500 proceeds to decision block 545 (as represented by the NO arrow), where the vehicle onboard computer system 54N or server / cloud 54B may determine whether the vehicle has reached the received destination.If the vehicle has reached the received destination, method 500 ends at 550 (as represented by the YES arrow). If the vehicle has not reached the destination, method 500 returns to decision block 535 (as represented by the NO arrow).

[0061] If the vehicle onboard computer system 54N or the server / cloud 54B determines that an adjustment to one or more road segment characterizations is necessary, the method 500 proceeds to block 540 (as represented by the YES arrow), where the vehicle onboard computer system 54N or the server / cloud 54B may re-optimize the optimized route. Then, at decision block 545, the vehicle onboard computer system 54N or the server / cloud 54B may determine whether the vehicle has reached the received destination. If the vehicle has reached the received destination, the method 500 ends at 550 (as represented by the YES arrow). If the vehicle has not reached the destination, the method 500 returns to block 535 (as represented by the NO arrow).

[0062] Alternatively, if the vehicle on-board computer system 54N or the server / cloud 54B determines that an adjustment to one or more road segment characterizations is required, the method 500 may return to block 525 (as represented by the dashed arrow) to determine if a better route exists instead of the optimized route provided with respect to the adjustment to one or more road segment characterizations.

[0063] In view of the disclosure contained herein, Fig. 6 illustrates a process flow 600 according to one or more embodiments. The process flow 600 may be implemented by the automotive on-board computing systems 54N and / or the server / cloud 54B. In general, the process flow 600 relates to the calculation, communication, and utilization of energy characterizations of quantized road segments that, in their entirety, represent an entire future route from a current location to a final destination. According to one or more embodiments, the utilization of the energy characterization occurs within the propulsion control system 59 to optimize real-time propulsion control decisions (e.g., improve propulsion resource efficiency) by incorporating predictive energy characterization information into the real-time control decisions.

[0064] Process flow 600 begins at block 630, where the automotive onboard computer system 54N and / or the server / cloud 54B identifies a route between a destination and a current location. The destination (e.g., final destination) and the vehicle's current location are both used as inputs to identify the route, and the route may be relabeled as the selected path from point A (e.g., the current location) to point B (e.g., the final destination). Note that process flow 600 does not correspond to route selection (e.g., using third-party mapping software) because the details of the identified route provide the necessary inputs for process flow 600.

[0065] At block 640, the automotive onboard computer system 54N and / or the server / cloud 54B quantizes the route into two or more road segments. That is, once the route from the current location to the final destination is identified, the route can be quantized into separate road segments. The number of road segments can be defined by an integer number N (e.g., any positive integer value equal to or greater than two). Quantization can be accomplished by dividing the route into equidistant segments, segments based on road types (e.g., highway vs. local roads), segments based on topography / terrain (e.g., hills vs. flat roads), and / or a combination thereof. At block 650, the automotive onboard computer system 54N and / or the server / cloud 54B characterizes the two or more road segments according to the propulsion resources available to a vehicle.The characterization of the road sections can be performed according to the process flow 800 of . Fig. 8 and the process flow 900 of Fig. 9A-9B, as described herein. Note that the energy characterization generates a required energy for each road segment (e.g., or a combination of potentially required energies (the entire curve), as shown in road segment characterization diagrams 405 and 410).

[0066] At block 660, the automotive onboard computer systems 54N and / or the server / cloud 54B generate at least two energy paths across the route based on the characterization of the two or more road segments. The at least two energy paths across the route are determined from the energy demand of each road segment. And at block 670, the automotive onboard computer systems 54N and / or the server / cloud 54B provide an optimized energy path from the at least two energy paths. Once the optimized energy path is determined, the direction of the short-term trajectory is used to control energy usage in the short term toward that direction. For example, if the optimal energy path indicates short-term battery depletion, the real-time control system can capture this general battery discharge state. This occurs with the new knowledge that a better recharging opportunity lies ahead.The path optimization of blocks 660 and 670 occurs in real time. Furthermore, the drive control system 59 uses the energy characterization with respect to the at least two energy paths to minimize overall fuel consumption while maintaining the desired final SoC. To do this, for example, the drive control system 59 controls a short-term control toward a short-term optimal path. Thus, if energy path 350 is the optimal path, the drive control system 59 controls the short-term energy on the energy path identified by line 370. In this way, the drive control system 59 aims to increase the SoC by 1% over the course of the first segment.

