Real-time vehicle speed curve prediction using connectivity for powertrain optimization

Abstract

A vehicle includes a controller communicatively coupled to an engine of the vehicle. The controller includes at least one processor and at least one memory device storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the following operations: receive information indicating a speed profile of the vehicle; receive multiple communication messages; identify a trigger condition in one or more of the received communication messages; in response to identifying the trigger condition, determine a change in the speed profile of the vehicle within a predefined prediction range; determine a control strategy based on the determined speed change; and implement the control strategy for the powertrain of the vehicle.

Description

[0001] Cross-references to related applications This application claims the benefit and priority of U.S. Provisional Patent Application No. 63 / 622,047, filed January 17, 2024, the entire contents of which are incorporated herein by reference and used for all purposes. Technical Field

[0002] This disclosure relates to systems and methods for predicting changes in the projected speed curve of a vehicle within a predicted range. These systems and methods determine the projected vehicle speed within the predicted range by converting qualitative or natural language keywords (e.g., keywords related to traffic, weather, construction, etc.) obtained via connectivity into quantitative adjustments. The predicted changes in the vehicle's projected speed curve within the predicted range are then used to optimize the vehicle's powertrain management. Background Technology

[0003] Route look-ahead systems are used to identify certain features ahead of a vehicle, such as road gradient and speed limits. Using this forward-looking information, vehicle operation can be planned or controlled in response to certain events (e.g., upcoming uphill or downhill events), thereby achieving various benefits such as improved fuel efficiency. Existing look-ahead strategies focus on short-term actions such as instantaneous adjustments to vehicle speed and are limited to relatively short look-ahead distances, such as less than or approximately two kilometers. Summary of the Invention

[0004] One embodiment relates to a vehicle. The vehicle includes a controller communicatively coupled to an engine of the vehicle. The controller includes at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the following operations: receive a plurality of communication messages; receive information indicating a speed profile of the vehicle; identify a trigger condition in one or more of the received communication messages; determine, in response to identifying the trigger condition, a change in the speed profile of the vehicle within a predefined prediction range; and implement a control strategy for the powertrain of the vehicle.

[0005] Another embodiment relates to a method for controlling a vehicle. The method includes: receiving multiple communication messages by a controller; receiving information indicating a speed profile of the vehicle by the controller; identifying a triggering condition in one or more of the received communication messages by the controller; determining, in response to identifying the triggering condition, a change in the speed profile of the vehicle within a predefined prediction range by the controller; and causing the vehicle to implement a control strategy for the powertrain of the vehicle by the controller.

[0006] Another embodiment relates to a system. The system includes at least one powertrain assembly and at least one processing circuit. The at least one processing circuit is coupled to the at least one powertrain assembly and includes at least one memory and at least one processor. The at least one processing circuit is operable to perform the following operations: receiving multiple communication messages; receiving information indicating a speed profile of a vehicle; identifying a triggering condition in one or more of the communication messages; determining a change in the speed profile of the vehicle within a predefined prediction range; and implementing a control strategy for the at least one powertrain assembly, the control strategy causing the at least one powertrain assembly to perform one or more powertrain actions based on the determined change in the speed profile of the vehicle within the predefined prediction range.

[0007] Numerous specific details are provided to provide a thorough understanding of embodiments of the subject matter of this disclosure. The features described in the subject matter of this disclosure may be combined in any suitable manner in one or more embodiments and / or implementations. In this respect, one or more features of one aspect of the invention may be combined with one or more features of different aspects of the invention. Furthermore, additional features may be recognized in some embodiments and / or implementations that may not be present in all embodiments or implementations. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of a networked transportation environment including an exemplary transportation connectivity system, according to an exemplary embodiment.

[0009] Figure 2 This is based on an exemplary embodiment. Figure 1 A schematic diagram of a vehicle in an exemplary networked transportation environment.

[0010] Figure 3 This is based on an exemplary embodiment. Figures 1 to 2 A schematic diagram of the control system of a vehicle.

[0011] Figure 4 It is a flowchart of a method for performing the following operations according to an exemplary embodiment: updating the projected speed curve of a vehicle within a forecast range by transforming keywords obtained via connectivity, and using such updates to optimize powertrain management.

[0012] Figure 5 This is a schematic diagram illustrating a vehicle receiving communication messages and identifying triggering conditions according to an exemplary embodiment. Detailed Implementation

[0013] The following describes in more detail various concepts and implementations related to methods, apparatuses, and systems for performing the following operations: updating the projected speed curve of a vehicle within a forecast range by converting certain content obtained via connectivity (i.e., one or more predefined keywords (e.g., keywords related to traffic, weather, construction, etc.)) into a quantitative adjustment to the projected vehicle curve, and using such adjustments to optimize the vehicle's powertrain management. Existing strategies for optimizing powertrain management are primarily based on route information (e.g., road gradient, speed limits) and do not consider longer-range look-ahead information regarding traffic, weather, road conditions, construction, roadside events, etc. These longer-range look-ahead conditions quickly render previous look-ahead strategies inaccurate due to computational "noise" caused by events or conditions two miles, five miles, ten miles, etc., ahead of the vehicle, and prevent advance planning for such events. In contrast, the methods, apparatus, and systems disclosed herein can optimize powertrain management within an extended predictive range (e.g., 2 miles ahead of the vehicle, 10 miles ahead of the vehicle, etc.) by utilizing predictive speed adjustments via a predicted speed (velocity or speed) curve, thereby achieving, substantially achieving, or attempting to achieve relevant benefits in terms of emissions compliance / targets, fuel economy, component durability, and / or other objectives.

[0014] In general, referring to the accompanying figures, the various embodiments disclosed herein relate to systems, apparatus, and methods for predicting the speed profile of a vehicle within a predicted range. The predicted range may include predefined distances, times, specified routes, known operating procedures, etc. Qualitative natural language keywords (related to traffic, weather, construction, roadside events, etc.) obtained via connectivity are converted into quantitative adjustments to the predicted vehicle speed, which are then used to optimize powertrain management.

[0015] As described herein, the target speed profile of a vehicle for a given route can be adjusted in real-time onboard (during operation) to accommodate forward-looking information received via V2X / C-V2X connectivity regarding traffic, weather, construction, roadside events, and other conditions affecting the vehicle's operation at future points in time and / or at an extended distance ahead of the vehicle (e.g., 5 miles, 10 miles, 20 miles). The vehicle's control system or controller can receive this forward-looking information, which includes data elements such as natural language keywords or phrases corresponding to route or roadside conditions (e.g., "vehicle stopped," "serious accident," "oil leak," "traffic congestion," etc.). For example, a received phrase such as "vehicle stopped" might indicate the need to slow down for a given distance at a future location on the route. Similarly, a received keyword such as "snow cleared" might indicate the possibility of increasing speed at another point on the route. Based on the received keywords, the vehicle's target speed profile can be increased, decreased, or otherwise adjusted by an absolute or relative amount at the relevant point on the route.

[0016] In some embodiments, the magnitude of the speed reduction is based on studies of the impact of traffic stabilization measures on vehicle speed. Studies have shown that the presence of speed bumps, S-curves, and lane narrowing can reduce vehicle speed by varying degrees (e.g., up to 50%, 56%, and 36%, respectively). Speed ​​adjustments due to other scenarios, such as traffic congestion, can be based on traffic flow theory and the relationship between traffic density and vehicle speed. By converting qualitative data (e.g., keywords and phrases) into quantitative predicted speed adjustments, powertrain control strategies can be selected based on predicted changes in vehicle speed to improve or achieve various powertrain objectives. Specifically, the controller can select various powertrain objectives, such as fuel economy, emissions compliance, and component durability, and subsequently influence one or more powertrain control strategies to facilitate the achievement of the selected objectives. For example, upon receiving a keyword or phrase indicating a traffic jam five miles ahead, the controller can translate the keyword into a percentage reduction in vehicle speed at five miles per hour. Then, since traffic jams often increase emissions, the system can select a "fuel emissions" target and execute an after-processing system regeneration event before the traffic jam occurs to prevent emissions from increasing upon arrival at the congestion point. This paper also discusses and envisions various other powertrain targets, control strategies, and exemplary scenarios.

[0017] As used herein, the term "prediction" and similar terms are used to refer to determining future values ​​based on data (e.g., sensor data, historical sensor data, real-time sensor data, etc.). In some embodiments, one or more models (e.g., statistical models, artificial intelligence models, machine learning models, etc.) may be used to perform predictions of future values. For example, predicting adjustments to vehicle speeds or changes in vehicle speed profiles may include using a combination of data (such as V2X / C-V2X connectivity (vehicle-to-everything, cellular vehicle-to-everything, such as pedestrians, infrastructure, networks, vehicles), historical sensor data, and / or real-time sensor data) and models, algorithms, or lookup tables to determine future quantitative changes in vehicle speed within a predicted range (e.g., a 36% reduction in speed at 10 miles, a stop-and-go situation lasting 10 miles at 15 miles, etc.). Before turning to the accompanying drawings, which illustrate certain exemplary embodiments in detail, it should be understood that this disclosure is not limited to the details or methods set forth in the specification or illustrated in the drawings. It should also be understood that the terminology used herein is for descriptive purposes only and should not be considered limiting.

[0018] Now for reference Figure 1 The diagram illustrates a networked transportation environment 10 according to an example embodiment. The networked transportation environment 10 may include one or more vehicles 100, a network 30, one or more external systems 200, and a telecomputing system 250. In some embodiments, one or more vehicles 100 may be configured as a fleet 110 of vehicles 100. The environment 10 is configured to allow the exchange of information or data (e.g., communication) between vehicles (such as vehicle 100) and one or more other components or other vehicles (such as other vehicles 100), a fleet 110 of one or more vehicles 100, the telecomputing system 250, external systems 200 (which may include roadside units (RSUs) 25), and one or more vehicle on-board units (OBUs, such as telematics systems, GPS devices, etc.). In this regard, for example, vehicles 100 may include telematics systems that facilitate the acquisition and transmission of acquired data regarding the operation of vehicles 100.

