In-SITU vehicle velocity profile prediction with connectivity for powertrain optimization

Abstract

A vehicle includes a controller communicably coupled to an engine of the vehicle. The controller includes at least one processor and at least one memory device storing instructions therein that, when executed by the at least one processor, cause the at least one processor to: receive information indicative of a velocity profile of the vehicle; receive a plurality of communication messages; identify a trigger condition in one or more of the received communication messages; responsive to identifying the trigger condition, determine a change in the velocity profile of the vehicle over a predefined prediction horizon; determine a control strategy based on the determined change in velocity; and implement the control strategy for a powertrain of the vehicle.

Description

IN-SITU VEHICLE VELOCITY PROFILE PREDICTION WITH CONNECTIVITY FOR POWERTRAIN OPTIMIZATIONCROSS-REFERENCE TO RELATED APPLICATIONS[00011 This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 622,047 filed on January 17, 2024, which is incorporated herein by reference in its entirety and for all purposes.TECHNICAL FIELD

[0002] The present disclosure relates to systems and methods for predicting changes to a projected velocity profile of a vehicle over a prediction horizon by translating qualitative or natural language keywords (e.g., relating to traffic, weather, construction, etc.) obtained via connectivity into quantitative adjustments to determine the projected vehicle velocity over the prediction horizon. The predicted changes to the projected velocity profile of the vehicle over the prediction horizon are then used to optimize powertrain management of the vehicle.BACKGROUND

[0003] Route look-ahead systems are used to identify certain characteristics ahead of a vehicle, such as road grade and speed limits. With this forward-looking information, operation of the vehicle may be planned / controlled for certain events (e.g., an upcoming uphill or downhill event) to achieve various benefits, such as improving fuel efficiency. Previous look-ahead strategies focus on short-term actions such as instantaneously adjusting vehicle speed and are limited to relatively short look-ahead distances such as less than or approximately two kilometers.SUMMARY

[0004] One embodiment relates to a vehicle. The vehicle includes a controller communicably coupled to an engine of the vehicle. The controller includes at least one processor and at least one memory storing instructions therein that, when executed by the at least one processor, cause the at least one processor to: receive a plurality of communication messages; receive information indicative of a velocity profile of the vehicle; identify a trigger condition in one or more of the received communication messages; responsive to identifying the triggercondition, determine a change in the velocity profile of the vehicle over a predefined prediction horizon; and implement a control strategy for a powertrain of the vehicle.10005] Another embodiment relates to a method for controlling a vehicle. The method includes: receiving, by a controller, a plurality of communication messages ; receiving, by the controller, information indicative of a velocity profile of a vehicle; identifying, by the controller, a trigger condition in one or more of the received communication messages; responsive to identifying the trigger condition, determining, by the controller, a change in the velocity profile of the vehicle over a predefined prediction horizon; and causing, by the controller, the vehicle to implement a control strategy for a powertrain of the vehicle.

[0006] Still another embodiment relates to a system. The system includes at least one powertrain component and at least one processing circuit. The at least one processing circuit is coupled to the at least one powertrain component and includes at least one memory and at least one processor. The at least one processing circuit is operable to receive a plurality of communication messages; receive information indicative of a velocity profile of a vehicle; identify a trigger condition in one or more of the communication messages; determine a change in the velocity profile of the vehicle over a predefined prediction horizon; and implement a control strategy with the at least one powertrain component that causes the at least one powertrain component to perform one or more powertrain actions based on the determined change in the velocity profile of the vehicle over the predefined prediction horizon.

[0007] Numerous specific details are provided to impart a thorough understanding of embodiments of the subject matter of the present disclosure. The described features of the subject matter of the present disclosure may be combined in any suitable manner in one or more embodiments and / or implementations. In this regard, one or more features of an aspect of the invention may be combined with one or more features of a different aspect of the invention. Moreover, additional features may be recognized in certain embodiments and / or implementations that may not be present in all embodiments or implementations.BRIEF DESCRIPTION OF THE FIGURES

[0008] FIG. l is a schematic diagram of a networked vehicle environment including exemplary vehicle connectivity systems, according to an exemplary embodiment.

[0009] FIG. 2 is a schematic diagram of a vehicle within the exemplary networked vehicle environment of FIG. 1, accordingly to an exemplary embodiment.

[0010] FIG. 3 is a schematic diagram of a control system of the vehicle of FIGS. 1-2, according to an exemplary embodiment.

[0011] FIG. 4 is a flow diagram of a method for updating a projected velocity profile of a vehicle over a prediction horizon by translating keywords obtained via connectivity and utilizing such updates to optimize powertrain management, according to an exemplary embodiment.

[0012] FIG. 5 is a schematic diagram of a vehicle receiving a communication message and flagging a trigger condition, according to an exemplary embodiment.DETAILED DESCRIPTION

[0013] Following below are more detailed descriptions of various concepts related to, and implementations of, methods, apparatuses, and systems for updating a projected velocity profile of a vehicle over a prediction horizon by translating certain content, namely one or more predefined keywords (e.g., relating to traffic, weather, construction, etc.), obtained via connectivity into quantitative adjustments to a projected vehicle speed profile and utilizing such adjustments to optimize powertrain management of a vehicle. Previous strategies to optimize powertrain management are primarily based on route information (e.g., road grade, speed limits) and do not account for longer-horizon look-ahead information regarding traffic, weather, road conditions, construction, roadside events, etc. These longer-horizon look-ahead conditions quickly cause inaccuracies in previous look-ahead strategies due to the computational “noise” caused by the events or conditions two miles, five miles, ten miles, etc. ahead of the vehicle and prevent planning in advance for such events. In contrast, the methods, apparatuses, and systems disclosed herein allow for optimization of powertrain management to occur over an extended prediction horizon (e.g., 2 miles ahead of the vehicle, 10 miles ahead of the vehicle, etc.) by utilizing predicted velocity adjustments via the projected velocity or speed profile to achieve, substantially achieve, or attempt to achieve associated benefits in emissions compliance / goals, fuel economy, component durability, and / or other objectives.

[0014] Referring to the Figures generally, the various embodiments disclosed herein relate to systems, apparatuses, and methods for predicting a velocity profile of a vehicle over a prediction horizon. The prediction horizon may include a predefined distance, time, designated route, known operation routines, etc. Qualitative natural language keywords (relating to traffic, weather, construction, roadside events, etc.) obtained via connectivity are translated into quantitative adjustments to projected vehicle speed, which are then used to optimize powertrain management.

[0015] As described herein, a target velocity profile of a vehicle for a given route may be adjusted in-situ (on-board during operation) to accommodate look-ahead information received via V2X / C-V2X connectivity regarding traffic, weather, construction, roadside events, and other conditions that affect vehicle operation at a future point in time and / or at some extended distance (e.g., 5 miles, 10 miles, 20 miles) ahead of the vehicle. A control system or controller of the vehicle may receive look-ahead information that includes data elements which comprise natural language keywords or phrases corresponding to a route / roadside condition (e.g., “stalled vehicle,” “serious accident,” “oil spill,” “heavy traffic,” etc.). For example, information received such as a “stalled vehicle” phrase may indicate a need to slow down at a future point in the route for some given distance. Likewise, information received such as a “snow-cleared” keyword may indicate the possibility of increasing speed during another portion of the route. Based on the keywords received, the target velocity profile of a vehicle may be increased, decreased, or otherwise adjusted by some absolute or relative quantity during relevant parts of the route.

[0001] In some embodiments, the amount of speed reduction is based on studies performed on the effect of traffic calming measures on vehicle speed, in which the presence of speed tables, chicanes, lane narrowing, and the like reduced vehicle speed by various amounts (e.g., up to 50%, 56%, and 36%, respectively). Speed adjustments due to other scenarios, such as heavy traffic, may be based on traffic flow theory and the relationship between traffic density and vehicle speed. By translating qualitative data (e.g., keywords and phrases) into quantitative predicted velocity adjustments, powertrain control strategies may be selected based on the predicted change in vehicle velocity to improve or meet various powertrain objectives. In particular, the controller may select various powertrain objectives, such as fuel economy, emissions compliance, component durability, and the like, then affect one or more of the powertrain control strategies in furtherance of the selected objective. For example,after receiving keywords or phrases indicating a traffic jam five miles ahead of the vehicle, the controller may translate the keywords into a reduction of vehicle speed by a certain percentage in five miles; then, because traffic jams tend to increase emissions, the system may select a “fuel emissions” objective, and cause an aftertreatment regeneration event in advance of the traffic jam to prevent increased emissions upon reaching the traffic jam. Various other powertrain objectives, control strategies, and exemplary scenarios are discussed herein and are contemplated in this disclosure.100171 As utilized herein, the term “predicting” and like terms are used to refer to determining a future value based on data (e.g., sensor data, historical sensor data, real-time sensor data, etc.). In some embodiments, predicting the future value may be performed using one or more models (e.g., statistical models, artificial intelligence models, machine learning models, etc.). For example, predicating an adjustment in vehicle velocity or a change in a vehicle velocity profile may include using data, such as V2X / C-V2X connectivity (V2 everything, cellular V to everything such as pedestrians, infrastructure, networks, vehicles), historical sensor data and / or real-time sensor data, with a model, algorithm, look-up table, or the like to determine a future quantitative change to vehicle velocity over a prediction horizon (e.g., reduced speed by 36% in 10 miles, stop-and-go conditions in 15 miles lasting for 10 miles, etc.). Before turning to the Figures, which illustrate certain exemplary embodiments in detail, it should be understood that the present disclosure is not limited to the details or methodology set forth in the description or illustrated in the Figures. It should also be understood that the terminology used herein is for the purpose of description only and should not be regarded as limiting.|0018| Turning now to Figure 1, a networked vehicle environment 10 is shown, according to an example embodiment. The networked vehicle environment 10 may include one or more vehicles 100, a network 30, one or more external systems 200, and a remote computing system 250. The one or more vehicles 100 may be structured as a fleet 110 of vehicles 100, in some embodiments. The environment 10 is structured to allow the exchange of information or data (e.g., communications) between a vehicle, such as vehicle 100, and one or more other components or other vehicles, such as other vehicles 100, the fleet 110 of one or more vehicles 100, remote computing systems 250, external systems 200 which may include roadside units (RSUs) 25, one or more vehicle on-board units (OBUs, such as telematics systems, GPS devices, etc.), and the like. In this regard and for example, the vehicle 100 mayinclude telematics systems that facilitate the acquisition and transmission of data acquired regarding the operation of the vehicle 100.100191 According to an example embodiment, the network 30 wirelessly communicably couples the remote computing system 250 to the vehicle 100 and the external systems 200. In an alternative embodiment, one or more of the external systems 200 are integrated into the remote computing system 250. In other embodiments, a wired network may be used at least in part.