[0067] At decision block 680, the automotive onboard computer system 54N and / or the server / cloud 54B checks whether the final goal has been reached. If the final goal has been reached, the process flow 600 continues to block 690 (e.g., as indicated by the YES arrow) and ends. If the final goal has not been reached, the process flow 600 returns to block 630 (e.g., as indicated by the NO arrow).

[0068] Fig. 7 illustrates a process flow 700 according to one or more embodiments. The process flow 700, from the perspective of the higher-level controller, proceeds through a series of operations (e.g., six). As illustrated, the process flow 700 includes route identification at dashed block 701, route quantization at dashed block 702, road segment characterization at dashed block 703, road segment characterization communication at dashed block 704, real-time control path optimization at dashed block 705, and real-time control at dashed block 706. Each of the six operations is explained in detail in the following sections.

[0069] As in Fig. 7, the route identification at dashed block 701 can be divided into blocks 713, 715, and 717. At block 713, the routing engine 58 identifies a final destination. Examples of identifying a final destination include, but are not limited to, receiving user input via an in-vehicle navigation system or mobile mapping applications. In turn, the different final destination is used as the new final destination. At block 715, the routing engine 58 identifies a current location. Examples of identifying a current location include, but are not limited to, using satellite communications with the in-vehicle navigation system and a combination of GPS and mobile mapping applications.

[0070] At block 717, the automotive onboard computer system 54N identifies a future route from the current location. The future route includes a complete route with all future map directions and specific road segments that will be traveled. And, although there may be a number of different routes to connect point A to point B, the process flow 700 is agnostic as to how the particular future route is selected. Rather, the process flow 700 strives to obtain the most accurate information (e.g., road types, topography / terrain, traffic, events) about the future route. As set forth herein, both the final destination and the current location of the vehicle are used as inputs to identify the future route from the current location (point A) to the final destination (point B). The future route is then identified as the chosen path to get from point A to point B.As an example of route identification, an optimally selected route can be chosen based on the fastest estimated travel time when driving from point A to point B. The route identification process can then repeat and be updated during the journey as point A changes and the algorithm runs until the vehicle arrives at point B.

[0071] The route quantization at dashed block 702 can be divided into blocks 722 and 724. At block 722, the automotive onboard computer system 54N quantizes the future route by selecting a series of separate road segments. That is, once the future route from the current location to the final destination has been identified, the route can be quantized into separate road segments as described here. Note that for propulsion control, the number of separate road segments N should be two or more, since the differences in the characterization of the segments are important information. Furthermore, the segments can become infinitely small as the distance of the entire route approaches zero (e.g., as the vehicle approaches the final destination). For example, the future route from point A to point B can be quantized into three separate road segments (e.g., N = 3).

[0072] At block 724, the automotive onboard computer system 54N (e.g., the routing engine 48) quantizes the future route by dividing the future route into the selected number of separate road segments. Continuing the three-segment example (e.g., N = 3), a first segment may be from point A to a first intersection with a first traffic light, a second segment may extend from this first intersection to a second intersection on a highway, and a third segment may cover a remaining portion of the future route along the highway to the final destination. In some cases, the first segment may generally represent a short-term segment from point A to a short-term future location, while the third segment generally represents the long-term segment.Furthermore, the on-vehicle computer system 54N may hold the second and third segments across multiple loops so that these segments are not recharacterized. Then, at a later time, the second and third segments may be moved and recharacterized when the vehicle 55 is closer to point B.