[0019] According to one example embodiment, network 30 wirelessly and communicatively couples remote computing system 250 to vehicle 100 and external system 200. In another embodiment, one or more external systems in external system 200 are integrated into remote computing system 250. In other embodiments, a wired network may be used at least partially.

[0020] The remote computing system 250 may be a computing system or device including one or more processing circuits, a network interface, and other computing systems and devices coupled to the network 30, enabling the exchange of information between a remote information source and the vehicle 100. Therefore, the remote computing system 250 may be an information source remote from the vehicle 100 and may include or be itself a remote server / computing system (e.g., a fleet operator and its computing system), a mobile computing device (e.g., a mobile phone, tablet computer, desktop computer, etc.), etc. In some embodiments, the remote computing system 250 may include cellular towers and / or cloud computing systems, etc. Thus, one or more processes or calculations of this disclosure may be performed and / or communicated by the remote computing system 250, for example, to reduce the onboard processing power required by the vehicle 100. Therefore, the vehicle 100 may form a V-2-X relationship with a remote information source (e.g., the remote computing system 250, external system 200, RSU 25), where “X” may be another vehicle 100, a remote server, pedestrian equipment, infrastructure, etc. The remote computing system 250 may be owned, associated with, managed by, or otherwise controlled by the provider. The provider entity may provide a variety of products and / or services, such as diagnostic services, component manufacturers (e.g., engines, aftertreatment systems, etc.).

[0021] like Figure 1 As shown, external system 200 includes route look-ahead system 210, weather system 220, and Global Positioning Satellite (GPS) system 230. In some embodiments, external system 200 includes fewer, more, or different systems. Route look-ahead system 210 may be a remote computing system configured to acquire route look-ahead data, including static information indicating road / route parameters ahead of the corresponding vehicle 100, which is substantially unchanged or changes only minimally over time. In other embodiments, route look-ahead system 210 may be included in vehicle 100 or in one or more geographically distributed systems within networked vehicle environment 10. For example, route look-ahead system 210 or a portion thereof may be included in at least a portion of controller 140. Road / route parameters can include information about road function class (e.g., highway / interstate, arterial road, collector road, local road, unclassified road, etc.), speed limits, road gradient, road slope, road curvature, bridges, gas stations, number of lanes, presence of emergency vehicles, traffic conditions, road surface conditions, and other information indicating the speed profile of vehicles within a predicted range (e.g., 2 miles, 5 miles, etc.). Additionally, road / route parameters can include information that changes over time, such as weather conditions, traffic conditions, and construction or emergencies along the route.

[0022] Weather system 220 may be a remote computing system configured to acquire weather data, including dynamic information indicating weather conditions ahead of the corresponding vehicle 100. Weather conditions may include information indicating road conditions (e.g., wet, icy, snowing, dry, snow cleared, etc.), weather conditions (e.g., rain, snow, temperature, humidity, etc.), and other weather-related information ahead of the corresponding vehicle 100.

[0023] GPS system 230 may be a remote computing system configured to: (i) receive information about the current location and desired destination of the corresponding vehicle 100; and (ii) generate GPS data that facilitates the determination of one or more routes based on the current location and desired destination. In some embodiments, the route of vehicle 100 is predicted by extrapolating the current location of vehicle 100 relative to a finite distance ahead of vehicle 100 (e.g., if there is no road available for turning within a distance X ahead, controller 140 assumes that vehicle 100 will continue along its current road). In other embodiments, the route of vehicle 100 may be received from GPS system 230 (e.g., the route between the starting location and the destination generated by GPS system 230 from the user may define the route). In still other embodiments, the route of vehicle 100 may be predicted based on historical / routine operating conditions associated with vehicle 100 (such as bus routes that travel along fixed routes at approximate times of day).

[0024] Now for reference Figure 2 The image illustrates a vehicle 100 in a networked environment 10 according to an example embodiment. The vehicle 100 may include a powertrain system 118, a vehicle subsystem 120, an operator input / output (I / O) device 130, a controller 140, a telematics unit 145, and one or more sensors 150, wherein the controller 140 is communicatively 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, wherein the differential 104 transmits power output from the engine 101 to a 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 sweepers, road sprinkler trucks, garbage trucks, wheel loaders, forklifts, long-haul trucks, medium-duty trucks (e.g., pickup trucks), cars, coupes, tanks, aircraft, boats, and any other type of vehicle.

[0025] Engine 101 may be an internal combustion engine (e.g., compression-ignition or spark-ignition), allowing it to be powered by any type of fuel (e.g., diesel, ethanol, gasoline, hydrogen, etc.). Engine 101 includes one or more cylinders and associated pistons. In the example shown, engine 101 is a diesel-powered compression-ignition engine. Atmospheric air mixes with fuel and burns, thereby powering the vehicle. The combustion of fuel and air in the compression chamber of engine 101 produces exhaust gases that are operatively discharged to an exhaust pipe and an exhaust aftertreatment system. In other embodiments, vehicle 100 is a fully electric vehicle (EV) or a hybrid electric vehicle (HEV). For example, vehicle 100 may include a plug-in fuel cell vehicle (FC), a plug-in hybrid electric vehicle, a battery electric vehicle (BEV), a range-extended electric vehicle (REEV), a diesel-electric vehicle, a non-plug-in FC, or a non-plug-in EV, etc.

[0026] The transmission 102 can be configured as any type of transmission, such as a continuously variable transmission (CVT), manual transmission, automatic transmission, semi-automatic transmission, dual-clutch transmission, etc. Therefore, as the transmission varies between geared and continuously variable configurations (e.g., a CVT), the transmission 102 can include various configurations (for geared transmissions) that produce different output speeds based on the input speeds received. Similar to the engine 101 and transmission 102, the drive shaft 103, differential 104, and / or final drive 105 can be configured in any configuration depending on the application (e.g., the final drive 105 is configured as a wheel in automotive applications, a propeller in marine applications, etc.). Furthermore, depending on the application, the drive shaft 103 can be configured as any type of drive shaft, including but not limited to one-piece, two-piece, and slide-in drive shafts.

[0027] In some embodiments, the vehicle subsystem 120 may include components, including mechanically or electrically driven vehicle components (e.g., HVAC systems, lights, pumps, fans, etc.). The vehicle subsystem 120 may also include an exhaust aftertreatment system in communication with the engine 101 to receive exhaust gases. 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 configured to receive exhaust gases from the engine 101 and oxidize hydrocarbons and carbon monoxide in the exhaust gases. The DPF 122 is arranged or positioned downstream of the DOC 123 and is configured to remove particulate matter (such as soot) from the exhaust gases flowing in the exhaust gas stream. The DPF 122 includes an inlet at which exhaust gases are received, and an outlet at which the exhaust gases exit after particulate matter has been substantially filtered out and / or converted into carbon dioxide. In some implementations, DPF 122 or other components may be omitted. For example, in a hydrogen internal combustion engine, the components of the aftertreatment system may differ. Additionally, although... Figure 2 The specific arrangement of the post-processing system 121 is shown, but the arrangement of the components within the post-processing system 121 may differ in other embodiments (e.g., the DPF 122 is located downstream of the SCR 124 and ASC 125, one or more components are omitted or added, etc.).

[0028] The aftertreatment system 121 may also include a reductant delivery system, which may include a decomposition chamber (e.g., a decomposition reactor, reactor piping, decomposition tube, reactor tube, etc.) to convert the reductant into ammonia. The reductant may be, for example, urea, diesel exhaust fluid (DEF), Adblue®, aqueous urea solution (UWS), aqueous urea solution (e.g., AUS32, etc.), and other similar fluids. Diesel exhaust fluid (DEF) is added to the exhaust gas stream to aid catalytic reduction. The reductant may typically be injected upstream of the SCR 124 (or specifically the SCR catalyst) via a DEF metering feeder, such that the SCR catalyst receives the mixture of reductant and exhaust gas. The reductant droplets then undergo evaporation, thermal decomposition, and decomposition processes to form gaseous ammonia within the decomposition chamber, the SCR catalyst, and / or the exhaust duct system, which exits the aftertreatment system 121. The aftertreatment system 121 may also include an oxidation catalyst (e.g., DOC 123) fluidly coupled to the exhaust duct system to oxidize hydrocarbons and carbon monoxide in the exhaust gas. To properly facilitate this reduction, DOC 123 may need to be at a specific operating temperature. In some embodiments, this specific operating temperature is approximately between 200°C and 500°C. In other embodiments, the specific operating temperature is the temperature at which the conversion efficiency of DOC 123 exceeds a predefined threshold (e.g., the conversion of HC to a less harmful compound, referred to as the HC conversion efficiency).

[0029] SCR 124 is configured to help reduce NOx emissions by accelerating the NOx reduction process between ammonia and NOx in exhaust gas, converting it into diatomic nitrogen and water. If the SCR catalyst is not at or above a specific temperature, the acceleration of the NOx reduction process will be limited, and SCR 124 may not operate at regulatory efficiency levels. In some embodiments, this specific temperature is approximately 200°C to 600°C. The SCR catalyst can be made from a combination of inactive materials and an active catalyst, such that the inactive material (e.g., a ceramic substrate) directs the exhaust gas to the active catalyst, which is any kind of material suitable for catalytic reduction (e.g., metal-exchanged zeolites (Fe or Cu / zeolite), base metal oxides (such as vanadium, molybdenum, tungsten, etc.)).