[0020] The remote computing system 250 may be a computing system or device that includes one or more processing circuits, network interfaces, and other computing systems and devices that couple to the network 30 and enables the exchange of information between the remote information source and a vehicle 100. Thus, the remote computing system 250 may be a source of information that is remote from the vehicle 100 and may include or be a remote server / computing system (e.g., a fleet operator and its computing system), a mobile computing device (e.g., mobile phone, tablet computer, desktop computer, etc.), etc. In some embodiments, the remote computing system 250 may include cellular towers, cloud computing systems, and / or the like. In this way, one or more processes or computations of this disclosure may be performed and / or communicated by the remote computing system 250, for example, to reduce the on-board processing power required by the vehicle 100. Thus, the vehicle 100 may form a V-2-X relationship with the remote information sources (e.g., remote computing system 250, external systems 200, RSUs 25) where “X” can be another vehicle 100, a remote server, pedestrian devices, infrastructure, etc. The remote computing system 250 may be owned by, associated with, managed by, or otherwise controlled by a provider. The provider institution or entity may provide various products and / or services, such as diagnostic services, a component manufacturer (e.g., of engines, aftertreatment systems, etc.), and so on.

[0021] As shown in FIG. 1, the external systems 200 include a route look-ahead system 210, a weather system 220, and a global positioning satellite (GPS) system 230. In some embodiments, the external systems 200 include fewer, more, or different systems. The route look-ahead system 210 may be a remote computing system that is structured to acquire route look-ahead data including static information indicative of road / route parameters ahead of a respective vehicle 100 that substantially do not change or substantially change with time. In other embodiments, the route look-ahead system 210 may be included in the vehicle 100 orgeographically dispersed across the one or more systems of the networked vehicle environment 10. For example, the route look-ahead system 210 or a part thereof may be included in at least part of the controller 140. The road / route parameters may include information regarding road function class (e.g., freeway / interstate, arterial roads, collectors, local roads, unclassified roads, etc.), speed limits, road grade, road slope, road curvature, bridges, fuel stations, number of lanes, presence of emergency vehicles, traffic conditions, road surface condition, and other information indicative of the velocity profile of a vehicle over the prediction horizon (e.g., 2 miles, 5 miles, etc.). Additionally, the road / route parameters may include information that changes with time such as weather conditions, traffic conditions, the presence of construction or emergency events occurring along a route, and the like.

[0022] The weather system 220 may be a remote computing system that is structured to acquire weather data including dynamic information indicative of weather conditions ahead of the respective vehicle 100. The weather conditions may include information indicative of road surface conditions (e.g., wet, icy, snowy, dry, snow-cleared, etc.), weather (e.g., rain, snow, temperature, humidity, etc.), and other weather-related information ahead of the respective vehicle 100.10023 ] The GPS system 230 may be a remote computing system that is structured to (i) receive information regarding a current location and a desired destination of a respective vehicle 100 and (ii) generate GPS data that facilitates determining one or more routes from the current location and the desired destination. In some embodiments, a route of the vehicle 100 is predicted by extrapolating a current location of the vehicle 100 relative a finite distance ahead of the vehicle 100 (e.g., the controller 140 assumes the vehicle 100 will continue traveling on the road the vehicle is currently on if there are no roads to turn onto for X distance). In other embodiments, the route of the vehicle 100 may be received from the GPS system 230 (e.g., a route generated by the GPS system 230 between a starting location and a destination received from the user may define the route). In still further embodiments, the route of the vehicle 100 may be predicted based on historical / routine operations associated with the vehicle 100, such as bus routes which follow a set route at an approximate time of day.

[0024] Referring now to FIG. 2, the vehicle 100 of the networked environment 10 is shown, according to an example embodiment. The vehicle 100 may include a powertrain system118, vehicle subsystems 120, operator input / output (I / O) device 130, a controller 140, a telematics unit 145, and one or more sensors 150 where the controller 140 is communicably coupled to each of the aforementioned components. The powertrain system includes an engine 101 and a transmission 102. The transmission 102 is operatively coupled to a drive shaft 103, which is operatively coupled to a differential 104, where the differential 104 transfers power output from the engine 101 to the final drive 105 to propel the vehicle 100. The vehicle 100 may be any type of on-road or off-road vehicle including, but not limited to, road sweeper vehicles, road sprinkler vehicles, refuse transfer vehicles, wheel-loaders, forklift trucks, line-haul trucks, mid-range trucks (e.g., pick-up truck, etc.), sedans, coupes, tanks, airplanes, boats, and any other type of vehicle.

[0025] The engine 101 may be an internal combustion engine (e.g., compression-ignition or spark-ignition), such that it can be powered by any fuel type (e.g., diesel, ethanol, gasoline, hydrogen, etc.). The engine 101 includes one or more cylinders and associated pistons. In the example shown, the engine 101 is a diesel -powered compression-ignition engine. Air from the atmosphere is combined with fuel, and combusted, to produce power for the vehicle. Combustion of the fuel and air in the compression chambers of the engine 101 produces exhaust gas that is operatively vented to an exhaust pipe and to the exhaust aftertreatment system. In other embodiments, the vehicle 100 is an all-electric vehicle (EV) or hybridelectric vehicle (HEV). For example, the vehicle 100 may include a plug-in fuel cell (FC), plug-in hybrid, battery EV (BEV), range extended EV (REEV), diesel-electric, non-plug-in FC, non-plug-in EV, or the like.

[0026] The transmission 102 may be structured as any type of transmission, such as a continuous variable transmission, a manual transmission, an automatic transmission, an automatic-manual transmission, a dual clutch transmission, and so on. Accordingly, as transmissions vary from geared to continuous configurations (e.g., continuous variable transmission), the transmission 102 may include a variety of settings (gears, for a geared transmission) that affect different output speeds based on an input speed received thereby. Like the engine 101 and the transmission 102, the drive shaft 103, the differential 104, and / or the final drive 105 may be structured in any configuration dependent on the application (e.g., the final drive 105 is structured as wheels in an automotive application and a propeller in a boat application, etc.). Further, the drive shaft 103 may be structured as any type ofdriveshaft including, but not limited to, a one-piece, two-piece, and a slip-in-tube driveshaft based on the application.

[0027] In some embodiments, the vehicle subsystems 120 may include components including mechanically driven or electrically driven vehicle components (e.g., HVAC system, lights, pumps, fans, etc.). The vehicle subsystems 120 may also include an exhaust aftertreatment system in exhaust-gas receiving communication with the engine 101. The aftertreatment system 121 may include a diesel particulate filter (DPF) 122, a diesel oxidation catalyst (DOC) 123, a selective catalytic reduction (SCR) system 124, and an ammonia slip catalyst (ASC) 125. The DOC 123 is structured to receive the exhaust gas from the engine 101 and to oxidize hydrocarbons and carbon monoxide in the exhaust gas. The DPF 122 is arranged or positioned downstream of the DOC 123 and structured to remove particulates, such as soot, from exhaust gas flowing in the exhaust gas stream. The DPF 122 includes an inlet, where the exhaust gas is received, and an outlet, where the exhaust gas exits after having particulate matter substantially filtered from the exhaust gas and / or converting the particulate matter into carbon dioxide. In some implementations, the DPF 122 or other components may be omitted. For example, in a hydrogen internal combustion engine, the components of the aftertreatment system may be different. Additionally, although a particular arrangement is shown for the aftertreatment system 121 in FIG. 2, the arrangement of components within the aftertreatment system 121 may be different in other embodiments (e.g., the DPF 122 positioned downstream of the SCR 124 and ASC 125, one or more components omitted or added, etc.).

[0028] The aftertreatment system 121 may further include a reductant delivery system which may include a decomposition chamber (e.g., decomposition reactor, reactor pipe, decomposition tube, reactor tube, etc.) to convert a reductant into ammonia. The reductant may be, for example, urea, diesel exhaust fluid (DEF), Adblue®, a urea water solution (UWS), an aqueous urea solution (e.g., AUS32, etc.), and other similar fluids. A diesel exhaust fluid (DEF) is added to the exhaust gas stream to aid in the catalytic reduction. The reductant may be injected upstream of the SCR 124 generally (or in particular, the SCR catalyst) by a DEF doser such that the SCR catalyst receives a mixture of the reductant and exhaust gas. The reductant droplets then undergo the processes of evaporation, thermolysis, and hydrolysis to form gaseous ammonia within the decomposition chamber, the SCR catalyst, and / or the exhaust gas conduit system, which leaves the aftertreatment system 121. The aftertreatment system 121 may further include an oxidation catalyst (e.g., the DOC 123)fluidly coupled to the exhaust gas conduit system to oxidize hydrocarbons and carbon monoxide in the exhaust gas. In order to properly assist in this reduction, the DOC 123 may be required to be at a certain operating temperature. In some embodiments, this certain operating temperature is approximately between 200-500 °C. In other embodiments, the certain operating temperature is the temperature at which the conversion efficiency of the DOC 123 exceeds a predefined threshold (e.g., the conversion of HC to less harmful compounds, which is known as the HC conversion efficiency).

[0029] The SCR 124 is configured to assist in the reduction of NOx emissions by accelerating a NOx reduction process between the ammonia and the NOx of the exhaust gas into diatomic nitrogen and water. If the SCR catalyst is not at or above a certain temperature, the acceleration of the NOx reduction process is limited, and the SCR 124 may not be operating at a level of efficiency to meet regulations. In some embodiments, this certain temperature is approximately 200-600°C. The SCR catalyst may be made from a combination of an inactive material and an active catalyst, such that the inactive material (e.g., ceramic substrate) directs the exhaust gas towards the active catalyst, which is any sort of material suitable for catalytic reduction (e.g., metal exchanged zeolite (Fe or Cu / zeolite), base metals oxides like vanadium, molybdenum, tungsten, etc.).10030 ] When ammonia in the exhaust gas does not react with the SCR catalyst (either because the SCR 124 is below operating temperature or because the amount of dosed ammonia greatly exceeds the amount of NOx), the unreacted ammonia may bind to the SCR catalyst, becoming stored in the SCR 124. This stored ammonia is released from the SCR 124 as the SCR 124 warms, which can cause issues if the amount of ammonia released is greater than the amount of NOx passing through (e.g., more ammonia than needed for the amount of NOx is dosed, which can lead to ammonia slip). In some embodiments, the ASC 125 (which also may be referred to as and / or operate as an ammonia oxidation catalyst (AMOX)) is included and structured to address ammonia slip by removing at least some excess or unreacted ammonia from the treated exhaust gas before the treated exhaust is released into the atmosphere. As exhaust gas passes through the ASC 125, some of the unreacted ammonia (e.g., unreacted with NOx) remaining in the exhaust gas is partially oxidized to NOx, which then consequently reacts with the remaining unreacted ammonia to form N2 gas and water. However, similar to the SCR catalyst, if the ASC 125 is not at or above a certain temperature, the acceleration of the NH3 oxidization process is limited and the ASC 125 may not beoperating at a level of efficiency to meet regulations or desired parameters. In some embodiments, this certain temperature is approximately 250-300°C.