[0073] The road segment characterization (e.g., the characterization of the two or more road segments according to the propulsion means available to the vehicle 55) at the dashed block 703 can be divided into blocks 731, 733, 735, 737, 738, and 739, which illustrate the execution of a control loop on each of the N segments. At block 731, the automotive onboard computer system 54N initializes a loop counter (e.g., j = 1). At block 733, the automotive onboard computer system 54N calls a function to start a loop for characterizing road segments. At block 735, the automotive onboard computer system 54N characterizes the road segment ("Characterize Road Segment j") to determine an energy required for that segment (e.g., from curves 405 and 410). The characterization of the road section can be done in two parts, such as a first part which is carried out according to the process flow 800 of Fig. 8, and a second part, which is executed according to the process flow 900 of Fig. 9A-9B, as described herein. At block 737, the automotive onboard computer system 54N stores the route characterization (e.g., an energy characterization or required energy for each road segment). At decision block 738, the automotive onboard computer system 54N determines whether a last segment has been reached by comparing the loop counter to the selected number of segments (e.g., j = N). If the last segment has not been reached, process flow 700 continues to block 739 where the loop counter is incremented (e.g., j = j + 1). Thereafter, process flow 700 returns to block 733. If the last segment has been reached, process flow 700 continues to block 704.

[0074] The characterization of the road section (e.g. Block 735) is now described in relation to the Fig. 8 and 9A-9B. In general, the process flow 800 of Fig. 8, according to one or more embodiments, a method for determining propulsion system speed and torque that receives the road segment as input and outputs the propulsion system speed and propulsion system torque as a function of time. Furthermore, from a high-level control perspective, process flow 800 performs a series of operations (e.g., five) on each of the N segments to generate a prediction of vehicle speed and vehicle acceleration as a function of time. As illustrated, process flow 800 includes route identification at dashed block 801, route characterization at dashed block 802, driver and traffic characterization at dashed block 803, vehicle characterization at dashed block 804, and propulsion system characterization at dashed block 805. Each of the five operations is explained in detail in the following sections.

[0075] The route identification at dashed block 801 can be described with respect to block 812. At block 812, the automotive on-board computer system 54N (e.g., routing engine 58) identifies the road segment (e.g., as described in blocks 731, 733, and 735 of Fig. 7). For example, the routing engine 58 identifies an entire route. While at block 702 of Fig. 7 the entire route is quantized into route segments. Block 735 identifies a suitable route segment that needs to be characterized (e.g. characterizes this identified route segment) by Fig. 8 and Fig. 9A-B are executed in a (continuous) loop through the N segments.

[0076] The route characterization process at dashed block 802 can be described with reference to block 822. At block 822, road markings and traffic information are identified at specific intervals along the road segment. That is, after the road segment has been identified, road markings and traffic information can be identified at specific intervals along the road segment (e.g., "route characterization"). For example, with respect to the first segment of the present three-segment example, there are multiple markers based on distance along the road segment.

[0077] The driver and traffic characterization process at dashed block 803 can be divided into blocks 832, 834, and 836. At block 832, the automotive on-board computer system 54N predicts a vehicle speed as a function of the road segment distance. The vehicle on-board computer system 54N may include waiting times related to stopping the vehicle 55. At block 834, the vehicle on-board computer system 54N determines the vehicle speed over time (e.g., a vehicle speed trajectory) using the vehicle speed. Furthermore, at block 836, the vehicle on-board computer system 54N determines the vehicle acceleration over time (e.g., a vehicle acceleration trajectory) using the vehicle speed.

[0078] The vehicle characterization process at dashed block 804 may be described with reference to block 842. Vehicle characterization may include determining vehicle characteristics (e.g., vehicle mass, rolling resistance, aerodynamic drag, etc.) and environmental conditions (e.g., grade and wind). At block 842, the vehicle characteristics and environmental conditions are used by the vehicle onboard computer system 54N to calculate the vehicle traction time trajectories required to generate the vehicle speed and vehicle acceleration (e.g., the vehicle speed and acceleration trajectories determined from blocks 834 and 836). The output is then vehicle traction power over time (e.g., vehicle traction power-time trajectories).

[0079] The drive system characterization process at dashed block 805 can be divided into blocks 852 and 854. At block 852, the vehicle onboard computer system 54 uses N wheel and tire sizes (e.g., and any multiplication by a vehicle differential) to convert the vehicle speed trajectory into drive speed over time (e.g., a drive speed trajectory or a transmission output speed). At block 854, the vehicle onboard computer system 54 uses N wheel and tire sizes (e.g., and any multiplication by a vehicle differential) to convert the vehicle traction time trajectories into drive system torque over time (e.g., a drive torque trajectory or a transmission output torque). Process flow 800 then proceeds to block 892 and exits.