[0030] When ammonia in the exhaust gas does not react with the SCR catalyst (because SCR 124 is below operating temperature, or because the metered amount of ammonia significantly exceeds the amount of NOx), unreacted ammonia may bind with the SCR catalyst and be stored in SCR 124. As SCR 124 heats up, this stored ammonia can be released from SCR 124, which can cause problems if the amount of ammonia released is greater than the amount of NOx flowing through (e.g., more ammonia than the metered amount of NOx required, which could lead to ammonia leakage). In some embodiments, ASC 125 (which may also be referred to as an ammonia oxidation catalyst (AMOX) and / or operates as an ammonia oxidation catalyst (AMOX)) is included and configured to address ammonia leakage by removing at least some of the excess or unreacted ammonia from the treated exhaust gas before it is released into the atmosphere. As exhaust gas flows through the ASC 125, some unreacted ammonia remaining in the exhaust gas (e.g., ammonia that has not reacted with NOx) is partially oxidized to NOx. The NOx then reacts with the remaining unreacted ammonia to form N2 gas and water. However, similar to SCR catalysts, the acceleration of the NH3 oxidation process is limited if the ASC 125 is not at or above a certain temperature, and the ASC 125 may not operate at an efficiency level that meets regulations or required parameters. In some embodiments, this specific temperature is approximately 250°C to 300°C.

[0031] Still referencing Figure 2 Operator input / output (I / O) device 130 is also shown. Operator I / O device 130 can be coupled to controller 140, enabling information exchange between controller 140 and I / O device 130, wherein this information can be... Figure 1 The determination of one or more components or controllers 140 (described below) is related. Operator I / O device 130 enables the operator of vehicle 100 to communicate with... Figure 1 The controller 140 in environment 10 communicates with one or more components. For example, operator input / output device 130 may include, but is not limited to, an interactive display, a touchscreen device, one or more buttons and switches, a voice command receiver, etc. Thus, operator input / output device 130 can provide the operator with one or more instructions or notifications, such as a fault indicator light (MIL). Additionally, the vehicle may include a port that allows the controller 140 to connect to or couple to a scanning tool, thereby obtaining fault codes and other information about the vehicle.

[0032] As discussed herein, vehicle 100 can receive various signals, data elements, and / or communications via one or more communication standards. Thus, in compliance with various communication standards, vehicle 100 and / or controller 140 can receive communication messages including one or more triggering conditions indicating route conditions or events that may affect the vehicle's speed within a predicted range (e.g., within the next 10 miles, within the next 30 miles, within the next 4 minutes, etc., at a certain segment of the route). A triggering condition is a predefined condition, situation, occurrence, or potential occurrence identified as potentially affecting the vehicle's speed. Triggering conditions can be identified based on natural language keywords and / or phrases present in one or more communication standards. For example, such standards may include SAE J2540-2 (… See SAE J2540-2, which was released in February 2002 and revised in December 2020, and is named as follows: ITIS Phrases List (International Travel) (Person Information System) (The full text of which is incorporated herein by reference) and SAE J2735 ( See SAE J2735, released in September 2015 and revised in March 2016, and named as follows: Dictionary of Dedicated Short Range Communication (DSRC) Message Sets (The full text of which is incorporated herein by reference).

[0033] Based on the received communication messages and applicable criteria, controller 140 can be configured to detect triggering conditions from the received communication messages, such as the presence of one or more predefined natural language keywords and / or phrases related to the speed profile of vehicle 100. Additionally, in some embodiments, the triggering conditions may be based on time, distance, and / or internal conditions of controller 140, which cause controller 140 to detect, after a predefined condition / trigger, whether one or more communication messages contain natural language keywords indicating a change in the speed profile or the likelihood of such a change. For example, controller 140 may determine whether the received communication message has changed or indicates a high-medium-low traffic condition / occurrence after each Xth (e.g., the 10th) communication message received or at predefined time intervals (e.g., 5-minute intervals) after a time / distance interval has elapsed. Natural language keywords and / or phrases can include single words, multiple words, text strings and / or alphanumeric phrases and / or words (e.g., “closed,” “road icing,” “traffic congestion ahead,” “severe flooding,” “severe flooding (3074),” “3074,” “lava flow,” etc.). For example, triggering conditions can include receiving data elements from the emergency vehicle via a V2V connection conforming to SAE J2735, including natural language keywords and / or phrases such as “SirenInUse ::= ENUMERATED{inUse (2)}”, indicating that the emergency vehicle is currently operating its siren at a specific distance of 100 from the vehicle. Controller 140 can convert qualitative keywords (e.g., “SirenInUse”) into quantitative speed adjustments (e.g., a 40% speed reduction within 5 miles) for example by using algorithms, lookup tables, or machine learning models that draw analogies between the presence of the emergency vehicle in use and lane narrowing or closure events.

[0034] In another example, the communication message may include text, numerical values, or both, to indicate one or more conditions or events (e.g., roadside conditions). Controller 140 may be configured to detect text, keywords, numerical sequences, or any combination thereof in the communication message and associate the detected data with natural language indicating speed curves and / or speed curve adjustments. For example, for a communication message containing “severe flood (3074)”, 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 flood conditions, or perform other appropriate associations / determinations based on the received communication message or a set of received communication messages. This analysis can similarly be performed without the natural language phrase “severe flood,” allowing controller 140 to associate various numerical sequences with various events / occurrences. As an example, controller 140 may store, retrieve, and use lookup tables to associate various numerical messages with various conditions / events that may affect the speed curve of a vehicle.

[0035] Furthermore, after predicting changes in the speed curve / its impact on the speed curve, controller 140 can then select a control strategy, determine the associated powertrain control actions, and execute the powertrain control actions. For example, if the vehicle 100's battery is low, controller 140 can select a "component durability" control strategy and, given an impending reduction in power demand, determine the associated control action, namely, "run engine 101 at high power for the next 3 miles," to charge the battery with excess power before reaching a portion of the route that includes the predicted deceleration event.

[0036] Therefore, based on at least one or more of the following: (1) the current operating parameters or expected future operating parameters of the vehicle 100, (2) the current speed profile, (3) the severity level or qualitative value associated with one or more detected triggering conditions, (4) the number of identified triggering conditions, or (5) the corresponding predicted impact on the speed profile, the controller 140 may, for example, select one or more control strategies for various parts along the route / predicted range. The controller 140 can then select corresponding control actions and, over time, initiate / change powertrain reference values ​​of the vehicle 100 to achieve the advantages associated with the control strategies.

[0037] because Figure 2 The components are shown as being embodied in the vehicle 100, so the controller 140 can be configured as one or more electronic control units (ECUs). Figure 3The functionality and structure of controller 140 are described in more detail below. Controller 140 may be separate from or included in 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 controller 140 are combined into a single unit. In another embodiment, one or more components of controller 140 may be geographically distributed throughout environment 10 and / or vehicle 100. In this regard, the various components of controller 140 discussed below may be distributed in separate physical locations or executed via remote computing system 250 to distribute / offload computational complexity and requirements throughout environment 10.

[0038] The vehicle 100 is also shown to include a telematics unit 145. The telematics unit 145 can be configured as any type of telematics control unit. Therefore, the telematics unit 145 may include, but is not limited to: a location positioning system (e.g., Global Positioning System) for tracking the vehicle's location (e.g., latitude and longitude data, altitude 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 communication interface for facilitating the exchange of data between the telematics unit 145 and one or more remote devices (e.g., providers / manufacturers of telematics devices, etc.). In this regard, the communication interface can be configured as any type of mobile communication interface or protocol, including but not limited to Wi-Fi, WiMAX, Internet, radio, Bluetooth, Zigbee, satellite, cellular networks, GSM, GPRS, and LTE, etc. The telematics unit 145 may also include a communication 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, wired connections may include serial cables, fiber optic cables, SAE J1939 buses, CAT5 cables, or any other form of wired connection. In contrast, wireless connections may include the Internet, Wi-Fi, Bluetooth, Zigbee, cellular networks, radio, etc. In one embodiment, a controller local area network (CAN) bus including any number of wired and wireless connections provides the exchange of signals, information, and / or data between controller 140 and telematics unit 145. In other embodiments, a local area network (LAN), wide area network (WAN), or an external computer (e.g., utilizing an Internet service provider via the Internet) may provide, facilitate, and support communication between telematics unit 145 and controller 140. In yet another embodiment, communication between telematics unit 145 and controller 140 is performed via the Unified Diagnostic Services (UDS) protocol. All such variations are intended to fall within the spirit and scope of this disclosure.

[0039] The telematics unit 145 may also include devices or systems installed within or located on the vehicle 100 to facilitate communication, monitoring, and / or control of the vehicle 100. The telematics unit 145 may transmit and / or receive communications or messages to and from external systems or networks. This may include transmitting and receiving data related to location, speed, status, etc. The telematics unit 145 may include vehicle-to-infrastructure (V2I) communication for facilitating communication between the vehicle and roadside infrastructure, vehicle-to-vehicle (V2V) communication devices for facilitating direct communication between vehicles, etc. In some embodiments, the functionality or features of the telematics unit 145 may be wholly or partially included in the controller 140. For example, the controller 140 may monitor and collect forward-looking information, such as road gradient, traffic, weather, and speed limit data. The controller 140 may receive data, messages, and other communications conforming to various communication standards. For example, in some embodiments, controller 140 may participate in dedicated short-range communication (DSRC) messaging (e.g., J2375 messaging, SAE2375 messaging, etc.), which includes predefined fields and data elements such as "icy weather," "traffic congestion," "smooth traffic," "safety checkpoint," "radar speed check," and "sports event." Controller 140 may detect the presence of these keywords as trigger conditions, which can ensure adjustments to the speed profile and the selection and implementation of one or more powertrain control actions.

[0040] Still referencing Figure 2As also shown in the figure, sensor 150 is included in vehicle 100. In some embodiments, sensor 150 may be coupled to controller 140 such that controller 140 can monitor and acquire data indicating current operating parameters of vehicle 100. Current operating parameters may include battery state of charge, temperature of aftertreatment system 121, emission level / volume, fuel consumption rate, reductant metering rate, power distribution, engine temperature, or engine fluid consumption rate, etc. Sensor 150 may include sensors positioned and / or configured to monitor operating characteristics or parameters of various components of vehicle 100. For example, sensor 150 may include a positioning sensor configured to facilitate monitoring the positioning of accelerators (e.g., accelerator pedal, throttle, etc.) and / or brakes (e.g., brake pedal, brake lever, etc.) of vehicle 100. Sensor 150 may include a speed sensor configured to facilitate monitoring and / or acquisition of data indicating the speed of vehicle 100 and / or the rotational speed of engine 101, which may be used to determine the speed of vehicle 100. Sensors may include radar, LIDAR detection systems, camera systems, and other systems for monitoring or collecting data about the operating status of vehicle 100 and its surrounding environment. For example, the camera system of vehicle 100 may be able to read / interpret visual indicators (e.g., signs, speed limits, lane markings, lane change signs / barriers, objects ahead, adjacent vehicles in front of / behind / to the side of the vehicle, oncoming vehicles, etc.).