[0031] Referring still to FIG. 2, an operator input / output (I / O) device 130 is also shown. The operator I / O device 130 may be coupled to the controller 140, such that information may be exchanged between the controller 140 and the I / O device 130, wherein the information may relate to one or more components of FIG. 1 or determinations (described below) of the controller 140. The operator I / O device 130 enables an operator of the vehicle 100 to communicate with the controller 140 and one or more components of the environment 10 of FIG. 1. For example, the operator input / output device 130 may include, but is not limited to, an interactive display, a touchscreen device, one or more buttons and switches, voice command receivers, etc. In this way, the operator input / output device 130 may provide one or more indications or notifications to an operator, such as a malfunction indicator lamp (MIL), etc. Additionally, the vehicle may include a port that enables the controller 140 to connect or couple to a scan tool so that fault codes and other information regarding the vehicle may be obtained.

[0032] As discussed herein, the vehicle 100 may receive various signals, data elements, and / or communications via one or more communications standards. In this way and consistent with various communications standards, the vehicle 100 and / or the controller 140 may receive communication messages comprising one or more trigger conditions indicative of a route condition, event, or the like that may affect vehicle speed over a prediction horizon (e.g., at some section of the route within the upcoming 10 miles, within the upcoming 30 miles, within the next 4 minutes, etc.). A trigger condition refers to a predefined condition, situation, occurrence, or potential occurrence that is determined to potentially affect a speed of the vehicle. Trigger conditions may be identified based on the receipt of natural language keywords and / or phrases present in one or more communications standards. For example, such standards may include SAE J2540-2 (see SAE J2540-2, dated February 2002, revised December 2020, and titled ITIS Phrase Lists (International Traveler Information Systems), which is incorporated herein by reference in its entirety) and SAE J2735 (see SAE J2735, dated September 2015, revised March 2016, and titled Dedicated Short Range Communications (DSRC) Message Set Dictionary, which is incorporated herein by reference in its entirety).

[0033] Depending on the communication message received and the applicable standard, the controller 140 may be configured to detect a trigger condition from the received communication message(s) such as the presence of one or more predefined natural language keywords and / or phrases relevant to the velocity profile of the vehicle 100. Additionally, in some embodiments, the trigger condition may be based on a time, a distance, and / or a condition internal to the controller 140 that causes the controller 140 to detect, after a predefined condition / trigger, whether one or more communication messages contains a natural language keyword indicative of a change or a potential for a change in the velocity profile. For example, the controller 140 may determine whether received communication messages alter / indicate a high-medium-low traffic state / occurrence every predefined time interval (e.g., 5-minute interval) after receipt of every Xth (e.g., 10th) communication message, at the passage of a time / distance interval, or the like. The natural language keywords and / or phrases may include single words, multiple words, strings of text, and / or alpha or alpha-numeric phrases and / or words (e.g., “closure,” “ice on road,” “heavy -traffic-ahead,” “major-flood,” “major-flood (3074),” “3074”, “lava-flow,” etc. For example, a trigger condition may include receipt of a data element from an emergency vehicle via V2V connection compliant with the SAE J2735 standard including the natural language keywords and / or phrases such as “SirenlnUse ::= ENUMERATED {inUse (2)},” indicating that the emergency vehicle is currently operating its siren at a certain distance from the vehicle 100. The controller 140 may translate the qualitative keywords (e.g., “SirenlnUse”) to a quantitative velocity adjustment (e.g., in 5 miles, speed will reduce by 40%), for example, by utilizing an algorithm, look-up table, machine learning model, or the like that analogizes the presence of an in-use emergency vehicle to a lane narrowing event or a lane closure.

[0034] In another example, a communication message may include either a text, a numeric value, or both text and a numeric value to indicate one or more conditions or events (e.g., roadside conditions). The controller 140 may be configured to detect text, a keyword, a numeric sequence, or any combination thereof in a communication message and associate the detected data with natural language indicative of a velocity profile and / or a velocity profile adjustment. For example, for a communication message containing “major-flood (3074),” the controller 140 may be configured to detect data such as “flood” and / or may be configured to detect “3074” and associate the integer “3074” with the condition of flooding, or perform other suitable associations / determinations based on the communication message received or a group of received communication messages. This analysis may similarly occur without thenatural language phrase of “major flood,” thereby enabling the controller 140 to associate various numeric sequences with various events / occurrences. As an example, a look-up table may be stored, retrieved, and used by the controller 140 to associate various numeric messages with various conditions / events that may impact / affect the velocity profile of the vehicle.

[0035] Additionally, after predicting the change / effect on the velocity profile, the controller 140 may then select a control strategy, determine an associated powertrain control action, and cause the powertrain control action. For example, if the vehicle 100 battery is low, the controller 140 may select a “component durability” control strategy, and, given the upcoming decrease in power demand, determine an associated control action of “operate engine 101 at high power within the next 3 miles” to charge the battery with the excess power before reaching the portion of the route containing the predicted slow-down event.

[0036] Thus, and depending at least on one or more of (1) the current operating parameters or expected future operating parameters of the vehicle 100, (2) the current velocity profile, (3) a severity level or qualitative value associated with one or more detected trigger conditions, (4) the number of trigger conditions identified, or (5) the corresponding predicted effect on the velocity profile, the controller 140 may, for example, select one or more control strategies for various portions along the route / prediction horizon. The controller 140 may then select corresponding control actions and initiate / vary the powertrain references of the vehicle 100 over time to achieve the benefit associated with the control strategy.

[0037] As the components of FIG. 2 are shown to be embodied in the vehicle 100, the controller 140 may be structured as one or more electronic control units (ECU). The function and structure of the controller 140 is described in greater detail in FIG. 3. The controller 140 may be separate from or included with at least one of a transmission control unit, an exhaust aftertreatment control unit, a powertrain control module, an engine control module, etc. In one embodiment, the components of the controller 140 are combined into a single unit. In another embodiment, one or more of the components of the controller 140 may be geographically dispersed throughout the environment 10 and / or the vehicle 100. In this regard, various components of the controller 140, discussed below, may be dispersed in separate physical locations, or performed via the remote computing system 250 to disperse / offload the computational complexity and demand across the environment 10.

[0038] The vehicle 100 is also shown to include a telematics unit 145. The telematics unit 145 may be structured as any type of telematics control unit. Accordingly, the telematics unit 145 may include, but is not limited to, a location positioning system (e.g., global positioning system) to track the location of the vehicle (e.g., latitude and longitude data, elevation data, etc.), one or more memory devices for storing the tracked data, one or more electronic processing units for processing the tracked data, and a communications interface for facilitating the exchange of data between the telematics unit 145 and one or more remote devices (e.g., a provider / manufacturer of the telematics device, etc.). In this regard, the communications interface may be configured as any type of mobile communications interface or protocol including, but not limited to, Wi-Fi, WiMAX, Internet, Radio, Bluetooth, Zigbee, satellite, radio, Cellular, GSM, GPRS, LTE, and the like. The telematics unit 145 may also include a communications interface for communicating with the controller 140 of the vehicle 100. The communication interface for communicating with the controller 140 may include any type and number of wired and wireless protocols (e.g., any standard under IEEE 802, etc.). For example, a wired connection may include a serial cable, a fiber optic cable, an SAE J1939 bus, a CAT5 cable, or any other form of wired connection. In comparison, a wireless connection may include the Internet, Wi-Fi, Bluetooth, Zigbee, cellular, radio, etc. In one embodiment, a controller area network (CAN) bus including any number of wired and wireless connections provides the exchange of signals, information, and / or data between the controller 140 and the telematics unit 145. In other embodiments, a local area network (LAN), a wide area network (WAN), or an external computer (for example, through the Internet using an Internet Service Provider) may provide, facilitate, and support communication between the telematics unit 145 and the controller 140. In still another embodiment, the communication between the telematics unit 145 and the controller 140 is via the unified diagnostic services (UDS) protocol. All such variations are intended to fall within the spirit and scope of the present disclosure.

[0039] The telematics unit 145 may also include a device or system that is installed within or located on the vehicle 100 that facilitates communication, monitoring, and / or control of the vehicle 100. The telematics unit 145 may send and / or receive communications or messages to / from external systems or networks. This can include sending and receiving data related to location, speed, status, and more. The telematics unit 145 may include Vehicle-to- Infrastructure (V2I) Communication to facilitate communication between vehicles and roadside infrastructure, Vehicle-to-Vehicle (V2V) communication devices to facilitate directcommunication between vehicles, etc. In some embodiments, the functionalities or features of the telematics unit 145 may be included, in whole or in part, in the controller 140. For example, the controller 140 may monitor and collect look-ahead information such as road grade, traffic, weather, speed limit data, and the like. The controller 140 may receive data, messages, and other communications comporting to various communications standards. For example, in some embodiments, the controller 140 may participate in Dedicated Short Range Communication (DSRC) messaging (e.g., J2375 messaging, SAE2375 messaging, etc.) that include predefined fields and data elements such as “icy weather, “traffic jam,” “light traffic,” “security-check-point,” “speed-checked-by-radar,” “sportingEvents,” and the like. The controller 140 may detect the presence of these keywords as trigger conditions that may warrant adjustment of the velocity profile and the selection and implementation of one or more powertrain control actions.