[0080] The process flow 900 of Fig. 9A-B, according to one or more embodiments, calculates an energy characterization of the drive system taking into account the drive speed and the torque trajectories (e.g., a drive torque trajectory or a drive torque trajectory from block 805 of Fig. 8). Process flow 900 corresponds to the calculation of the minimum input energy required to drive a road segment. Note that the drive speed multiplied by the drive torque yields the drive power, and a time integration of the drive power over the road segment yields the output energy required for the road segment. It should further be noted that if there are no drive system losses, the input energy may equal the output energy. Process flow 900 also determines how the energy characterization is a function of the specific drive system. If the drive system is a conventional powertrain, the energy characterization of process flow 900 is the single value that indicates the minimum fuel energy required to drive the road segment (e.g.,calculated using the transmission that yields the best engine operating point and the best fuel consumption across the entire road segment). If the propulsion system is an electric vehicle, the energy characterization of process flow 900 is the minimum battery energy required to power the road segment. For hybrid propulsion systems with multiple (e.g., usually two) energy sources, the energy characterization of process flow 900 indicates a relationship between the multiple energy sources (e.g., fuel and battery energies). More specifically, process flow 900 indicates the minimum fuel energy required as a function of battery energy by optimizing all degrees of freedom of the propulsion control. For example, if the vehicle has traveled the road segment and the battery SoC has been maintained from start to finish (e.g.,If the battery SoC is below the minimum value (e.g., no net state of charge change), process flow 900 determines what minimum amount of fuel will be consumed at that neutral battery SoC. Process flow 900 also repeats for several different changes in battery SoC. The desired end result is an energy characterization curve as described herein (e.g., curves 405 and 410).

[0081] Process flow 900 includes a characterization of the drive system at dashed block 901, a binary quantization of the road segment at dashed block 902, a micro-characterization of the road segment at dashed block 905, and a micro-combination of the road segment at dashed block 906. In general, blocks 902, 905, and 906 take a route segment in time, break that segment into smaller micro-route segments, characterize the micro-route segments, and then combine the characterization of the micro-segments (e.g., such as curves 405 and 410 to curves 415) until a combined single characterization for the route segment is calculated. The smaller the micro-failure (e.g., greater than 2^n), the more accurate the characterization, resulting in greater computational power. These operations are explained in detail in the following sections.

[0082] The drive system characterization process at dashed block 901 can be divided into blocks 912 and 914. At block 912, the automotive onboard computer system 54N receives the drive system speed over time (e.g., a drive speed trajectory or a transmission output speed trajectory). Note that while the drive speed trajectory or the transmission output speed trajectory may be a standalone function, in this embodiment, it is processed by block 852 as part of the broader block 735 of Fig. 7. At block 914, the vehicle onboard computer system 54N receives the drive system torque over time (e.g., a drive torque trajectory or a transmission output torque trajectory from block 854).

[0083] The binary microbinary quantization operation of the road segment at dashed block 902 can be divided into blocks 922 and 924. At block 922, the binary quantization operation of the road segment selects a number of separate time intervals (e.g., 2^r time intervals). At block 924, the binary quantization operation of the road segment separates the total time into the number of individual time intervals.

[0084] The micro-characterization process of the road segment at dashed block 905 begins at block 951, where the on-board vehicle computer system 54N (e.g., routing engine 58) initializes a loop interval counter (e.g., f = 1). At block 952, the automotive on-board computer system 54N calls a function ("Route Micro Segment Characterization Loop f") to characterize the individual time intervals. At block 953, the on-board vehicle computer system 54N determines an average speed of the propulsion system during this time interval (e.g., f-th interval) using the propulsion speed trajectory from block 912. At block 954, the vehicle on-board computer system 54N determines an average torque of the propulsion system during this time interval (e.g., f-th interval) using the propulsion torque trajectory from block 914.At block 955, the automotive onboard computer system 54N characterizes the power of the propulsion system at a single input speed and a single input torque (using the average input speed and average input torque from blocks 953 and 954, respectively). At block 956, the automotive onboard computer system 54N characterizes the power of the propulsion system (using the separate time intervals, the individual input speed and input torque from blocks 954 and 955, and the propulsion power characteristic multiplied by the time interval). In this context, the automotive onboard computer system 54N multiplies the power by the time interval.