[0041] Sensor 150 may include an aftertreatment sensor (e.g., a NOx sensor, a temperature sensor, etc.) configured to acquire data / information indicating the temperature of components of the exhaust aftertreatment system 121, the temperature of the exhaust gas, and / or the composition of the exhaust gas, and / or monitor the above. Sensor 150 may include a sensor configured to acquire data indicating the torque and / or power output of the main drive (e.g., engine 101). Sensor 150 may also include a sensor configured to facilitate the determination of the current transmission gear selection of transmission 102. Other sensors 150 may monitor engine coolant temperature, engine oil temperature, vehicle state of charge, electric motor speed, tire pressure, tire temperature, road inclination, ambient temperature, service braking status, gear selection, and transmission fluid temperature, etc. Thus, controller 140 can receive data (e.g., current vehicle operating parameters), given the current operating state of vehicle 100 and a predicted speed curve within a predicted range, and select an appropriate control strategy from that data.

[0042] Controller 140 is configured to at least partially control the operation of vehicle 100 and associated subsystems such as engine 101, operator input / output (I / O) devices 130, aftertreatment system 121, etc. For aftertreatment system 121, controller 140 is configured to control one or more components of aftertreatment system 121 to achieve specific operating parameters. For example, controller 140 may control the heater or DEF metering feeder of aftertreatment system 121 to improve the conversion efficiency of SCR 124, thereby achieving a specific NOx output value or initiating regeneration operation, etc. Similarly, for engine 101, controller 140 may control power distribution, IC engine operating mode, heating strategy, etc. Controller 140 may also be configured to manage battery charge, fuel consumption rate, engine temperature / intake air volume, battery temperature, external heater control, and various other powertrain reference values. Communication between and within components can be via any number of wired or wireless connections. For example, wired connections may include serial cables, fiber optic cables, CAT5 cables, or any other form of wired connection. In contrast, wireless connectivity can include the Internet, Wi-Fi, cellular networks, radio, etc. In one embodiment, a Controller Area Network (CAN) bus provides the exchange of signals, information, and / or data. A CAN bus includes any number of wired and wireless connections. Because the controller 140 is communicatively coupled to... Figures 1 to 2 The system and components, therefore the controller 140 is configured to... Figure 1 One or more of the components shown receive data. The structure and function of controller 140 will be explained in [the following text is missing from the original] Figure 3 Further details are provided below.

[0043] Now for reference Figure 3 This illustrates an example embodiment. Figure 2 A schematic diagram of the controller 140 of the vehicle 100 and certain other systems. (e.g.) Figure 3As shown, controller 140 may include processing circuitry 302 with processor 304 and memory 306, speed profile circuitry 320, detection circuitry 322, prediction circuitry 324, optimization circuitry 326, and communication interface 310. Controller 140 is configured or constructed to receive / generate information regarding the operation of vehicle 100 and the speed profile of vehicle 100. Controller 140 is also configured to detect triggering conditions from signals received by vehicle 100 indicating impending route conditions and events that may affect the speed profile of the vehicle. Controller 140 is further configured to predict / calculate corresponding changes in the speed profile of vehicle 100 / the impact on the speed profile of the vehicle after detecting a triggering condition, and to select a control strategy and determine whether powertrain control actions are feasible and / or guaranteed in response to the predicted changes in the speed profile of vehicle 100. If so, controller 140 is configured or constructed to execute powertrain control actions.

[0044] In this way, controller 140 can "optimize" the performance of one or more components of vehicle 100, such as powertrain system 118. Through "optimization," controller 140 can determine baseline operating parameters and then adjust one or more conditions associated with those parameters so that the operating parameters remain at or above a threshold. For example, "optimizing" fuel economy by controller 140 could include determining a baseline fuel consumption rate without implementing control strategies and / or control actions, and then adjusting fuel injection so that the speed of vehicle 100 is limited and fuel economy remains at or above a threshold fuel consumption rate. Similarly, for hybrid vehicles, controller 140 can optimize or improve the vehicle's state of charge (SOC) by determining a baseline charge level and preventing that baseline charge level from exceeding a calculated current level, thereby maintaining SOC and preventing the battery from dropping below a desired SOC threshold before and / or within a predicted range during the route.

[0045] In one configuration, the speed curve circuit 320, detection circuit 322, prediction circuit 324, and optimization circuit 326 are embodied as machine- or computer-readable media (e.g., instructions) executable by a processor (such as processor 304). As described herein, and among other uses, the machine-readable media facilitates the performance of certain operations to achieve the reception and transmission of data. For example, the machine-readable media can provide instructions (e.g., commands, etc.) to, for example, acquire data. In this respect, the machine-readable media may include programmable logic defining the frequency of data acquisition (or data transmission). The computer-readable media instructions may include code that can be written in any programming language (including, but not limited to, Java and any conventional procedural programming language, such as the "C" programming language or similar programming languages). The computer-readable program code can be executed on one processor or multiple remote processors. In the latter scenario, the remote processors can be connected to each other via any type of network (e.g., CAN bus, etc.).

[0046] In another configuration, the speed curve circuit 320, detection circuit 322, prediction circuit 324, and optimization circuit 326 are embodied as hardware units (such as electronic control units). Therefore, the speed curve circuit 320, detection circuit 322, prediction circuit 324, and optimization circuit 326 can be embodied as one or more circuit components, including but not limited to processing circuitry, network interfaces, peripheral devices, input devices, output devices, sensors, etc. In some embodiments, the speed curve circuit 320, detection circuit 322, prediction circuit 324, and optimization circuit 326 can take the form of one or more analog circuits, electronic circuits (e.g., integrated circuits (ICs), discrete circuits, system-on-a-chip (SOC) circuits, microcontrollers, etc.), telecommunications circuits, hybrid circuits, and any other type of "circuit". In this respect, the speed curve circuit 320, detection circuit 322, prediction circuit 324, and optimization circuit 326 can include any type of component for performing or facilitating the implementation of the operations described herein.

[0047] For example, the circuits 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, etc. The speed curve circuit 320, detection circuit 322, prediction circuit 324, and optimization circuit 326 may also include programmable hardware devices, such as field-programmable gate arrays, programmable array logic, or programmable logic devices. The speed curve circuit 320, detection circuit 322, prediction circuit 324, and optimization circuit 326 may include one or more memory devices for storing instructions that can be executed by a processor of the speed curve circuit 320, detection circuit 322, prediction circuit 324, and optimization circuit 326. The one or more memory devices and the processor may have the same definitions as provided below with respect to memory 306 and processor 304. In some hardware unit configurations, and as mentioned above, the speed curve circuit 320, detection circuit 322, prediction circuit 324, and optimization circuit 326 may be geographically distributed across different locations within the vehicle 100 and / or environment 10. Alternatively, and as shown in the figures, the speed curve circuit 320, detection circuit 322, prediction circuit 324, and optimization circuit 326 may be embodied in or within a single unit / housing, shown as controller 140. Furthermore, the determination of the speed curve circuit 320, detection circuit 322, prediction circuit 324, and optimization circuit 326 may be performed by any one of the different components or a combination thereof, and the execution order may differ from the order described above.

[0048] In the illustrated example, controller 140 includes processing circuitry 302 having processor 304 and memory 306. Processing circuitry 302 may be configured or constructed to execute or implement the instructions, commands, and / or control processes described herein with respect to speed curve circuitry 320, detection circuitry 322, prediction circuitry 324, and optimization circuitry 326. The depicted configuration represents speed curve circuitry 320, detection circuitry 322, prediction circuitry 324, and optimization circuitry 326 as a machine or computer-readable medium storing instructions. However, as mentioned above, this illustration is not intended to be limiting, as this disclosure contemplates other embodiments in which the respective circuits or at least one of them are configured as hardware units. All such combinations and variations are intended to fall within the scope of this disclosure.

[0049] At least one processor 304 may be implemented as a single-chip processor 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. The processor may be a processor, a microprocessor, a set of processors, etc. The processor may also be implemented as a combination of computing devices (such as a combination of a DSP and a microprocessor), multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration. In some embodiments, one or more processors may be shared by multiple circuits (e.g., speed curve circuit 320, detection circuit 322, prediction circuit 324, and optimization circuit 326 may include or otherwise share the same processor, which in some example embodiments may execute instructions stored or otherwise accessed via different regions of memory). Alternatively or additionally, one or more processors may be configured to perform or otherwise perform certain operations independently of one or more coprocessors. In other example embodiments, two or more processors may be bus-coupled to enable independent, parallel, pipelined, or multithreaded instruction execution. All such variations are intended to fall within the scope of this disclosure.

[0050] At least one memory 306 (e.g., memory, memory cell, storage device) may include one or more devices (e.g., RAM, ROM, flash memory, hard disk storage devices) for storing data and / or computer code to perform or facilitate the various processes, layers, and modules described herein. Memory 306 may be communicatively connected to processor 304 to provide processor 304 with computer code or instructions for performing at least some of the processes described herein. Furthermore, memory 306 may be or include tangible, non-transient volatile memory or non-volatile memory. Therefore, memory 306 may include database components, object code components, script components, or any other type of information structure to support the various activities and information structures described herein.