[0040] Still referring to FIG. 2, as also shown, sensors 150 are included in the vehicle 100. In some embodiments, the sensors 150 may be coupled to the controller 140, such that the controller 140 can monitor and acquire data indicative of a current operating parameter of the vehicle 100. The current operating parameters may include a state of charge of a battery, a temperature of the aftertreatment system 121, an emissions level / amount, a fuel consumption rate, a reductant dosing rate, a power split, a temperature of the engine, an engine fluid consumption rate, or the like. The sensors 150 may include sensors positioned and / or structured to monitor operating characteristics or parameters of various components of the vehicle 100. By way of example, the sensors 150 may include a position sensor structured to facilitate monitoring the position of the accelerator (e.g., accelerator pedal, accelerator throttle, etc.) and / or the brake (e.g., brake pedal, brake lever, etc.) of the vehicle 100. The sensors 150 may include a speed sensor structured to facilitate monitoring and / or acquire data indicative of a speed of the vehicle 100 and / or the engine 101 speed that may be used to determine the speed of the vehicle 100. The sensors may include radar, LIDAR detection systems, camera systems, and other systems for monitoring or collecting data on the operating state of the vehicle 100 and its surroundings. For example, a camera system of the vehicle 100 may be capable of reading / interpreting visual indicators (e.g., signs, speed limits, lane markings, lane change signs / barrels, look ahead objects, adjacent vehicles on the front / rear / side of the vehicle, on-coming vehicles, etc.).

[0041] The sensors 150 may include aftertreatment sensors (e.g., NOx sensors, temperature sensors, etc.) structured to acquire data / information indicative of and / or monitor the temperature of components of the exhaust aftertreatment system 121, the temperature of the exhaust gases, and / or the composition of the exhaust gasses. The sensors 150 may include sensors structured to acquire data indicative of a torque and / or power output of the primary driver (e.g., the engine 101). The sensors 150 may also include sensors structured to facilitate determining a current transmission gear selection of the transmission 102. Other sensors 150 may monitor engine coolant temperatures, oil temperatures, vehicle state-of-charge, electric motor speed, tire pressure, tire temperature, road angularity, ambient temperature, service brake status, gear selection, transmission fluid temperature, and the like. In this way, the controller 140 may receive data (e.g., current vehicle operating parameters) from which to select an appropriate control strategy given the current operating state of the vehicle 100 and the predicted velocity profile over the prediction horizon.

[0042] The controller 140 is structured to control, at least partly, the operation of the vehicle 100 and associated sub-systems, such as the engine 101, the operator input / output (I / O) device 130, the aftertreatment system 121, etc. For the aftertreatment system 121, the controller 140 is structured to control one or more components of the aftertreatment system 121 to achieve certain operating parameters. For example, the controller 140 may control a heater or DEF doser of the aftertreatment system 121 to increase a conversion efficiency of the SCR 124 to achieve a particular value for NOx output, begin a regeneration operation, or the like. Similarly, for the engine 101, the controller 140 may control a power split, IC engine operation mode, heating strategy, etc. The controller 140 may also be configured to manage battery charge, fuel consumption rate, engine temperatures / air intake, battery temperature, external heater controls, and other various powertrain references. Communication between and among the components may be via any number of wired or wireless connections. For example, a wired connection may include a serial cable, a fiber optic cable, a CAT5 cable, or any other form of wired connection. In comparison, a wireless connection may include the Internet, Wi-Fi, cellular, radio, etc. In one embodiment, a controller area network (CAN) bus provides the exchange of signals, information, and / or data. The CAN bus includes any number of wired and wireless connections. Because the controller 140 is communicably coupled to the systems and components of FIGS. 1-2, the controller 140 is structured to receive data from one or more of the components shown in FIG. 1. The structure and function of the controller 140 is further described in regard to FIG. 3.

[0043] Referring now to FIG. 3, a schematic diagram of the controller 140 of the vehicle 100 of FIG. 2 along with certain other systems, is shown, according to an example embodiment. As shown in FIG. 3, the controller 140 may include a processing circuit 302 having a processor 304 and a memory 306, a velocity profile circuit 320, a detection circuit 322, a prediction circuit 324, an optimization circuit 326, and a communications interface 310. The controller 140 is configured or structured to receive / generate information regarding operation of the vehicle 100 and a velocity profile for the vehicle 100. The controller 140 is also configured to detect trigger conditions from signals received by the vehicle 100 indicative of upcoming route conditions and events that may affect the vehicle velocity profile. The controller 140 is also configured to, after detecting the trigger condition, predict / calculate a corresponding change / effect upon the velocity profile of the vehicle 100 and to select a control strategy and determine if a powertrain control action is feasible and / or warranted in response to the predicted change in the velocity profile of the vehicle 100. If so, the controller 140 is configured or structured to cause the powertrain control action.

[0044] In this way, the controller 140 may “optimize” performance of one or more components of the vehicle 100, such as the powertrain system 118. By “optimizing,” the controller 140 may determine a baseline operating parameter then meter one or more conditions related to the operating parameter such that the operating parameter remains at or above a threshold. For example, “optimizing” fuel economy by the controller 140 may include determining a baseline fuel consumption rate without implementing a control strategy and / or control action, then metering fuel injections so that speed of the vehicle 100 is limited and fuel economy remains at or above a threshold fuel consumption rate. Similarly, for hybrid vehicle, the controller 140 may optimize or improve vehicle state of charge (SOC) by determining a baseline charge and preventing the baseline charge from exceeding a calculated amount of amperes, thereby maintaining the SOC and preventing the battery from draining below a desired SOC threshold before a desired point in the route and / or over the prediction horizon.

[0045] In one configuration, the velocity profile circuit 320, the detection circuit 322, the prediction circuit 324, and the optimization circuit 326 are embodied as machine or computer-readable media (e.g., instructions) that is executable by a processor, such as processor 304. As described herein and amongst other uses, the machine-readable media facilitates performance of certain operations to enable reception and transmission of data. Forexample, the machine-readable media may provide an instruction (e.g., command, etc.) to, e.g., acquire data. In this regard, the machine-readable media may include programmable logic that defines the frequency of acquisition of the data (or, transmission of the data). The computer readable media instructions may include code, which may be written in any programming language including, but not limited to, Java or the like and any conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program code may be executed on one processor or multiple remote processors. In the latter scenario, the remote processors may be connected to each other through any type of network (e.g., CAN bus, etc.).

[0046] In another configuration, the velocity profile circuit 320, the detection circuit 322, the prediction circuit 324, and the optimization circuit 326 are embodied as hardware units, such as electronic control units. As such, the velocity profile circuit 320, the detection circuit 322, the prediction circuit 324, and the optimization circuit 326 may be embodied as one or more circuitry components including, but not limited to, processing circuitry, network interfaces, peripheral devices, input devices, output devices, sensors, etc. In some embodiments, the velocity profile circuit 320, the detection circuit 322, the prediction circuit 324, and the optimization circuit 326 may take the form of one or more analog circuits, electronic circuits (e.g., integrated circuits (IC), discrete circuits, system on a chip (SOCs) circuits, microcontrollers, etc.), telecommunication circuits, hybrid circuits, and any other type of “circuit.” In this regard, the velocity profile circuit 320, the detection circuit 322, the prediction circuit 324, and the optimization circuit 326 may include any type of component for accomplishing or facilitating achievement of the operations described herein.

[0047] For example, a circuit as described herein may include one or more transistors, logic gates (e.g., NAND, AND, NOR, OR, XOR, NOT, XNOR, etc ), resistors, multiplexers, registers, capacitors, inductors, diodes, wiring, and so on). The velocity profile circuit 320, the detection circuit 322, the prediction circuit 324, and the optimization circuit 326 may also include programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like. The velocity profile circuit 320, the detection circuit 322, the prediction circuit 324, and the optimization circuit 326 may include one or more memory devices for storing instructions that are executable by the processor(s) of the velocity profile circuit 320, the detection circuit 322, the prediction circuit 324, and the optimization circuit 326. The one or more memory devices andprocessor(s) may have the same definition as provided below with respect to the memory 306 and processor 304. In some hardware unit configurations and as alluded to above, the velocity profile circuit 320, the detection circuit 322, the prediction circuit 324, and the optimization circuit 326 may be geographically dispersed throughout separate locations in the vehicle 100 and / or the environment 10. Alternatively, and as shown, the velocity profile circuit 320, the detection circuit 322, the prediction circuit 324, and the optimization circuit 326 may be embodied in or within a single unit / housing, which is shown as the controller 140. Further, the determinations of the velocity profile circuit 320, the detection circuit 322, the prediction circuit 324, and the optimization circuit 326 may be combined and carried out by any one or a combination of different components and in an order other than that described above.

[0048] In the example shown, the controller 140 includes the processing circuit 302 having the processor 304 and the memory 306. The processing circuit 302 may be structured or configured to execute or implement the instructions, commands, and / or control processes described herein with respect to the velocity profile circuit 320, the detection circuit 322, the prediction circuit 324, and the optimization circuit 326. The depicted configuration represents the velocity profile circuit 320, the detection circuit 322, the prediction circuit 324, and the optimization circuit 326 as machine or computer-readable media storing instructions. However, as mentioned above, this illustration is not meant to be limiting as the present disclosure contemplates other embodiments where the respective circuits or at least one circuit of the same is configured as a hardware unit. All such combinations and variations are intended to fall within the scope of the present disclosure.

[0049] The at least one processor 304 may be implemented as a single- or multi-chip processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A processor may be a processor, a microprocessor, a group of processors, etc. A processor also may be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. In some embodiments, the one or more processors may be shared by multiple circuits (e.g., the velocity profile circuit 320, the detection circuit 322, the prediction circuit 324, and the optimization circuit 326 may comprise or otherwise share the sameprocessor which, in some example embodiments, may execute instructions stored, or otherwise accessed, via different areas of memory). Alternatively or additionally, the one or more processors may be structured to perform or otherwise execute certain operations independent of one or more co-processors. In other example embodiments, two or more processors may be coupled via a bus to enable independent, parallel, pipelined, or multithreaded instruction execution. All such variations are intended to fall within the scope of the present disclosure.

[0050] The at least one memory 306 (e.g., memory, memory unit, storage device) may include one or more devices (e.g., RAM, ROM, Flash memory, hard disk storage) for storing data and / or computer code for completing or facilitating the various processes, layers and modules described in the present disclosure. The memory 306 may be communicably connected to the processor 304 to provide computer code or instructions to the processor 304 for executing at least some of the processes described herein. Moreover, the memory 306 may be or include tangible, non-transient volatile memory or non-volatile memory. Accordingly, the memory 306 may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described herein.