[0085] At block 957, the automotive on-board computer system 54N stores the characterization of the route microsegment (e.g., [0, f]). At decision block 958, the vehicle on-board computer system 54N determines whether a last time interval has been reached by comparing the loop interval counter to the selected number of time intervals (e.g., m = 2^r). If the last time interval has not been reached, process flow 900 continues to block 959, where the loop interval counter is incremented (e.g., f = f + 1). Then, process flow 900 returns to block 952. If the last time interval is reached, process flow 900 continues to block 961 of Fig. 9B, as represented by the dot-dash circle A.

[0086] The microcharacterization of the road segment at dashed block 906 begins at block 961, where the on-board computer system 54N initializes an outer loop counter (e.g., k = 1). At block 962, the automotive on-board computer system 54N calls a function ("Binary Characterization Outer Loop k"). At block 963, the automotive on-board computer system 54N initializes an inner loop counter (e.g., m = 1). At block 964, the automotive on-board computer system 54N calls a function ("Binary Characterization Inner Loop m").

[0087] At block 965, the automotive onboard computer system 54N combines the characterization (e.g., [k - 1, 2m - 1] and [k - 1, 2m]). At block 966, the automotive onboard computer system 54N stores the combined characterization (e.g., [k, m]). At decision block 968, the automotive onboard computer system 54N determines whether the inner loop is complete (e.g., m = 2^(tk)). If the inner loop is not complete, process flow 900 continues to block 970, where the inner loop counter is incremented (e.g., m = m + 1). Then, process flow 900 returns to block 964. If the inner loop is complete, process flow 900 continues to decision block 978.

[0088] At decision block 978, the automotive onboard computer system 54N determines whether the outer loop is complete (e.g., k = t). If the outer loop is not complete, process flow 900 continues to block 980, where the outer loop counter is incremented (e.g., k = k + 1). Then, process flow 900 returns to block 962. If the outer loop is complete, process flow 900 continues to block 981. At block 981, the automotive onboard computer system 54N stores the segment characterization (e.g., [n, 1]). Then, process flow 900 continues to block 992 and exits.

[0089] Return to block 704 of the process flow of Fig.7, the information about the characterization (e.g., the energy characterization or the required energy for each road segment) is communicated. According to one or more embodiments, the route identification, the route quantization, the road segment characterization may be located and executed in a single controller (e.g., the vehicle on-board computer system 54N or the routing engine 58 of the navigation processing system 57), while the real-time control path optimization and the real-time control may be located and executed in another controller (e.g., the propulsion control system 59). In this context, the energy characterization or the required energy is communicated from the automotive on-board computer system 54N or the routing engine 58 to the propulsion controller (e.g., the road segment characterization communication operation). The energy characterization or the required energy may be the fuel energy (e.g.,all types of fuels used in propulsion applications, such as E10 / E85 gasoline, diesel, hydrogen, natural gas, etc.) and battery energy (e.g. all batteries used in propulsion applications from high voltage to low voltage).

[0090] For example, communicating the energy characterization (e.g., an entire characterization curve) for each of the quantized road segments is communicating tactical information to the propulsion control system 59 to optimize propulsion system operation by understanding, from an energy perspective, what is imminent in the short term and what is imminent beyond the short term. In this sense, quantization provides a comparative characterization between the near term and the far term. Therefore, communicating the energy characterization encompasses all propulsion systems (e.g., conventional, hybrid, electric, etc.). That is, communicating the entire characterization curve, which includes two energy sources while only one may be present, works for all vehicles. According to one or more embodiments, conventional powertrains with the 12V battery can be treated like a two-source system.

[0091] The real-time control path optimization at dashed block 705 can be divided into blocks 753 and 755. At block 753, the drive control system 59 performs an optimization of the energy path segment path based on the current objectives. At block 755, the drive control system 59 determines a travel direction for a short-term segment (e.g., the first segment).