[0051] Communication interface 310 may include any combination of wired and / or wireless interfaces (e.g., jacks, antennas, transmitters, receivers, transceivers, wired terminals) for data communication with various systems, devices, or networks configured to enable in-vehicle communication (e.g., between and within the components of the vehicle) and out-of-vehicle communication (e.g., using a remote server). For example, and regarding out-of-vehicle / system communication, communication interface 310 may include Ethernet cards and ports for transmitting and receiving data via Ethernet-based communication networks, and / or Wi-Fi transceivers for communication via wireless communication networks. Communication interface 310 may be configured to communicate via local area networks or wide area networks (e.g., the Internet) and may use various communication protocols (e.g., IP, LON, Bluetooth, ZigBee, radio, cellular networks, near-field communication). Furthermore, communication interface 310 may cooperate with or coordinate with telematics unit 145 to communicate with other vehicles or remote computing systems (e.g., in a fleet of one or more vehicles 100). In some embodiments, and as mentioned above, the functionality of the telematics unit 145 may be included in the communication interface 310 to enable various communications. In this way, the telematics unit 145 can be excluded from the vehicle 100, so that communication with external devices such as the telecomputing system 250 is accomplished via the controller 140 (e.g., via the communication interface 310).

[0052] Speed ​​profile circuit 320 is configured or constructed to receive data indicating the speed profile of vehicle 100. The speed profile may include projected speed over time, projected speed at various predefined points within a certain distance (e.g., the next 20 miles of the route), or projected average speed at one or more locations along the route. The speed profile refers to the vehicle's likely speed at an imminent time and / or the speed of the vehicle within a predetermined look-ahead range, which may be distance-based (e.g., within the next 30 miles) and / or time-based (e.g., within the next 10 minutes). For example, speed profile circuit 320 may be configured to receive the speed profile from one or more components in environment 10. In such embodiments, speed profile circuit 320 may receive GPS data, route information, map / route planning data from route look-ahead system 210, pre-planned or conventional driving routes (e.g., bus routes and delivery routes) from telecomputing system 250, or data from other suitable sources. In other embodiments, speed profile circuit 320 may generate a portion or all of the speed profile. For example, speed curve circuit 320 can collect data indicating a speed curve and use such data to extrapolate, estimate, or otherwise generate speed curves within a forecast range (e.g., 2 miles, 5 miles, 10 miles). Speed ​​curve circuit 320 can determine the speed of a vehicle (e.g., in miles per hour or another metric). The determined speed can indicate the current speed of the vehicle or an estimated future speed. For example, speed curve circuit 320 can be communicatively coupled to an engine system (e.g., via a speed sensor coupled to the engine) to obtain data indicating the engine's rotational speed (e.g., in revolutions per minute). This data indicating the speed curve of vehicle 100 can also be received from sensor 150 (e.g., vehicle speed sensor, radar sensor, road tilt sensor), from telematics unit 145, route look-ahead system 210, weather system 220, GPS system 230, RSU 25, or any other relevant internal or external system. Speed ​​curve circuit 320 calculates expected or potential future speeds within a long-term forecast range, as these future speeds may affect powertrain control, aftertreatment operations, etc. Therefore, compared to existing forward-looking systems, identifying and discerning possible vehicle speeds within the prediction range allows vehicle 100 to develop and implement robust control strategies that improve engine emissions, exhaust gas temperature, aftertreatment performance, and other operating parameters over extended time periods and / or distances.

[0053] The speed curve circuit 320 can also be configured or constructed to determine a “default” or “target speed” associated with the route as the starting speed curve. The “default” and / or “target speed” of vehicle 100 refers to the desired speed of the vehicle without considering the impact of route look-ahead information on the vehicle's driving, and can be set / specified by the vehicle's driver (e.g., via cruise control input), set / specified according to local laws (e.g., the target speed equals the speed limit), and / or set / specified 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 the driver wishes to travel at 65 mph based on that speed limit, the speed curve circuit 320 can determine that, for a given length of route, the speed curve of vehicle 100 can be set to 60 mph (i.e., the speed limit) or 65 mph (i.e., the driver's preference). If the speed limit changes (e.g., the speed limit decreases from 10 miles to 50 mph), the speed curve circuit 320 can specify a separate speed (e.g., 50 mph) as the current speed within that portion of the speed curve. Thus, the speed curve can be defined as the expected speed at a given distance from the starting position or the expected speed at a given travel time, etc.

[0054] Detection circuitry 322 is configured or constructed to detect the presence of one or more trigger conditions in one or more communication messages received by controller 140. As discussed above, when vehicle 100 is in operation / driving, vehicle 100 may periodically, sporadically, continuously, etc., receive communication messages conforming to standard data protocols. These standard data protocols may include data elements, components, packets, byte values, or other characteristics transmitted, received, or otherwise conveyed via communication messages. For example, vehicle 100 may receive a message indicating the presence of a post office, accommodation facility, or picnic pavilion, etc., from RSU 25, which is 20 miles away from vehicle 100. Message / data elements may include a J2540 ASN.1 representation, which defines a “structure” with a set of named values ​​and associated integer values. For example, received messages may include “post office (13070)”, “picnic pavilion (13072)”, etc.

[0055] The detection circuit 322 can monitor some or all of the communication messages received by the vehicle 100 and identify / detect those messages containing one or more trigger conditions. As mentioned above, trigger conditions can be identified based on the analysis of communication messages containing natural language keywords and / or phases to identify situations or occurrences that may change the speed profile of the vehicle 100 or are related to the speed profile of the vehicle. Specifically, a trigger condition may be the receipt of a communication message, which includes keywords, phrases, text lines, natural language data elements, etc., corresponding to a condition or event that may affect the speed profile of the vehicle 100. The communication message may include a predefined data indicator, and the controller 140 is configured to associate the predefined data indicator with the trigger condition. In some embodiments, the communication message may contain a trigger condition even if it does not cause a change in the speed profile (e.g., receiving a communication message that is a duplicate of a previously received message, and the speed profile does not need to be updated). For example, vehicle 100 may receive a message with a "snow cleared" trigger condition indicating that the road is clear for 10 to 20 miles ahead, and subsequently receive messages from different sources with a "snow cleared" trigger condition indicating that the road is clear for 12 to 14 miles ahead. In some embodiments, controller 140 may determine that the second message does not change the speed profile, but may still identify the second message as a message containing the trigger condition.

[0056] Using the example of RSU 25 above, vehicle 100 can receive a communication message from RSU 25 located 20 miles away from vehicle 100. This communication message may indicate the presence of a post office near or at RSU 25. Detection circuitry 322 can monitor or otherwise identify received communication messages conforming to a standard (e.g., J2540 standard) that include natural language and the associated integer “post office (13070)”. Detection circuitry 322 may be pre-programmed to determine, or can (e.g., via lookup tables, algorithms, or machine learning models), that the location of a post office may not affect the vehicle's speed profile (e.g., the keyword is not associated with speed limits and traffic conditions, such that the currently predicted speed profile will not change at the post office or along the route of vehicle 100, or the effect of the given keyword “post office” on the speed profile cannot be predicted with sufficient accuracy). Therefore, controller 140 can determine that the communication message does not contain a trigger condition, 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., it may ignore the communication message).

[0057] Alternatively, for example, vehicle 100 may receive another / different communication message from RSU 25, which is 20 miles away from vehicle. This communication message may indicate the presence of an obstacle on the road near or at RSU 25. Detection circuitry 322 may monitor or otherwise identify received communication messages conforming to a standard (e.g., J2540 standard) that include natural language and / or integers associated with natural language; for example, detection circuitry 322 may detect one or more data fields present in “Obstruction ::= INTEGER {Obstruction on the road (1281)}”, such as “obstacle,” “obstacle on the road,” “1281,” etc. The presence of an obstacle on the road 20 miles ahead of vehicle 100 may affect the speed profile of vehicle 100. Therefore, detection circuitry 322 may identify / recognize that a communication message containing triggering conditions has been received (e.g., predefined logs based on triggering conditions, algorithm-based, machine learning model-based, or other heuristic methods, etc.). In some embodiments, detection circuit 322 can identify specific natural language text, critical tasks, etc., including triggering conditions (e.g., obstacles, barriers, etc. on the road). For example, detection circuit 322 can store one or more tables (or lists, databases, etc.) that define (e.g., identify, classify, store, etc.) various data elements (e.g., "iced road," "closed," "construction," etc.) within the communication message as triggering conditions. Furthermore, machine learning models, AI models, or other heuristics can identify triggering conditions based on one or more keywords received within a defined time period. For example, if detection circuit 322 receives the keywords "hAZMAT unit," "medical rescue team," "dangerous load," and "advise local drivers to avoid the area" within a 60-second interval, it can determine that the communication message includes natural language, data elements, and phrases as triggering conditions based on the content of the communication message, the proximity of the time, and its reception location (e.g., based on the received message / natural language text, controller 140 can determine that a hazardous chemical leak may occur ahead and predict deceleration / stopping / detour that will affect the speed of the vehicle, thus detecting the triggering condition).

[0058] After detecting / identifying that a communication message contains a triggering condition, detection circuit 322 marks, identifies, recognizes, forwards, or otherwise communicates the received identified communication message containing the triggering condition to prediction circuit 324. In some embodiments, detection circuit 322 may store, copy, and / or record the identified communication message containing the triggering condition in one or more databases / memories. Thus, prediction circuit 324 may receive a batch, set, or multiple communication messages at a time (e.g., it may process received messages containing triggering conditions every 5 minutes, after a threshold number of communication messages have been received, etc.). Once identified, prediction circuit 324 uses the triggering condition to determine the quantitative impact of the triggering condition on the vehicle speed curve. For example, while detection circuit 322 may detect triggering conditions that could potentially affect vehicle speed to some extent, prediction circuit 324 may apply algorithms, heuristics, traffic flow theory, and machine learning models, etc., to determine a quantitative value associated with the keyword (e.g., 30% deceleration within 5 miles, 52% acceleration within 10 to 14 miles, etc.). In some embodiments, quantified speed adjustments may include a percentage change in speed, a predefined (i.e., absolute) or calculated change in speed (e.g., decelerating by 5 mph, accelerating by 2 mph per minute when the vehicle is within a 10 to 12 mile section of the route ahead, reducing the speed to half the speed limit, etc.), a change in speed to a calculated or predefined speed (e.g., reducing the speed to 25 mph, increasing the speed to 37 mph, etc.), or another suitable quantified adjustment.