[0051] The communications interface 310 may include any combination of wired and / or wireless interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wire terminals) for conducting data communications with various systems, devices, or networks structured to enable in-vehicle communications (e.g., between and among the components of the vehicle) and out-of-vehicle communications (e.g., with a remote server). For example and regarding out-of-vehicle / system communications, the communications interface 310 may include an Ethernet card and port for sending and receiving data via an Ethernet-based communications network and / or a Wi-Fi transceiver for communicating via a wireless communications network. The communications interface 310 may be structured to communicate via local area networks or wide area networks (e.g., the Internet) and may use a variety of communications protocols (e.g., IP, LON, Bluetooth, ZigBee, radio, cellular, near field communication). Furthermore, the communications interface 310 may work together or in tandem with the telematics unit 145 to communicate with other vehicles (e.g., in the fleet of one or more vehicles 100) or remote computing systems. In some embodiments and as mentioned above, the functionality of the telematics unit 145 may be included in the communications interface co310 to enable various communications. In this way, the telematics unit 145 may be excluded from the vehicle 100 such that the out-of-vehicle communications with, for example, remote computing system 250 are done via the controller 140 (e.g., via the communications interface 310).

[0052] The velocity profile circuit 320 is structured or configured to receive data indicative of a velocity profile of the vehicle 100. The velocity profile may include a projected speed over time, a projected speed at various predefined points for a certain distance (e.g., the next upcoming 20 miles of a route), an average speed at one or more locations along a projected route, or the like. The velocity profile refers to an upcoming likely speed of the vehicle and / or a speed of the vehicle along a predetermined look-ahead horizon which may be distance-based (e.g., over the next 30 miles, etc.) and / or time-based (e.g., over the next 10 minutes, etc.). For example, the velocity profile circuit 320 may be structured to receive a velocity profile from one or more components of the environment 10. In such embodiments, the velocity profile circuit 320 may receive GPS data, route information, map / pathing data from route look-ahead systems 210, preplanned or routine paths of travel (e.g., bus routes, delivery routes, and the like) from remote computing systems 250, or other suitable sources. In other embodiments, the velocity profile circuit 320 may generate some or all of the velocity profile. For example, the velocity profile circuit 320 may collect data indicative of a velocity profile and use such data to extrapolate, estimate, or otherwise generate a velocity profile over a prediction horizon (e.g., 2 miles, 5 miles, 10 miles). The velocity profile circuit 320 may determine a speed of the vehicle (e.g., in miles-per-hour or another metric). The determined speed may indicate a current vehicle speed or an estimated future vehicle speed. For example, the velocity profile circuit 320 may be communicably coupled to the engine system (e.g., via a speed sensor coupled to the engine) to acquire data indicative of a rotational speed of the engine (e.g., in revolutions-per-minute). These data indicative of a velocity profile of the vehicle 100 may also be received from the sensors 150 (e.g., a vehicle speed sensor, radar sensors, road angularity sensors), from the telematics unit 145, the route look-ahead system 210, the weather system 220, the GPS system 230, RSUs 25, or any other relevant internal or external system. The velocity profile circuit 320 calculates anticipated or potential future speeds over a long-term prediction horizon because these future speeds may affect powertrain control, aftertreatment operation, etc. Accordingly, determining and recognizing the likely vehicle speeds over the prediction horizon allows the vehicle 100 to develop and implement robust control strategies that improve engine emissions, exhausttemperatures for emissions, aftertreatment performance, and other operating parameters over an extended time period and / or distance when compared to previous look-ahead systems.10053] The velocity profile circuit 320 may further be configured or structured to determine a “default” or “target speed” associated with a route as a starting velocity profile. The “default” and / or “target speed” for the vehicle 100 refers to a desired travel speed for the vehicle without considering the effect of route look-ahead information upon vehicle travel, and may be set by / designated by a driver of the vehicle (e.g., via a cruise control input), by local laws (e.g., the target speed is equal to a speed limit), and / or by a remote user (e.g., at a fleet command center). For example, if the speed limit is listed as 60 miles-per-hour (mph) and a driver desires to travel at 65 mph based on that speed limit, the velocity profile circuit 320 may determine that the velocity profile of the vehicle 100 may be set to 60 mph (i.e., the speed limit) or to 65 mph (i.e., the driver preference) for a determined length of the route. If the speed limit changes (e.g., speed limit reduces to 50 mph in 10 miles), the velocity profile circuit 320 may designate a separate speed (e.g., 50 mph) as the current speed during that portion of the velocity profile. In this way, the velocity profile may be defined as a projected speed at a given distance from a starting location, a projected speed at a given time of travel, or the like.

[0054] The detection circuit 322 is structured or configured to detect whether one or more trigger conditions are present in one or more communication messages received by the controller 140. As discussed above, the vehicle 100 may periodically, sporadically, constantly, etc. receive communication messages compliant with standard data protocols as the vehicle 100 operates / travels. These standard data protocols may include data elements, components, packets, byte values, or other features that are sent, received, or otherwise communicated via the communication message. For example, the vehicle 100 may receive a message from an RSU 25 located 20 miles from the vehicle 100 that indicates the presence of post-office, lodging, picnic shelter, or the like. The message / data element may include a J2540 ASN.l representation that defines “Structures” with a set of named values and associated integer value. For example, a message received may include “post-office (13070),” “picnic-shelter (13072),” etc.

[0055] The detection circuit 322 may monitor some or all of the communication messages received by the vehicle 100 and identify / detect those messages that contain a trigger condition or trigger conditions. As mentioned above, the trigger condition(s) may beidentified based on analysis of communication messages containing natural language keywords and / or phases to identify situations or occurrences that may alter or be relevant to the velocity profile of a vehicle 100. Specifically, a trigger condition may be the receipt of a communication message that includes keywords, phrases, lines of text, natural language data elements, etc. within that communication message that corresponds to a condition or event that may affect the velocity profile of the vehicle 100. The communication messages may include a predefined data indicator that the controller 140 is configured to associate with a trigger condition. In some embodiments, a communication message may contain a trigger condition even if it has no change on the velocity profile (e.g., a communication message is received that is duplicative of a previously received message and no change is needed to update the velocity profile). For example, a vehicle 100 may receive a message having a “snow-cleared” trigger condition indicating clear roads ten to twenty miles ahead, then receive a message having a “snow cleared” trigger condition from a different source, indicating clear roads twelve to fourteen miles ahead. The controller 140 may determine that the velocity profile is not changed by the second message but may nonetheless identify the second message as a message containing a trigger conditions, in some embodiments.

[0056] Using the example regarding the RSU 25 above, the vehicle 100 may receive a communication message from an RSU 25 located 20 miles from the vehicle 100. The communication message may indicate the presence of a post office near or at the RSU 25. The detection circuit 322 may monitor or otherwise identify receipt of the communication message compliant with a standard (e.g., the J2540 standard) that includes the natural language and associated integer “post-office (13070).” The detection circuit 322 may be preprogrammed to conclude or may determine (e.g., via a look-up table, an algorithm, a machine learning model, or the like) that the location of a post office will likely not affect the velocity profile of the vehicle (e.g., the keyword is not associated with a speed limit, traffic conditions, and the like such that, at the post office or along the route of the vehicle 100, the currently predicted velocity profile will not change, that the effect on the velocity profile given the “post-office” keyword is not predictable with sufficient accuracy, etc.).Accordingly, the controller 140 may determine that the communication message contains no trigger condition, the detection circuit 322 may not associate the communication message with a trigger condition, and / or the detection circuit will not detect a trigger condition within the communication message (e.g., may ignore the communication message).

[0057] Alternatively, and for example, the vehicle 100 may receive another / a different communication message from an RSU 25 located 20 miles from the vehicle. The communication message may indicate the presence of an obstruction on the roadway near or at the RSU 25. The detection circuit 322 may monitor or otherwise identify receipt of the communication message compliant with a standard (e.g., the J2540 standard) that includes the natural language and / or the integer associated with the natural language; for example, the detection circuit 322 may detect one or more of the data fields present in: “Obstruction ::= INTEGER {obstruction-on-roadway (1281)}” such as “obstruction,” “obstruction-on- roadway,” “1281,” etc. The presence of an obstruction on the roadway along the route of the vehicle 20 miles ahead may affect the velocity profile of the vehicle 100. Accordingly, the detection circuit 322 may flag / identify that a communication message containing a trigger condition has been received (e.g., based on a predefined log of trigger conditions, based on an algorithm, based on a machine learning model or other heuristic, etc.). In some embodiments, the detection circuit 322 may identify the specific natural language text, key work, etc. that comprises the trigger condition (e.g., obstruction-on-roadway, obstruction, etc.). For example, the detection circuit 322 may store one or more tables (or lists, databases, etc.) that define (e.g., identify, classify, store, etc.) various data elements within a communication message (e.g., “icy road”, “closure”, “construction”, etc.) as trigger conditions. Further, machine learning models, Al models, or other heuristics may identify trigger conditions based on one or more keywords received within a defined time period. For example, the detection circuit 322 receiving the keywords “hAZMAT-unit,” “medical-rescue-unit,” “hazardous-loads,” “local-drivers-are-recommended-to-avoid-the-area” within a 60 second interval may determine that the communication messages include natural language, data elements, phrases and the like that are trigger conditions based on their content, the proximity in time, and location of their receipt (e.g., the controller 140 may determine, based on the received messages / natural language text, that a hazardous chemical spill may be ahead and to anticipate a slowdown / stop / detour which affects vehicle velocity, therefore trigger conditions are detected).

[0058] After detecting / identifying that a communication message contains a trigger condition, the detection circuit 322 marks, flags, identifies, forwards, or otherwise communicates to the prediction circuit 324 that the identified communication message containing the trigger condition has been received. In some embodiments, the detection circuit 322 may store, copy, and / or log the identified communication messages containing thetrigger condition in one or more databases / memories. In this way, the prediction circuit 324 may receive a batch, group, or multiple communication messages at a time (e.g., may process received messages containing trigger conditions every 5 minutes, once a threshold number of communication messages has been received, etc.). Once flagged, the trigger condition is used by the prediction circuit 324 to determine the quantitative effect of the trigger condition on the vehicle velocity profile. For example, while the detection circuit 322 may detect trigger conditions that likely affect vehicle velocity by some amount, the prediction circuit 324 applies algorithms, heuristics, traffic flow theory, machine learning models, and the like to determine the quantitative value associated with the keyword (e.g., slow down by 30% in 5 miles, speed up by 52% in 10 miles through 14 miles, etc.). In some embodiments, the quantitative velocity adjustment may include a percentage change in velocity, a change in velocity by a predefined (i.e., absolute) or calculated amount (e.g., slow down by 5 mph, increase speed by 2 mph every minute while the vehicle is within an area along the route 10- 12 miles ahead, reduce speed by half of the speed limit, etc.), a change in velocity to a calculated or predefined speed (e.g., reduce speed to 25 mph, increase speed to 37 mph, etc.), or another suitable quantitative adjustment.