[0092] For example, in the real-time control of hybrid propulsion systems, one aspect of propulsion system optimization is how to manage the battery to maximize fuel economy. Propulsion system optimization uses a battery state of charge (SoC) and a battery SoC target. If the battery SoC is higher than the battery SoC target, propulsion system optimization tends to discharge the battery. Conversely, if the battery SoC is lower than the battery SoC target, propulsion system optimization tends to charge the battery. If the battery SoC is equal to the battery SoC target, propulsion system optimization neither charges nor discharges the battery (e.g., note that if a net desire is no discharge or charge, propulsion system optimization can charge or discharge in the short term as long as it is restored later).These are decisions that take place in real time, and the path optimization that takes place in real-time control optimization is an energy path optimization.

[0093] Continuing the quantization example included herein (e.g., N = 3), if the propulsion control system 59 has the energy characterization for the three segments, the propulsion control system 59 solves an energy path optimization problem to optimize the energy path. If the battery SoC currently meets the battery SoC target, an energy path optimization problem can be solved to minimize overall fuel consumption while maintaining the desired final SoC. According to one or more embodiments, the optimal path could be determined by a single-source or single-destination optimization technique, shortest path formulation, and / or weighting methods (e.g., if the "weight" of each segment path is equal to the fuel consumption for that path, if the "weight" of each segment is known from the system energy characterization).

[0094] The real-time control at dashed block 706 can be divided into blocks 764 and 768. At block 764, the propulsion control system 59 uses a road segment to influence the real-time propulsion control. For example, the propulsion control system 59 uses a short-term road segment direction and biases the energy over the duration of that road segment to an optimized amount, approximately corresponding to an average net power. At block 768, the propulsion control system 59 executes the real-time propulsion decision. In this way, the propulsion control system 59 provides the optimal energy path in the short term, and fuel consumption is minimized. For example, even if the SoC is on target and the real-time control system would otherwise bias toward maintaining the battery SoC, new information from the optimal energy path can provide a short-term energy path that includes a 1% battery charge.To achieve this, the automotive onboard computer system 54N behaves as if the battery SoC target were 1% higher than it currently is. The end result is an optimal control strategy for the propulsion system that charges the battery in the short term because there is a better future opportunity to discharge the battery prematurely (e.g., when the vehicle 55 is in the third segment). The end result is an energy-path-optimized control strategy that incorporates the energy opportunities of the future route (e.g., good future charging or discharging opportunities) to influence propulsion control decisions in real time.

[0095] At decision block 770, the automotive onboard computer system 54N determines whether the final goal has been reached. If the final goal has not been reached, process flow 700 continues to block 715. If the revenue block has been reached, process flow 700 continues to block 792 and ends.

[0096] The technical effects and advantages of the disclosed embodiments include, among others, providing a complete path optimization for a route that minimizes overall fuel consumption for a vehicle traveling the route. Accordingly, when traveling through an optimized route, a vehicle's propulsion control system 59 can minimize fuel consumption for the vehicle when charging or discharging the battery to maintain a desired state of charge.

[0097] Accordingly, the embodiments disclosed herein describe a system that characterizes vehicle route information and driver information to determine the optimal operation of a vehicle and propulsion, which could be consumed and strategically utilized by an integrated propulsion system control system. The system can therefore make real-time decisions about an embedded propulsion control system that can be used to traverse road sections of a drivable route. The system can consider large amounts of map data, driver-specific data, and vehicle information to generate smaller data sets that can be used to directly, relevantly, and actionably operate the available propulsion resources to traverse the drivable route.

[0098] Embodiments of the present disclosure may overcome typical vehicle routing and propulsion management solutions that merely determine a route that attempts to maintain a current state of charge for a battery by considering current and future opportunities for charging and discharging the battery, as well as fuel expenditures along the route. Taking into account current and future opportunities for charging the battery and fuel costs, as well as other considerations such as map data and driver-specific data, an optimized route may be calculated and provided to the user, resulting in more efficient use of the battery while traversing the route.

[0099] The system may include an efficient communications interface structure capable of communicating with a vehicle's propulsion controller to control the propulsion control system 59 and thus make real-time decisions regarding fuel and battery utilization. The communications interface may distinguish between short-term fuel / battery energy characteristics for a traveled route and long-term characteristics for the traveled route. The system may effect "macro-level" path optimization along the route, including real-time adjustment along the route.