[0059] Prediction circuitry 324 is configured or constructed to predict / determine, based on received triggering conditions, quantified changes in the speed profile of vehicle 100 within a prediction range and / or the impact on the speed profile of that vehicle. As discussed above, the prediction range may include a predetermined distance ahead of the vehicle, such as a route 5 miles ahead, between 2 and 20 miles ahead, or up to 30 miles ahead. The prediction range may also include time intervals, such as the future travel time of vehicle 100. For example, the speed profile may be represented as a predicted speed at a specific point in absolute time (e.g., time elapsed since the start of the route, time of day, etc.) or relative time (e.g., time 5 minutes from the current time). The prediction range may also be specific to a particular route or operation of vehicle 100. For example, for a city bus, the prediction range may be the complete daily route schedule; while for a long-haul truck, considering expected delivery times, the prediction range may include the route from the pickup location to the delivery location, etc.

[0060] Prediction circuit 324 can use triggering conditions and current operating parameters of vehicle 100 (e.g., data from sensor 150, engine 101, etc.), route look-ahead information, and / or weather information to determine the expected changes in the speed curve within the prediction range. Prediction circuit 324 can transform, convert, or otherwise correlate natural language keywords and / or phrases into quantifiable speed adjustments by associating events / conditions described by natural language keywords and / or phrases with different traffic geometries, road conditions, and their impact on the speed of vehicle 100.

[0061] For example, prediction circuit 324 can receive keywords including "construction". Prediction circuit 324 can determine that a lane pattern change is expected based on natural language keywords and / or phrases, because the keyword "construction" is associated with a specific distance / location within the prediction range. Prediction circuit 324 can determine that a lane change caused by construction resembles the geometry of an S-curve. Therefore, prediction circuit 324 can calculate a speed adjustment (e.g., reducing speed by approximately 56%) based on known or theoretical speed changes that occur when a driver encounters an S-curve.

[0062] Similarly, prediction circuit 324 can receive keywords regarding the type of accident ahead, such as "shoulder closure," "disabled vehicle," and / or "accident." Based on these keywords, prediction circuit 324 can determine that an event associated with the keyword (e.g., shoulder blockage ahead, vehicle blocking shoulder / part of the road ahead, one lane blocked at an accident scene, etc.) is likely to occur at a specific distance / location within the prediction range, as the keyword data structure may include location information. Prediction circuit 324 can determine that these events resemble lane narrowing. Therefore, prediction circuit 324 can calculate speed adjustments (e.g., reducing speed by approximately 50%) based on known or theoretical speed changes that occur when a driver encounters lane narrowing or lane reduction. Prediction circuit 324 can also apply equations and algorithms based on traffic theory and the relationship between vehicle speed and traffic density to determine predicted changes in the vehicle speed curve. For example, prediction circuit 324 can receive trigger conditions such as "holiday traffic" or "traffic is smoother than usual" and calculate the associated decreases and increases in the vehicle speed curve, respectively.

[0063] Speed ​​curve circuit 320 can receive signals and / or data from prediction circuit 324 indicating corresponding changes or updates to the speed curve of a vehicle. Speed ​​curve circuit 320 can then revise, regenerate, alter, or otherwise update the speed curve based on the signals and / or data received from prediction circuit 324. For example, speed curve circuit 320 can reduce a predicted speed of 50 mph 5 to 10 miles ahead to 25 mph 5 to 10 miles ahead. In another example, speed curve circuit 320 can generate additional speed predictions (e.g., if a speed of the upcoming 10 miles was previously predicted, after receiving information, speed curve circuit 320 predicts a vehicle speed of an additional 10 miles, such that the speed curve covers the upcoming 20 miles).

[0064] The optimization circuit 326 is configured or constructed to select one or more control strategies based on changes in the operating parameters of vehicle 100 and / or the speed profile of vehicle 100 within a predicted range. The control strategy may be a specific powertrain control strategy targeting one or more predefined objectives or characteristics of vehicle 100, such as fuel economy, component durability, emissions compliance, handling / performance, etc. The control strategy may, in turn, control the vehicle's maximum permissible speed, transmission shift capability (e.g., whether downshifting by two gears is permitted), maximum and / or minimum permissible engine speeds, torque limits, etc. 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 hourly reductant metering rate), or other suitable parameters. The optimization circuit 326 can compare the predicted operating parameters on the initial speed curve with the predicted operating parameters on the updated speed curve (e.g., it can predict the impact of changes in the speed curve) to determine a control strategy and / or select powertrain control actions associated with the control. Thus, in response to changes in the speed curve, the optimization circuit 326 can determine a control strategy and / or control actions that improve the efficiency of the vehicle 100's operating parameters within the predicted range.

[0065] "Improving efficiency" can refer to making vehicle 100 operate with operating parameter values ​​that are closer to predefined or target values. For example, optimization circuit 326 can select a "fuel economy" optimization strategy to improve the efficiency of the vehicle's fuel consumption rate. Thus, for example, the determination of efficiency can be based on the fact that the operating parameters of the fuel consumption rate (i.e., miles per gallon) are closer to the minimum fuel consumption rate after optimization than when no optimization is taken. In this way, optimization circuit 326 can determine whether the optimization action is efficient based on a comparison of the predicted operating parameters after optimization and the predicted operating parameters without optimization (e.g., considering changes in the speed curve).

[0066] The optimization circuit 326 can also select an "emissions compliance" optimization strategy. The emissions compliance optimization strategy may include one or more targets, indicators, and / or objectives that the optimization circuit 326 can choose to achieve, such as target emission ranges, minimum smoke generation, minimum noise levels (e.g., from engine 101), minimum / target reductant metering rates, regeneration plans, etc. Predefined objectives can be set by the driver or a remote third party (e.g., a fleet operator) via communication interface 310. Regarding emissions, certain jurisdictions set limits on the amount of emissions (e.g., NOx) that the vehicle 100 may produce during operation. Therefore, the optimization circuit 326 can analyze the speed profile of the vehicle 100 and select changes in powertrain reference values ​​to reduce the amount of NOx / CO2 produced by the vehicle 100. For example, upon receiving a message including a trigger condition indicating traffic congestion within fifteen miles, the optimization circuit 326 can receive a speed profile with reduced vehicle speeds between fourteen and seventeen miles ahead. The optimization circuit 326 can then analyze current operating parameters (e.g., aftertreatment temperature, battery state of charge, etc.) and adjust powertrain reference values ​​to avoid increased emissions during traffic congestion. Optimization actions could include operating at higher power within nine to twelve miles to increase aftertreatment temperature and use excess power to charge the battery, allowing the battery to power the aftertreatment system heater during traffic congestion, thereby reducing emissions. In contrast, if the powertrain reference values ​​were not adjusted based on predicted traffic congestion levels, the vehicle's emissions would be higher.

[0067] Furthermore, the optimization circuit 326 can select a "component durability" optimization strategy. The optimization circuit 326 can select a component durability strategy in response to detecting changes in the speed profile or receiving a communication message indicating route conditions that allow for variations in powertrain reference values. This can extend the life of one or more vehicle components or reduce the load on one or more vehicle components. For example, in response to a change in the speed profile caused by a 15-mile downhill section, the optimization circuit 326 can adjust the powertrain reference values ​​to charge the battery during the downhill / speed increase. Additionally, the optimization circuit 326 can determine if the NOx concentration / emission rate is too high before the start of a climb, determine the control actions of the preheating aftertreatment system (e.g., calculate optimal power distribution, engine operating mode, heating strategy, etc.), and then reassess after the aftertreatment system reaches the target temperature and / or after encountering a slope.

[0068] The optimization circuit 326 is configured or constructed to select one or more powertrain optimization actions based on one or more selected optimization strategies. For example, the optimization circuit 326 may select powertrain optimization actions that improve the vehicle's fuel efficiency, component durability, or emissions compliance. The optimization circuit 326 can determine whether the powertrain optimization action will improve vehicle performance by comparing the predicted operating parameters of the vehicle after the powertrain optimization action with the predicted operating parameters of the vehicle without the powertrain optimization action. Thus, the optimization circuit 326 determines the benefit of the powertrain optimization action based, for example, whether the operating parameters are closer to a target value (e.g., target aftertreatment temperature), a minimum value (e.g., minimum NOx emissions), a maximum value (e.g., a fully charged battery), or other suitable values.

[0069] Furthermore, optimization circuitry 326 and / or controller 140 are configured to cause vehicle 100 to perform one or more selected powertrain optimization actions. Thus, after selecting one or more powertrain optimization actions, controller 140 and / or optimization circuitry 326 can signal to one or more components of vehicle 100, or otherwise modify powertrain reference values ​​of vehicle 100 to change the vehicle's operating parameters. For example, powertrain optimization actions may include changing vehicle power distribution, changing temperature values ​​associated with vehicle 100 (e.g., aftertreatment system temperature, exhaust temperature, etc.), changing reductant metering rates, changing fuel consumption rates / amounts, or executing / delaying regeneration events, etc. Controller 140 and / or optimization circuitry 326 are configured to perform powertrain optimization actions at any point within the predicted range. Therefore, in some embodiments, the powertrain reference values ​​may not change immediately or instantaneously. For example, in some embodiments, controller 140 and / or optimization circuitry 326 can cause powertrain optimization actions to occur within ten miles, over the next twelve to sixteen miles, including a first event within five miles, a second event within ten miles, a third event within twelve miles, and so on. Therefore, controller 140 can be configured to optimize powertrain reference values ​​to meet emissions compliance, fuel economy, and other objectives throughout the forecast range and at look-ahead distances exceeding two kilometers.

[0070] Go to Figure 4 The diagram illustrates exemplary steps of a method for controlling a vehicle 100 according to an exemplary embodiment. In other embodiments, the steps may be rearranged in various orders, may be repeated, may be combined with other steps, or may include additional or intermediate steps, etc.