[0059] The prediction circuit 324 is configured or structured to predict / determine a quantitative change in and / or an effect on the velocity profile of the vehicle 100 over a prediction horizon based on the received trigger condition(s). As discussed above, the prediction horizon may include a predetermined distance ahead of the vehicle such as the route 5 miles ahead of the vehicle 100, between 2 miles and 20 miles ahead, up to 30 miles ahead, etc. The prediction horizon may also include a time interval such as a travel time ahead of the vehicle 100. For example, the speed profile can be represented as a predicted speed at a certain point at an absolute time (e.g., a time from the start of the route, a time of day, etc.) or a relative time (e.g., a time 5 minutes from the current time). The prediction horizon may also be specific to certain routes or operations of a vehicle 100. For example, the prediction horizon for a city bus may be an entire daily route schedule and the prediction horizon of a long-haul truck may include the route from its pickup location to its delivery location considering the desired delivery time, etc.

[0060] The prediction circuit 324 may utilize the trigger condition, as well as current vehicle 100 operating parameters (e.g., data from sensors 150, engine 101, etc.), route-look ahead information, weather information, and / or the like to determine the expected change invelocity profile over the prediction horizon. The prediction circuit 324 may translate, convert, or otherwise correlate the natural language keywords and / or phrases into quantifiable speed adjustments by associating the event / condition described by the natural language keyword and / or phrase to different traffic geometries, road conditions, and their impact on the velocity of the vehicle 100.

[0061] For example, the prediction circuit 324 may receive keywords including “construction.” The prediction circuit 324 may determine from the natural language keyword and / or phrase that a shift in lane pattern is expected due to the construction keyword being associated with a specific distance / location along the prediction horizon. The prediction circuit 324 may determine that a lane shift due to construction is analogous to chicane lane geometry. Accordingly, the prediction circuit 324 may calculate a speed adjustment based on known or theoretical velocity changes that occur when a driver encounters a chicane (e.g., approximately 56% reduction in speed).

[0062] Similarly, the prediction circuit 324 may receive keywords regarding accident-type conditions ahead, such as “shoulder-closure,” “disabled vehicle,” and / or “accident.” The prediction circuit 324 may determine from the keyword that events associated with the keyword (e.g., blockage on shoulder ahead, vehicle blocking shoulder / portion of road ahead, one lane blocked at accident site, etc.) may occur at a specific distance / location along the prediction horizon because location information may be included with the keyword data structure. The prediction circuit 324 may determine that these events are analogous to lane narrowing. Accordingly, the prediction circuit 324 may calculate a speed adjustment based on known or theoretical velocity changes that occur when a driver encounters lane narrowing or a reduction in lanes (e.g., approximately 50% reduction in speed). The prediction circuit 324 may also apply equations, algorithms, and the like based on traffic theory and the relationship between vehicle speed and traffic density to determine a predicted change in the vehicle velocity profile. For example, the prediction circuit 324 may receive trigger conditions such as “holiday -traffic” or “traffic-lighter-than-normal” and calculate associated decreases and increases, respectively, to the vehicle velocity profile.

[0063] The velocity profile circuit 320 may receive a signal and / or data from the prediction circuit 324 indicating a respective change or update in the velocity profile of the vehicle. The velocity profile circuit 320 may then revise, regenerate, alter, or otherwise update the velocity profile based on the signal and / or data received from the prediction circuit 324. For example,the velocity profile circuit 320 may reduce a predicted speed of 50 mph at 5-10 miles ahead to 25 mph at 5-10 miles ahead. In another example, the velocity profile circuit 320 may generate additional velocity predictions (e.g., previously, velocity was predicted for upcoming 10 miles, after receiving information, the velocity profile circuit 320 predicts vehicle velocity for an additional 10 miles such that the velocity profile covers the upcoming 20 miles).

[0064] The optimization circuit 326 is configured or structured to select one or more control strategies based on the operating parameters of the vehicle 100 and / or the change in the velocity profile of the vehicle 100 over the prediction horizon. The control strategies may be specific powertrain control strategies for one or more predefined goals or properties of the vehicle 100 such as fuel economy, component durability, emissions compliance, handling / performance, etc. The control strategies may in turn control the maximum allowed speed of the vehicle, transmission shift capabilities (e.g., whether downshifting two gears is allowed), maximum and / or minimum allowed engine speeds, torque limits, and so on. The operating parameters may include current operating parameters (e.g., current vehicle 100 speed), predicted operating parameters (e.g., estimated aftertreatment system temperature for the next 5 miles), past operating parameters (e.g., fuel consumption rate for the previous 5 miles), average operating parameters (e.g., average reductant dose rate per hour), or other suitable parameters. The optimization circuit 326 may compare predicted operating parameters over an initial velocity profile with predicted operating parameters over an updated velocity profile (e.g., may predict the effect of a change in the velocity profile) to determine a control strategy and / or to select powertrain control action associated with the control. In this way, responsive to a change in the velocity profile, the optimization circuit 326 may determine a control strategy and / or control action(s) that increase the efficiency of the operating param eter(s) of the vehicle 100 over the prediction horizon.

[0065] “Increase the efficiency” may refer to causing the vehicle 100 to travel at operating parameter values closer to a predefined or target value(s). For example, the optimization circuit 326 may select a “fuel economy” optimization strategy to increase the efficiency of the fuel consumption rate of the vehicle. Accordingly, and for example, the determination of efficiency may be based on whether the operating parameter of fuel-consumption rate (i.e., miles-per-gallon) after an optimization action is closer to a minimum fuel consumption rate than the fuel consumption rate if no optimization action were to be taken. In this way, the- lioptimization circuit 326 may determine whether an optimization action would be efficient based on a comparison of predicted operating parameters after taking the optimization action to predicted operating parameters without taking the optimization action (e.g., considering the change in the velocity profile).

[0066] The optimization circuit 326 may also select an “emissions compliance” optimization strategy. The emissions compliance optimization strategy may include one or more goals, targets, and / or objectives that the optimization circuit 326 may select to achieve such as target range of emissions, a minimum amount of smoke generation, a minimum amount of noise (e.g., from the engine 101), a minimum / target reductant dosing rate, a regeneration schedule, etc. The pre-defined objective may be set by a driver or by a remote third party (e.g., a fleet operator) via the communications interface 310. Regarding the amount of emissions, in some jurisdictions, a limit is placed on the amount of emissions (e.g., NOx) that can be produced by a vehicle 100 during operation. Accordingly, the optimization circuit 326 may analyze the velocity profile of the vehicle 100 and select a change in powertrain references that reduces the amount of NOx / CO2 produced by the vehicle 100. For example, upon receiving a message including a trigger condition indicating a traffic jam in fifteen miles, the optimization circuit 326 may receive a velocity profile with a reduced vehicle velocity fourteen to seventeen miles ahead. The optimization circuit 326 may then analyze current operating parameters (e.g., aftertreatment temperature, state of charge of the battery, etc.) and alter the powertrain references to avoid increased emissions at the traffic jam. The optimization action may include operating at a higher power in nine to twelve miles to increase aftertreatment temperature and use the excess power to charge the battery, thus allowing the battery to power the aftertreatment system heater during the traffic jam, lowering emissions compared to a vehicle which operates without altering its powertrain references in light of the traffic jam on the prediction horizon.

[0067] Further, the optimization circuit 326 may select a “component durability” optimization strategy. The optimization circuit 326 may select a component durability strategy in response to detecting a change in velocity profile or receiving a communication message indicative of a route condition that allows for a change in powertrain references which extends the lifetime or reduces the load on one or more vehicle components. For example, in response to a change in velocity profile caused by a downhill stretch of route in fifteen miles, the optimization circuit 326 may adjust the powertrain references to charge thebattery during the downhill stretch / speed increase. Further, the optimization circuit 326 may determine that a NOx concentration / emission rate is too high before a hill climb starts, determine a control action to warm up the aftertreatment system (e.g., calculate a best power split, an engine operation mode, a heating strategy, etc.), then re-evaluate once the aftertreatment system reaches a target temperature and / or the hill is encountered.

[0068] The optimization circuit 326 is configured or structured to select one or more powertrain optimization actions based on the selected optimization strategy or selected optimization strategies. For example, the optimization circuit 326 may select a powertrain optimization action that will increase the fuel efficiency of the vehicle, component durability, emissions compliance, or the like. The optimization circuit 326 may determine whether a powertrain optimization action will improve vehicle performance by comparing the predicted operating parameters of the vehicle after conducting the powertrain optimization action with the predicted operating parameters of the vehicle if the powertrain optimization action is not taken. In this way, the optimization circuit 326 whether the powertrain optimization action is beneficial, for example, based on whether the operating parameters are closer to a target value (e.g., a target aftertreatment temperature), a minimum value (e.g., a minimum NOx emission value), a maximum value (e.g., full battery charge), or another suitable value.100691 Further, the optimization circuit 326 and / or the controller 140 is configured to cause the vehicle 100 to perform the one or more selected powertrain optimization actions. In this way, after selecting the one or more powertrain optimization actions, the controller 140 and / or the optimization circuit 326 may send a signal to one or more components of the vehicle 100 or otherwise change the powertrain references of the vehicle 100 to change the operating parameters of the vehicle. For example, the powertrain optimization action may include changing a vehicle power split, changing a temperature value associated with the vehicle 100 (e.g., an aftertreatment system temperature, exhaust temperature, etc.), changing a reductant dosing rate, changing a fuel consumption rate / amount, causing / delaying a regeneration event, or the like. The controller 140 and / or optimization circuit 326 is configured to cause the powertrain optimization action at any point over the prediction horizon. Accordingly, in some embodiments, the powertrain references may not be altered immediately or instantaneously. For example, in some embodiments, the controller 140 and / or the optimization circuit 326 may cause the powertrain optimization action to occur in ten miles, to occur over the course of the next twelve to sixteen miles, to comprise a firstevent in five miles, a second event in ten miles, a third event in twelve miles, etc. Accordingly, the controller 140 may be configured to optimize powertrain references to meet emissions compliance, fuel economy, and other goals over the entirety of the prediction horizon and at look-ahead distances exceeding two kilometers.