[0100] It should be understood that although the embodiments are described as being implemented on a traditional processing system, the embodiments may be implemented in connection with any other type of computing environment now known or later developed. For example, the prior techniques may be implemented using cloud computing. Cloud computing is a service delivery model that enables convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a service provider.It should be noted that the computing environment 50 associated with a geostationary-based transmission resource allocation system for vehicle sensors may be implemented in a cloud computing environment, and cell length, latitude, and location information may be stored locally and / or remotely, such as in the cloud computing environment.

[0101] The present disclosure may be a system, a method, and / or a computer-readable storage medium. The computer-readable storage medium may include computer-readable program instructions thereon for causing a processor to perform aspects of the present disclosure.

[0102] The computer-readable storage medium may be a tangible device capable of storing and enforcing instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.A non-exhaustive list of specific examples of the computer-readable storage medium includes the following: a portable computer diskette, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable read-only memory (CD-ROM), a digital versatility disk (DVD), a memory stick, a mechanically encoded device, and any suitable combination of the foregoing. A computer-readable storage medium, as used herein, is not to be understood as a transitory signal per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber optic cable), or electrical signals transmitted through a wire.

[0103] The computer-readable program instructions may also be loaded into a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable device, or other device to produce a computer-implemented process such that the instructions, executing on the computer, other programmable device, or other device, perform the functions / actions specified in the flowchart and / or block diagram block or blocks.

Claims

[1] A method (900) comprising: identifying (630), by a processor, a route between a destination and a current location; quantizing (640), by the processor, the route into two or more road segments; Characterizing (650), by the processor, the two or more road segments according to propulsion resources available to a vehicle, including Driver and traffic characterization, wherein the processor predicts a vehicle speed as a function of the distance of a road segment, determines a speed curve of the vehicle based on the predicted speed, and determines an acceleration curve of the vehicle based on the predicted speed; Vehicle characterization, where the processor determines vehicle characteristics and environmental conditions to calculate vehicle traction force time trajectories, Drive system characterization, wherein the processor converts the vehicle speed curve into a drive speed curve and converts the tractive force time curve into a drive system torque curve; performing a binary quantization of the road segments by the processor, whereby the total time of a route section is divided into a predetermined number of time intervals; Performing a micro-characterization of the road segments by the processor, including: ◯ Determining the average speed of the drive system during each time interval based on the drive speed history curve; ◯ Determining the average torque of the drive system during each time interval based on the torque curve; ◯ Characterise the drive power in each time interval based on the average speed and torque of the drive system in that interval; ◯ Characterizing the drive energy in each time interval based on the drive power in the respective time interval; ◯ Combining the characterized driving energy for all time intervals of a respective road segment and storing these combinations as the respective energy characterization of the road segment; Generating (660), by the processor, at least two energy paths over the route based on the characterization of the two or more road segments; and Providing (670), by the processor, an optimized energy path from the at least two energy paths., Making real-time decisions, by the processor, regarding the control of the drive system, including the control of the vehicle's available drive resources. [2] The method (900) of claim 1, wherein the characterization of each of the two or more road segments is based on a fuel energy versus battery energy curve. [3] The method (900) of claim 1, wherein characterizing each of the two or more road segments comprises a route characterization, wherein the processor identifies road markings and traffic information at specified intervals along each of the two or more road segments. [4] The method (900) of claim 1, wherein the characterization of each of the two or more road segments comprises a propulsion system characterization that uses wheel and tire sizing of the vehicle to convert vehicle speed and traction force time trajectories into propulsion system speed and torque trajectories. [5] The method (900) of claim 4, wherein characterizing each of the two or more road segments comprises calculating an energy characterization of the propulsion system taking into account the propulsion system output speed and the torque trajectories. [6] The method (900) of claim 1, wherein the characterization of the two or more road segments is provided from a routing engine executed by the processor to a drive controller executed by a second processor. [7] The method (900) of claim 1, wherein the optimized energy path is provided based on short-term energy characteristics and long-term energy characteristics.

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