[0071] At step 410, controller 140 receives information indicating a speed profile of vehicle 100 within a predicted range. The speed profile predicts the speed of vehicle 100 at various points within the predicted range (e.g., the vehicle's route or the time period during which vehicle 100 travels). For example, the speed profile may include a dataset indicating that vehicle 100 travels at 50 mph from 0 to 5 miles, gradually increases its speed to 75 mph from 5 to 6 miles, maintains 75 mph from 6 to 10 miles, then rapidly decreases its speed to 30 mph from 10 to 10.25 miles, and then maintains 30 mph from 10.25 to 15 miles. Controller 140 may receive the speed profile based on information received from external system 200, remote computing system 250, RSU 25, telematics unit 145, sensor 150, etc. In other embodiments, controller 140 may generate or calculate the speed profile based on received information indicating the expected future speed of vehicle 100. The controller 140 may also store one or more speed curves in the memory 306 to calculate and compare operating parameters after changes in the speed curves. In some embodiments, the controller 140 receives one or more signals or datasets, etc., and generates a predicted speed curve via the speed curve circuit 320.

[0072] At step 420, controller 140 receives a communication message. The communication message may include one or more triggering conditions, such as natural language text, keywords, or phrases indicating changes in the speed profile. Alternatively, the communication may not include triggering conditions. For example, the communication message may include natural language text or keywords that do not correspond to changes in vehicle speed (e.g., a communication message containing only the natural language phrase "picnic location" might not be identified as a triggering condition based on the lack of a clear correlation between the presence of picnic locations along the route and changes in vehicle speed). The communication message may include standard messaging protocols such as J2375 messaging, SAE2375 messaging, etc. Communication messages including triggering conditions may include, but are not limited to, messages with natural language, such as "traffic congestion, lane closure, shoulder closure, traffic flow, construction, lane change, icing, rain, skidding, accident," etc.

[0073] At step 430, controller 140 detects and / or determines whether a received communication message includes a trigger condition. For example, controller 140 may include detection circuitry 322 that monitors and / or receives communication messages and identifies / recognizes those communication messages that include trigger conditions. Therefore, trigger conditions may include predefined conditions, situations, occurrences, or potential occurrences determined to potentially affect the speed of vehicle 100. Detection of trigger conditions may be based on analysis / recognition of natural language keywords and / or phrases within the received communication message. Controller 140 and / or detection circuitry 322 may ignore, discard, or otherwise identify communication messages that do not include trigger conditions, causing prediction circuitry 324 to disregard these messages. 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, within a predefined distance, during a predefined event, etc.). For example, in some embodiments, triggering conditions may be based on time / duration time intervals within controller 140 (e.g., controller 140 may process communication messages and / or detect triggering conditions for all communication messages received within a predefined time window (e.g., a 5-minute window)). Controller 140 may correlate the effects on speed profiles caused by communication messages with triggering conditions that are received within a predefined time (e.g., 10 seconds) and cumulatively determine such effects. Additionally, controller 140 may group and process communication messages containing data, etc., indicating speed profiles over a length of route between X and Y miles ahead of the vehicle. In other embodiments, controller 140 may identify whether communication messages associated with the same geographic location include triggering conditions (e.g., determining whether all communication messages associated with a route between five and six miles ahead include triggering conditions). In other embodiments, controller 140 may be configured to individually identify whether a single communication message includes a triggering condition.

[0074] Furthermore, controller 140 can be configured to adjust and / or determine how to detect triggering conditions based on one or more criteria such as available bandwidth or connectivity strength. For example, in areas with poor connectivity, or in situations where memory is insufficient or other processes require memory, controller 140 can determine to detect triggering conditions from individual communication messages upon receipt (e.g., "immediately"), rather than storing sets of communication messages and then detecting and / or batch-processing the impact on the rate profile of the communication messages (if any). In this way, controller 140 can choose a method that requires less memory or bandwidth to detect triggering conditions and / or process communication messages, adapting to situations with limited memory, poor connectivity, etc. In additional embodiments, controller 140 can 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, within a predefined time interval (e.g., 5 minutes) window, controller 140 can receive communication messages that include natural language and / or integers associated with "traffic congestion". Before determining the impact on the speed curve, controller 140 may process other communication messages and detect triggering conditions and / or natural language keywords, such as “road clear,” “detour ahead,” “obstacle cleared,” or “route open,” which indicate the existence of conditions that contradict, oppose, are inconsistent with, or otherwise affect the accuracy of the “traffic congestion” condition. Therefore, controller 140 may determine a confidence value (e.g., the ratio of communication messages confirming / affirming a condition to communication messages opposing / disagreeing with it) based on the number of communication messages indicating certain predefined natural language conditions. In some embodiments, the confidence value may be used to reduce, mitigate, adjust, or otherwise alter the predicted impact on the speed curve (e.g., a “traffic congestion” condition with 100% confidence may correspond to a 40% deceleration, while a “traffic congestion” condition with 42% confidence may correspond to a 22% deceleration or another value less than 42%).

[0075] At step 440, after detecting / determining that the received communication message includes at least one trigger condition, controller 140 determines and / or predicts changes in the speed profile within the prediction range / the impact on the speed profile within the prediction range based on the trigger condition. Controller 140 may convert natural language keywords, phrases, and qualitative data into quantitative speed adjustments within the prediction range. In some embodiments, controller 140 receives forward-looking information and keywords within the prediction range and estimates speed changes by associating natural language events (e.g., “accident,” “shoulder closure”) with traffic stabilization measures (e.g., S-curves, lane narrowing, speed bumps, etc.) that cause predictable changes in vehicle speed. For example, controller 140 may determine that the trigger condition indicates an impending lane change (e.g., due to construction, due to an accident, etc.) and may adjust the speed profile by analogizing the event to an S-curve to reduce speed by an absolute or relative amount within that segment of the route. In other embodiments, controller 140 may determine that the trigger condition indicates an impending lane or shoulder closure and may adjust the speed profile by analogizing the event to a lane narrowing to reduce speed by an absolute or relative amount within that segment of the route. Controller 140 can also determine that the triggering condition indicates the presence of an uneven road surface or an icy / slippery / wet road surface at a future location on the route (or may indicate other road conditions requiring caution). Therefore, controller 140 can adjust the speed profile by analogizing the event to a speed bump to reduce speed by a certain absolute or relative amount within that segment of the route. Similarly, the controller can determine that the triggering condition indicates an increase / decrease in traffic density at a future location on the route and can adjust the speed profile to reduce / increase speed by a certain absolute or relative amount within that segment of the route.

[0076] At step 450, controller 140 selects an optimization strategy based on changes in the speed curve and determines one or more powertrain optimization actions corresponding to that optimization strategy. Controller 140 may select the control strategy based on at least one of the following: current operating parameters of vehicle 100, predicted future operating parameters of vehicle 100 within a predicted range, predicted changes in the speed curve, and / or triggering conditions (e.g., natural language keywords or phrases identifying events affecting the speed curve). In some embodiments, each optimization strategy may include a database containing associated and / or predefined powertrain optimization actions. Powertrain optimization actions may include changing the temperature of vehicle 100, changing the power distribution of vehicle 100, delaying aftertreatment thermal management operations, triggering a regeneration event at a specified time, or otherwise changing one or more powertrain reference values ​​within a predicted range.

[0077] At step 460, controller 140 causes one or more powertrain optimization actions to occur within the predicted range. For example, controller 140 may transmit signals to engine 101, vehicle subsystem 120, aftertreatment system 121, and / or their components at a specified time within the predicted range to cause the selected powertrain optimization actions to achieve the objectives of the optimization strategy. Controller 140 may store one or more commands, signals, data objects, and / or instructions in memory 306 for a specified delay period (e.g., 5 minutes, 10 miles from the current location, etc.) before executing the powertrain optimization actions. Therefore, controller 140 can receive subsequent changes in the vehicle 100 speed profile and revise, update, edit, or prevent the powertrain optimization actions from occurring in response to the current vehicle speed profile.

[0078] Go to Figure 5 This illustrates an example of a vehicle 100 receiving an example communication message 500 according to an exemplary embodiment. Figure 5 As shown, vehicle 100 can travel along route 172. The prediction range can include predicted distances along route 172, such as 0 to 20 miles ahead of vehicle 100, and the driving time of vehicle 100 in the next 0 to 10 minutes. Vehicle 100 can be communicatively coupled to environment 10 and can receive one or more communication messages 500 from external system 200, remote computing system 250, RSU 25, etc. Figure 5 As shown, the communication message 500 may include example trigger conditions 510, such as natural language phrases like "reduced to one lane" or "reduced to one lane id=_777". The controller 140 may receive the communication message and / or may detect the example trigger condition 510 when / after the vehicle 100 receives the communication message. The controller 140 may then determine a quantified speed adjustment based on the trigger condition. For example, a "lane reduction to one" traffic condition starting ten miles ahead and lasting for two miles may be associated with a lane narrowing traffic geometry ten to twelve miles from the vehicle 100. The controller 140 may predict that such lane narrowing will result in a 36% speed reduction along the two-mile route from ten to twelve miles ahead. The controller 140 may select a control strategy based on the expected change in the speed profile and current vehicle operating parameters. For example, if the temperature of the aftertreatment system 121 is below a threshold temperature, the controller 140 can select an emissions compliance optimization strategy and select control actions that cause the vehicle to operate in a manner that generates higher exhaust temperatures, so that the aftertreatment temperature remains high during deceleration and the aftertreatment system 121 operates more efficiently (e.g., the vehicle 100 emits less NOx than it would when no powertrain optimization action is performed).

[0079] As used herein, the terms “about,” “approximately,” “substantially,” and similar terms are intended to have a broad meaning consistent with common and accepted usage by one of ordinary skill in the art to which the subject matter of this disclosure pertains. Those skilled in the art who read this disclosure will understand that these terms are intended to allow for the description of certain features described and claimed, without limiting the scope of these features to the precise numerical ranges provided. Therefore, these terms should be interpreted as indicating that non-substantial or irrelevant modifications or alterations to the described and claimed subject matter are considered to be within the scope of this disclosure as set forth in the appended claims.