[0070] Turning to FIG. 4, a flowchart illustrating exemplary steps of a method for controlling a vehicle 100 is shown, according to an exemplary embodiment. In other embodiments, the steps may be rearranged in various order, may be repeated, may be combined with other steps, may include additional or intervening steps, or the like.[00711 At step 410, the controller 140 receives information indicative of a velocity profile of a vehicle 100 over a prediction horizon. The velocity profile predicts the velocity of the vehicle 100 at various points over the prediction horizon (e.g., the route of the vehicle or time period of travel of the vehicle 100). For example, the velocity profile may include a dataset indicating that the vehicle 100 will travel at 50mph for 0-5 miles, gradually increase velocity to 75 mph from 5-6 miles, remain at 75 mph for 6-10 miles, then rapidly decrease speed to 30 mph from 10-10.25 miles, before remaining at 30 mph for 10.25-15 miles. The controller 140 may receive the velocity profile based on information received from external systems 200, remote computing systems 250, RSUs 25, telematics unit 145, sensors 150, etc. In other embodiments, the controller 140 may generate or calculate the velocity profile based on received information indicative of an expected future speed of the vehicle 100. The controller 140 may also store one or more velocity profiles in memory 306 to calculate and compare operating parameters after a change in the velocity profile. In some embodiments, the controller 140 receives one or more signals, data sets, or the like and generates a predicted velocity profile via a velocity profile circuit 320.

[0072] At step 420, the controller 140 receives a communication message. The communication message may include one or more trigger conditions such as natural language text, keywords, phrases, or the like indicative of a change in the velocity profile. The communication may alternatively include no trigger condition(s). For example, the communication message may include natural language text, keywords, or the like that do not correspond to a change in the velocity of the vehicle (e.g., a communication message including only the natural language “picnic-location” may not be identified as a trigger condition based on the absence of a clear correlation between the presence of a picnic location along a route and a change in vehicle velocity). The communication messages mayinclude standard messaging protocols such as J2375 messaging, SAE2375 messaging, etc. Communication messages that include trigger conditions may include, but are not limited to, messages that have natural language such as “heavy traffic, lane closed, shoulder closed, light traffic, construction, lane shift, icy, rainy, slick, accident,” etc.

[0073] At step 430, the controller 140 detects and / or determines whether the received communication message comprises a trigger condition. For example, the controller 140 may include a detection circuit 322 that monitors and / or receives communication messages and flags / identifies those communication messages that include trigger conditions. Accordingly, a trigger condition may include a predefined condition, situation, occurrence, or potential occurrence that is determined to potentially affect a speed of the vehicle 100. The detection of the trigger condition may be based on an analysis / identification of natural language keywords and / or phrases within a received communication message. The controller 140 and / or detection circuit 322 may ignore, disregard, or otherwise identify the communication messages that do not include trigger conditions such that those messages are not considered by the prediction circuit 324. The controller 140 may be configured to identify individual communication messages and / or may be configured to identify, detect, store, or otherwise process groups of communication messages (e.g., communication messages received within a time period, over a predefined distance, during a predefined event, etc.). For example, in some embodiments, the trigger condition may be based on a time / duration interval internal to the controller 140 (e.g., the controller 140 may process communication messages and / or detect trigger conditions for all communication messages received during a predefined time window (e.g., a 5-minute window)). The controller 140 may associate and cumulatively determine an effect on the velocity profile caused by communication messages having trigger conditions that are received within a predefined time (e.g., 10 seconds) of each other. Additionally, the controller 140 may group and process communication messages containing data indicative of the velocity profile over a length of the route between X-Y miles ahead of the vehicle, etc. In other embodiments, the controller 140 may identify whether communication messages related to the same geographic location comprise trigger conditions (e.g., determine whether all communication messages relating to the route five to six miles ahead comprise trigger conditions). In other embodiments, the controller 140 may be configured to individually identify whether single communication messages comprise a trigger condition.

[0074] Further, the controller 140 may be configured to adjust and / or determine how to detect trigger conditions based on one or more criteria such as available bandwidth, a connectivity strength, or the like. For example, in a region of poor connectivity or in an instance where memory is low or demanded by other processes, the controller 140 may determine to detect trigger conditions from individual communication messages as they are received (e.g., “on-the-fly”) rather than storing groups of communication messages then detecting trigger conditions and / or processing the effect, if any, on the velocity profile of the communication messages as a batch. In this way, the controller 140 may select a manner of detecting trigger conditions and / or processing communication messages that requires less memory or less bandwidth to account for the limited memory, low connectivity, etc. In additional embodiments, the controller 140 may be configured to determine a confidence value associated with a communication message based on other communication messages and / or data received within a predetermined time period / interval. For example, over a predefined time interval (e.g., 5-minute) window, the controller 140 may receive a communication message including the natural language and / or an integer associated with “heavy traffic.” Before determining the effect on the velocity profile, the controller 140 may process other communication messages and detect trigger conditions and / or natural language keywords such as “road clear,” “detour ahead,” “blockage cleared,” “route open,” or the like that indicate conditions counter to, opposite, inconsistent with, or that otherwise affect the accuracy of the “heavy traffic” condition. Accordingly, the controller 140 may determine a confidence value based on the number of communication messages indicating certain predefined natural language conditions (e.g., a ratio of communication messages confirming / affirming a condition to communication messages opposing / diff ering from the condition). The confidence value may, in some embodiments, be used to reduce, mitigate, adjust, or otherwise alter the predicted effect on the velocity profile (e.g., a “heavy traffic” condition with 100% confidence may correspond to a 40% speed decrease while a “heavy traffic” condition with 42% confidence may correspond with a 22% speed decrease or another value less than a 42% speed decrease).

[0075] At step 440, the controller 140, after detecting / determining that the received communication message comprises at least one trigger condition, determines and / or predicts a change / effect on the velocity profile over a prediction horizon in light of the trigger condition(s). The controller 140 may translate the natural language keywords, phrases, and qualitative data into quantitative velocity adjustments over the prediction horizon. In someembodiments, the controller 140 receives look-ahead information and keywords over the prediction horizon and estimates a change in velocity by correlating the natural language event (e.g., “accident,” “shoulder-closed”) to traffic calming measures that result in predictable changes in vehicle velocity (e.g., chicanes, lane narrowing, speed tables, etc.). For example, the controller 140 may determine that a trigger condition indicates an upcoming lane shift (e.g., due to construction, due to an accident, etc.), and may adjust the velocity profile to reduce speed by some absolute or relative amount during that section of the route by analogizing the event to a chicane. In other embodiments, the controller 140 may determine that a trigger condition indicates an upcoming lane or shoulder closure and may adjust the velocity profile to reduce speed by some absolute or relative amount during that section of the route by analogizing the event to lane narrowing. The controller 140 may further determine that trigger conditions indicate the presence of an uneven road surface or an icy / slick / slippery road surface (or other road conditions that may indicate need for caution) at a future location in the route. Accordingly, the controller 140 may adjust the velocity profile to reduce speed by some absolute or relative amount during that section of the route by analogizing the event to a speed table. Likewise, the controller may determine that a trigger condition indicates the presence of increased / decreased traffic density at a future location on the route and may adjust the velocity profile to reduce / increase speed by some absolute or relative amount, respectively, during that section of the route.

[0076] At step 450, the controller 140, based on the change in the velocity profile, selects an optimization strategy and determines one or more powertrain optimization actions corresponding to the optimization strategy. The controller 140 may select a control strategy based on at least one of the current operating parameters of the vehicle 100, the predicted future operating parameters of the vehicle 100 over the prediction horizon, the predicted change in the velocity profile, and / or the trigger condition(s) (e.g., the natural language keyword, phrase, or the like identifying an event that affects the velocity profile). In some embodiments, each optimization strategy may include a database containing associated and / or predefined powertrain optimization actions. The powertrain optimization actions may include changing a temperature of the vehicle 100, changing a power split of the vehicle 100, delaying an aftertreatment thermal management operation, triggering a regeneration event at a designated time, or otherwise altering one or more powertrain references over the course of the prediction horizon.

[0077] At step 460, the controller 140, causes the one or more powertrain optimization actions to occur over the prediction horizon. For example, the controller 140 may send a signal to the engine 101, the vehicle subsystems 120, the aftertreatment system 121, and / or components thereof at a designated time over the prediction horizon to cause the selected powertrain optimization action to achieve the goal of the optimization strategy. The controller 140 may store one or more commands, signals, data objects, and / or instructions in the memory 306 for a designated delay period (e.g., 5 minutes, 10 miles from the current location, etc.) before causing the powertrain optimization action. Accordingly, the controller 140 may receive subsequent changes in the vehicle 100 velocity profile and revise, update, edit, or prevent powertrain optimization actions from occurring in response to the current vehicle velocity profile.

[0078] Turning to FIG. 5, an illustration of a vehicle 100 receiving an example communication message 500 is shown, according to an exemplary embodiment. As shown in FIG. 5, the vehicle 100 may travel along a route 172. The prediction horizon may include a predicted distance along the route 172, such as 0-20 miles ahead of the vehicle 100, 0 to 10 minutes of driving time ahead of the vehicle 100, etc. The vehicle 100 may be communicatively coupled to the environment 10 and may receive one or more communication messages 500 from external systems 200, remote computing systems 250, RSUs 25, etc. As shown in FIG. 5, the communication message 500 may include an example trigger condition 510 such as the natural language “reduced to one lane”, “reduced to one lane id=_777,” etc. The controller 140 may receive the communication message and / or may detect the example trigger condition 510 as / after it is received by the vehicle 100. The controller 140 may then determine a quantitative velocity adjustment based on the trigger condition. For example, “reduced to one lane” for two miles starting in ten miles may be correlated to a lane narrowing traffic geometry at ten to twelve miles from the vehicle 100. The controller 140 may predict that such lane narrowing will result in a speed reduction of 36% over the course of the two-mile length of route ten to twelve miles ahead. The controller 140 may select a control strategy based on the expected change in the velocity profile and the current vehicle operating parameters. For example, if the aftertreatment system 121 temperature is below a threshold temperature, the controller 140 may select an emissions compliance optimization strategy and select a control action that causes the vehicle to operate in a manner that produces higher exhaust temperatures, so that aftertreatment temperatures remain higher during the slow-down and the aftertreatment system 121 operates moreefficiently (e.g., the vehicle 100 emits less NOx than it otherwise would have if it had not conducted the powertrain optimization action).|0079| As utilized herein, the terms “approximately,” “about,” “substantially”, and similar terms are intended to have a broad meaning in harmony with the common and accepted usage by those of ordinary skill in the art to which the subject matter of this disclosure pertains. It should be understood by those of skill in the art who review this disclosure that these terms are intended to allow a description of certain features described and claimed without restricting the scope of these features to the precise numerical ranges provided. Accordingly, these terms should be interpreted as indicating that insubstantial or inconsequential modifications or alterations of the subject matter described and claimed are considered to be within the scope of the disclosure as recited in the appended claims.