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

[0081] As used herein, the term “coupled” and its variations mean two components connected directly or indirectly to each other. Such a connection can be stationary (e.g., permanent or fixed) or movable (e.g., removable or releasable). Such a connection can be achieved when two components are directly coupled to each other, when two components are coupled to each other using one or more separate intermediate components, or when two components are coupled to each other using an intermediate component that is integrally formed with one of the two components to form a single whole. If “coupled” or its variations are modified by an additional term (e.g., direct coupling), the general definition of “coupled” provided above is modified by the common linguistic meaning of the additional term (e.g., “direct coupling” means joining two components without any separate intermediate component), resulting in a narrower definition than the general definition of “coupled” provided above. Such a coupling can be mechanical, electrical, or fluid. For example, circuit A being communicatively “coupled” to circuit B can mean that circuit A communicates directly with circuit B (i.e., without intermediaries) or indirectly with circuit B (e.g., through one or more intermediaries).

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

[0083] Although Figure 3 Various circuits with specific functions are shown herein; however, it should be understood that controller 140 may include any number of circuits for performing the functions described herein. Additional circuits with additional functions may also be included. Furthermore, controller 140 may control other activities beyond the scope of this disclosure.

[0084] As mentioned above, and in one configuration, the "circuit" can be implemented in a machine-readable medium for use with various types of processors (such as...). Figure 3 The executable code is executed by the processor 304. Executable code may include, for example, one or more physical or logical blocks of computer instructions, which may be organized, for example, into objects, procedures, or functions. However, an executable program does not need to be physically located together, but may include different instructions stored in different locations that, when logically combined, comprise circuitry and implement the intended purpose of that circuitry. In practice, the circuitry of computer-readable program code can be a single instruction or multiple instructions, and can even be distributed across several different code segments, different programs, and several memory devices.

[0085] While the term "processor" has been briefly defined above, the terms "processor" and "processing circuitry" are intended to be interpreted broadly. In some embodiments, one or more processors may be located external to a device (e.g., an onboard vehicle controller), for example, one or more processors may be remote processors (e.g., cloud-based processors) or included therein. In this regard, a given circuitry or its components may be locally configured (e.g., as part of a local server, local computing system, etc.) or remotely configured (e.g., as part of a remote server, such as a cloud-based server). For this purpose, "circuitry" as described herein may include components distributed across one or more locations.

[0086] Embodiments within the scope of this disclosure include program products that include computer or machine-readable media for carrying or having computer or machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available medium accessible by a computer. A computer-readable medium can be a tangible computer-readable storage medium storing computer-readable program code. A computer-readable storage medium can be, for example, but not limited to, electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor systems, apparatuses, or devices, or any suitable combination of the foregoing. More specific examples of computer-readable media may include, but are not limited to, portable computer floppy disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable optical disc read-only memory (CD-ROM), digital versatile optical disc (DVD), optical storage devices, magnetic storage devices, holographic storage media, micromechanical storage devices, or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium can be any tangible medium capable of containing and / or storing computer-readable program code used and / or in conjunction with an instruction execution system, apparatus, or device. Machine-executable instructions include, for example, instructions and data that cause a computer or processing machine to perform a specific function or a set of functions.

[0087] Computer-readable media can also be computer-readable signal media. Computer-readable signal media can include propagated data signals in which computer-readable program code is contained, for example, in baseband or as part of a carrier wave. Such propagated signals can take any of a variety of forms, including, but not limited to, electrical, electromagnetic, magnetic, optical, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium that is not a computer-readable storage medium and can convey, propagate, or transmit computer-readable program code for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable program code contained on a computer-readable signal medium can be transmitted using any suitable medium, including but not limited to wireless, wired, fiber optic cable, radio frequency (RF), or similar media, or any suitable combination thereof.

[0088] In one embodiment, a computer-readable medium may include a combination of one or more computer-readable storage media and one or more computer-readable signal media. For example, computer-readable program code may be transmitted as an electromagnetic signal via an optical fiber cable for processor execution, or it may be stored in a RAM storage device for processor execution.

[0089] Computer-readable program code used to perform the operations of various aspects of this disclosure may be written in any combination of one or more other programming languages, including object-oriented programming languages ​​(such as Java, Smalltalk, C++, or similar languages) and conventional procedural programming languages ​​(such as the "C" programming language or similar programming languages). The computer-readable program code may execute entirely on the local computer, partially on the local computer, as a standalone computer-readable package, partially on the local computer and partially on a remote computer, etc. In the latter scenario, the remote computer may be connected to the local computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0090] Program code may also be stored in a computer-readable medium that can instruct a computer, other programmable data processing apparatus or other device to operate in a particular manner, such that instructions stored in the computer-readable medium produce an article of writing, which includes instructions that implement the functions / actions specified by one or more boxes in a schematic flowchart and / or schematic block diagram.

[0091] Although the accompanying drawings and description may illustrate a particular order of method steps, such order may differ from the order depicted and described unless otherwise specified above. Similarly, unless otherwise stated above, two or more steps may be performed simultaneously or partially simultaneously. Such variations may depend, for example, on the chosen software and hardware system and the designer's choices. All such variations are within the scope of this disclosure.

[0092] It is important to note that the construction and arrangement of the apparatus and systems illustrated in the various exemplary embodiments are merely illustrative. Additionally, any element disclosed in one embodiment may be combined with or used in conjunction with any other embodiment disclosed herein.

Claims

1. A means of transport, the means of transport comprising: A controller communicatively coupled to the engine of the vehicle, the controller including at least one processor and at least one memory device storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the following operations: Receive multiple communication messages; Receive information indicating the speed profile of the vehicle; Identify the triggering condition in one or more of the received communication messages; After identifying the triggering conditions, determine the change of the vehicle's speed curve within a predefined prediction range; as well as Based on the speed curve of the vehicle and the determined changes of the vehicle within the predefined prediction range, a control strategy is implemented for the powertrain of the vehicle.

2. The vehicle of claim 1, wherein the speed curve of the vehicle further includes a prediction of the speed of the vehicle within the predefined prediction range, wherein the prediction of the speed of the vehicle is based on route information about the vehicle.

3. The vehicle according to claim 1, wherein the control strategy includes: Fuel economy optimization strategies; Emissions compliance optimization strategies; and Component durability optimization strategies.

4. The vehicle according to claim 1, wherein implementing the control strategy comprises at least one of the following: Change the temperature of the components of the vehicle; Perform post-processing system regeneration before the predefined conditions are met; or Within the predicted range, one or more powertrain reference values ​​are changed.

5. The vehicle according to 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 the current operating parameters of the vehicle.

6. The vehicle according to claim 1, wherein the triggering condition includes natural language keywords or phrases.

7. The vehicle according to claim 6, wherein the natural language keywords or phrases correspond to data indicators of one or more standard messaging protocols.

8. The vehicle of claim 7, wherein the triggering condition indicates one or more of the following: an impending lane pattern change, an impending lane closure, an impending shoulder closure, an impending weather condition, an impending traffic condition, or an impending construction condition.

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

10. The vehicle according to claim 1, wherein the predefined prediction range is a predefined distance in front of the current position of the vehicle.

11. The means of transport according to claim 1, wherein: The change in the speed curve of the vehicle within the predefined prediction range indicates that the vehicle speed is about to decrease; and Implementing the control strategy includes operating the powertrain at a higher power output than the power output of the powertrain before the start of the predicted range, in order to increase the temperature of the vehicle's aftertreatment system.

12. The means of transport according to claim 1, wherein: The change in the speed curve of the vehicle within the predefined prediction range indicates that the vehicle speed is about to decrease; and Implementing the control strategy includes: operating the powertrain at a higher power output than the powertrain's current power output before the start of the prediction range; The excess power generated by the higher power output is used to charge the battery; and the battery is used to power the heater of the after-treatment system.

13. A method, the method comprising: The controller receives multiple communication messages from at least one remote computing system; The controller receives information indicating the speed profile of the vehicle. The controller identifies the triggering condition from one or more of the received communication messages; After identifying the triggering conditions, the controller determines the change of the vehicle's speed curve within a predefined prediction range; as well as The controller causes the vehicle to implement a control strategy for its powertrain based on changes in the vehicle's speed curve within a predefined prediction range.

14. The method of claim 13, wherein the triggering condition includes natural language keywords or phrases, and the natural language keywords or phrases are included in at least one of the plurality of messages.

15. The method of claim 14, wherein determining the change in the velocity curve 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 before the start of the prediction range.

17. A system comprising: At least one powertrain component; and At least one processing circuit coupled to the at least one powertrain assembly, the at least one processing circuit including at least one memory coupled to the at least one processor, the at least one processing circuit being operable to perform the following operations: Receive multiple communication messages; Receive information indicating the speed profile of vehicles; Identify the triggering condition in one or more communication messages within the communication messages; Determine the variation of the speed curve of the vehicle within a predefined prediction range; as well as A control strategy is implemented for the at least one powertrain component, the control strategy causing the at least one powertrain component to perform one or more powertrain actions based on determined changes in the speed curve of the vehicle within the predefined prediction range.

18. The system of claim 17, wherein implementing the control strategy on the at least one powertrain component comprises: The at least one powertrain component is operated at a higher power output than the current power output of the at least one powertrain component before the start of the prediction range; as well as The vehicle's battery is charged using excess power from at least one powertrain component.

19. The system of claim 17, wherein the triggering condition is a natural language keyword or phrase in one or more communication messages, the natural language keyword or phrase indicating a change in the speed curve or the probability of such a change; and The control strategy is selected based on the change in the speed curve or the probability of that change.

20. The system of claim 17, wherein the control strategy includes: The at least one powertrain component is operated at a higher power output than the current power output of the at least one powertrain component before the start of the prediction range, in order to increase the exhaust gas temperature, thereby increasing the temperature of the aftertreatment system relative to the current aftertreatment system temperature.

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