[0080] It should be noted that the term “exemplary” and variations thereof, as used herein to describe various embodiments, are intended to indicate that such embodiments are possible examples, representations, or illustrations of possible embodiments (and such terms are not intended to connote that such embodiments are necessarily extraordinary or superlative examples).

[0081] The term “coupled” and variations thereof, as used herein, means the joining of two members directly or indirectly to one another. Such joining may be stationary (e.g., permanent or fixed) or moveable (e.g., removable or releasable). Such joining may be achieved with the two members coupled directly to each other, with the two members coupled to each other using one or more separate intervening members, or with the two members coupled to each other using an intervening member that is integrally formed as a single unitary body with one of the two members. If “coupled” or variations thereof are modified by an additional term (e.g., directly coupled), the generic definition of “coupled” provided above is modified by the plain language meaning of the additional term (e.g., “directly coupled” means the joining of two members without any separate intervening member), resulting in a narrower definition than the generic definition of “coupled” provided above. Such coupling may be mechanical, electrical, or fluidic. For example, circuit A communicably “coupled” to circuit B may signify that the circuit A communicates directly with circuit B (i.e., no intermediary) or communicates indirectly with circuit B (e.g., through one or more intermediaries).

[0082] References herein to the positions of elements (e.g., “top,” “bottom,” “above,” “below”) are merely used to describe the orientation of various elements in the FIGURES. It should be noted that the orientation of various elements may differ according to other exemplary embodiments, and that such variations are intended to be encompassed by the present disclosure.

[0083] While various circuits with particular functionality are shown in FIG. 3, it should be understood that the controller 140 may include any number of circuits for completing the functions described herein. Additional circuits with additional functionality may also be included. Further, the controller 140 may further control other activity beyond the scope of the present disclosure.

[0084] As mentioned above and in one configuration, the “circuits” may be implemented in machine-readable medium for execution by various types of processors, such as the processor 304 of FIG. 3. Executable code may, for instance, comprise one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables need not be physically located together, but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the circuit and achieve the stated purpose for the circuit. Indeed, a circuit of computer readable program code may be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices.{0085] While the term “processor” is briefly defined above, the term “processor” and “processing circuit” are meant to be broadly interpreted. In some embodiments, the one or more processors may be external to the apparatus (e.g., onboard vehicle controller), for example the one or more processors may be or included with a remote processor (e.g., a cloud-based processor). In this regard, a given circuit or components thereof may be disposed locally (e.g., as part of a local server, a local computing system, etc.) or remotely (e.g., as part of a remote server such as a cloud-based server). To that end, a “circuit” as described herein may include components that are distributed across one or more locations.

[0086] Embodiments within the scope of the present disclosure include program products comprising computer or machine-readable media for carrying or having computer or machine-executable instructions or data structures stored thereon. Such machine-readablemedia can be any available media that can be accessed by a computer. The computer readable medium may be a tangible computer readable storage medium storing the computer readable program code. The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer readable medium may include but are not limited to a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), an optical storage device, a magnetic storage device, a holographic storage medium, a micromechanical storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, and / or store computer readable program code for use by and / or in connection with an instruction execution system, apparatus, or device. Machine-executable instructions include, for example, instructions and data which cause a computer or processing machine to perform a certain function or group of functions.

[0087] The computer readable medium may also be a computer readable signal medium. A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electrical, electro-magnetic, magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport computer readable program code for use by or in connection with an instruction execution system, apparatus, or device. Computer readable program code embodied on a computer readable signal medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, Radio Frequency (RF), or the like, or any suitable combination of the foregoing.]0 88| In one embodiment, the computer readable medium may comprise a combination of one or more computer readable storage mediums and one or more computer readable signal mediums. For example, computer readable program code may be both propagated as anelectro-magnetic signal through a fiber optic cable for execution by a processor and stored on RAM storage device for execution by the processor.10089] Computer readable program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more other programming languages, including an object-oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program code may execute entirely on a local computer, partly on the local computer, as a stand-alone computer- readable package, partly on the local computer and partly on a remote computer, etc. In the latter scenario, the remote computer may be connected to the local computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0090] The program code may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function / act specified in the schematic flowchart diagrams and / or schematic block diagrams block or blocks.

[0091] Although the figures and description may illustrate a specific order of method steps, the order of such steps may differ from what is depicted and described, unless specified differently above. Also, two or more steps may be performed concurrently or with partial concurrence, unless specified differently above. Such variation may depend, for example, on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure.

[0092] It is important to note that the construction and arrangement of the apparatus and system as shown in the various exemplary embodiments is illustrative only. Additionally, any element disclosed in one embodiment may be incorporated or utilized with any other embodiment disclosed herein.

Claims

WHAT IS CLAIMED IS:

1. A vehicle, comprising: a controller communicably coupled to an engine of the vehicle, the controller comprising at least one processor and at least one memory device storing instructions therein that, when executed by the at least one processor, cause the at least one processor to: receive a plurality of communication messages; receive information indicative of a velocity profile of the vehicle; identify a trigger condition in one or more of the received communication messages; after identifying the trigger condition, determine a change in the velocity profile of the vehicle over a predefined prediction horizon; and implement a control strategy with a powertrain of the vehicle based on the determined change in the velocity profile vehicle of the vehicle over the predefined prediction horizon.

2. The vehicle of claim 1, wherein the velocity profile of the vehicle further comprises a prediction of the velocity of the vehicle over the predefined prediction horizon, wherein the prediction of the velocity of the vehicle is based on route information regarding the vehicle.

3. The vehicle of claim 1, wherein the control strategies comprise: a fuel economy optimization strategy; an emissions compliance optimization strategy; and a component durability optimization strategy.

4. The vehicle of claim 1, wherein implementing the control strategy comprises at least one of: changing a temperature of a component of the vehicle; causing an aftertreatment system regeneration in advance of a predefined condition; or altering one or more powertrain references over the course of the prediction horizon.

5. The vehicle of claim 1, wherein the instructions, when executed by the at least one processor, further cause the at least one processor to select the control strategy based on a current operating parameter of the vehicle.

6. The vehicle of claim 1, wherein the trigger condition comprises a natural language keyword or phrase.

7. The vehicle of claim 6, wherein the natural language keyword or phrase corresponds to a data indicator of one or more standard messaging protocols.

8. The vehicle of claim 7, wherein the trigger condition is indicative of one or more of an upcoming lane pattern shifting, an upcoming lane closure, an upcoming shoulder closure, an upcoming weather condition, an upcoming traffic condition, or an upcoming construction condition.

9. The vehicle of claim 1, wherein implementing the control strategy comprises increasing a fuel consumption rate of the vehicle relative to a current fuel consumption rate.

10. The vehicle of claim 1, wherein the predefined prediction horizon is a predefined distance ahead of a current location of the vehicle.

11. The vehicle of claim 1, wherein: the change in the velocity profile of the vehicle over the predefined prediction horizon indicates an upcoming decrease in vehicle speed of the vehicle; and implementing the control strategy comprises operating the powertrain at a higher power output relative to a power output of the powertrain prior to a start of the prediction horizon to increase a temperature of an aftertreatment system of the vehicle .

12. The vehicle of claim 1, wherein: the change in the velocity profile of the vehicle over the predefined prediction horizon indicates an upcoming decrease in vehicle speed of the vehicle; and implementing the control strategy comprises operating the powertrain at a higher power output relative to a current power output of the powertrain prior to a start of theprediction horizon, using excess power from the higher power output to charge a battery, and using the battery to power a heater of an aftertreatment system.

13. A method comprising: receiving, by a controller, a plurality of communication messages from at least one remote computing system; receiving, by the controller, information indicative of a velocity profile of a vehicle; identifying, by the controller, a trigger condition in one or more of the received communication messages; after identifying the trigger condition, determining, by the controller, a change in the velocity profile of the vehicle over a predefined prediction horizon; and causing, by the controller, the vehicle to implement a control strategy for a powertrain of the vehicle based on the determined change in the velocity profile of the vehicle over the predefined prediction horizon.

14. The method of claim 13, wherein the trigger condition comprises a natural language keyword or phrase that is included in at least one of the plurality of messages.

15. The method of claim 14, wherein determining the change in the velocity profile is based on the natural language keyword or phrase detected in at least one of the plurality of messages.

16. The method of claim 13, wherein the control strategy for the powertrain is implemented prior to a start of the prediction horizon.

17. A system comprising: at least one powertrain component; and at least one processing circuit coupled to the at least one powertrain component, the at least one processing circuit comprising at least one memory coupled to at least one processor, the at least one processing circuit operable to: receive a plurality of communication messages; receive information indicative of a velocity profile of a vehicle; identify a trigger condition in one or more of the communication messages;determine a change in the velocity profile of the vehicle over a predefined prediction horizon; and implement a control strategy with the at least one powertrain component that causes the at least one powertrain component to perform one or more powertrain actions based on the determined change in the velocity profile of the vehicle over the predefined prediction horizon.

18. The system of claim 17, wherein implementing the control strategy with the at least one powertrain component comprises: operating the at least one powertrain component at a higher power output relative to a current power output of the at least one powertrain component prior to a start of the prediction horizon; and using excess power from the at least one powertrain component to charge a battery of the vehicle.

19. The system of claim 17, wherein the trigger condition is a natural language keyword or phrase in the one or more communication messages that is indicative of a change or potential for a change in the velocity profile; and wherein the control strategy is selected based on the change or potential for the change in the velocity profile.

20. The system of claim 17, wherein the control strategy comprises causing the at least one powertrain component to operate at a higher power output relative to a current power output of the at least one powertrain component prior to a start of the prediction horizon to increase an exhaust gas temperature to increase a temperature of an aftertreatment system relative to a current aftertreatment system temperature.

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